Visuality

The Practice & Art of Thinking


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Chaos, Complexity and Sense Making in Problem Solving

It occurred to me recently that these concepts can be daunting and a serious turn-off for some. They are, however, very useful concepts, especially in the highly-changing and complex environment we live in. If we want to solve problems, it would be smart for us to understand them.

To attempt to explain them, let us consider the game of pick-up sticks also known as Mikado (originating in Hungary).

image source unknown.

image source unknown.

When we drop the sticks, they all fall in a heap and there is no rhyme nor reason about the way they land. They are just a bunch of beautifully shaped sticks, randomly scattered on a table. It appears chaotic.

Problems at first seem very much like this – they are confusing and jumbled up – a little like life can be. We touch on something and something else is affected. Just like the sticks, you attempt to move one stick and other sticks move.

What one really needs to do is approach the situation and look for the patterns, the leverage points, and the relationships between the sticks. We need to create meaning and make sense of the data (sticks) in front of us.

The sticks stop appearing to be chaotic because we have been through a process of sense making and essentially ‘tamed’ the chaos. We have attached meaning to the relationships between the sticks, the spaces and gaps. We start to observe and spot the emerging points where action can take place.

After ‘listening’ to the data – observation and learning – we start to tame the chaos. It becomes familiar and we feel comfortable with the patterns that once were so strange and unfamiliar. Nothing has changed; all we did was go through a process of reasoning and sense making.

Problems are very much like this. At first, we are faced with unfamiliar and a highly-interconnected jumble of issues but, as we slowly interact with the data, we learn and make sense. We start to be able to plot the landscape and it becomes familiar. At this point, we are able to develop a theory that explains the situation and we are able to identify solutions to the problem.

I have not explained what chaos, complexity or even sense making is here because I have done so in many previous posts. What I wanted to achieve in this post is to use a metaphor of the game ‘pick-up sticks’ to illustrate how simple these concepts can be and how they are interrelated.

Reality remains chaotic and complex – the sticks remain a heap – all that has changed is that we are able to articulate and understand it. We tame it not by having changed anything; the only thing that has changed is that we have internalized and understood the reality. Reality becomes familiar and we are able to navigate through the various situations.

Image source - LOR

Image source – LOR

 


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REASON – A USEFUL COMPONENT FOR PROBLEM SOLVING

I normally would present the complete ‘picture’ – in this case, the ‘method’ for problem solving – and then explain what I mean by the individual components of the method. This assumes that the method consists of a combined set of components, which is the case here.

I am using this approach for the simple reason that I think the ‘journey’ (to use an over-used term) is important, allowing us to develop and visualise a personal version of the whole, which would be negated if presented with the conclusion upfront. I will add, many principles of the method have already been touched on throughout this blog.

The method consists of a mix of concepts and specific principles. In this post I will be looking briefly at aspects of the concept of ‘Reason’.

The notion ‘reason’ is important but we tend to park it in the ‘hard basket’ for the simple reason that it has connotations of ‘philosophy’ and such discussion can have the feeling of talking to someone who has forgotten to take their medication – it all seems a bit ‘random’ – as some New Zealanders would say.

Let us quickly look at why reason is important. A great example is that captured in one of Popper’s books (title at end of post), where he suggests that a healthy society is one that is open to ideas and reason. In contrast, non-open societies are (as Popper states) ‘barbaric’, with no capacity for sympathy or understanding for others and diversity of opinion (I could not agree more. We can currently see this happening in our world).

urlWithout the ability to reason, we cannot progress and understand the world around us. We reason to consciously make sense of things and to establish and verify facts. This is exactly what one needs to do to get to grips with any problem situation.

We tend to confuse the term ‘reason’ with logic and use them interchangeably but they are different. To explain, we can use the example of the difference between movement and locomotion. All locomotion is movement but not all movement is locomotion. A tree moves but it does not locomote because it is rooted in the ground. Likewise, all logic is reason but not all reason conforms to the standards of logic.

If logic is the map of what’s really “out there”, reason is the process of trying to read and follow the map. Using another example – reason is the application of logic to one’s perception of the real world, like engineering is the application of physics.

Simply put, reasoning is the actual process of evaluating information and applying logic to arrive at an appropriate (correct) conclusion.

It might now be clear to see why refining our skill of reasoning is essential to good problem solving. It should be considered as the overall concept of the method of problem solving – the balancing force.

I have used the metaphor of the spinning top before (see here in relation to design thinking). We could consider reason as the overall resultant force that keeps the spinning top performing at its optimum – in this case, good problem solving could be seen as a spinning top humming away, where reason is the element that feeds all the other forces and ensures they are performing at peak condition.

