problem solving process in networking

Basic Network Troubleshooting: A Complete Guide

The basics of network troubleshooting have not changed much over the years. When you’re network troubleshooting, a lot can be required to solve the problem. You could be solving many different issues across several different systems on your complex, hybrid network infrastructure. A network observability solution can help speed up and simplify the process.

The Network is the Key

“The network is down!” — I’m sure you heard that before.

Despite your best efforts as a network engineer, network failures happen, and you have to fix them. Hopefully, you’ve implemented a network observability platform in advance, so you should be collecting a wealth of information about your network, making troubleshooting easier.

But what happens when it’s time to activate troubleshooting mode?

In this post, I’m going to talk about the steps to troubleshoot your network. And then I’ll provide some best practices, as well as provide examples of troubleshooting with Kentik’s network observability solutions.

What is Network Troubleshooting?

Network troubleshooting is the process of solving problems that are occurring on your network, using a methodical approach. A simple definition for what can often be a hard task!

When users complain, whether they’re internal or external to your organization — or ideally, before they do — you need to figure out what the cause of their problem is. The goal is to troubleshoot and fix whatever issue underlies the problems.

Troubleshooting requires taking a methodical approach to resolving the issue as quickly as possible. Unfortunately for you, the user doesn’t care what your service-level objective for fixing the problem is. In today’s “gotta have it fast” culture, more often than not, you need to fix it now — or revenue is affected.

Let’s get into some ways you can troubleshoot your network and reduce your mean time to repair (MTTR).

Basic Network Troubleshooting Processes

Identify the problem.

When you’re troubleshooting network issues , complexity and interdependency make it complex to track down the problem. You could be solving many different issues across several different networks and planes (underlay and overlay) in a complex, hybrid network infrastructure.

The first thing you want to do is identify the problem you’re dealing with. Here are some typical network-related problems:

• A configuration change broke something . On a network, configuration settings are constantly changing. Unfortunately, configuration change accidents can happen that bring down parts of the network.
• Interface dropping packets . Interface issues caused by misconfigurations, errors, or queue limits lead to network traffic failing to reach its destination. Packets simply get dropped.
• Physics limitations on connectivity . Sometimes, your connections don’t have enough bandwidth. Or the latency is too much between source and destination. These lead to network congestion, slowness, or timeouts.
• Problems in the cloud . Intra- or inter-cloud connectivity problems can have their own unique set of causes and challenges. Often driven by someone else’s congestion, oversubscription, or software failures.

Find Your Network Troubleshooting Tools

Fixing these kinds of troubleshooting problems needs more than identification. To paraphrase French biologist Louis Pasteur — where observation is concerned, chance favors only the prepared mind.

No network engineer can troubleshoot without being prepared with their tools and telemetry. So once you’ve identified that there is a problem, it’s time to use your network troubleshooting tools.

Ideally, you have tools and telemetry in advance, so your network observability toolchain is using AI to automatically identify problems and linking you to a jumping off point so you can drive down both MTTK (Mean Time to Know) and either MTTR (Mean Time to Repair) or MTTI (Mean Time to Innocence).

Here are a few examples of basic network troubleshooting tools:

• Tracert/ Trace Route
• Ipconfig/ ifconfig
• Pathping/MTR

The First Step — Ping Affected Systems

When your network is down, slow, or suffers from some other problem, your first job is to send packets across the network to validate the complaint. Send these pings using the Internet Control Message Protocol (ICMP) or TCP to one or any of the network devices you believe to be involved.

The ping tool is a utility that’s available on practically every system, be it a desktop, server, router, or switch.

There’s a sports analogy that says “the most important ability is availability” for systems. If you can’t reach it, it’s not available to your users.

Sending some ICMP packets across the network, especially from your users’ side, will help answer that question, if your platform isn’t presenting the path to you automatically. In some cases if ICMP is filtered, you can usually switch to TCP (Transmission Control Protocol) and use tcping, telnet, or another TCP-based method to check for reachability.

Get the Path with Traceroute

If you’re not getting any ping responses, you need to find out where the ping is stopping. You can use another ICMP-based tool to help, and that’s traceroute.

Your ping could be getting stopped because ICMP isn’t allowed on your network or by a specific device. If that’s the case, you should consider TCP Traceroute on Linux, which switches to TCP packets.

From traceroute, since you will see the path of IP-enabled devices your packets take, you will also see where the packets stop and get dropped. Once you have that, you can further investigate why this packet loss is happening. Could it be a misconfiguration issue such as a misconfiguration of IP addresses or subnet mask? A misapplied access list?

Test Your Network with Synthetic Monitoring

Tool such as Kentik Synthetic Monitoring enable you to continuously test network performance (via ICMP, TCP, HTTP, and other tests) so you can uncover and solve network issues before they impact customer experience. Ping and traceroute tests performed continuously with public and/or private agents generate key metrics (latency, jitter, and loss) that are evaluated for network health and performance.

To get ahead of the game, Kentik also allows you to set up autonomous tests, so there’s already test history to your top services and destinations. You can also run these continuously (every second, like the ping command default) for high resolution.

Device Commands and Database Logs

Now that you’ve identified the network device or group of devices that could be the culprit, log into those devices and take a look. Run commands based on your device’s network operating system to see some of the configuration.

Take a look at the running configuration to see what interfaces are configured to get to the destination. You can take a look at system logs that the device has kept for any routing or forwarding errors. You can also look at antivirus logs on the destination systems that could be blocking access.

At this point, you may find yourself unable to get enough detail about the problem. Command line tools are telling you how things should work. What if everything’s working the way it should? What now? Or you might be getting overwhelmed by the amount of log data.

Device Configuration Changes

Many network outages relate to changes that humans made! Another key step on the troubleshooting path is to see if anything changed at about the same time as issues started.

This information can be found in logs of AAA (Authentication, Authorization, and Accounting) events from your devices. Ideally stored centrally, but often also visible by examining the on-device event log history.

Packets and Flows

The old saying about packet captures is that packets don’t lie! That’s also true for flow data, which summarizes packets.

Both packets and flows provide information about the source and destination IP addresses, ports, and protocols.

When getting flow data, you’re not as in the weeds as during a packet capture, but it’s good enough for most operational troubleshooting. Whether it’s with NetFlow, sFlow, or IPFIX , you’ll be able to see who’s talking to whom and how with flow data going to a flow collector for analysis.

Capturing packet data is truly getting into the weeds of troubleshooting your network. If it’s unclear from flow, and often if it’s a router or other system bug, you may need to go to the packets.

Unless you have expensive collection infrastructure, it’s also often more time consuming for you than any of the other tools above. Whether it’s tcpdump, Wireshark, or SPAN port, you’ll be able to get needle-in-the-haystack data with packet captures.

One great middle ground is augmented flow data, which captures many of the elements of packets. This can be great if you can get performance data, but not all network devices can watch performance and embed in flow — in fact, the higher speed the device, the less likely it is to support this kind of enhancement.

Collecting and analyzing packets and flows is where you start to venture into the next step. You’re using a mix of utility tools (tcpdump) and software (Wireshark, flow collector). If you’re expecting to keep a low MTTR, you need to move up the stack to software systems.

Up the Stack

If you can’t find issues using these tools and techniques at the network level, you may need to peek up the stack because it could be an application, compute, or storage issue. We’ll cover more on this cross-stack debugging in a future troubleshooting overview.

Kentik Network Observability

Of course, network performance monitoring (NPM) and network observability solutions such as Kentik can greatly help avoid network downtime, detect network performance issues before they critically impact end-users, and track down the root cause of network problems

In today’s complex and rapidly changing network environments, it’s essential to go beyond reactive troubleshooting and embrace a proactive approach to maintaining your network. Network monitoring and proactive troubleshooting can help identify potential issues early on and prevent them from escalating into more severe problems that impact end users or cause downtime.

Kentik’s Network Observability solutions, including the Network Explorer and Data Explorer, can be invaluable tools in implementing proactive troubleshooting strategies. By providing real-time and historical network telemetry data and easy-to-use visualization and analysis tools, Kentik enables you to stay ahead of potential network issues and maintain high-performing, reliable, and secure network infrastructure.

Network Explorer Solution

Kentik Network Explorer provides an overview of the network with organized, pre-built views of activity and utilization, a Network Map, and other ways to browse your network, including the devices, peers, and interesting patterns that Kentik finds in the traffic.

To make NetOps teams more efficient, Kentik provides troubleshooting and capacity management workflows. These are some of the most basic tasks required to operate today’s complex networks, which span data center, WAN, LAN, hybrid and multi-cloud infrastructures.

The Network Explorer combines flow, routing, performance, and device metrics to build the map and let you easily navigate. And everything is linked to Data Explorer if you need to really turn the query knobs to zoom way in.

Data Explorer Solution

If you can’t find the obvious issue with something unreachable or down, it’s key to look beyond the high level and into the details of your network.

Kentik Data Explorer provides a fast, network-centric, easy-to-use interface to query real-time and historic network telemetry data. Select from dozens of dimensions or metrics, 13 different visualizations and any data sources. Set time ranges and search 45 days or more of retained data. Query results within seconds for most searches.

This lets you see traffic, routing, performance, and device metrics in total, by device, region, customer, application, or any combination of dimensions and filters that you need to zoom in and find underlying issues.

Kentik’s Data Explorer provides graphs or table views of network telemetry useful for all types of troubleshooting tasks.

Software Tools Help Facilitate Network Troubleshooting

Marc Andreessen of Netscape fame once said that, “software is eating the world.” But software has made things a lot easier when it comes to network troubleshooting. It has taken over from the manual tools run from a terminal or network device.

There are software tools that ping not just to one device but multiple devices simultaneously for availability and path. Many are flow and packet data stores with software agents sending network data. All this is done and put on a nice dashboard for you. Network troubleshooting is still hard, but software makes it easier.

However, in this cloud-native and multi-cloud infrastructure era, some software makes it easier than others. For that, you need to move beyond traditional monitoring software because it’s not enough anymore. You need to move to observability software.

With software tools like products from Kentik, you can use the devices to send data to observe the state of your network instead of pulling it from the network.

Network Troubleshooting Best Practices

Whether you’re using network observability tools, or have a network small enough where the other tools are sufficient, here are some best practices you should consider.

Develop a Checklist

You should develop a checklist of steps like what I’ve outlined above when troubleshooting.

In his book The Checklist Manifesto , Dr. Atul Gawande discusses how checklists are used by surgeons, pilots, and other high-stress professionals to help them avoid mistakes. Having a checklist to ensure that you go through your troubleshooting steps promptly and correctly can save your users big headaches. And save you some aggravation.

Over time, this checklist will likely become second nature, and having and following it ensures you’re always on top of your game.

Ready Your Software Tools

You want to have already picked the network troubleshooting tools you need to troubleshoot a network problem before you get an emergency call. That isn’t the time to research the best software tool to use. By then, it’s too late.

If you run into a network troubleshooting problem that took longer than you hoped with one tool, research other tools for the next time. But do this before the next big problem comes along.

Get Documentation

It’s tough to jump on a network troubleshooting call and not know much about the network you’re going to, well, troubleshoot. IT organizations are notorious for not having enough documentation. At times, you know it’s because there aren’t enough of you to go around.

But you have to do what you can. Over time, you should compile what you learn about the network. Document it yourself if you have to, but have some information. Identify who owns what and what is where. Otherwise, you could spend lots of troubleshooting time asking basic questions.

Prepare Your Telemetry

In addition to having the software to move with speed, you’ll need to be already sending, saving, and ideally detecting anomalies over your network telemetry. For more details on network telemetry, see our blog posts “The Network Also Needs to be Observable” and “Part 2: Network Telemetry Sources” .

Follow the OSI

If you closely follow the toolset above, you may have noticed that I’m moving up the stack with each tool.

In some ways, I’m following the Open Systems Interconnection (OSI) stack. When troubleshooting, you want to start at the physical layer and work your way up. If you start by looking at the application, you’ll be masking potential physical connection problems such as interface errors or routing issues happening at layer 3. Or any forwarding problems at layer 2.

So follow the stack, and it won’t steer you wrong.

Preparedness and Network Troubleshooting

And there it is. When the network is down, troubleshooting can be a daunting task, especially in today’s hybrid infrastructure environments .

But if you follow the steps I’ve outlined, you can make things easier on yourself. Create your network troubleshooting checklist, decide on your toolset, and get ready. If it’s not down now, the network will likely be down later today.

Now that you know this about network troubleshooting, you’ll be ready when the network issues affect traffic in the middle of the night. You won’t like it; nobody likes those 1:00 A.M. calls. But you’ll be prepared.

Explore more from Kentik

• Multi-Cloud Observability
• Network Monitoring System (NMS)
• SD-WAN Monitoring
• Network Security and Compliance
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• Harden Zero-Trust Cloud Network Policy
• Investigate Security Incidents
• Visualize All Cloud and Network Traffic
• Troubleshoot Any Network
• Understand Internet Performance
• Consolidate Legacy Tools
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Network Troubleshooting: A Guide for IT Professionals

Imagine your organization’s network suddenly goes down, halting operations and causing revenue loss by the minute. While this scenario is unsettling, it goes to show why network troubleshooting skills are essential for today’s IT professionals.

The smooth operation of network infrastructure not only supports your organization’s day-to-day operations, but also safeguards against disruptions and downtime. In a time where unplanned network outages can cost mid-sized businesses an average of $1,850 and large enterprises $25,402 per minute , having a proactive plan in place to resolve issues quickly is a must. 

A variety of issues can call for network troubleshooting. In this article, we’ll share the most common problems IT teams run into and provide a basic framework for you to follow in any scenario, so you can get your business back up and running swiftly. 

We’ll explore:  

• What is troubleshooting in networking?
• Common network issues that need troubleshooting

Network troubleshooting tools recommended by IT pros 

• Basic network troubleshooting steps 
• Best practices for troubleshooting network problems 
• How network management tools can help 

Let’s get you set up for success. 

What is network troubleshooting? 

Network troubleshooting is the systematic process of diagnosing and resolving problems within an IT network. It involves identifying the root cause of a network issue and implementing a solution to restore functionality. 

Today, 91% of organizations experience at least one network outage per quarter. In an era where we heavily rely on technology for daily business operations, downtime has the potential to cause major financial, reputational, and productivity losses. 

Take Facebook for example, which lost an estimated $90 million during a 14-hour outage in 2019. Or, there’s payment processor Square , which experienced a systems outage in 2023 that cost its small business customers thousands in lost revenue. Minimizing this damage and enhancing network reliability requires strong network troubleshooting capabilities.  

Common network problems that need troubleshooting

Network troubleshooting issues can range far and wide from minor inconveniences to major disruptions.

Some of the most common problems IT teams are up against include: 

• Network congestion , which occurs when a network is overloaded with data (or high bandwidth usage), causing slowdowns and delays in data transmission. This can result from a variety of factors, including excessive demand for network resources during peak usage times or distributed denial-of-service (DDoS) attacks that intentionally flood the network with excessive traffic.
• Faulty network configurations , which refer to incorrect or inefficient settings in network devices or software that hinder proper network operations. These configurations can range from improper IP addressing, subnet masks, and gateway settings to incorrect DNS configurations, leading to issues like network inaccessibility, slow performance, and inability to connect to external networks.
• Hardware failures , which involve the malfunctioning of physical components in the network infrastructure—such as routers, switches, or cables—disrupting network connectivity. These failures can be caused by wear and tear, manufacturing defects, or external factors like power surges and physical damage, leading to partial or complete network outages that require hardware replacement or repair.
• IP address conflicts , which arise when two devices on the same network are assigned the same IP address, leading to connection issues for the affected devices. These conflicts can prevent one or both devices from accessing network resources and the internet, and typically require manual intervention to resolve, such as renewing IP addresses or adjusting DHCP (Dynamic Host Configuration Protocol) settings.
• Network security breaches , which may involve unauthorized access or attacks on the network, compromising data integrity, confidentiality, and network availability. Breaches can occur through various means, including malware, phishing, or exploiting vulnerabilities within the network, and can have severe consequences such as data theft, service disruption, and financial loss.

These common issues give insight into the broad scope of problems IT professionals must be prepared to tackle—and having a reliable toolkit is a great place to start. 

To effectively troubleshoot network problems, IT professionals should have a variety of software and hardware tools at their disposal.

Here’s what we’re seeing industry experts recommend . 

Software tools and applications

For network diagnostics.

• Ping commands : Essential for basic connectivity tests.
• Traceroute : Key for identifying the path data takes across the network.
• My traceroute (MTR) : Popular for its combined features of ping and traceroute.
• Wireshark : The go-to tool for in-depth packet analysis.
• Zenmap / Nmap : Widely used for network exploration and security auditing.
• PingPlotter : Useful for visualizing Ping and traceroute results over time.

For network performance and monitoring 

• iperf3 : A benchmark tool for measuring network bandwidth.
• Wireshark : For monitoring network performance issues.
• LibreNMS : A full-featured network monitoring system that’s community-supported.
• Tcpdump : A lightweight and versatile packet analysis tool.
• Graylog : For monitoring and analyzing network logs for performance insights.
• Netflow collector : For analyzing network traffic flows.

Hardware tools

Software tools and applications can be of great value when it comes to network troubleshooting, but you’ll need a few tangible hardware tools in your kit as well. 

