solve complex problems

How to be successful at problem-solving (3 criteria)



We spend much time and effort on problem-solving. The moment we are dealing with complex issues, the game changes completely. The logic and approach needs to be different. It requires a change in mindset to be able to solve complex problems.

Most of us have a good idea of how to solve problems. The obstacle is that the process we use is one of solving simple or complicated problems, not complex ones.

We are well into the 21st century, and very few institutions (particularly professional bodies) train on complex problem-solving. The prevailing mindset is very much one of causality. This approach and attitude is a total waste of time when you are trying to deal with anything complex.

The 'scientific method' is useful for solving some types of problems. With linear problems, the objective is to understand the logic and use causality to get to the root cause of the problem. However, when it comes to social issues where humans are involved, the scientific method will not help. We need something that can deal with complexity. 

SenseCatcher together with the Visual SenseMaking framework, we can confidently do a great job with complex problems.

Three simple criteria will help you get on the right track:

  • Understand - what is the nature of your problem (simple, complicated or complex)?
  • How to - get to grips with complexity.
  • Use Visual SenseMaking framework to solve the problem with SesneCatcher.


Types of Problems

complicated problems

Example of a Complicated Problem - Behaviour is predictable. This is not Complex.

The first check is to make sure you know what type of problem you are looking at: 

  • is it simple?
  • is it complicated? or
  • is it complex?

The best way to identify if you are dealing with simple, complicated or complex problems, is to compare them – click on the button below to see the table.

Many promise the ultimate guide to problem-solving in terms of steps. From the table above, you can see it is pointless to provide a set of steps. It works for complicated problems but not for complex issues. That is why understanding the next two aspects is essential.


Problem Solving

There are two parts to solving problems:

Problem-solving is nothing more than finding a process of tackling a challenge that does not have a known solution.

  1. Understanding the problem - we avoid using the term 'defining the problem' as this suggests that we can identify all aspects of the problem from the outset. At best, we discover.
  2. How do we use what we know to get the outcomes we want?

The repertoire involves a series of iterative and interactive cycles:

  • Understanding the situation
  • Integration of information
  • Modifying, revising, and refining information
  • cyclical process

    Before we can get to the framework, we need to understand the nature of complexity and complex adaptive systems (CAS).



    Complexity is a fundamental feature of our 'real' world. We cannot explain by using the analytical method. In modern science, if something is too complex to be understood, we break it down into manageable portions. Subunits are be analyzed separately and then put together again - 'presto' we solve the problem. However, this reductionist method does not work with complex issues.

    When we do solve complex problems using the scientific approach (trust me - many people do precisely this), we create more problems than we solve.

    complex systems

    The major weakness in current science is that to understand a problem, we cut it up into separate components and analyze these independently.

    With complex systems, it does not work. Not only do they consist of the sum of its parts, but there are intricate, interwoven relationships between the components. The moment the system becomes split up, the analytical method destroys the very thing that we need to understand. We are not dealing with machines but live dynamic entities – the dynamics are where the problems are triggered.

    We have been aware of the complexity of the world as far back as 1736 (Euler). Still, it was only in the 1950s that work on complexity started to become prominent. Now, with the exponential growth of technology, it is urgent to understand what we are dealing with, especially when it involves humans. We have to deal with what we do not understand, and we need a new way of thinking – complexity.


    Complex Adaptive Systems (CAS)


    CAS is a particular class of complex systems that has the capacity for adaptation, change, and evolution. Complex systems have many elements, rich interactions and many working parts - all of which are connected. There is much that is unknown, and the behaviour is unpredictable.

    Complex systems are somewhere between order and chaos.

    Complex Adaptive Systems

    The easiest way to describe CAS is to identify their characteristics – click the button below to see the main properties of CAS.

    Complexity is the dynamic properties of the system and results from the interactions between the parts (agents) within the system. A system never exists within a tight, closed boundary. The boundaries are fuzzy, and any unit of the system needs to be seen as being complex in its own right, and simultaneously part of a more extensive system. 


    For example, an organization consists typically of several departments. Those departments are composed of people (agents) who are members of clubs, families, and societies — each with their own cultures, aspirations, goals and values. The agents learn and adapt their behaviours in response to their environment and its history – the dynamic is constant and unpredictable. 


    Implementing any changes or addressing any problems cannot proceed with a cause-and-effect approach. It only leads to more significant problems, and any 'solutions' can trigger unexpected consequences. 


    We cannot focus on the individual components of the system, but instead on the interactions. Visual SenseMaking is very useful and powerful, as it allows us to do just that. The properties of the system emerge from the interactions; not within the individual components. 


    Another critical issue is that the internal structure of any complex system is new – it is self-organizing. Related to problem-solving, humans have a fantastic ability to circumvent orders, hence why imposing a 'solution' is quite meaningless. 


    Let's take the 'family' as an example of a complex adaptive system. Most families exist as a group, despite the challenges that occur now and then. However, occasionally there can be an abrupt breakup to the surprise of the other family members when an individual sudenly has a tantrum and breaks away from the rest of the family. What might appear to be bizarre behaviour is nothing more than a nonlinear reaction to change, seen as a disproportionate response in the other members of the family.