From this, it is not too difficult to see why it is vital that we are eloquent at reasoning and understand the importance of reason in relation to good problem solving practice. The cartoon below by Luiz Oswaldo Carneiro Rodrigues stresses the point that data should not be forced and distorted to do what you want it to do. One needs to use reason at all times.

Treat Your Data with Reason - Not With Force. Cartoon by LOR

Treat Your Data with Reason – Not With Force. Cartoon by LOR

I look briefly at reason and ‘listening to data’ shortly in the future.

Book: By Karl Popper – The Poverty of Historicism; The Open Society and Its Enemies. First published in London in 1945, Russia in 1992 and US 2013. This book was on the Modern Library Board’s 100 best nonfiction books of the 20th century. It criticizes Marx, amongst others, for relying on historicism to underpin their political philosophies.


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Problems – What is this?

When we say we have a problem or we need to solve a problem, we tend to think of a situation that is negative or unpleasant and, at the back of our minds, we would simply like the problem to ‘go away’. The reason for this tends to be that we were not expecting the circumstances to arise in the first place. It will also require some energy to deal with and we are often taken ‘off course’ – this can be frustrating and cause us to have negative feelings towards this thing we refer to as problems.

This negative feeling is probably due to the fact that we are not equipped to deal with unknowns and uncertainty.

If we could have a method or the knowledge that, if we approach ‘problems’ in a systematic way, then we might not go through the uncomfortable stage of wrestling with the confusion and uncertainty. A method would immediately give us the confidence that we will be able to sort the problem out and become productive very quickly and not spend time in a state of confused negative energy.

Original from CartoonStock - adopted by Rui Martins

Original from CartoonStock – adopted by Rui Martins

Essentially, we need to realise that problems are not all bad – it is simply the attitude we have towards them. Professionals who routinely solve problems for a living, especially problems within their field of expertise, will generally look forward to solving ‘the problem’. They intuitively know how to go about solving the problem and probably have developed an explicit, or at least tacit way, to solve problems of the nature they are frequently faced with.

This is all good so far. However, when things change – and change is becoming the norm – professionals can quickly fall into a routine or frame of mind and they will not notice that some initial conditions have changed. This is where the problem becomes serious. It is serious because professionals will be solving the wrong problem.

They have not understood what the problem is that they are solving. Most problems will have many variables that act on the problem and all variables need to be addressed for the problem to be solved. Making sense of these variables is not easy and, depending on the complexity of the problem, it can be quite difficult to get a good understanding of the variables and dynamics between them.

Shifting target - image recreated by Rui Martins. Original unknown.

Shifting target – image recreated by Rui Martins. Original unknown.

The level of complexity increases exponentially and very often one is not able to quantify the variables as an exact science. Many problems need to be solved sooner rather than later and one cannot research the problem to death to make sure we know what it is that we are dealing with. We need a fairly good method that enables us to quickly gain a deep understanding of the issues we are dealing with and what the root causes are – so that we can solve ‘the problem.

I do not like processes that require long upfront research periods. My objection is simple, things change too fast and often the terms of reference do not get adjusted. By the time we understand the problem, it has morphed into something else and we end up solving half of the problem or the wrong problem.

In the next few months, I will be proposing an approach to problem solving that will enable us to have a good degree of confidence that we can make sense of any simple or complex problem. Before this though, I will be exploring a variety of issues and revisiting some topics I have blogged about previously within a problem solving perspective.


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Japan’s Educational Reforms – Paving the Way for Better Problem Solvers

In 2007, Japan’s Prime Minister made education his nation’s top agenda.

Why?

Innovation – is the transformation of knowledge and ideas into commercially successful products. Innovation has been the key factor behind the rise in living standards since the Industrial Revolution. We talk of the knowledge society and knowledge worker as a result.

The problem is solved only temporarily - the umbrella will soon fill up with water! Image source unknown

The problem is solved only temporarily – the umbrella will soon fill up with water! Image source unknown

The driver behind transforming ideas into products is creativity and the process is one of solving a problem – in this case it is one of innovation.

Getting back to Japan. The country has a long history of importing ideas from the rest of the world. It has been good at adapting and transforming ideas – it was essentially an incremental innovator.

Japan improved and tweaked products and processes that had been developed by other countries. This took place in closed networks of organisations, where promotion was seniority-based. There was lifetime employment, internal research and the norm was to have in-house training. The system worked for awhile, however, like any relatively closed system, it cannot adapt to change. The destiny, as Prigogine would say, is for the organization to fail.

After the 90s, Japan started to invest seriously in research and innovation. It spent 3.2% of GDP on R&D. Japan knew that it needed to shift from catching up with the rest of the world towards developing its own fundamental product innovation through creativity. Despite this goal, its leading organisations like Sharp, Sony and Panasonic are struggling.