When it comes to network maintenance and troubleshooting, you’ll want to invest in a cable tracer, which identifies and traces specific network cables in bundles or infrastructure. You may also need a crimper , which attaches connectors to cable ends—essential for customizing network cables. 

For network setup and configuration, add these tools to your kit. First, a USB to Serial connector, which connects computers to your network devices’ console ports for configuration. Next, a DB9 to RJ45, which converts DB9 serial to RJ45 connection. Lastly, a RJ45 Female to Female , which extends your Ethernet cables by connecting two RJ45 cables. These are just the basics, but you can dive deeper into our top recommendations for hardware-based SysAdmin tools.  

Free guides and resources  

In addition to these software and hardware tools, there are many free online resources that provide valuable insights and detailed steps for network troubleshooting.

Here’s a roundup of Auvik resources you may find helpful and the key takeaways from each.

• How to Find and Fix Asymmetric Routing Issues : Explore the challenges asymmetric routing poses for stateful firewalls, and discover strategies for diagnosing and resolving related issues. 
• Troubleshooting and Configuring Multicast Protocols : Learn troubleshooting techniques for common multicast issues and the role of PIM (Protocol Independent Multicast) and IGMP (Internet Group Membership Protocol) in facilitating multicast packet delivery.
• How to Troubleshoot Network Connectivity Issues : Find out how to troubleshoot common network connectivity issues reported by end users, so you can streamline the issue resolution process and reduce downtime.

7 basic network troubleshooting steps

What is the first step that needs to be taken when troubleshooting a network incident?

No matter the scenario, these basic network troubleshooting steps serve as a solid framework to work within.

1. Identify the problem

Start with a comprehensive assessment by documenting the symptoms, identifying affected systems, and determining the scope and impact of your network issue. Determine whether you’re dealing with an internal or external connectivity issue by completing the following steps: 

• Check your hardware : ‘Did you try turning it off and on again?’ While it might seem painfully obvious, faulty hardware is a common source of network issues. Verify that all of your network devices, including routers, switches, and cables, are powered on and functioning correctly. 
• Use ipconfig: Use the ipconfig command on Windows to confirm that your computer has received a valid IP address from the DHCP server. An invalid IP will begin with the numbers 169 and can prevent network access.
• Do a ping test: Send a ping to both your local router and external websites to identify potential connectivity issues within your local network or to the internet. 
• Do a DNS check: Performing a DNS check will ensure your computer can resolve domain names into IP addresses. If it can’t, websites may not load. 
• Check malware protection: Ensure your virus and malware protection software is working correctly and that nothing has been flagged that may impact network performance.
• Review database logs: Examine logs from your network devices and servers for any anomalies or errors that could indicate underlying issues affecting network performance or accessibility.

2. Establish a probable cause 

Analyze the data you collected in step one to formulate theories about potential root causes. Consider recent changes, common challenges, and known vulnerabilities that could contribute to the issue. Remember not to rule out the obvious such as an unplugged cord or a power supply issue. 

3. Test the theory

Perform targeted tests to validate your theory using the tools and techniques at your disposal. For example, if your theory is that your router has gone haywire, you might try replacing it with a new one to test whether it resolves the issue. If it doesn’t, return to step two and come up with a new theory. 

4. Establish a plan of action

Based on the outcomes of your testing, outline a detailed plan to address the root cause. This plan should include steps for implementation, a breakdown of the resources required, and the potential impact on users and business operations. Make sure to communicate effectively across the business to minimize disruption to customers and staff. 

5. Implement the solution

Execute the plan, making adjustments as necessary. Monitor the implementation process to ensure that it proceeds as expected without introducing new issues.

6. Verify full-system functionality

After applying the fix, conduct thorough tests to confirm that the original issue is resolved and that there are no residual effects. This includes checking network performance and gathering user feedback.

7. Document the process

Create a detailed report of the incident, including the initial symptoms, diagnosed cause, solution implementation, and verification process. Network documentation will be helpful for future troubleshooting and preventing similar issues.

Best practices for troubleshooting network problems  

Following best practices for network troubleshooting will enhance your efficiency and effectiveness when issues arise. Here are our top recommendations: 

• Maintain detailed documentation to better understand your network’s current and historical configurations, which will help you to diagnose issues quicker. 
• Use a systematic approach to problem-solving to help methodically narrow down the cause of the problem through a process of elimination. 
• Stay current with the latest network technologies and vulnerabilities so you can proactively anticipate and mitigate potential issues. 
• Foster a culture of continuous learning on your IT team to encourage ongoing skills development and ensure you’re prepared to tackle emerging network challenges. 

How network management tools can help with troubleshooting 

Network management tools can help you prevent network issues and facilitate easier troubleshooting when they do occur. They offer real-time network monitoring , automated alerting systems, and detailed reports on network performance—all of which work to identify potential problems before they escalate. 

These tools can also streamline the troubleshooting process by providing real-time diagnostic data and actionable insights. For example, Auvik’s internet connection check can help you quickly determine whether a network issue is internal or external by tracking abnormalities in packet loss and round trip time. The quicker you can identify the issue, the less downtime and impact on your business.

Network monitoring and troubleshooting with Auvik 

Auvik’s cloud-based network monitoring and management software helps you respond to network issues in real-time, protecting your users from unnecessary downtime and lost productivity. We streamline and simplify troubleshooting to help you keep your users connected to business critical resources.

With Auvik, you can: 

• Visualize your network: Get true network visibility and control with our real-time, automatically updating maps.
• Speed up troubleshooting: Benefit from automated device documentation and config backups that keep all of your information in one place.
• Get ahead of network issues: Find peace of mind with alerts that let you know whenever something significant changes in your network. 
• Reduce on-site visits: Gain access to your networks and make command line changes without ever having to leave your desk.

Try Auvik for yourself with a free trial .

Consider yourself a master at troubleshooting 

Network troubleshooting takes technical knowledge, analytical skills, and the right tools.

Now that you have an understanding of common network issues, a list of tools to leverage for effective troubleshooting, and a consistent framework to follow, you can better prepare your organization’s network to remain robust, secure, and fully operational.

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How Visibility Makes Empathy Easier, From the IT Desk to the End-User

What is edge computing, getting started with syslog in auvik, what’s your shadow it risk factor.

• The impact shadow IT has on an organization
• How to evaluate tools
• Tips on security
• A quiz to help you determine the severity of shadow IT in your org
• Solutions to solve these problems

Troubleshooting Overview

Table Of Contents Troubleshooting Overview Symptoms, Problems, and Solutions General Problem-Solving Model Preparing for Network Failure Troubleshooting Overview Dependency on network resources has grown tremendously over the past ten years. In today's world, a company's success is highly dependent on its network availability. As a result, companies are increasingly less tolerant of network failures. Therefore, network troubleshooting has become a crucial element to many organizations. Not only has the dependency for network grown, but the industry also is moving toward increasingly complex environments, involving multiple media types, multiple protocols, and often interconnection to unknown networks. These unknown networks may be defined as a transit network belonging to a Internet service provider (ISP), or a telco that interconnects private networks. The convergence of voice and video into data networks has also added to the complexity and the importance of network reliability. More complex network environments mean that the potential for connectivity and performance problems in internetworks is high, and the source of problems is often elusive. Symptoms, Problems, and Solutions Failures in internetworks are characterized by certain symptoms. These symptoms might be general (such as clients being incapable of accessing specific servers) or more specific (routes not existing in a routing table). Each symptom can be traced to one or more problems or causes by using specific troubleshooting tools and techniques. After being identified, each problem can be remedied by implementing a solution consisting of a series of actions. This book describes how to define symptoms, identify problems, and implement solutions in generic environments. You should always apply the specific context in which you are troubleshooting to determine how to detect symptoms and diagnose problems for your specific environment. General Problem-Solving Model When you're troubleshooting a network environment, a systematic approach works best. An unsystematic approach to troubleshooting can result in wasting valuable time and resources, and can sometimes make symptoms even worse. Define the specific symptoms, identify all potential problems that could be causing the symptoms, and then systematically eliminate each potential problem (from most likely to least likely) until the symptoms disappear. Figure 1-1 illustrates the process flow for the general problem-solving model. This process flow is not a rigid outline for troubleshooting an internetwork; it is a foundation from which you can build a problem-solving process to suit your particular environment. Figure 1-1 General Problem-Solving Model The following steps detail the problem-solving process outlined in Figure 1-1 : Step 1 When analyzing a network problem, make a clear problem statement. You should define the problem in terms of a set of symptoms and potential causes. To properly analyze the problem, identify the general symptoms and then ascertain what kinds of problems (causes) could result in these symptoms. For example, hosts might not be responding to service requests from clients (a symptom). Possible causes might include a misconfigured host, bad interface cards, or missing router configuration commands. Step 2 Gather the facts that you need to help isolate possible causes. Ask questions of affected users, network administrators, managers, and other key people. Collect information from sources such as network management systems, protocol analyzer traces, output from router diagnostic commands, or software release notes. Step 3 Consider possible problems based on the facts that you gathered. Using the facts, you can eliminate some of the potential problems from your list. Depending on the data, for example, you might be able to eliminate hardware as a problem so that you can focus on software problems. At every opportunity, try to narrow the number of potential problems so that you can create an efficient plan of action. Step 4 Create an action plan based on the remaining potential problems. Begin with the most likely problem, and devise a plan in which only one variable is manipulated. Changing only one variable at a time enables you to reproduce a given solution to a specific problem. If you alter more than one variable simultaneously, you might solve the problem, but identifying the specific change that eliminated the symptom becomes far more difficult and will not help you solve the same problem if it occurs in the future. Step 5 Implement the action plan, performing each step carefully while testing to see whether the symptom disappears. Step 6 Whenever you change a variable, be sure to gather results. Generally, you should use the same method of gathering facts that you used in Step 2 (that is, working with the key people affected, in conjunction with utilizing your diagnostic tools). Step 7 Analyze the results to determine whether the problem has been resolved. If it has, then the process is complete. Step 8 If the problem has not been resolved, you must create an action plan based on the next most likely problem in your list. Return to Step 4, change one variable at a time, and repeat the process until the problem is solved. Note If you exhaust all the common causes and actions—either those outlined in this book or ones that you have identified for your environment—you should contact your Cisco technical support representative. Preparing for Network Failure It is always easier to recover from a network failure if you are prepared ahead of time. Possibly the most important requirement in any network environment is to have current and accurate information about that network available to the network support personnel at all times. Only with complete information can intelligent decisions be made about network change, and only with complete information can troubleshooting be done as quickly and as easily as possible. During the process of network troubleshooting, the network is expected to exhibit abnormal behavior. Therefore, it is always a good practice to set up a maintenance time window for troubleshooting to minimize any business impact. Always document any changes being made so that it is easier to back out if troubleshooting has failed to identify the problem within the maintenance window. To determine whether you are prepared for a network failure, answer the following questions: • Do you have an accurate physical and logical map of your internetwork? Does your organization or department have an up-to-date internetwork map that outlines the physical location of all the devices on the network and how they are connected, as well as a logical map of network addresses, network numbers, subnetworks, and so forth? • Do you have a list of all network protocols implemented in your network? For each of the protocols implemented, do you have a list of the network numbers, subnetworks, zones, areas, and so on that are associated with them? • Do you know which protocols are being routed? For each routed protocol, do you have correct, up-to-date router configuration? • Do you know which protocols are being bridged? Are any filters configured in any bridges, and do you have a copy of these configurations? • Do you know all the points of contact to external networks, including any connections to the Internet? For each external network connection, do you know what routing protocol is being used? • Do you have an established baseline for your network? Has your organization documented normal network behavior and performance at different times of the day so that you can compare the current problems with a baseline? If you can answer yes to all questions, you will be able to recover from a failure more quickly and more easily than if you are not prepared. Lastly, for every problem solved, be sure to document the problems with solutions provided. This way, you will create a problem/answer database that others in your organization can refer to in case similar problems occur later. This will invariably reduce the time to troubleshoot your networks and, consequently, minimize your business impact.

Dominick Fair

Chief Technology Officer

15 Common Network Problems & How To Solve Them

So, your network crashed. It’s a great day.

You just lost an entire morning’s work because your network quit on you- and this is not some college research paper, this is your business .

Life is crazy and busy enough already! You don’t need more network and IT stressors added to it.

The only thing you need is a business network that makes your job and your life fluid.

It doesn’t matter if you live in Frederick, Bethesda , Rockville, McLean or Alexandria- you live in the future tech-hub of the world. Amazon is moving in, and Google has already caught on. You need protected, reliable and progressive IT.

If your business network breaks down, we have you covered.

We know about the uncommon network problems on the backend. But, did you know there are common network problems that can sometimes be prevented with the services of a managed service provider?

Here’s some information that may help you take a positive step towards never having to deal with annoying network issues again:

1. Networking Issues

IT management companies can always help businesses with their networking issues. But not all IT companies which help you with network issues are created equal. 

Some aren’t worth the time to call them. Some have great network solutions for issues but cost too much. What if we were to tell you about a company that can handle your networking issues at an affordable price? 

Maybe you’re skeptical at this point, so we’ll go over 15 of the most common network problems. Then we will tell you why we’re the best IT company in your area.

Here are a few common network problems:

• Your network is too slow. You have rebooted your computer. You have deleted files and folders that take up a lot of memory. But you still have a slow network speed. 
• The Wi-Fi signal is strong in some areas and weak in other areas of the office. It makes no sense. It continues, no matter how you rearrange the furniture.
• The IP addresses have snafus. Sometimes there may be duplicate IP addresses. Sometimes there can be IP address exhaustion. 
• Network path cannot be found. This is also knowns as a DNS problem. If there is a network error message that drives most people to distraction, it is that one. 
• Unable to connect to a printer or file on a network share program. That can put a group meeting in an indefinite hold pattern if not fixed as soon as possible.

We know how aggravating and stressful these network issues can be. As an IT management company with clients from Virginia to DC and Maryland, we also know how important our network solutions are.

They are the reasons our solutions need to work the first time, every time.

2. Network Problems

We are presenting you with common network issues and problems that affect all businesses from time to time. We also have solutions for these problems.

Here’s a small cheat sheet of brief solutions you may try for a couple of these ongoing network problems.

Here are more everyday common network problems;

• Cyber Security or hacking your network . Only about 14 percent of small businesses can mitigate cyber risks and their network’s vulnerability. Of the 14%, 60 percent of them go out of business within six months. 
• Data Back-ups . You have a lot of data that needs to be backed up at the end of each workday. But sometimes that doesn’t happen right, or it doesn’t happen at all. It’s a nightmare to figure out what happened.
• The Cloud and those who don’t understand it, misuse it, keep incorrect data on it or cannot figure it out so they get rid of it on their computer. 
• No IT plan at all? This is when we have our work cut out for us and we love every minute. Because it allows us to help a business develop an IT plan that grows and develops with them.
• Account Privilege Abuse. That’s when someone who shouldn’t be using company networks does. It can be a nightmare because  60% of all security breaches on your network are done from the inside. That means it is someone who works with or for you.

There are solutions to these network problems we work with every day. But the longer the problems go on, the more difficult it is to fix at every level. We get it done, but it is time-intensive. 

3. Common Network Problems

The last five common network problems are some of our unique network problems. But that being said, they are still common enough to include on our the list. They are:

• Misconfigurations cause as much as 80% of all network problems. Setting parameters manually may be something you can do. But when it is done wrong, it can cause untold IT headaches.
• VLAN issues happen when a VLAN is not configured to the correct port, which supports its services. 
• Wireless connections don’t work. There is nothing worse than going to use the wireless connection with your boatload of work only to realize that you cannot get access to the connection.
• Unable to establish a VPN connection. Many people may not know what or how the VPN connects so this can be a real stress-inducer IT problem.
• Ongoing network processes are using up all the memory. This one can be a puzzler without an IT management group which can help you walk through what is going on.

The value of an IT management company cannot be overstated. We say the heartbeat of your business is your network.  There is no substitute you can put in the place of a network that crashed. Because it doesn’t exist.

Reliable networks are a necessity for businesses everywhere. If your network problems cause you to lose business, for a day or even an hour- it is going to make a huge difference in office flow and client relations on your end.

Managed IT can prevent these problems before they happen.

Common Network Problems & Solutions

Some answers to a few of the common network problems we mentioned above you may find surprising. Some will seem too easy to be true but sometimes it works out that way.

But there are other network problems that can take the average IT knowledgeable person, more days than they want to figure it all out. This is often because of all the moving parts in network systems and servers.

The good news is that there isn’t a single network problem or issue we haven’t seen already. When it comes to complicated and in-depth network problems, we know what to do and how to do it almost as soon as we make it.

Here are some short answers to what can be difficult network problems.

• When your network is too slow , it can be the router’s positioning causing wireless interference. We test in all positions when we need to.
• The WiFi signal is strong and weak throughout the building may be because of your network’s administrator interface. It needs to be set to optimum performance.
• Data Back-up issues can be caused by hardware and software problems so all need to be checked out. You can back up directly to an outside server or another outside source as a temporary measure.
• VLAN issues require you to check the cabling and the interface first before doing anything else.
• Continuous loss of memory because your network has a memory hog somewhere. Damaged hardware or a capacity bottleneck can cause this. Either can be fixed in a straight-forward manner.

Network Issues 

There is no network issue you have which does not have a solution or answer. That’s what we will as truth in our industry. We give you the IT management services and answers to meet your network needs.