    Solving problems of this nature, understanding the dynamics and relationships within the system, is where the substance of the problem lies.

    'the most important thing in communication

    is to hear what isn't being said'.

    How CAS Behaves


    When dealing with complex problems, the focus of attention should be on the interactions (dynamics) between the components (agents) and the effects of the stimulus inputs. The properties of the system emerge from the interactions. They are not contained within the individual components.

    Complex adaptive systems (CAS):

    • are modulated – they are unforecastable and unpredictable,
    • there is no linear causality; it has disposition and propensity (disposition is about probability - about how the system might change - not a predictive mindset),
    • propensity - is an aspect of the system that appears stable, but you do not know why.

    We start with understanding the state of the system. Then we nudge the system in the direction we want. While at the same time observing the effects of the input(s) along the way.

    The focus should be about the journey, responding to changes along the way – steering the system in the direction we desire. It is not about deciding on the endpoint (solution), nor forcing / imposing changes onto the system.

    The approach works from the inside out, not the outside in - Bottom-up.

    Every management method in the last few decades starts by defining where things should be and then filling in the gaps. Complex adaptive systems reverse this thinking.

    The only three things we can manage in a complex system are:

    complex adaptive systems

    When managing a CAS, we are maintaining an ecosystem (systems within systems). Whatever we do, we can be confident that there will be unintended consequences. One size does not fit all – solutions and mechanisms used yesterday do not apply to today. We need to get a mindset of flow and adhere to the three principles mentioned above. A solution is not the blueprint (strict rules) of the desired destination. It is more like a compass that sets a direction, allowing for the emergence of the probable that will inevitably adjust the route.

    The critical thing to remember is that even if we have a complete understanding of the individual parts of a system, we will not understand the behaviour of the bigger system. CAS systems have a high capacity to adapt, giving them resilience when faced with change, inputs or perturbations.

    Vector Compplex systems

    Outcomes and outputs cannot define complex systems. They are vectors. By this, we mean individual agents have their agendas and roles, which change over time, pulling and pushing the system in unpredictable ways with different intensities, directions and rates.

    The mindset of complex problem solving is a 'verb'. It is about the activity and discovery (a dance). The process is both dynamic and, as information unravels, it becomes an emergent property. The process both -influences and gives the 'vector' direction. The mindset is about anticipating change.

    Problem-solving perceived as a 'noun.' The focus shifts to the 'end solution' driven by the hypothesis. The mindset and everything we do becomes about predicting change. 

    The noun view assumes we know a priori the path that the problem-solving process will take – this is appropriate for complicated problems but not for complex social issues. By taking the 'verb' position, we can estimate its range of reasonable possibilities. Establishing an elastic and permeable boundary, we create a notion of containment that acts as a dynamic scaffold.

    Paying attention to the tension in the process is vital. It allows for a broad range of ideas, perspectives and unexpected interactions between individuals to emerge. These have the potential to create new meaning and new directions.


    We can only understand a complex system by interacting with it, not by analyzing it or modelling it. Actions and interventions (inputs) must be as a result of the data, not before the data – the attitude is one of discovery. For example, the stock market exhibits adaptive behaviour and emergence. We see up and downtrends in the stock market, and yet there is no mathematical model that can predict which way the market will go next. Nor is there an organization or group dictating the direction of the stock market. No institution sets the prices or demand.

    interact with complex adaptive system

    A mind-shift is required. Put aside the need for the 'correct' and clear-cut answer. Such an attitude assumes that complex problems are nothing more than a machine, and all that is required is to find the broken piece and replace it. It is naïve and misleading to operate in a cause and effect frame of mind and dive into a bag of tricks for the solution. The real world, and the problems worth solving, are complex, and there are no recipes for them. The transition is made easy with Visual Thinking. Before you know it, you will be very comfortable in dealing with complexity and uncertainty.

    Visual SenseMaking Framework (the 3rd Criteria)

    We recommend you read the in-depth explanation of Visual SenseMaking (VSMF).

    Visual SenseMaking engages with complexity to reveal patterns which explain and map the thinking process. The framework is a dynamic architecture of discovery.

    SenseCatcher - Tool for complex problem-solving

    SenseCatcher incorporates the principles of VSMF.

    It enables you to map and visualize the dynamics in a complex system. You can see the interactions and relationships between the different components of the problem, and track the changes over time.

    The map records a history of the system and captures the rich data collected.

    Visual problem solving provides us with a framework of discovery and sees things beyond biases and constraints.

    Visual mapping - SenseCatcher enables you to effectively solve complex problems visually, making the process of solving complex problems possible.

    In basic terms, you must both collect data and make sense of what you have.

    Your aim is to develop a narrative that gives structure to the complex system you are at.

    With SenseCatcher you can construct a picture (Visual SenseMap) of the situation, track the interactions, gather the data and respond accordingly.


    SenseCatcher is an intuitive tool. You do not need to know the theory to use SenseCatcher.

    However, the theory will most certainly help - it describes, explains and predicts complex process and behaviours.

    Visual Thinking, complexity and sense-making are intertwined. Understanding this mix provides us with a means to both get a grasp and take action in situations where things are just a little too much for our cognitive capacity.

    SenseCatcher is empowering, we can now do thinks and 'go places'!

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