There are some fundamentals at play here. Japanese culture is conservative. Its educational system has been one of rote learning; its researchers are not the best in the world. The companies remain bureaucratic and hierarchical and lack the dynamism and agility that is required for innovation to flourish.

Image source unknown. A little old fashioned I would say!

Image source unknown. A little old fashioned I would say!

The Japanese Prime Minister identified that the schooling system had to shift from rote learning (maintenance of the status quo) to one where the emphasis was on learning to solve problems. This is a very important and fundamental shift. It recognizes that a healthy reality is an open, dynamic and complex system. The map to guide any enterprise into the future is not cast in stone. It emerges and gets addressed constantly where the attitude is one of solving problems creatively. Knowledge is temporary – projects are the new organizational structure – they are temporary knowledge organisations. This term was coined by me (Rui Martins) and Kim Sbarcea in 2003.


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Humans are Highly Rational – Really?

Last week, I proposed that it is fallacious to believe that we can manage change by design and control, coupled with the belief that humans are perfectly rational at all times.

To shed some light on just how rational we humans are, I will look at what Daniel Kahneman has to say on the topic.

Daniel Kahneman is a psychologist who received the 2002 Nobel Memorial Prize in Economics. I am reading his book ‘Thinking, Fast and Slow’ (2011). It is a visual feast – I could not agree more with Kahneman’s thesis.

Firstly, the prevailing economic assumption is that all our thinking on decision making and planning is based on the following assumption: …agents are motivated by pure self interest, are capable of making rational economic decisions even in very complex situations.

spock-to-destroy-your-home-planets-ecosystem-for-imaginary-wealth-is-highly-illogical

There are a few problems with the above statement. Perfect knowledge does not and will never exist, nor will any individual have access to all knowledge. This means that all economic activity implies risk – we make decisions under conditions of uncertainty and limited information, particularly relating to the future.

Secondly, humans are individuals. We do not all like the same things; we are culturally different and so on. Utility function is not a constant but a variable. The other major problem with the statement is that it ignores the reality that humans have inner conflicts. We struggle between short-term and long-term goals (e.g., eating chocolate cake and losing weight) or between individual goals and societal values. Such conflicts may lead to “irrational” behaviour involving inconsistency, psychological paralysis, neurosis and psychic pain. Further irrational human behavior can occur as a result of habit, laziness, mimicry and simple obedience.

Kahneman’s central message is very important – human reason, if left to its own devices, is apt to engage in a number of fallacies and systematic errors. Rational behavior in decision making and planning is a ridiculous premise to build any theory on. Yet, many theories and models are anchored in this fallacy.

Let me give you a very simple example – it is called the law of small numbers – people seem to be very comfortable to draw very important conclusions from little evidence. We are really talking about rules of thumb also known as heuristics. The issue stems from the fact that people are not comfortable with uncertainty, coupled with the fact that as humans we are constantly seeking patterns. We have a tendency (referred to as system one thinking – also known as spontaneous non-reflective thinking) to draw conclusions and have a bias for small non represented numbers.

A good example of this is the ‘Mozart effect’. A study proposed that playing classical music to babies and young children might make them smarter. This study triggered a massive industry of books, CD and videos overnight.

The study by psychologist Frances Rauscher was based upon observations of just 36 college students. However, the test was only conducted once where students who had listened to Mozart “seemed” to show a significant improvement in their performance in an IQ test. Before long, the media got wind of this and people were convinced that listening to Mozart made them smarter. However, in 2007 a serious and rigorous extensive study was conducted by the Ministry of Education and Research in Germany, they concluded that the phenomenon was “nonexistent”.

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If we think of this, we seem to do this all the time. We are prone to generalizations and biases. The amazing fact is that we actually believe this stuff and act on it. The risk is huge, particularly if we are going to build a discipline like change management around very fragile theory and assumptions.

We have the capacity to be rational – it is referred to as system 2 thinking, but the problem is that it is slow. We of the human species like fast thinking. We like decisive people, people who think on their feet. We see that as being ‘smart’. Yet, Kahneman proves that is not correct. It is a very important discovery that Kahneman bring to us – if we want to make better decisions in our society and personal lives, we must become aware of the biases built in to us. I seriously recommend this book, we will start to select better leaders and make better decisions.


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How do we deal with change?

The next few posts are loosely connected under the theme of ‘rationality’. They explore rationality from different perspectives.

Change is not an easy thing to manage. To be able to use a systematic approach, like a formula, to orchestrate change would be great. There are many methods and theories that have been written about the promise to facilitate change.

Some approaches are quite complicated and require the ‘conditions to be just right” for the method to work. In other words, they are fragile and might only work in very specific conditions.