View pricing on our management plans that vary and are based on your particular needs. We have starter package plans for new businesses. We also have our obsidian package plan for established, larger businesses.

All of our packages can handle any of your current or potential network issues and provide your business with the answers and solutions to get you back on track. Our mission is to create fluidity in the workspace so that you and your business can get back to what it does best in its day-to-day operations.

All technology today evolves at the speed of a week-to-week and sometimes day to day basis. Problems in networking, cloud computing, and remote access are what we fix for our customers every day.

Your business should be able to hop online with the touch of a button and click of a mouse. Many of our calls come when people have tried to fix their network issues themselves but think they may have made things worse. We can fix those network issues too. 

Our IT Service Management Plans

Our IT service management plans allow us to be there for you so you never have to worry about having network problems or issues. 

We offer timely network solutions that give you peace of mind. Also, they get your business back up to speed as quickly as possible.  

Wouldn’t it be great to know that your network protection is just around the corner with 24/7 support?

SADOS offers more than solutions to your network problems. We offer specialized architecture and deployment. SADOS does maintenance and LAN/WAN network monitoring in our managed service packages.

We’re only one call away . Reach out to us before you need us in a network emergency. 

Try our FREE AI-powered  help desk. No login required.

Need a quick fix?

Try our free ai-powered tech support.

Describe the problem you are facing. Be friendly, descriptive. Remember that AI can make mistakes. If you’re having an IT emergency, please contact us .

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• Open access
• Published: 22 October 2020

Patterns of ties in problem-solving networks and their dynamic properties

• Dan Braha 1 , 2  

Scientific Reports volume  10 , Article number:  18137 ( 2020 ) Cite this article

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• Computational biology and bioinformatics
• Engineering
• Mathematics and computing
• Systems biology

Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs—synchronizability—and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.

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Introduction.

Problem-solving is a natural and ubiquitous human activity 1 , 2 , and is concerned with devising courses of action aimed at changing existing situations into preferred ones 3 . In this sense, the problem-solving activity is key to a variety of fields from engineering and medicine to economics and social planning. Some authors even argue for a direct resemblance between problem-solving processes and the structure of the scientific method 4 .

As problem-solving becomes complex and dynamic, the limited ability of humans to handle complexity and large amount of information is accounted for by decomposing the complex system into components that are relatively independent 1 , 2 , 3 . The problem is then solved collectively by multiple groups (a “group” can include one or more individuals), each solving problems with some degree of independence of others 1 , 2 , 3 , 5 , 6 . In such a coordinated problem-solving environment, the decentralized groups make decisions on the basis of information that is available to them locally via the network of interactions with other groups. Often, in such a network of interactions, decentralized groups make decisions even though the local information is not available or known with certainty 2 . In this case, decentralized groups need to make assumptions about the information generated by other units, and use these assumptions in their decisions. Groups update their decisions due to the availability of new information generated by other groups, such as changes in input, updates of shared assumptions, components, boundaries, or the discovery of errors 2 . This interdependence between the various groups makes collaborative problem-solving fundamentally iterative 2 , 5 in the sense that as new information becomes available, the decentralized decisions are repeated to come closer to the problem-solving goals or specifications. This iterative problem-solving process proceeds until convergence occurs 2 , 5 .

One way of thinking about coordinated problem-solving is to view it as involving both exploratory and exploitative behaviors. By receiving information locally via the network of interactions, decentralized groups gather information (exploration) that will ultimately be valuable in discovering a problem solution (exploitation). The tradeoff between the exploration of new possibilities and the exploitation of old certainties 6 , 7 , 8 is a central concern for a wide range of adaptive processes 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 . Gathering too much information via the network of interactions is likely to increase the quality of the over-all coordinated activity at the cost of slower convergence rates. On the other hand, carrying out exploitation more rapidly than exploration, e.g., by limiting the explorative information-gathering via local interactions, is likely to result in rapid convergence of the coordinated activity at the cost of lacking the ability to adapt to significant changes in information, requirements or constraints mediated by connected groups in the network. The latter might result in a suboptimal overall solution. Thus, it is reasonable to expect that the structure—both global and local—of the network of group interactions represents a balance between exploration and exploitation. Another useful view of coordinated problem-solving is to look at it as a social process involving cooperation among self-oriented individuals or groups 4 , 19 . According to this view, self-oriented individuals or groups cooperate and coordinate with each other, despite being driven to make local decisions that aim to achieve their local goals and viewpoints. This perspective is closely related to understanding the emergence of cooperation among selfish individuals—a key problem in biology 20 , 21 , network science 22 , 23 , and the social sciences 24 . In decentralized problem-solving, cooperation among groups is promoted by several communication mechanisms including the creation of shared meaning, high-level goals and views, and common knowledge 4 . Similar to exploration and exploitation, it is likely that the structure of ties in problem-solving networks captures the tension between cooperation and competition, and might account for the long-term cooperative actions between self-oriented groups.

Following the above discussion, it is important to understand how the output dynamics of one group can be affected by changes in the dynamics of other groups via the network of interactions. An important step in this direction is graph-based representations of real-world problem-solving. Steward 25 applied a square-matrix format (the adjacency matrix of a network) to represent a network of engineering task interactions, and used the method to determine a logical sequence for the tasks being modeled. This method was elaborated and extended in a variety of ways (e.g., using clustering analysis), including applications to a wide range of technical domains 5 , 26 , 27 , 28 , 29 . Braha and Bar-Yam 1 , 2 , 30 and Braha 31 used complex network theory to identify global statistical features that are shared across a variety of large-scale, real-world engineering problem-solving networks (ranging in size from 120 to 889 nodes), highlighting the similarity between problem-solving networks and other complex networks that occur in nature and society 32 . These global statistical features include sparseness of connections, the “small-world” property characterized by highly clustered nodes and short distance between any two nodes, uneven node degree distributions characterized by a few very highly connected nodes (critical nodes or ‘hubs’), asymmetry between the distributions of incoming and outgoing information flows, disassortativity among nodes, community structure (modularity), and hierarchical network organization 1 , 2 , 30 , 31 .

As discussed above, although the network structure of collaborative problem-solving is static the nodes (subproblems/tasks) represent values that change in time. Understanding the interplay between network structures and the global dynamics and performance of real-world problem-solving networks has been investigated by several researchers. Empirical work on small groups by organizational scientists show a mixed relationship between network density and performance 33 , 34 , 35 , 36 , 37 , 38 . A meta-analysis 34 of 37 studies of teams (ranging in size from 3 to 15 members) showed a modest positive relationship between team’s performance and network structure such as density of ties and the centrality of team’s leaders in the network. Other studies found an inversely U-shaped relationship between density of ties and team performance 35 , 36 , 37 , while other studies found no association at all 38 . Several laboratory-based experiments (such as graph-coloring tasks) examined the effect of density, clustering, and efficiency (measured in terms of average path length) on the balance between problem-solving exploration and exploitation 18 , 39 , 40 , 41 , 42 , 43 . For example, it was suggested that clustering inhibits exploration of new solutions (e.g., by copying and refining solutions of others) and promotes exploration of new knowledge and facts 18 . Research that examines the relationship between structure and performance also benefited from simulations and agent-based modeling 6 , 44 , 45 . This research found, for example, that efficient network structures (e.g., fully connected networks) that facilitate fast diffusion of information quickly converge on solutions that are better than the ones corresponding to inefficient networks. However, in the long run, inefficient networks that facilitate exploration of new information could perform significantly better than inefficient networks 6 .

By integrating theory, computational modeling, and empirical data of large-scale problem-solving networks, Braha and Bar-Yam 1 , 2 , 30 provided explanations for how the various organizing principles observed in real-world problem-solving networks affect overall system dynamics. Sparseness and small-world properties were explained in terms of efficiency, exploration, and integration. The average connectivity and the extent of degree correlations in the network determine whether collaborative problem-solving converge on an equilibrium, and how rapidly decentralized groups synchronize their activity. In particular, positively correlated networks tend to slow synchronization and convergence to an equilibrium. The right-skewed degree distributions and the characteristic feature of ‘hubs’ (highly connected nodes) lead to ultra-robustness under the circumstances of adverse fluctuations that affect randomly selected nodes. On the other hand, the right-skewed distributions also make problem-solving networks more fragile and vulnerable to adverse fluctuations that occur at highly connected nodes; a condition that slows synchronization and convergence. At the same time, the right-skewed distributions enable remarkable improvement of synchronization performance when resources are preferentially allocated to the highly connected nodes in the network.

Although global topological features (such as path lengths and degree distributions) provide important insight into problem-solving networks 1 , 2 , a more refined analysis of repeated patterns of ties (subgraphs) in problem-solving networks is needed to truly understand their large-scale dynamical properties. The frequency of a subgraph with a particular arrangement of ties (typically a subgraph of three or four nodes) in a particular network is the number of different matches of this subgraph, where topologically identical arrangements are counted as the same type of subgraph. Network motifs are defined as subgraphs that are overrepresented in the real-world network relative to their appearance in an ensemble of appropriately randomized networks 46 , 47 , 48 . Identifying subgraphs that are underrepresented in the real-world network (anti-motifs) is of equal importance in characterizing the network structure. The same network motifs may appear in diverse networks 46 , 49 , suggesting that motifs can delineate broad families of networks 47 , each family is characterized by common basic functionalities (e.g., networks that process information are distinct from networks that process energy flow and transfer). The exploration of subgraphs with two nodes (dyads) and three nodes (triads) has a long and rich history 50 , 51 , beginning with the work of Holland and Leinhardt 52 who used three-node subgraphs to study social networks. Similar techniques were applied in a variety of fields, including ecology 53 , 54 , 55 , systems biology 48 , 49 , economics and finance 56 , 57 , and neuroscience 58 . The dynamic and functional properties of network motifs in biological and ecological networks were explored both analytically and empirically 48 , 49 , 59 . Significant effort has shown that abundant motifs in diverse transcription networks (both sensory and developmental) perform a variety of regulatory and information processing functions, such as balancing homeostasis and plasticity 59 . It was also argued that the patterns of ties of biological and ecological network motifs make them more locally stable—roughly speaking, the tendency for system perturbations to damp out, returning the system to some persistent equilibrium 60 . The stability analysis applied in these works is based on calculating the eigenvalues of randomly generated matrices (representing the Jacobian of an underlying dynamical system)—an approach introduced by Gardner and Ashbey 61 , and extended to study ecological stability 53 , 60 . For example, Pimm 53 showed that subgraphs that are commonly found in real ecological food webs (including subgraphs of three, or four species) also tend to be more locally stable. Similar concepts were applied to several biological networks (including transcription, signal transduction, and neuronal networks) where it was shown that the stability properties of all structurally distinct three- or four-node subgraphs are highly correlated with their abundance in the network 49 . The approach presented in the current paper follows this direction of comparing subgraph abundance in real networks with their dynamic properties.

In this paper, we extend our previous work on complex problem-solving networks 1 , 2 , 30 by analyzing the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. We attempt to answer the pertinent question of what determines the frequency of network subgraphs in real problem-solving networks. It has been shown that the system-level structure of many complex systems is best approximated by a hierarchical network organization with seamlessly nested modularity 62 , a property also observed in problem-solving networks 30 . A nested hierarchical organization of problem-solving networks means that there are many highly integrated small groups of individuals, which assemble into a few larger groups, which in turn can be integrated into even larger groups. It is plausible to reason that rapid and effective synchronization of the problem-solving activity evolves by the accumulation of rapidly synchronized intermediate configurations, which are interconnected to form more synchronized complex structures. We thus hypothesize that real problem-solving networks will be biased towards repeated patterns of ties in which it is easier to obtain problem-solving synchronization. In this paper, we show that network subgraphs embedded in a variety of real problem-solving networks can emerge based on such considerations. In particular, we find a high correlation between a dynamic property of a network subgraph—synchronizability—and its frequency and statistical significance in real problem-solving networks. In this paper, synchronizability is characterized as the probability of rapidly coordinating the problem-solving activities, and is determined for each three- and four-node subgraph based on a corresponding dynamical model of collective problem-solving. The results in this paper show that highly-synchronizable subgraphs are overrepresented in the real problem-solving networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that the dynamical properties of subgraphs affect their prevalence, and thus the global structure of problem-solving networks.

Modeling setup

Before studying the dynamic properties of subgraphs, we need to develop a generic dynamical model of collective problem-solving. To this end, we use the well-characterized stochastic model presented in 2 . Consider the scenario of solving a complex problem, which involves a large number of decentralized groups each of which solves a simpler subproblem task. As shown in Fig.  1 A, subproblem tasks are represented as the nodes of a directed network, and a directed link from one subproblem to another represents the information dependency between the two subproblems. Each node in the network can be in two states: ‘open’ (if the subproblem is ‘unresolved’) or ‘closed’ (if the subproblem is ‘resolved’). At each time step, a node is selected at random. If the node is in a ‘closed’ state (Fig.  1 B, top), its state can be changed depending on the number of ‘open’ nodes connected to it through incoming links. These ‘open’ nodes send out new information that might lead to the reopening of a neighboring ‘closed’ subproblem. More specifically, each ‘open’ subproblem causes a connected ‘closed’ subproblem to reopen its state with a reopening probability \(\upbeta \) . The strength of this reopening probability \(\upbeta \) plays an important role in determining the synchronization of the problem-solving activity. If the node is in an ‘open’ state (Fig.  1 B, bottom), its state can be changed depending on two simultaneous conditions: (1) the node is not influenced by any of its neighboring ‘open’ nodes (each occurring with probability \(1-\upbeta \) ), and (2) the node switches to a ‘closed’ state (with probability \(\updelta \) ). Condition 2 reflects the fact that, with the absence of nearest-neighbor influences, each group attempts to solve its subproblem in a self-directed way. As with the reopening probability \(\upbeta \) , the strength of the self-directed probability \(\updelta \) affects the synchronization of the problem-solving activity. Without loss of generality, we assume homogeneity with \({\upbeta }_{\mathrm{i}}=\upbeta \) and \({\updelta }_{\mathrm{i}}=\updelta \) for all nodes in the network—considered as typical average values.

A dynamic network model of collective problem-solving. ( A ) The problem-solving network consists of nodes, representing subproblems attempted by decentralized groups. The groups interact with one another via directed communication links. In the diagram, blue and red nodes represent ‘closed’ and ‘open’ subproblems, respectively. ( B ) The stochastic rules that govern the dynamics of the network. The model involves two parameters—the reopening probability \(\upbeta \) , and the self-directed probability \(\updelta \) .

As the problem-solving activity evolves, ‘open’ subproblems are resolved, and may be reopened due to influences propagated by nearest-neighbor ‘open’ nodes. A mean-field analysis of large-scale problem-solving networks shows that—depending on \(\upbeta \) , \(\updelta \) , and the topology of the network—the process continues until either all subproblems are solved and full synchronization is achieved, or until the network settles into a quasi-equilibrium state with a non-zero fraction of ‘open’ subproblems 2 . The latter outcome reflects partial synchronization, and is an undesirable characteristic of the problem-solving activity. To illustrate this dynamical behavior, we show in Fig.  2 two typical simulation runs of the dynamic network model. The underlying network in this case is a real-world vehicle problem-solving network (see “ Data ” section), which includes 120 nodes (subproblems) and 417 links. We fix the self-directed probability \(\updelta =0.5\) , and vary the reopening probability \(\upbeta \) . Two different types of dynamical behavior are seen: while rapid synchronization is obtained for \(\upbeta =0.2\) , increasing the reopening probability to \(\upbeta =0.25\) results in poor synchronization.

Typical simulation runs of the dynamic problem-solving model on a real-world vehicle problem-solving network with 120 nodes (subproblems) and 417 links (information flows). The graphs show the percentage of solved subproblems over time, for two simulation scenarios.

The dynamic analysis can also be applied at the level of a single network subgraph. For a given pair of parameters \(\upbeta \) and \(\updelta \) , we apply the stochastic problem-solving model to a network subgraph and estimate (using Monte Carlo simulations) the probability of full synchronization after an arbitrarily chosen number of iterations. The effect of \(\upbeta \) and \(\updelta \) , as they vary from \(0\) to \(1\) , on the synchronization properties of several three-node network subgraphs is illustrated in Fig.  3 . In general, different combinations of the two parameters \(\upbeta \) and \(\updelta \) result in different probabilities of subgraph’s synchronizability. For the two parameter values this is indicated by the heatmap in Fig.  3 , which shows distinct dynamic properties exhibited by different three-node subgraphs. Intuitively, we see that the feedforward loop subgraph (Fig.  3 A) is more likely to synchronize for a wide range of parameter values than the mutual-in subgraph (Fig.  3 B), which includes a single two-node feedback loop. The mutual-in subgraph in turn is more likely to synchronize than other subgraphs that include a mixture of more complicated feedback loops (e.g. the mutual-cascade and the clique subgraphs in Fig.  3 C,D). In the real world, model parameters are not constants but vary according to some distribution that depends on a variety of factors. In order to operationalize the concept of subgraph’s synchronizability, we sample a large number of parameter values \(\upbeta \) and \(\updelta \) from a uniform \((0, 1)\) distribution, and compute the average synchronization probability over all realizations. This results in a synchronizability metric score (SM-score), which is assigned to all possible three- or four-node subgraphs, and then compared with subgraph frequency in real problem-solving networks.

Synchronization probability of three-node subgraphs. The dynamic properties of several three-node subgraphs, with increasing number and length of feedback loops, are explored: Feedforward Loop ( A ), Mutual-In ( B ) Mutual Cascade ( C ), and Clique ( D ). The synchronization probability for a given pair of parameters \(\upbeta \) and \(\updelta \) is determined by generating 100 realizations of the problem-solving model, and calculating the percentage of simulations in which full synchronization (i.e. all subproblems are solved) is achieved after an arbitrarily chosen number of 120 iterations. The synchronizability metric score (SM-metric) of a subgraph is obtained by averaging over 10,000 samples of the two parameters \(\upbeta \) and \(\updelta \) .

We compiled well-characterized data from the literature, quantifying relationships between subproblems tasks in diverse problem-solving environments and geographical locations. The original data is part of a commonly used method (Dependency Structure Matrix, DSM), which uses a matrix representation of a directed graph to graphically depict information dependencies between the elements of a complex system 25 , 26 , 27 , 28 , 29 . Mapping the interdependencies among subproblem tasks in these data was conducted primarily using structured interviews with experienced individuals involved in the problem-solving activity. The problem-solving network data considered here include: vehicle development (‘Veh1’, see Supplementary Data S1 online) with 120 nodes and 417 directed links 2 ; real estate development (‘Red’, see Supplementary Data S2 online) with 91 nodes and 1148 directed links 63 ; strategy and knowledge development (‘Knwl’, see Supplementary Data S3 online) with 62 nodes and 285 directed links 64 ; microprocessor development (‘Mip’, see Supplementary Data S4 online) with 60 nodes and 301 directed links 65 ; bioscience facility development (‘Bio’, see Supplementary Data S5 online) with 53 nodes and 230 directed links 66 ; vehicle development (‘Veh2’, see Supplementary Data S6 online) with 44 nodes and 249 directed links 67 ; and equipment development (‘Equip’, see Supplementary Data S7 online) with 43 nodes and 120 directed links 68 .

We calculate the SM scores for all topologically distinct 13 three-node and 199 four-node directed subgraphs. We further enumerate the frequency of all three- and four-node subgraphs in each of the seven real problem-solving networks, where the frequency of a subgraph in a particular network is the number of different matches of this subgraph. The frequency of subgraphs is then compared with their assigned SM scores. To get a sense of the relative order between the various subgraphs, we rank the three-node subgraphs by their assigned SM scores (largest to smallest) as well as by their frequencies (largest to smallest) in the real networks (Fig.  4 ). As Fig.  4 shows, a strong relationship is suggested between synchronizability and subgraph frequency. In general, subgraphs that are more synchronizable tend to be more abundant in the real networks. Of no less importance is the fact that the ranking of subgraph frequency is quite consistent across the diverse problem-solving networks, suggesting that the non-random nature of problem-solving networks is closely linked to the synchronizability of network subgraphs.

Ranking of all topologically distinct 13 three-node subgraphs according to their SM scores and occurrence in real problem-solving networks. The three-node subgraphs are ranked from largest to smallest SM scores (first column, top to bottom). We use the quartiles of the SM scores to divide the subgraphs into four natural SM classes (low SM score, moderately-low SM score, moderately-high SM score, and high SM score). The ranking of subgraphs, from largest to smallest frequencies (top to bottom), are shown in columns 2–8 where each column represents a particular real network (networks are ordered from larger to smaller). A monotonic association between synchronizability and subgraph frequency as well as between subgraph frequency in different problem-solving networks is suggested by the graphical representation. More rigorous nonparametric statistical analyses (Spearman’s correlation and Kruskal–Wallis tests) are used in the text to substantiate this suggestion.

We apply several statistical tests to further substantiate the above observations. A series of Spearman rank correlations were conducted in order to determine if there were any relationships between SM score and subgraph frequency, in different problem-solving networks. Spearman’s rank correlation measures the strength and direction of monotonic association between two variables, and is determined by calculating Pearson’s correlation on the ranked values of the data. Figure  5 shows the Spearman’s correlation coefficients for three-node subgraphs. A two-tailed test of significance indicates the there is a strong monotonic relationship between SM score and subgraph frequency ( \(0.84{\le r}_{s}(13)\le 0.91\) , \(\mathrm{p}<0.001\) ). On average, the higher the SM score of a subgraph, the more abundant the subgraph in the real network. The results are extended to four-node subgraphs as shown in Fig.  6 . The 199 four-node subgraphs offer a fuller description of patterns of local interconnections than the three-node subgraphs, and can help refine the association between synchronizability and subgraph frequency. Results of the Spearman correlations indicate that there is a strong monotonic association between SM score and subgraph frequency (the vast majority of \({\mathrm{r}}_{\mathrm{s}}(199)\) are between \(0.71\) and \(0.85\) , \(\mathrm{p}<0.001\) ). Surprisingly, even with the increased variability introduced by four-node subgraphs, the general trend is the same as in the three-node analysis.

Spearman's rank correlations between three-node subgraph SM score and three-node subgraph frequency, for all real problem-solving networks. The panels show scatter plots of ranks of subgraph frequencies (ranking from high to low) versus ranks of SM scores (ranking from high to low), for each of the real networks.

Spearman's rank correlations between four-node subgraph SM score and four-node subgraph frequency, for all real problem-solving networks. The panels are as in Fig.  5 .

To further explore the association between synchronizability and subgraph frequency, we use the quartiles of the SM scores to divide the subgraphs into four natural synchronizability classes (high SM score, moderately-high SM score, moderately-low SM score, and low SM score). Figure  7 presents box plots of the frequencies of three-node subgraphs (grouped by the different synchronizability classes), for all of the real problem-solving networks. Figure  8 shows the results for four-node subgraphs. A box plot (also called a box-and-whisker plot) is a standard graphical tool (see caption of Fig.  7 for details) used in statistics 69 and other quantitative sciences 70 , 71 , 72 , 73 to visualize summary statistics for sample data 69 , compare groups of data 70 , 71 , 72 , or identify extreme events 73 . On average, the box plots indicate that the higher the synchronizability class of subgraphs, the higher the frequencies of the subgraphs in the real network. A Kruskal–Wallis test 70 —a nonparametric alternative to the one-way ANOVA—shows that there is a statistically significant difference in subgraph frequency between the different synchronizability classes ( \(\mathrm{p}\le 0.05\) for three-node subgraphs, and \(\mathrm{p}<0.001\) for four-node subgraphs).

Box plots of three-node subgraph frequencies contrasted by subgraph synchronizability class. Synchronizability classes 1, 2, 3, and 4 correspond to low SM score, moderately-low SM score, moderately-high SM score, and high SM score, respectively. The box plot is a five-number summary of the empirical distribution 69 , 70 , 71 , 72 , 73 . The outer edges of the box represent the first quartile \(\mathrm{Q}1\) (the 25th percentile), and the third quartile \(\mathrm{Q}3\) (the 75th percentile). The middle red line of the box indicates the median (or the 50th percentile). The length of the box, \(\mathrm{Q}3-\mathrm{Q}1\) , is the interquartile range (IQR), which measures the spread in the data. The dashed line (“upper whisker”) that extends from \(\mathrm{Q}3\) is the smallest between the maximum value of the sample and \(\mathrm{Q}3+1.5\times \mathrm{IQR}\) , and the dashed line (“lower whisker”) that extends from \(\mathrm{Q}1\) is the largest between the minimum value of the sample and \(\mathrm{Q}1-1.5\times \mathrm{IQR}\) . Observations that are farther than \(1.5\times \mathrm{IQR}\) from the top or bottom of the box indicate outliers, and are shown as red + signs. The p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph frequency between the different synchronizability classes. Each panel corresponds to a particular problem-solving network.

Box plots of four-node subgraph frequencies contrasted by subgraph synchronizability class. The box plots and panels are defined as in Fig.  7 .

Although the above results are revealing, there might be confounding and selection biases acting on the network’s subgraphs, which could introduce spurious relationships between subgraph synchronizability and subgraph frequency. To avoid any such selective biases, we extend the previous analysis by controlling for possible confounding factors. First, the unobserved mechanism that drives the organization of the problem-solving network might be a confounding factor. Therefore, we employ a random null model and focus on the statistical significance of subgraphs rather than looking at their counts in the network. The statistical significance of a subgraph is determined by comparing its frequency in the real network relative to its mean frequency in an ensemble of a large number of randomly generated networks 46 , 47 , 48 , 52 . Statistically significant subgraphs (also called network motifs) are defined as subgraphs that occur in a real network much more often than in random networks. Using a random null model, which is free of any type of selective bias, will enable to adequately account and control for the non-random nature of problem-solving networks, and test the hypothesis that subgraphs with higher-synchronizability are more overrepresented—when compared to randomized networks—in real problem-solving networks.

To generate the ensemble of randomized networks, we employ the simplest Erdös-Rényi (ER) random graph model 32 , which was used before to detect network motifs 48 , 49 . To make a meaningful comparison, each simulated random network in the ensemble is constrained to have the same number of nodes and directed links as in the corresponding real network. The choice of the ER random graph model is motivated by the fact that it is devoid of any organizing principles 48 , 49 . This reduces the risk of confounding by unmeasured factors, and makes the association between subgraph abundance and subgraph dynamics clearer. The statistical significance of each (three- or four-node) subgraph is measured by calculating the Z-score, which is defined as the difference between the subgraph frequency in the real network and the mean frequency in a large ensemble of randomly generated ER networks, divided by the standard deviation of the frequency values for the randomized networks 46 , 47 , 48 , 52 . The Z-scores of three-node subgraphs was used in 47 to calculate the significance profile of a directed network, obtained by normalizing the vector of Z-scores. These methods were found to be useful in clustering networks into distinct families based on the correlations between their significance profiles 47 . Since the most abundant subgraphs in sparse networks tend to have fewer edges than less abundant subgraphs 48 , 49 , it is necessary in subsequent analysis to also control and eliminate the influence of edge number on subgraph significance, when studying the relationship between the significance of three- and four-node subgraphs and their dynamic properties. This is achieved, as suggested in 49 , by dividing the subgraphs into density classes, each of which containing subgraphs with the same number of directed links.

Before presenting the main findings, it is instructive to examine the significance profiles of the 13 possible connected subgraphs for the real problem-solving networks. Figure  9 (left panel) shows the significance profiles of the 13 possible three-node subgraphs for the problem-solving networks. Results of Spearman correlations (Fig.  9 , right panel) of the significance profiles indicate a strong monotonic relationship between subgraph abundance in different unrelated problem-solving networks (the vast majority of \({\mathrm{r}}_{\mathrm{s}}(13)\) are between \(0.7\) and \(0.98\) , \(\mathrm{p}<0.001\) ), furthering the hypothesis that subgraph abundance is driven by dynamic properties of local network structures. Subgraphs 4, 5, 8, 12 and 13 have the highest normalized Z-scores, and subgraphs 3 and 9 the lowest. The feedforward loop (subgraph 4) is composed of a problem-solving activity that sends information to another activity, and both send information to a third activity. The regulated and regulating mutual subgraphs (subgraphs 5 and 8, respectively) show mutual information feedback between two activities that send to or receive information from a third activity. The semi-clique (subgraph 12) and clique (subgraph 13) display three activities that repeatedly interact with each other, either directly or via transitive interactions. The cascade (subgraph 3) and the feedback loop (subgraph 9) are rare. What could explain these patterns?

Spearman’s rank correlation matrix (right panel) of the three-node significance profiles (left panel) for the directed problem-solving networks (‘Red’ through ‘Equip’; for comparison, ‘Veh1’ was excluded due to an undefined Z-score of subgraph 13). (Left panel) The three-node significance profiles for the problem-solving networks. The significance profile shows the normalized Z-score for each of the 13 connected subgraphs. (Bottom of left panel) The 13 connected subgraphs are ordered from left to right by their SM scores, and each is colored according to its synchronizability class (red, yellow, green, and blue corresponding to high, moderately-high, moderately-low, and low SM scores, respectively). The normalized Z-score of all subgraphs is determined by comparison to 500 randomized ER networks. (Right panel) Spearman’s rank correlations among pairs of significance profiles. The panels show scatter plots of ranks of subgraph normalized Z-scores in one network (ranking from high to low) versus ranks of subgraph normalized Z-scores in another network (ranking from high to low).

The bar graphs (Fig.  9 , left panel) show the normalized Z-scores of the 13 subgraphs, which are ordered from left to right by their SM scores. Subgraphs 1, 2, and 3 represent a density class with 2 edges; subgraphs 4, 6, 7, and 9 represent a density class with 3 edges; subgraphs 5, 8, 10, and 11 represent a density class with 4 edges; and subgraphs 12 and 13 represent density classes with 5 and 6 edges, respectively. Remarkably, the comparison of the significance of subgraphs within a particular density class reveals a general trend of significant overrepresentation of subgraphs with higher synchronizability, and weak overrepresentation—and sometimes underrepresentation—of subgraphs with lower synchronizability. Subgraphs 1 and 2 consistently have higher normalized Z-scores than subgraph 3. In almost all cases, the order of normalized Z-scores among pairs of 3-edge subgraphs (4, 6, 7, and 9) is consistent with their SM score ranking. Subgraph 4, which has the highest SM score, is significantly overrepresented in all networks relative to all other 3-edge subgraphs; and subgraph 9, which has the lowest SM score, is underrepresented in all networks relative to other 3-edge subgraphs. In almost all cases, the order of normalized Z-scores among pairs of 4-edge subgraphs (5, 8, 10, and 11) is consistent with their SM score ranking. Subgraphs 5 and 8, with moderate SM scores, are significantly more overrepresented than subgraphs 10 and 11, which have lower SM scores.

The interplay between subgraph abundance and synchronizability can also be seen by comparing the SM classes of subgraphs, within a particular density group, with their Z-scores. The 2-edge subgraphs (1, 2, and 3) belong to the high SM class, and both the 3-edge subgraphs (4, 6, 7, and 9) and 4-edge subgraphs (5, 8, 10, and 11) belong to multiple SM classes (Fig.  9 , bottom left). Figures  10 and 11 show scatter plots of Z-score versus SM class for 3-edge and 4-edge subgraphs, respectively. We see that in almost all cases subgraphs of higher SM classes are more overrepresented in real problem-solving networks than subgraphs of lower SM classes. The complexity and variety of the 199 four-node directed subgraphs enables a more comprehensive statistical analysis of the relationship between subgraph abundance and subgraph synchronizability. A series of Kruskal–Wallis tests, one for each subgraph density class, are conducted to examine this relationship for four-node subgraphs. Figures  12 , 13 , 14 show that—in each of the 4-edge, 5-edge, and 6-edge density classes—there is a statistically significant difference in subgraph Z-score between the different synchronizability classes (p ≤ 0.05 for most cases). The comparison of the dynamic properties of subgraphs to their Z-scores reveals that—compared to a random null model—subgraphs with higher synchronizability are more enhanced in the real networks, while low- synchronizable subgraphs are more suppressed. This suggests that the dynamic properties of network subgraphs have an influence on their abundance in the network and correspondingly the overall organization of the problem-solving network.

Z-score classified by synchronizability class, for three-node subgraphs in the 3-edge density class. The panels show scatter plots of subgraph SM class (SM classes 1, 2, 3, and 4 correspond to low, moderately-low, moderately-high, and high SM scores, respectively) versus subgraph Z-score, for each of the real networks.

Z-score classified by synchronizability class for three-node subgraphs in the 4-edge density class. The panels show scatter plots of subgraph SM class (SM classes are numbered as in Fig.  10 ) versus subgraph Z-score, for each of the real networks.

Box plots of Z-scores, for four-node, 4-edge subgraphs, contrasted by subgraph SM class (SM classes are numbered as in Fig.  10 ). In most cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph abundance (relative to a random null model) between the different synchronizability classes. Each panel corresponds to a particular problem-solving network. The box plots are defined as in Fig.  7 .

Box plots of Z-scores, for four-node, 5-edge subgraphs, contrasted by subgraph synchronizability class. Details and panels are as in Fig.  12 . In most cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph abundance (relative to a random null model) between the different synchronizability classes. The box plots are defined as in Fig.  7 .

Box plots of Z-scores, for four-node, 6-edge subgraphs, contrasted by subgraph synchronizability class. Details and panels are as in Fig.  12 . In most cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph abundance (relative to a random null model) between the different synchronizability classes. The box plots are defined as in Fig.  7 .

Although the Z-score is a commonly used measure for detecting statistically significant subgraphs 46 , 47 , 48 , 51 , it does not provide full information on the relative importance of subgraphs. The relative importance of a subgraph can also be understood in terms of the relative difference between the subgraph frequency in the target network and the expected frequency in the random networks. One way to define the relative difference of two numbers is to take their difference divided by some function of the two numbers 74 . Following 47 , 74 , we define the relative difference (RD-score) of each (three- or four-node) subgraph as the difference between the subgraph frequency in the real network and the mean frequency in ER networks, divided by the sum of these two frequencies. In order to compare networks of different sizes, the vector of subgraph RD-scores can be normalized, obtaining the RD profile of the directed network 47 . The Z and RD scores do not necessarily overlap 47 —a subgraph can be detected as statistically significant (high Z-score) due to a narrow distribution of subgraph incidence in the randomized networks, but still have a slight difference between its abundance in the real network relative to random networks (small RD-score).

We compare the problem-solving networks based on the RD profiles of the 13 possible three-node subgraphs (Fig.  15 , left panel). Results of Spearman correlations (Fig.  15 , right panel) indicate a very strong monotonic relationship between subgraph RD-scores in different unrelated problem-solving networks ( \({\mathrm{r}}_{\mathrm{s}}(13)\) are between \(0.8\) and \(0.98\) , \(\mathrm{p}<0.001\) ), suggesting (as also implied by Fig.  9 ) that the networks have similar key subgraphs that were developed to have similar dynamic properties. Subgraphs 4, 5, 8, 12 and 13 have the highest normalized RD-scores, and subgraphs 3 and 9 the lowest, which is consistent with the results obtained for the Z-score. The bar graphs (Fig.  15 , left panel) show the normalized RD-scores of the 13 subgraphs, which are ordered from left to right by their SM scores. Remarkably, and consistent with the results for the Z-score, the comparison of the RD-scores of subgraphs within a particular density class (i.e., 3-edge and 4-edge) reveals a general trend of large subgraph RD-scores having high SM classes, and small subgraph RD scores—and sometimes negative RD-scores—having low SM classes. This trend is further substantiated when analyzing the 199 four-node subgraphs. Figures  16 , 17 , 18 show that—in each of the 4-edge, 5-edge, and 6-edge density classes—there is a statistically significant difference in subgraph RD-score between the different synchronizability classes (Kruskal–Wallis, p ≤ 0.05 for most cases). The excellent correlation between subgraph synchronizability and both subgraph abundance (Z-score) and relative difference (RD-score) indicates that subgraphs with higher synchronizability are both more statistically significant and occur in real networks at numbers that are significantly larger than those in randomized networks. In other words, highly-synchronizable subgraphs are both significant and important in real problem-solving networks. This suggests, once again, that the topology of problem-solving networks is deeply related to the dynamic properties of regular patterns of local interconnections, which constitute the basic building blocks of the networks.

Spearman’s rank correlation matrix (right panel) of the three-node RD profiles (left panel) for the directed problem-solving networks (‘Red’ through ‘Equip’; for comparison, ‘Veh1′ was excluded due to an undefined RD-score of subgraph 13). (Left panel) The three-node RD profiles for the problem-solving networks. The RD profile shows the normalized RD-score for each of the 13 connected subgraphs. (Bottom of left panel) The 13 connected subgraphs are ordered from left to right by their SM scores, and each is colored as described in Fig.  9 . The normalized RD-score of all subgraphs is determined by comparison to 500 randomized ER networks. (Right panel) Spearman’s rank correlations among pairs of RD profiles. The panels show scatter plots of ranks of subgraph normalized RD-scores in one network (ranking from high to low) versus ranks of subgraph normalized RD-scores in another network (ranking from high to low).

Box plots of RD-scores, for four-node, 4-edge subgraphs, contrasted by subgraph SM class (SM classes are numbered as in Fig.  10 ). In all cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph RD-score between the different synchronizability classes. Each panel corresponds to a particular problem-solving network. The box plots are defined as in Fig.  7 .

Box plots of RD-scores, for four-node, 5-edge subgraphs, contrasted by subgraph synchronizability class. Details and panels are as in Fig.  16 . In most cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph RD-score between the different synchronizability classes. The box plots are defined as in Fig.  7 .

Box plots of RD-scores, for four-node, 6-edge subgraphs, contrasted by subgraph synchronizability class. Details and panels are as in Fig.  16 . In most cases, the p‐value of the Kruskal–Wallis tests is less than 0.05, indicating a statistically significant difference in subgraph RD-score between the different synchronizability classes. The box plots are defined as in Fig.  7 .

In this paper, we study the relationship between the dynamic properties of three- and four-node subgraphs and their frequency in directed problem-solving networks. The dynamic behavior of a subgraph is characterized in terms of its synchronizability—measured by the probability of rapidly converging to a problem-solving equilibrium. We give an evidence that highly synchronizable subgraphs are more overrepresented and critical in real problem-solving networks.

What is the origin of the correlation revealed between synchronizability and subgraph prevalence? Although it is difficult to fully disentangle the causative relation between the dynamical properties of subgraphs and their abundance, the results in this paper suggest that both global constraints and properties of local subgraphs influence the occurrence of subgraphs in a given problem-solving network. Collaborative problem-solving activity is organized into a nested hierarchical structure with semi-independent activities at different scales performing particular tasks that contribute to the overall performance of the network. In such a nested hierarchy, subgraphs—patterns of local collaborative activities—do not exist in isolation but are embedded in a heavily interconnected network of “subgraphs within subgraphs”, which are connected to each other via patterns of local ties that are themselves subgraphs at higher scales 30 . Moreover, as pointed out in 75 , 76 , real complex network connections are often highly fluid; even when there exists an underlying fixed topological structure, connections between nodes (and so subgraphs) can adaptively become active or inactive over time. In complex problem-solving networks, we might expect the dynamics of lower-level subgraphs to change faster (i.e., on a shorter time scale) than higher-level configurations. The nested hierarchical organization along with the separation of time scales exerts a powerful effect on the synchronization and convergence of the problem-solving activity. In the short run, lower-level subgraphs will tend to rapidly synchronize to an approximate equilibrium state nearly independently of others. These accumulated intermediate steps can have dramatic effect on the rapid synchronization of the network as a whole. In other words, complex problem-solving networks will synchronize much more rapidly if there are synchronizable intermediate configurations than if there are not. It is thus plausible, as our results also suggest, that problem-solving networks are biased towards subgraphs in which it is easier to synchronized.

As was shown in Fig.  3 , subgraphs that include feedback loops (e.g., the regulated mutual, semi-clique, and clique subgraphs) are less likely to synchronize. We therefore expect that these feedback-based subgraphs will be underrepresented in the networks (when compared with random networks). However, our study reveals that, while being relatively rare, these subgraphs are sometimes overrepresented in the networks. This suggests that other factors and selective pressures, besides synchronizability, are important in determining the prevalence of network subgraphs. One possible explanation might be that high performance of problem-solving networks usually involves a trade-off between the exploration for new information that could impede effective synchronization, and the exploitation of existing knowledge that facilitates effective synchronization. It is thus plausible that the abundance of highly-synchronizable subgraphs combined with the existence of subgraphs that include feedback loops reflect the balance between these exploratory and exploitative activities in real problem-solving networks.

For small-scale problem-solving networks, it is possible to generate highly optimizable interconnections that promote synchronization among group members since linking constraints are easy to satisfy. As the complexity of problem-solving crosses over some threshold, the purposeful attainment of highly-synchronizable structures becomes more difficult. In such cases, it is possible that the observed diversity of subgraphs in problem-solving networks reflects the cumulative effect of various short-term adaptive processes that combine to produce change in the local network structure over long-periods of time. The network structure of the problem-solving activity is modified to meet changes in the requirements of previously implemented solutions. For example, starting from previous group interactions underlying vehicle development the problem-solving network will change organically as incremental or innovative changes occur at the subsystem and component levels of the car. Similar considerations apply to many other problem-solving processes including drug development, economic development, or social planning. In each problem-solving phase, various group interactions are attempted, their consequences are observed, and this information is used to guide the “re-wiring” of group interactions. The results in this paper might suggest that one of the forces behind the link rearrangement of group interactions is adaptation, present at different levels, towards effective synchronization within constraints of the problem-solving activity. The evolution of problem-solving networks shares some basic characteristics with evolutionary change in biotic populations, which involves a process of variation, selection, and transmission. If a collection of organizational patterns (subgraphs) differ in the efficiency with which they can synchronize, selection processes will favor such patterns of ties. Over time, such selection processes will lead to increased abundance of the more fit patterns of ties. The results in this paper suggest that selectivity of synchronizable configurations may guide the evolution of the problem-solving network, a mechanism that is essential for its subsequent rapidity. Changes in the structure of organizational interconnections emerge not only through selection, but also through a variety of transmission mechanisms, including mimicry, copying, learning, and re-use of network motifs 1 , 2 . Organizations that are involved in problem-solving activities attempt to implement ‘best practices’ or adopt organizational forms extracted from other problem-solving activities that were successful in the past 77 . Such imitation or copying processes would give rise to an increasing number of synchronizable network forms of organization.

The similarity in the local structure of distinct large-scale problem-solving networks, based on their subgraph significance and relative difference profiles (Figs.  9 and 15 ), leads to the intriguing possibility that subgraph abundance in problem-solving networks is a manifestation of organizational “routines.” The concept of routines plays a key role in organizational theory, strategic management, and evolutionary economics 78 , 79 , 80 . Routines are defined as recurrent patterns of interactions executed by various actors, which represent behaviors, knowledge, or capabilities held in organizational memory 79 , 81 . Organizational routines were proposed as analogous to biological genes 79 in which they are passed on by various transmission processes, such as learning and copying. Our theory could provide a bridge between the science of complex networks and the well-established concept of organizational routines. In particular, the study of network subgraphs could provide a powerful perspective on organizational routines. Our results show that large-scale problem-solving networks share repeated patterns of interdependent activities (routines) that are not idiosyncratic to a specific problem-solving organization but are universal across many distinct organizations. These repeated patterns might then be considered as problem-solving routines—patterns of activity ties occurring in the network at numbers that are significantly higher than those in randomized networks, and that are conserved across a large number of distinct problem-solving networks. The main result in this paper is that the abundance of these problem-solving routines is highly correlated with their ability to synchronize and coordinate the problem-solving activity.

In conclusion, the results in this paper reveal a strong association between a dynamic property of network subgraphs—synchronizability—and the frequency and significance of these subgraphs in real-world problem-solving networks. We suggested the possibility that selective pressures that favor more synchronizable subgraphs could account for the diverse abundance of subgraphs in problem-solving networks. Our empirical results show that unrelated problem-solving networks display very similar local network structure, defined in terms of the significance and relative difference profiles of three- and four-nodes connected subgraphs. These observations led us to the hypothesis that network subgraphs represent organizational routines that enable better coordination and control of the problem-solving activity as well as the exchange and sharing of knowledge within and across problem-solving activities.

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How to improve your problem solving skills and build effective problem solving strategies

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Effective problem solving is all about using the right process and following a plan tailored to the issue at hand. Recognizing your team or organization has an issue isn’t enough to come up with effective problem solving strategies. 

To truly understand a problem and develop appropriate solutions, you will want to follow a solid process, follow the necessary problem solving steps, and bring all of your problem solving skills to the table.  

We’ll first guide you through the seven step problem solving process you and your team can use to effectively solve complex business challenges. We’ll also look at what problem solving strategies you can employ with your team when looking for a way to approach the process. We’ll then discuss the problem solving skills you need to be more effective at solving problems, complete with an activity from the SessionLab library you can use to develop that skill in your team.

Let’s get to it! 

What is a problem solving process?

• What are the problem solving steps I need to follow?

Problem solving strategies

What skills do i need to be an effective problem solver, how can i improve my problem solving skills.

Solving problems is like baking a cake. You can go straight into the kitchen without a recipe or the right ingredients and do your best, but the end result is unlikely to be very tasty!

Using a process to bake a cake allows you to use the best ingredients without waste, collect the right tools, account for allergies, decide whether it is a birthday or wedding cake, and then bake efficiently and on time. The result is a better cake that is fit for purpose, tastes better and has created less mess in the kitchen. Also, it should have chocolate sprinkles. Having a step by step process to solve organizational problems allows you to go through each stage methodically and ensure you are trying to solve the right problems and select the most appropriate, effective solutions.

What are the problem solving steps I need to follow? 

All problem solving processes go through a number of steps in order to move from identifying a problem to resolving it.

Depending on your problem solving model and who you ask, there can be anything between four and nine problem solving steps you should follow in order to find the right solution. Whatever framework you and your group use, there are some key items that should be addressed in order to have an effective process.

We’ve looked at problem solving processes from sources such as the American Society for Quality and their four step approach , and Mediate ‘s six step process. By reflecting on those and our own problem solving processes, we’ve come up with a sequence of seven problem solving steps we feel best covers everything you need in order to effectively solve problems.

1. Problem identification 

The first stage of any problem solving process is to identify the problem or problems you might want to solve. Effective problem solving strategies always begin by allowing a group scope to articulate what they believe the problem to be and then coming to some consensus over which problem they approach first. Problem solving activities used at this stage often have a focus on creating frank, open discussion so that potential problems can be brought to the surface.

2. Problem analysis 

Though this step is not a million miles from problem identification, problem analysis deserves to be considered separately. It can often be an overlooked part of the process and is instrumental when it comes to developing effective solutions.

The process of problem analysis means ensuring that the problem you are seeking to solve is the right problem . As part of this stage, you may look deeper and try to find the root cause of a specific problem at a team or organizational level.

Remember that problem solving strategies should not only be focused on putting out fires in the short term but developing long term solutions that deal with the root cause of organizational challenges. 

Whatever your approach, analyzing a problem is crucial in being able to select an appropriate solution and the problem solving skills deployed in this stage are beneficial for the rest of the process and ensuring the solutions you create are fit for purpose.

3. Solution generation

Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or problem solving activities designed to produce working prototypes of possible solutions. 

The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold. 

4. Solution development

No solution is likely to be perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically. 

During this stage, you will often ask your team to iterate and improve upon your frontrunning solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.

Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose. 

5. Decision making 

Nearly there! Once your group has reached consensus and selected a solution that applies to the problem at hand you have some decisions to make. You will want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.

The decision making stage is a part of the problem solving process that can get missed or taken as for granted. Fail to properly allocate roles and plan out how a solution will actually be implemented and it less likely to be successful in solving the problem.

Have clear accountabilities, actions, timeframes, and follow-ups. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group. 

Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved. 

6. Solution implementation 

This is what we were waiting for! All problem solving strategies have the end goal of implementing a solution and solving a problem in mind. 

Remember that in order for any solution to be successful, you need to help your group through all of the previous problem solving steps thoughtfully. Only then can you ensure that you are solving the right problem but also that you have developed the correct solution and can then successfully implement and measure the impact of that solution.

Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way.

7. Solution evaluation 

So you and your team developed a great solution to a problem and have a gut feeling its been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback. You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives. 

None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.

Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization. 

It’s worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time. Data and insight is invaluable at every stage of the problem solving process and this one is no different.

Problem solving workshops made easy

Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques , actions, and processes. Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?

Broadly, the problem solving steps outlined above should be included in any problem solving strategy though choosing where to focus your time and what approaches should be taken is where they begin to differ. You might find that some strategies ask for the problem identification to be done prior to the session or that everything happens in the course of a one day workshop.

The key similarity is that all good problem solving strategies are structured and designed. Four hours of open discussion is never going to be as productive as a four-hour workshop designed to lead a group through a problem solving process.

Good problem solving strategies are tailored to the team, organization and problem you will be attempting to solve. Here are some example problem solving strategies you can learn from or use to get started.

Use a workshop to lead a team through a group process

Often, the first step to solving problems or organizational challenges is bringing a group together effectively. Most teams have the tools, knowledge, and expertise necessary to solve their challenges – they just need some guidance in how to use leverage those skills and a structure and format that allows people to focus their energies.

Facilitated workshops are one of the most effective ways of solving problems of any scale. By designing and planning your workshop carefully, you can tailor the approach and scope to best fit the needs of your team and organization. 

Problem solving workshop

• Creating a bespoke, tailored process
• Tackling problems of any size
• Building in-house workshop ability and encouraging their use

Workshops are an effective strategy for solving problems. By using tried and test facilitation techniques and methods, you can design and deliver a workshop that is perfectly suited to the unique variables of your organization. You may only have the capacity for a half-day workshop and so need a problem solving process to match. 

By using our session planner tool and importing methods from our library of 700+ facilitation techniques, you can create the right problem solving workshop for your team. It might be that you want to encourage creative thinking or look at things from a new angle to unblock your groups approach to problem solving. By tailoring your workshop design to the purpose, you can help ensure great results.

One of the main benefits of a workshop is the structured approach to problem solving. Not only does this mean that the workshop itself will be successful, but many of the methods and techniques will help your team improve their working processes outside of the workshop. 

We believe that workshops are one of the best tools you can use to improve the way your team works together. Start with a problem solving workshop and then see what team building, culture or design workshops can do for your organization!

Run a design sprint

Great for: 

• aligning large, multi-discipline teams
• quickly designing and testing solutions
• tackling large, complex organizational challenges and breaking them down into smaller tasks

By using design thinking principles and methods, a design sprint is a great way of identifying, prioritizing and prototyping solutions to long term challenges that can help solve major organizational problems with quick action and measurable results.

Some familiarity with design thinking is useful, though not integral, and this strategy can really help a team align if there is some discussion around which problems should be approached first. 

The stage-based structure of the design sprint is also very useful for teams new to design thinking.  The inspiration phase, where you look to competitors that have solved your problem, and the rapid prototyping and testing phases are great for introducing new concepts that will benefit a team in all their future work. 

It can be common for teams to look inward for solutions and so looking to the market for solutions you can iterate on can be very productive. Instilling an agile prototyping and testing mindset can also be great when helping teams move forwards – generating and testing solutions quickly can help save time in the long run and is also pretty exciting!

Break problems down into smaller issues

Organizational challenges and problems are often complicated and large scale in nature. Sometimes, trying to resolve such an issue in one swoop is simply unachievable or overwhelming. Try breaking down such problems into smaller issues that you can work on step by step. You may not be able to solve the problem of churning customers off the bat, but you can work with your team to identify smaller effort but high impact elements and work on those first.

This problem solving strategy can help a team generate momentum, prioritize and get some easy wins. It’s also a great strategy to employ with teams who are just beginning to learn how to approach the problem solving process. If you want some insight into a way to employ this strategy, we recommend looking at our design sprint template below!

Use guiding frameworks or try new methodologies

Some problems are best solved by introducing a major shift in perspective or by using new methodologies that encourage your team to think differently.

Props and tools such as Methodkit , which uses a card-based toolkit for facilitation, or Lego Serious Play can be great ways to engage your team and find an inclusive, democratic problem solving strategy. Remember that play and creativity are great tools for achieving change and whatever the challenge, engaging your participants can be very effective where other strategies may have failed.

LEGO Serious Play

• Improving core problem solving skills
• Thinking outside of the box
• Encouraging creative solutions

LEGO Serious Play is a problem solving methodology designed to get participants thinking differently by using 3D models and kinesthetic learning styles. By physically building LEGO models based on questions and exercises, participants are encouraged to think outside of the box and create their own responses. 

Collaborate LEGO Serious Play exercises are also used to encourage communication and build problem solving skills in a group. By using this problem solving process, you can often help different kinds of learners and personality types contribute and unblock organizational problems with creative thinking. 

Problem solving strategies like LEGO Serious Play are super effective at helping a team solve more skills-based problems such as communication between teams or a lack of creative thinking. Some problems are not suited to LEGO Serious Play and require a different problem solving strategy.

Card Decks and Method Kits

• New facilitators or non-facilitators 
• Approaching difficult subjects with a simple, creative framework
• Engaging those with varied learning styles

Card decks and method kids are great tools for those new to facilitation or for whom facilitation is not the primary role. Card decks such as the emotional culture deck can be used for complete workshops and in many cases, can be used right out of the box. Methodkit has a variety of kits designed for scenarios ranging from personal development through to personas and global challenges so you can find the right deck for your particular needs.

Having an easy to use framework that encourages creativity or a new approach can take some of the friction or planning difficulties out of the workshop process and energize a team in any setting. Simplicity is the key with these methods. By ensuring everyone on your team can get involved and engage with the process as quickly as possible can really contribute to the success of your problem solving strategy.

Source external advice

Looking to peers, experts and external facilitators can be a great way of approaching the problem solving process. Your team may not have the necessary expertise, insights of experience to tackle some issues, or you might simply benefit from a fresh perspective. Some problems may require bringing together an entire team, and coaching managers or team members individually might be the right approach. Remember that not all problems are best resolved in the same manner.

If you’re a solo entrepreneur, peer groups, coaches and mentors can also be invaluable at not only solving specific business problems, but in providing a support network for resolving future challenges. One great approach is to join a Mastermind Group and link up with like-minded individuals and all grow together. Remember that however you approach the sourcing of external advice, do so thoughtfully, respectfully and honestly. Reciprocate where you can and prepare to be surprised by just how kind and helpful your peers can be!

Mastermind Group

• Solo entrepreneurs or small teams with low capacity
• Peer learning and gaining outside expertise
• Getting multiple external points of view quickly

Problem solving in large organizations with lots of skilled team members is one thing, but how about if you work for yourself or in a very small team without the capacity to get the most from a design sprint or LEGO Serious Play session? 

A mastermind group – sometimes known as a peer advisory board – is where a group of people come together to support one another in their own goals, challenges, and businesses. Each participant comes to the group with their own purpose and the other members of the group will help them create solutions, brainstorm ideas, and support one another. 

Mastermind groups are very effective in creating an energized, supportive atmosphere that can deliver meaningful results. Learning from peers from outside of your organization or industry can really help unlock new ways of thinking and drive growth. Access to the experience and skills of your peers can be invaluable in helping fill the gaps in your own ability, particularly in young companies.

A mastermind group is a great solution for solo entrepreneurs, small teams, or for organizations that feel that external expertise or fresh perspectives will be beneficial for them. It is worth noting that Mastermind groups are often only as good as the participants and what they can bring to the group. Participants need to be committed, engaged and understand how to work in this context. 

Coaching and mentoring

• Focused learning and development
• Filling skills gaps
• Working on a range of challenges over time

Receiving advice from a business coach or building a mentor/mentee relationship can be an effective way of resolving certain challenges. The one-to-one format of most coaching and mentor relationships can really help solve the challenges those individuals are having and benefit the organization as a result.

A great mentor can be invaluable when it comes to spotting potential problems before they arise and coming to understand a mentee very well has a host of other business benefits. You might run an internal mentorship program to help develop your team’s problem solving skills and strategies or as part of a large learning and development program. External coaches can also be an important part of your problem solving strategy, filling skills gaps for your management team or helping with specific business issues. 

Now we’ve explored the problem solving process and the steps you will want to go through in order to have an effective session, let’s look at the skills you and your team need to be more effective problem solvers.

Problem solving skills are highly sought after, whatever industry or team you work in. Organizations are keen to employ people who are able to approach problems thoughtfully and find strong, realistic solutions. Whether you are a facilitator , a team leader or a developer, being an effective problem solver is a skill you’ll want to develop.

Problem solving skills form a whole suite of techniques and approaches that an individual uses to not only identify problems but to discuss them productively before then developing appropriate solutions.

Here are some of the most important problem solving skills everyone from executives to junior staff members should learn. We’ve also included an activity or exercise from the SessionLab library that can help you and your team develop that skill. 

If you’re running a workshop or training session to try and improve problem solving skills in your team, try using these methods to supercharge your process!

Active listening

Active listening is one of the most important skills anyone who works with people can possess. In short, active listening is a technique used to not only better understand what is being said by an individual, but also to be more aware of the underlying message the speaker is trying to convey. When it comes to problem solving, active listening is integral for understanding the position of every participant and to clarify the challenges, ideas and solutions they bring to the table.

Some active listening skills include:

• Paying complete attention to the speaker.
• Removing distractions.
• Avoid interruption.
• Taking the time to fully understand before preparing a rebuttal.
• Responding respectfully and appropriately.
• Demonstrate attentiveness and positivity with an open posture, making eye contact with the speaker, smiling and nodding if appropriate. Show that you are listening and encourage them to continue.
• Be aware of and respectful of feelings. Judge the situation and respond appropriately. You can disagree without being disrespectful.   
• Observe body language. 
• Paraphrase what was said in your own words, either mentally or verbally.
• Remain neutral. 
• Reflect and take a moment before responding.
• Ask deeper questions based on what is said and clarify points where necessary.   

Active Listening   #hyperisland   #skills   #active listening   #remote-friendly   This activity supports participants to reflect on a question and generate their own solutions using simple principles of active listening and peer coaching. It’s an excellent introduction to active listening but can also be used with groups that are already familiar with it. Participants work in groups of three and take turns being: “the subject”, the listener, and the observer.

Analytical skills

All problem solving models require strong analytical skills, particularly during the beginning of the process and when it comes to analyzing how solutions have performed.

Analytical skills are primarily focused on performing an effective analysis by collecting, studying and parsing data related to a problem or opportunity. 

It often involves spotting patterns, being able to see things from different perspectives and using observable facts and data to make suggestions or produce insight. 

Analytical skills are also important at every stage of the problem solving process and by having these skills, you can ensure that any ideas or solutions you create or backed up analytically and have been sufficiently thought out.

Nine Whys   #innovation   #issue analysis   #liberating structures   With breathtaking simplicity, you can rapidly clarify for individuals and a group what is essentially important in their work. You can quickly reveal when a compelling purpose is missing in a gathering and avoid moving forward without clarity. When a group discovers an unambiguous shared purpose, more freedom and more responsibility are unleashed. You have laid the foundation for spreading and scaling innovations with fidelity.

Collaboration

Trying to solve problems on your own is difficult. Being able to collaborate effectively, with a free exchange of ideas, to delegate and be a productive member of a team is hugely important to all problem solving strategies.

Remember that whatever your role, collaboration is integral, and in a problem solving process, you are all working together to find the best solution for everyone. 

Marshmallow challenge with debriefing   #teamwork   #team   #leadership   #collaboration   In eighteen minutes, teams must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one marshmallow. The marshmallow needs to be on top. The Marshmallow Challenge was developed by Tom Wujec, who has done the activity with hundreds of groups around the world. Visit the Marshmallow Challenge website for more information. This version has an extra debriefing question added with sample questions focusing on roles within the team.

Communication  

Being an effective communicator means being empathetic, clear and succinct, asking the right questions, and demonstrating active listening skills throughout any discussion or meeting. 

In a problem solving setting, you need to communicate well in order to progress through each stage of the process effectively. As a team leader, it may also fall to you to facilitate communication between parties who may not see eye to eye. Effective communication also means helping others to express themselves and be heard in a group.

Bus Trip   #feedback   #communication   #appreciation   #closing   #thiagi   #team   This is one of my favourite feedback games. I use Bus Trip at the end of a training session or a meeting, and I use it all the time. The game creates a massive amount of energy with lots of smiles, laughs, and sometimes even a teardrop or two.

Creative problem solving skills can be some of the best tools in your arsenal. Thinking creatively, being able to generate lots of ideas and come up with out of the box solutions is useful at every step of the process. 

The kinds of problems you will likely discuss in a problem solving workshop are often difficult to solve, and by approaching things in a fresh, creative manner, you can often create more innovative solutions.

Having practical creative skills is also a boon when it comes to problem solving. If you can help create quality design sketches and prototypes in record time, it can help bring a team to alignment more quickly or provide a base for further iteration.

The paper clip method   #sharing   #creativity   #warm up   #idea generation   #brainstorming   The power of brainstorming. A training for project leaders, creativity training, and to catalyse getting new solutions.

Critical thinking

Critical thinking is one of the fundamental problem solving skills you’ll want to develop when working on developing solutions. Critical thinking is the ability to analyze, rationalize and evaluate while being aware of personal bias, outlying factors and remaining open-minded.

Defining and analyzing problems without deploying critical thinking skills can mean you and your team go down the wrong path. Developing solutions to complex issues requires critical thinking too – ensuring your team considers all possibilities and rationally evaluating them. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Data analysis 

Though it shares lots of space with general analytical skills, data analysis skills are something you want to cultivate in their own right in order to be an effective problem solver.

Being good at data analysis doesn’t just mean being able to find insights from data, but also selecting the appropriate data for a given issue, interpreting it effectively and knowing how to model and present that data. Depending on the problem at hand, it might also include a working knowledge of specific data analysis tools and procedures. 

Having a solid grasp of data analysis techniques is useful if you’re leading a problem solving workshop but if you’re not an expert, don’t worry. Bring people into the group who has this skill set and help your team be more effective as a result.

Decision making

All problems need a solution and all solutions require that someone make the decision to implement them. Without strong decision making skills, teams can become bogged down in discussion and less effective as a result. 

Making decisions is a key part of the problem solving process. It’s important to remember that decision making is not restricted to the leadership team. Every staff member makes decisions every day and developing these skills ensures that your team is able to solve problems at any scale. Remember that making decisions does not mean leaping to the first solution but weighing up the options and coming to an informed, well thought out solution to any given problem that works for the whole team.

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

Dependability

Most complex organizational problems require multiple people to be involved in delivering the solution. Ensuring that the team and organization can depend on you to take the necessary actions and communicate where necessary is key to ensuring problems are solved effectively.

Being dependable also means working to deadlines and to brief. It is often a matter of creating trust in a team so that everyone can depend on one another to complete the agreed actions in the agreed time frame so that the team can move forward together. Being undependable can create problems of friction and can limit the effectiveness of your solutions so be sure to bear this in mind throughout a project. 

Team Purpose & Culture   #team   #hyperisland   #culture   #remote-friendly   This is an essential process designed to help teams define their purpose (why they exist) and their culture (how they work together to achieve that purpose). Defining these two things will help any team to be more focused and aligned. With support of tangible examples from other companies, the team members work as individuals and a group to codify the way they work together. The goal is a visual manifestation of both the purpose and culture that can be put up in the team’s work space.

Emotional intelligence

Emotional intelligence is an important skill for any successful team member, whether communicating internally or with clients or users. In the problem solving process, emotional intelligence means being attuned to how people are feeling and thinking, communicating effectively and being self-aware of what you bring to a room. 

There are often differences of opinion when working through problem solving processes, and it can be easy to let things become impassioned or combative. Developing your emotional intelligence means being empathetic to your colleagues and managing your own emotions throughout the problem and solution process. Be kind, be thoughtful and put your points across care and attention. 

Being emotionally intelligent is a skill for life and by deploying it at work, you can not only work efficiently but empathetically. Check out the emotional culture workshop template for more!

Facilitation

As we’ve clarified in our facilitation skills post, facilitation is the art of leading people through processes towards agreed-upon objectives in a manner that encourages participation, ownership, and creativity by all those involved. While facilitation is a set of interrelated skills in itself, the broad definition of facilitation can be invaluable when it comes to problem solving. Leading a team through a problem solving process is made more effective if you improve and utilize facilitation skills – whether you’re a manager, team leader or external stakeholder.

The Six Thinking Hats   #creative thinking   #meeting facilitation   #problem solving   #issue resolution   #idea generation   #conflict resolution   The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Flexibility 

Being flexible is a vital skill when it comes to problem solving. This does not mean immediately bowing to pressure or changing your opinion quickly: instead, being flexible is all about seeing things from new perspectives, receiving new information and factoring it into your thought process.

Flexibility is also important when it comes to rolling out solutions. It might be that other organizational projects have greater priority or require the same resources as your chosen solution. Being flexible means understanding needs and challenges across the team and being open to shifting or arranging your own schedule as necessary. Again, this does not mean immediately making way for other projects. It’s about articulating your own needs, understanding the needs of others and being able to come to a meaningful compromise.

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Working in any group can lead to unconscious elements of groupthink or situations in which you may not wish to be entirely honest. Disagreeing with the opinions of the executive team or wishing to save the feelings of a coworker can be tricky to navigate, but being honest is absolutely vital when to comes to developing effective solutions and ensuring your voice is heard. 

Remember that being honest does not mean being brutally candid. You can deliver your honest feedback and opinions thoughtfully and without creating friction by using other skills such as emotional intelligence. 

Explore your Values   #hyperisland   #skills   #values   #remote-friendly   Your Values is an exercise for participants to explore what their most important values are. It’s done in an intuitive and rapid way to encourage participants to follow their intuitive feeling rather than over-thinking and finding the “correct” values. It is a good exercise to use to initiate reflection and dialogue around personal values.

Initiative 

The problem solving process is multi-faceted and requires different approaches at certain points of the process. Taking initiative to bring problems to the attention of the team, collect data or lead the solution creating process is always valuable. You might even roadtest your own small scale solutions or brainstorm before a session. Taking initiative is particularly effective if you have good deal of knowledge in that area or have ownership of a particular project and want to get things kickstarted.

That said, be sure to remember to honor the process and work in service of the team. If you are asked to own one part of the problem solving process and you don’t complete that task because your initiative leads you to work on something else, that’s not an effective method of solving business challenges.

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Impartiality

A particularly useful problem solving skill for product owners or managers is the ability to remain impartial throughout much of the process. In practice, this means treating all points of view and ideas brought forward in a meeting equally and ensuring that your own areas of interest or ownership are not favored over others. 

There may be a stage in the process where a decision maker has to weigh the cost and ROI of possible solutions against the company roadmap though even then, ensuring that the decision made is based on merit and not personal opinion. 

Empathy map   #frame insights   #create   #design   #issue analysis   An empathy map is a tool to help a design team to empathize with the people they are designing for. You can make an empathy map for a group of people or for a persona. To be used after doing personas when more insights are needed.

Being a good leader means getting a team aligned, energized and focused around a common goal. In the problem solving process, strong leadership helps ensure that the process is efficient, that any conflicts are resolved and that a team is managed in the direction of success.

It’s common for managers or executives to assume this role in a problem solving workshop, though it’s important that the leader maintains impartiality and does not bulldoze the group in a particular direction. Remember that good leadership means working in service of the purpose and team and ensuring the workshop is a safe space for employees of any level to contribute. Take a look at our leadership games and activities post for more exercises and methods to help improve leadership in your organization.

Leadership Pizza   #leadership   #team   #remote-friendly   This leadership development activity offers a self-assessment framework for people to first identify what skills, attributes and attitudes they find important for effective leadership, and then assess their own development and initiate goal setting.

In the context of problem solving, mediation is important in keeping a team engaged, happy and free of conflict. When leading or facilitating a problem solving workshop, you are likely to run into differences of opinion. Depending on the nature of the problem, certain issues may be brought up that are emotive in nature. 

Being an effective mediator means helping those people on either side of such a divide are heard, listen to one another and encouraged to find common ground and a resolution. Mediating skills are useful for leaders and managers in many situations and the problem solving process is no different.

Conflict Responses   #hyperisland   #team   #issue resolution   A workshop for a team to reflect on past conflicts, and use them to generate guidelines for effective conflict handling. The workshop uses the Thomas-Killman model of conflict responses to frame a reflective discussion. Use it to open up a discussion around conflict with a team.

Planning 

Solving organizational problems is much more effective when following a process or problem solving model. Planning skills are vital in order to structure, deliver and follow-through on a problem solving workshop and ensure your solutions are intelligently deployed.

Planning skills include the ability to organize tasks and a team, plan and design the process and take into account any potential challenges. Taking the time to plan carefully can save time and frustration later in the process and is valuable for ensuring a team is positioned for success.

3 Action Steps   #hyperisland   #action   #remote-friendly   This is a small-scale strategic planning session that helps groups and individuals to take action toward a desired change. It is often used at the end of a workshop or programme. The group discusses and agrees on a vision, then creates some action steps that will lead them towards that vision. The scope of the challenge is also defined, through discussion of the helpful and harmful factors influencing the group.

Prioritization

As organisations grow, the scale and variation of problems they face multiplies. Your team or is likely to face numerous challenges in different areas and so having the skills to analyze and prioritize becomes very important, particularly for those in leadership roles.

A thorough problem solving process is likely to deliver multiple solutions and you may have several different problems you wish to solve simultaneously. Prioritization is the ability to measure the importance, value, and effectiveness of those possible solutions and choose which to enact and in what order. The process of prioritization is integral in ensuring the biggest challenges are addressed with the most impactful solutions.

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

Project management

Some problem solving skills are utilized in a workshop or ideation phases, while others come in useful when it comes to decision making. Overseeing an entire problem solving process and ensuring its success requires strong project management skills. 

While project management incorporates many of the other skills listed here, it is important to note the distinction of considering all of the factors of a project and managing them successfully. Being able to negotiate with stakeholders, manage tasks, time and people, consider costs and ROI, and tie everything together is massively helpful when going through the problem solving process. 

Record keeping

Working out meaningful solutions to organizational challenges is only one part of the process.  Thoughtfully documenting and keeping records of each problem solving step for future consultation is important in ensuring efficiency and meaningful change. 

For example, some problems may be lower priority than others but can be revisited in the future. If the team has ideated on solutions and found some are not up to the task, record those so you can rule them out and avoiding repeating work. Keeping records of the process also helps you improve and refine your problem solving model next time around!

Personal Kanban   #gamestorming   #action   #agile   #project planning   Personal Kanban is a tool for organizing your work to be more efficient and productive. It is based on agile methods and principles.

Research skills

Conducting research to support both the identification of problems and the development of appropriate solutions is important for an effective process. Knowing where to go to collect research, how to conduct research efficiently, and identifying pieces of research are relevant are all things a good researcher can do well. 

In larger groups, not everyone has to demonstrate this ability in order for a problem solving workshop to be effective. That said, having people with research skills involved in the process, particularly if they have existing area knowledge, can help ensure the solutions that are developed with data that supports their intention. Remember that being able to deliver the results of research efficiently and in a way the team can easily understand is also important. The best data in the world is only as effective as how it is delivered and interpreted.

Customer experience map   #ideation   #concepts   #research   #design   #issue analysis   #remote-friendly   Customer experience mapping is a method of documenting and visualizing the experience a customer has as they use the product or service. It also maps out their responses to their experiences. To be used when there is a solution (even in a conceptual stage) that can be analyzed.

Risk management

Managing risk is an often overlooked part of the problem solving process. Solutions are often developed with the intention of reducing exposure to risk or solving issues that create risk but sometimes, great solutions are more experimental in nature and as such, deploying them needs to be carefully considered. 

Managing risk means acknowledging that there may be risks associated with more out of the box solutions or trying new things, but that this must be measured against the possible benefits and other organizational factors. 

Be informed, get the right data and stakeholders in the room and you can appropriately factor risk into your decision making process. 

Decisions, Decisions…   #communication   #decision making   #thiagi   #action   #issue analysis   When it comes to decision-making, why are some of us more prone to take risks while others are risk-averse? One explanation might be the way the decision and options were presented.  This exercise, based on Kahneman and Tversky’s classic study , illustrates how the framing effect influences our judgement and our ability to make decisions . The participants are divided into two groups. Both groups are presented with the same problem and two alternative programs for solving them. The two programs both have the same consequences but are presented differently. The debriefing discussion examines how the framing of the program impacted the participant’s decision.

Team-building 

No single person is as good at problem solving as a team. Building an effective team and helping them come together around a common purpose is one of the most important problem solving skills, doubly so for leaders. By bringing a team together and helping them work efficiently, you pave the way for team ownership of a problem and the development of effective solutions. 

In a problem solving workshop, it can be tempting to jump right into the deep end, though taking the time to break the ice, energize the team and align them with a game or exercise will pay off over the course of the day.

Remember that you will likely go through the problem solving process multiple times over an organization’s lifespan and building a strong team culture will make future problem solving more effective. It’s also great to work with people you know, trust and have fun with. Working on team building in and out of the problem solving process is a hallmark of successful teams that can work together to solve business problems.

9 Dimensions Team Building Activity   #ice breaker   #teambuilding   #team   #remote-friendly   9 Dimensions is a powerful activity designed to build relationships and trust among team members. There are 2 variations of this icebreaker. The first version is for teams who want to get to know each other better. The second version is for teams who want to explore how they are working together as a team.

Time management 

The problem solving process is designed to lead a team from identifying a problem through to delivering a solution and evaluating its effectiveness. Without effective time management skills or timeboxing of tasks, it can be easy for a team to get bogged down or be inefficient.

By using a problem solving model and carefully designing your workshop, you can allocate time efficiently and trust that the process will deliver the results you need in a good timeframe.

Time management also comes into play when it comes to rolling out solutions, particularly those that are experimental in nature. Having a clear timeframe for implementing and evaluating solutions is vital for ensuring their success and being able to pivot if necessary.

Improving your skills at problem solving is often a career-long pursuit though there are methods you can use to make the learning process more efficient and to supercharge your problem solving skillset.

Remember that the skills you need to be a great problem solver have a large overlap with those skills you need to be effective in any role. Investing time and effort to develop your active listening or critical thinking skills is valuable in any context. Here are 7 ways to improve your problem solving skills.

Share best practices

Remember that your team is an excellent source of skills, wisdom, and techniques and that you should all take advantage of one another where possible. Best practices that one team has for solving problems, conducting research or making decisions should be shared across the organization. If you have in-house staff that have done active listening training or are data analysis pros, have them lead a training session. 

Your team is one of your best resources. Create space and internal processes for the sharing of skills so that you can all grow together. 

Ask for help and attend training

Once you’ve figured out you have a skills gap, the next step is to take action to fill that skills gap. That might be by asking your superior for training or coaching, or liaising with team members with that skill set. You might even attend specialized training for certain skills – active listening or critical thinking, for example, are business-critical skills that are regularly offered as part of a training scheme.

Whatever method you choose, remember that taking action of some description is necessary for growth. Whether that means practicing, getting help, attending training or doing some background reading, taking active steps to improve your skills is the way to go.

Learn a process 

Problem solving can be complicated, particularly when attempting to solve large problems for the first time. Using a problem solving process helps give structure to your problem solving efforts and focus on creating outcomes, rather than worrying about the format. 

Tools such as the seven-step problem solving process above are effective because not only do they feature steps that will help a team solve problems, they also develop skills along the way. Each step asks for people to engage with the process using different skills and in doing so, helps the team learn and grow together. Group processes of varying complexity and purpose can also be found in the SessionLab library of facilitation techniques . Using a tried and tested process and really help ease the learning curve for both those leading such a process, as well as those undergoing the purpose.

Effective teams make decisions about where they should and shouldn’t expend additional effort. By using a problem solving process, you can focus on the things that matter, rather than stumbling towards a solution haphazardly. 

Create a feedback loop

Some skills gaps are more obvious than others. It’s possible that your perception of your active listening skills differs from those of your colleagues. 

It’s valuable to create a system where team members can provide feedback in an ordered and friendly manner so they can all learn from one another. Only by identifying areas of improvement can you then work to improve them. 

Remember that feedback systems require oversight and consideration so that they don’t turn into a place to complain about colleagues. Design the system intelligently so that you encourage the creation of learning opportunities, rather than encouraging people to list their pet peeves.

While practice might not make perfect, it does make the problem solving process easier. If you are having trouble with critical thinking, don’t shy away from doing it. Get involved where you can and stretch those muscles as regularly as possible. 

Problem solving skills come more naturally to some than to others and that’s okay. Take opportunities to get involved and see where you can practice your skills in situations outside of a workshop context. Try collaborating in other circumstances at work or conduct data analysis on your own projects. You can often develop those skills you need for problem solving simply by doing them. Get involved!

Use expert exercises and methods

Learn from the best. Our library of 700+ facilitation techniques is full of activities and methods that help develop the skills you need to be an effective problem solver. Check out our templates to see how to approach problem solving and other organizational challenges in a structured and intelligent manner.

There is no single approach to improving problem solving skills, but by using the techniques employed by others you can learn from their example and develop processes that have seen proven results. 

Try new ways of thinking and change your mindset

Using tried and tested exercises that you know well can help deliver results, but you do run the risk of missing out on the learning opportunities offered by new approaches. As with the problem solving process, changing your mindset can remove blockages and be used to develop your problem solving skills.

Most teams have members with mixed skill sets and specialties. Mix people from different teams and share skills and different points of view. Teach your customer support team how to use design thinking methods or help your developers with conflict resolution techniques. Try switching perspectives with facilitation techniques like Flip It! or by using new problem solving methodologies or models. Give design thinking, liberating structures or lego serious play a try if you want to try a new approach. You will find that framing problems in new ways and using existing skills in new contexts can be hugely useful for personal development and improving your skillset. It’s also a lot of fun to try new things. Give it a go!

Encountering business challenges and needing to find appropriate solutions is not unique to your organization. Lots of very smart people have developed methods, theories and approaches to help develop problem solving skills and create effective solutions. Learn from them!

Books like The Art of Thinking Clearly , Think Smarter, or Thinking Fast, Thinking Slow are great places to start, though it’s also worth looking at blogs related to organizations facing similar problems to yours, or browsing for success stories. Seeing how Dropbox massively increased growth and working backward can help you see the skills or approach you might be lacking to solve that same problem. Learning from others by reading their stories or approaches can be time-consuming but ultimately rewarding.

A tired, distracted mind is not in the best position to learn new skills. It can be tempted to burn the candle at both ends and develop problem solving skills outside of work. Absolutely use your time effectively and take opportunities for self-improvement, though remember that rest is hugely important and that without letting your brain rest, you cannot be at your most effective. 

Creating distance between yourself and the problem you might be facing can also be useful. By letting an idea sit, you can find that a better one presents itself or you can develop it further. Take regular breaks when working and create a space for downtime. Remember that working smarter is preferable to working harder and that self-care is important for any effective learning or improvement process.

Want to design better group processes?

Over to you

Now we’ve explored some of the key problem solving skills and the problem solving steps necessary for an effective process, you’re ready to begin developing more effective solutions and leading problem solving workshops.

Need more inspiration? Check out our post on problem solving activities you can use when guiding a group towards a great solution in your next workshop or meeting. Have questions? Did you have a great problem solving technique you use with your team? Get in touch in the comments below. We’d love to chat!

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Into all problem-solving, a little dissent must fall

Events of the past several years have reiterated for executives the importance of collaboration and of welcoming diverse perspectives when trying to solve complicated workplace problems. Companies weren’t fully prepared for the onset of a global pandemic, for instance, and all that it engendered—including supply chain snarls and the resulting Great Attrition  and shift to remote (and now hybrid) work, which required employers to fundamentally rethink their talent strategies . But in most cases leaders have been able to collaborate their way through the uncertainty, engage in rigorous debate and analyses about the best steps to take, and work with employees, suppliers, partners, and other critical stakeholders to react and, ultimately, recover.

And It’s not just COVID-19: many organisations have had to rethink their business strategies and practices in the wake of environmental concerns, the war in Ukraine, and social movements sparked by racial injustice, sexual misconduct, and widespread economic inequity . Ours are fast-moving, complex times, rich not just in worrisome challenges but also in exciting potential—organisations that enable innovation will find ample opportunities to thrive. So now more than ever, decision makers can’t act alone; they must bring diverse perspectives to the table and ensure that those voices are fully heard . 1 Sundiatu Dixon-Fyle, Kevin Dolan, Vivian Hunt, and Sara Prince, “ Diversity wins: How inclusion matters ,” McKinsey, May 19, 2020.

But while many leaders say they welcome dissent, their reactions often change when they actually get some. They may feel defensive. They may question their own judgment. They may resent having to take time to revisit the decision-making process. These are natural responses, of course; employees’ loyalty and affirmation are more reassuring to leaders than robust challenges from the group. There is discomfort, too, for potential dissenters; it is much safer to keep your thoughts to yourself and conform  than to risk expulsion from the group. 2 Derived from this work on the evolutionary origins of social and political behavior: Christopher Boehm, Hierarchy in the Forest: The Evolution of Egalitarian Behavior , Cambridge, Massachusetts: Harvard University Press, 2001.

What’s missing in many companies, in our experience, is the use of “contributory dissent” or the capabilities required to engage in healthy if divergent discussions about critical business problems. Contributory dissent allows individuals and groups to air their differences in a way that moves the discussion toward a positive outcome and doesn’t undermine leadership or group cohesion . 3 McKinsey itself has established obligation to dissent as one of its core values alongside those focused on client service and talent development. For more, see Bill Taylor, “True leaders believe dissent is an obligation,” Harvard Business Review , January 12, 2017.

McKinsey’s research and experience in the field point to several steps leaders can take to engage in healthy dissent and build a culture where constructive feedback is expected and where communication is forthright. These include modeling “open” behaviors, embedding psychological safety  and robust debate into decision-making processes, and equipping employees with the communication skills that will allow them to contribute dissenting opinions effectively.

In this article we outline the steps leaders can take to encourage healthy dissent, and the actions teams and individuals can take to share their voices and perspectives most effectively. It takes both sides, after all, to engage in robust debate, find the right solutions, and enable lasting, positive change.

How leaders can encourage contributory dissent

Senior leaders in an organisation play a central role in ensuring that individuals and teams see contributory dissent as a normal part of any discussion. They can signal the importance of dissent by taking a series of steps to institutionalise the practice within an organisation and empower employees to share their ideas freely and productively. Specifically, senior leaders should strive to inspire rather than direct employees to collaborate, explicitly demand dissent and, taking that one step further, actively engage with naysayers (see sidebar “How to encourage healthy dissent”). 4 Leaders can also draw on McKinsey’s “influence model” for changing mindsets and behaviors: role modeling, fostering understanding and conviction, reinforcing with formal mechanisms, and developing talent and skills. For more, see Tessa Basford and Bill Schaninger, “ The four building blocks of change ,” McKinsey Quarterly , April 11, 2016.

Inspire, don’t direct

How to encourage healthy dissent.

To encourage dissent through personal leadership:

Lead to inspire, not to direct:

• Empower the group to come up with ideas: “None of us knows the answer yet, but we can work it out together if we harness the best of everyone’s thinking.”

Foster dissent by actively seeking it:

• Explicitly seek dissent; give people permission and encouragement.
• Consider including dissent as a stated organisational value.
• Make provision for open discussion in the buildup to decisions.

Welcome open discussion when it comes:

• Listen to dissenters and naysayers, and thank them for their insights.
• Recognise this as a usefully unfiltered channel for understanding the organisation’s perceptions on issues.
• Seek to bring dissenters along the decision journey, so they become positive influencers later during implementation.
• Employ deliberate techniques such as red teaming and pre-mortems to widen the debate and mitigate groupthink.

As the inspirational speaker Simon Sinek put it, “The role of a leader is not to come up with all the great ideas. The role of a leader is to create an environment in which great ideas can happen.” 5 Simon Sinek, Start with Why: How Great Leaders Inspire Everyone to Take Action , New York, NY: Portfolio, 2009. That is especially important for fostering an atmosphere of collaboration and contributory dissent. Rather than immediately jump into a discussion about solutions, one senior leader in an international organisation addressed his team’s anxiety in the wake of a crisis. “Let me guess,” he said, “you’re all feeling confused and uncertain about the way ahead. Terrific. I’m so glad we are of one mind and that we all understand our situation correctly! I’m sure that we can work it out together, but it’s going to require the best of everyone’s thinking. Let’s get started.” His authenticity and understated humor allowed him to connect with the group and inspired them to keep calm, carry on, and generate solutions that the leader alone couldn’t have come up with. Harvard professor Ron Heifetz describes this as creating a holding environment, a key element of adaptive leadership. 6 Ronald A. Heifetz and Mary Linksy, Leadership on the Line: Staying Alive through the Dangers of Leading , Boston, MA: Harvard Business School Press, 2002; Ronald Heifetz, Alexander Grashow, and Marty Linksy, The Practice of Adaptive Leadership: Tools and Tactics for Changing Your Organization and the World , Boston, MA: Harvard Business Press, 2009.

Explicitly demand dissent

It’s not enough for leaders to give people permission to dissent; they must demand it of people. In many companies, individuals and teams may (understandably) default to collegiality, not realizing that there are ways to challenge ideas while still respecting colleagues’ roles and intellect. It’s on senior leaders, then, to help employees understand where the boundaries are. In World War 1, Australia’s General Sir John Monash was determined to develop better tactics to overcome the catastrophic impasse of trench warfare. He knew there were answers to be found from the experience of soldiers in the trenches, but he needed to loosen the military discipline of blind obedience: “I don’t care a damn for your loyal service when you think I am right; when I really want it most is when you think I am wrong.” Monash scheduled open battle planning sessions and pulled in advice from whoever offered it. In doing so, he built ownership of and confidence in his plans among all ranks. The resulting orchestration of tanks, artillery, aircraft, and troops led to rapid advances along the Somme Valley, and Monash garnered respect and appreciation from his troops, whose chances of survival and ultimate victory had increased markedly.

Actively engage with naysayers

Taking the demand imperative one step further, it’s beneficial for leaders to actively seek out the views of vocal naysayers , who can turn into influential champions just by being part of the conversation. They can immediately improve the nature of business debate and may boost the quality of the final decision, although engaging with naysayers can be tough. Some dissenting opinions can be ill-informed or uncomfortable to hear. The objective for senior leaders, then, is to put their discomfort aside and listen for signs of cognitive dissonance within an organisation. As an example, front-line employees may say things like “We’re not considered strategic thinkers,” or “The company doesn’t put people first,” while senior management may actually feel as though they have made strides in both of those areas. Still, leaders need to absorb such comments, treat them as useful data points, assess their validity, and engage in what may be a challenging discussion. They may want to use red teams  and premortems , in which teams at the outset anticipate all the ways a project could fail, to frame up dissenting opinions, mitigate groupthink, and find a positive resolution. These behaviours also serve to enhance organizational agility and resilience .

How leaders can establish psychological safety

Senior leaders need to establish a work environment in which it is safe to offer dissenting views. The McKinsey Health Institute’s work on employee well-being points to a strong correlation between leadership behaviors, collaborative culture, and resistance to mental health problems and burnout : only 15 percent of employees in environments with low inclusivity and low support for personal growth are highly engaged, compared with 38 percent in high-scoring environments. 7 “ Addressing employee burnout: Are you solving the right problem? ,” McKinsey, May 27, 2022. Leaders can build psychological safety (where team members feel they can take interpersonal risks and remain respected and accepted) and set the conditions for contributory dissent by rethinking how they engage in debate—both the dynamics and the choreography of it.

The dynamics of debate

The poet and playwright Oscar Wilde described a healthy debating culture as one in which people are “playing gracefully with ideas”— listening to, and even nourishing, opposing points of view in a measured and respectful way. 8 The Complete Works of Oscar Wilde, Volume 2: De Profundis, “Epistola: In Carcere et Vinculis,” Oxford, United Kingdom: Clarendon Press, 2005. Indeed, the best ideas can emerge at the intersection of cultures and opinions. In 15th century Florence, for instance, the Medici family attracted and funded creators from across the arts and sciences to establish an epicenter of innovative thinking that sparked the Renaissance. 9 Frans Johansson, The Medici Effect: Breakthrough Insights at the Intersection of Ideas, Concepts, and Culture , Boston, MA: Harvard Business School Press, 2004. Closer to this century, we have seen cross-discipline innovations like the application of biologists’ research on ant colonies to solve problems in telecommunications routing. And in the business world, extraordinary innovations have been achieved by open-minded leaders bringing together smart people and creating the conditions for playful exploration.

To achieve a state of “graceful play,” senior leaders must carefully manage group dynamics during debates. Rather than lead with their own opinions, for instance, which might immediately carry outsize weight in the group and stifle discussion, senior leaders can hold back and let others lead the discussion . They can lean in to show genuine curiosity or to explicitly recognise when a dissenting view has changed their thinking. But by letting other, more junior voices carry the agenda and work through ideas, however imperfect, senior leaders can establish a climate of psychological safety—and garner more respect from colleagues long term. 10 Amy C. Edmondson, The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth , Hoboken, NJ: John Wiley & Sons, 2019.

Leaders will also need to be aware of cultural differences that may crop up during debates. For example, many Australians speak candidly and are happy to address issues squarely. By contrast, the concept of “face” is so important in many Asian cultures that a more circumspect approach is taken. And the Pacific and Maori cultures emphasize displays of both strength and respect. 11 Erin Meyer, The Culture Map: Breaking through the Invisible Boundaries of Global Business , Philadelphia, PA: PublicAffairs, 2014. These differences in debate dynamics really matter. They can be a great source of hybrid vigour, 12 “Heterosis, also called hybrid vigour: the increase in such characteristics as size, growth rate, fertility, and yield of a hybrid organism over those of its parents. The first-generation offspring generally show, in greater measure, the desired characteristics of both parents.” Encyclopedia Britannica , accessed September 19, 2022. if sensitively managed, or a source of conflict and disenfranchisement if not. To approach these differences in a positive way, senior leaders could undertake a mapping exercise that identifies the different styles of the cultures present, thereby providing validation and enabling pragmatic measures to integrate them.

Choreographing debate

Beyond just managing debate dynamics, business leaders must take a hand in choreographing the debate and, specifically, in helping to design collective-thinking processes  so people know how best to play their part. Business leaders may adopt a structured approach  to brainstorming, for instance, or plan strategic off-site schedules that combine deliberate thinking with “distracted” thinking—taking time to engage in a social activity, for instance—to take advantage of employees’ deep-thinking processes.

How deliberate choices by the leader can optimise a decision-making process

A leader must consciously assess each new situation and design the collective-thinking process accordingly, then articulate this so that people know how best to play their part.

In doing so, the leader should consider an array of questions, the answers to which will determine the context, for example:

• What does success look like?
• Will the organisation underwrite initial failures in the interests of agility and innovation?
• How broad and freethinking an analysis is required?
• What are the explicit expectations for contributory dissent?
• Are any topics and behaviours out of bounds?
• Who will lead the discussion, and how will comments be captured?
• Does urgency mean that it’s better to be directive?
• Who will be consulted?
• Which decisions can be delegated, and to whom?
• Whose support needs to be built?
• What parameters and boundaries exist?
• Are there interim decisions and communications required?
• What form should the deliverable outcomes take?
• When are the deliverables required?
• Direction setting on these parameters by the leader focuses the team, while also creating space for creativity and iterative learning.

To create a sustainable structure for debate, business leaders will need to consider questions relating to team structure and rules of engagement: What does success look like when it comes to contributory dissent? What topics and behaviors are out of bounds? Who will lead the discussion, and how will comments be captured? Who has the final say on decisions, or which decisions can be delegated, and to whom? (For a more comprehensive explanation, see sidebar “How deliberate choices by the leader can optimise a decision-making process.”)

Having these parameters in place can free up the team to think more creatively about the issue at hand. Establishing such protocols can also make it easier to raise dissenting opinions. At one company, people are asked to call out their underlying values or potential biases when expressing a dissenting view. During meetings of the promotion committee, for instance, a statement like “I think we are making the wrong decision” would be rephrased as “I am someone who values experience over collaboration, and this decision would risk losing too much institutional knowledge.”

How individuals and teams can engage and dissent

As we’ve shared, senior leaders can take steps to set conditions for robust discussion and problem-solving, but individuals and teams themselves must also have the right mindsets and skills for contributory dissent to work well (see sidebar “How teams and individuals can dissent effectively”). In particular, they must embrace the obligation to dissent, actively make space to analyse ideas that are different from their own, and then find ways to either iterate on others’ ideas or respectfully agree to disagree.

Embrace the obligation to dissent

How teams and individuals can dissent effectively.

For dissent to be effective, its delivery requires courage and tactical skills underpinned by sincere respect and grace. Speaking up with respect is the right thing to do, and the responsibility to do so exists, even if there is uncertainty. The following guidelines are useful in enabling effective dissent:

Prepare a welcome for dissenting views:

• Understand the context and motivations of others, appreciate their views, and syndicate your own.
• Stop and strategise before wading into the conversations, establish a solid platform for agreement, and explicitly seek permission to dissent.

Play the long game:

• Be open minded and iterative. Don’t expect to succeed on the first try.
• Listen to others for what their views might add rather than to defend your own.

Withhold assent if you need to, but do it carefully:

• Withholding assent is a legitimate option if done judiciously.
• Minimise offense to and loss of face for the decision maker.
• If principles or legality is at stake, document your dissent.

Individuals and teams need to exhibit a certain amount of humility and confidence in order to speak truth to power with respect; they must be sure for themselves that doing so is the right thing to do. To build this confidence, individuals and teams should remember that the very act of dissent can be valuable, even if the contribution itself isn’t 100 percent baked. Others can react or build on the dissenting view—which, in itself, can be a satisfying process for a dissenter. If the ultimate decision isn’t what they proposed, they still helped shape it by offering and testing a worthy possibility.

Make space to analyse different views

Individuals and teams may need time to determine their positions on an issue. During this period, it’s important to be (and seen to be) open-minded and respectful of others’ views. That means asking lots of questions, gathering information, assessing others’ motivations, and acknowledging their views before syndicating alternatives of your own. Much of this fact gathering can be done one-on-one, in a nonconfrontational way, in offline conversations rather than in a tension-filled meeting room. In these conversations, individuals could start by reaffirming a shared commitment to finding a solution to the issue at hand, their respect for the decision-making process and the group, and areas of broad agreement. They could also signal their possible intention to dissent and seek permission to do so rather than confronting people head-on. People will find it harder to refuse that permission, and will be less likely to get defensive, when approached with statements like “This is a great discussion, and I love the vision of where we are headed, but would it be OK for us to explore some alternatives for how to get there?”

Agree to iterate …

Individuals and teams that decide to offer dissenting views should agree to iterate on other solutions, rather than digging in. Their dissenting opinions should be cogent, persuasive, and open-minded—but dissenters shouldn’t expect to change hearts and minds on the first try. They should plant seeds gently and bide their time; they might even see their idea come back as someone else’s. The critical skill required here is active, open listening: dissenters should listen carefully for others’ additive insights and find ways to build on them. In their contributory dissent, individuals and teams can take a moment to summarize what others have said and then use statements like “Can I offer another take?” and then allow the momentum of the conversation to take over.

… or agree to disagree

But what happens if, after all the considered and tactful input, the dissenter still believes a decision is heading in the wrong direction? In our experience, withholding assent then becomes a legitimate option: people shouldn’t agree if they don’t agree. This is where all the careful, respectful groundwork the dissenter has done can pay dividends. In fact, a dissenting view gains even more power when an individual can say something like, “I still believe in my alternate solution, but I’m grateful for the opportunity to contribute to this process, and I respect that you have the final say.” In this case, the dissenter is supporting the leader while flagging that the open debate hasn’t convinced them to change their initial view.

Of course, withholding assent should be a relatively rare action, taken only after an individual or team has shown that they can accommodate other views and have aligned with the consensus when they believe it’s right to do so. Think of US Supreme Court associate justice Ruth Bader Ginsburg, who joined the consensus view on many decisions but who is especially celebrated for the positive changes that arose from her highly influential dissenting opinions on issues such as gender equity, human rights, and religious freedom.

Contributory dissent can help strengthen employee engagement, unlock hidden insights, and help organisations solve tough challenges. But putting it into practice takes courage and humility, and it won’t just happen by accident. Leaders need to be intentional about welcoming challenges to their plans and opinions, even when it’s uncomfortable to do so. They need to establish cultures and structures where respectful debate can occur and where individuals and teams feel free to bring innovative—and often better—alternative solutions to the table.

Ben Fletcher is a senior partner in McKinsey’s Sydney office, Chris Hartley is a partner in the Melbourne office, Rupe Hoskin is a senior expert in the Canberra office, and Dana Maor is a senior partner in the Tel Aviv office.

The authors wish to thank Jacqueline Brassey, Nikki Dines, Richard Fitzgerald, Sam Hemphill, Ayush Jain, Jemma King, and Martin Nimmo for their contributions to this article.

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The Mind-Expanding Value of Arts Education

As funding for arts education declines worldwide, experts ponder what students — and the world at large — are losing in the process.

By Ginanne Brownell

This article is part of our special report on the Art for Tomorrow conference that was held in Florence, Italy.

Awuor Onguru says that if it were not for her continued exposure to arts education as a child, she never would have gotten into Yale University.

Growing up in a lower-middle-class family in Nairobi, Kenya, Ms. Onguru, now a 20-year-old junior majoring in English and French, started taking music lessons at the age of four. By 12, she was playing violin in the string quartet at her primary school, where every student was required to play an instrument. As a high school student on scholarship at the International School of Kenya, she was not only being taught Bach concertos, she also became part of Nairobi’s music scene, playing first violin in a number of local orchestras.

During her high school summer breaks, Ms. Onguru — who also has a strong interest in creative writing and poetry — went to the United States, attending the Interlochen Center for the Arts ’ creative writing camp, in Michigan, and the Iowa Young Writers’ Studio . Ms. Onguru, who recently returned to campus after helping organize Yale Glee Club’s spring tour in Kenya, hopes to become a journalist after graduation. She has already made progress toward that goal, serving as the opinion editor for the Yale Daily News, and getting her work published in Teen Vogue and the literary journal Menacing Hedge.

“Whether you’re in sports, whether you end up in STEM, whether you end up in government, seeing my peers — who had different interests in arts — not everyone wanted to be an artist,” she said in a video interview. “But they found places to express themselves, found places to be creative, found places to say things that they didn’t know how else to say them.”

Ms. Onguru’s path shows what a pivotal role arts education can play in a young person’s development. Yet, while the arts and culture space accounts for a significant amount of gross domestic product across the globe — in the United Kingdom in 2021, the arts contributed £109 billion to the economy , while in the U.S., it brought in over $1 trillion that year — arts education budgets in schools continue to get slashed. (In 2021, for instance, the spending on arts education in the U.K. came to an average of just £9.40 per pupil for the year .)

While experts have long espoused the idea that exposure to the arts plays a critical role in primary and secondary schooling, education systems globally have continually failed to hold it in high regard. As Eric Booth, a U.S.-based arts educator and a co-author of “Playing for Their Lives: The Global El Sistema Movement for Social Change Through Music,” said: “There are a whole lot of countries in the world that don’t have the arts in the school, it just isn’t a thing, and it never has been.”

That has led to the arts education trajectory heading in a “dark downward spiral,” said Jelena Trkulja, senior adviser for academic and cultural affairs at Qatar Museums , who moderated a panel entitled “When Arts Education is a Luxury: New Ecosystems” at the Art for Tomorrow conference in Florence, Italy, organized by the Democracy & Culture Foundation, with panels moderated by New York Times journalists.

Part of why that is happening, she said, is that societies still don’t have a sufficient and nuanced understanding of the benefits arts education can bring, in terms of young people’s development. “Arts education is still perceived as an add-on, rather than an essential field creating essential 21st-century skills that are defined as the four C’s of collaboration, creativity, communication and critical thinking,” Dr. Trkulja said in a video interview, “and those skills are being developed in arts education.”

Dennie Palmer Wolf, principal researcher at the U.S.-based arts research consultancy WolfBrown , agreed. “We have to learn to make a much broader argument about arts education,” she said. “It isn’t only playing the cello.”

It is largely through the arts that we as humans understand our own history, from a cave painting in Indonesia thought to be 45,000 years old to “The Tale of Genji,” a book that’s often called the world’s first novel , written by an 11th-century Japanese woman, Murasaki Shikibu; from the art of Michelangelo and Picasso to the music of Mozart and Miriam Makeba and Taylor Swift.

“The arts are one of the fundamental ways that we try to make sense of the world,” said Brian Kisida, an assistant professor at the University of Missouri’s Truman School of Public Affairs and a co-director of the National Endowment for the Arts-sponsored Arts, Humanities & Civic Engagement Lab . “People use the arts to offer a critical perspective of their exploration of the human condition, and that’s what the root of education is in some ways.”

And yet, the arts don’t lend themselves well to hard data, something educators and policymakers need to justify classes in those disciplines in their budgets. “Arts is this visceral thing, this thing inside you, the collective moment of a crescendo,” said Heddy Lahmann , an assistant professor of international education at New York University, who is conducting a global study examining arts education in public schools for the Community Arts Network. “But it’s really hard to qualify what that is.”

Dr. Lahmann’s early research into the decrease in spending by public schools in arts education points to everything from the lack of trained teachers in the arts — partly because those educators are worried about their own job security — to the challenges of teaching arts remotely in the early days of the Covid pandemic. And, of course, standardized tests like the Program for International Student Assessment, which covers reading, math and science, where countries compete on outcomes. “There’s a race to get those indicators,” Dr. Lahmann said, “and arts don’t readily fit into that.” In part, that is because standardized tests don’t cover arts education .

“It’s that unattractive truth that what gets measured gets attended to,” said Mr. Booth, the arts educator who co-authored “Playing for Their Lives.”

While studies over the years have underscored the ways that arts education can lead to better student achievement — in the way that musical skills support literacy, say, and arts activities lead to improved vocabulary, what have traditionally been lacking are large-scale randomized control studies. But a recent research project done in 42 elementary and middle schools in Houston, which was co-directed by Dr. Kisida and Daniel H. Bowen, a professor who teaches education policy at Texas A&M, is the first of its kind to do just that. Their research found that students who had increased arts education experiences saw improvements in writing achievement, emotional and cognitive empathy, school engagement and higher education aspirations, while they had a lower incidence of disciplinary infractions.

As young people are now, more than ever, inundated with images on social media and businesses are increasingly using A.I., it has become even more relevant for students these days to learn how to think more critically and creatively. “Because what is required of us in this coming century is an imaginative capacity that goes far beyond what we have deliberately cultivated in the schooling environment over the last 25 years,” said Mariko Silver, the chief executive of the Henry Luce Foundation, “and that requires truly deep arts education for everyone.”