Change management has evolved over the last fifty years. Up to the beginning of the 1990s, the focus was on establishing the foundations and exploring how humans experience change. The underlying theory was based on psychology and human behaviour.

The next decade up to 2000, the principles were applied to business and organizational change management. Up to 2012, we saw the formalization of the profession and the establishment of procedures and rigorous methods. We are now seeing the maturing of the discipline.

There is much to be done for the practice of change management tends to be reactive and ad hoc. The body of knowledge is contradictory and lacking in empirical evidence. There are many hypotheses that have not been challenged or tested. The discipline is now ready to learn – we hope.

We need firstly to remind ourselves what the main objective of the discipline is. It aims to facilitate the organization to continually renew its direction, structure and capabilities to ensure that the ever-changing needs of external and internal customers are met. The organization wants to survive into the future and change management is there to ensure that happens.

This is a great objective, however, we now know that, as reported by various writes (e.g. Balogun and Hope Hailey) that there is a 70% failure rate of all change programs that have been initiated. Clearly, something is not working.

Change management is based on the work of Kurt Lewin in 1947. The model is not only linear and static, but assumes that one can design and rationally plan for change to take place.

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This is the fundamental problem. There are massive contradictions in the discipline. The main overarching principle is based on the assumption that one can plan and control the change process. It is like saying that you know exactly what is going to happen on your 5 year old’s birthday party with 50 sugar-hyped children. The parents that dance with the flow and channel the energy might possibly live to see the end of the day. Those that think they have everything under control are living with the fairies in the bottom of the garden. Anyone who believes that one can manage change through control and design is living with the fairies.

The paradigm for change management is stuck with systems thinking (first order systems – I will explain this one day soon). Systems thinking talks about emergence and interconnectivity, yet it fundamentally believes that one can design ‘the system’ – control it and plan so that it produces the designed outcomes – yes there is a blatant contradiction. It assumes that all people will always behave in a rational way – the change manager believes s/he knows what people think and therefore is able to have the organization change in the planned way. Sounds like beautiful music.

The problem is that people do not march to the manager’s sound of music. Actually, not even the manager marches to his/her own sound of music. Intuitively we know this yet we plan and believe that if we do ‘x’ and ‘y’ and then ‘z’ will happen and when it does not happen we are innocently surprised.

So, what is the problem with our thinking?

logicChange management deals at the core with people. It is not about managing self-contained neatly inert objects. One is dealing with dynamics and agents behave in a way that is not predictable. As soon as some small change happens (often not detectable), there is a dramatic amplifying effect on the behaviour of the agents (known as the butterfly effect). Individuals will make decisions based on the information they have just received and that same piece of information will be interpreted differently by different agents.

Sounds like chaos – remember the 5 year old’s party? That is exactly what one is dealing with – the attitude one should have is how do we manage highly complex and chaotic systems? This attitude is fundamentally different to that of managing something that we think we can control and predict rationally.

It is not that difficult. It simply requires a fundamental internalization of the way things behave and how to manage them. Once you realize that people are complex adaptive systems, the management approach must be different. You cannot use the same tools that you would use to manage highly predictable and fully-understood behavior (utopia).

Control and micromanagement is really a sign of ignorance. Management by algorithms is a joke to say the least. People who like closure and are not able to sway with constant uncertainty need to change jobs or, better still, need to go to ‘finishing school’ – try the liberal arts!

I would even go further and propose that the nature of thinking required to manage complex situations under conditions of uncertainty is the type of thinking similar to that of visual thinking.

Next week, I will look at the work of Daniel Kahneman.


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Heat Maps

Heat Maps is a simple yet powerful technique that leverages the human’s superior visual cognitive capacity to gain deeper and faster insights into data and information.

Cormac Kinney coined the term in 1991 to describe a 2D-display depicting real-time financial market information.

A heat map is any data visualization that uses color to represent data values in a two-dimensional image. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.

There are many different types of heat maps used in different disciplines, each referred to by the term “heat map”, even though they use different visualization techniques.

The reason heat maps work so well is because of our ‘Pre-attentive Processing’ ability. The term refers to the ability of the low-level human visual system to rapidly identify properties, such as color and size, in less than 250 milliseconds.

According to Van der Heijden, we unconsciously accumulate information from the environment ,which is pre-attentively processed. The brain filters and processes what is important based on what stands out the most. Once the individual’s attention is captured (based on what is of relevance to what the individual is thinking), at that point the information is selected for further and more complete analysis by conscious (attentive) processing.

It is a two-stage process, which happens in seconds, and this is why heat maps are so powerful.

Heat maps are being used in many sectors and that is great. The best way to demonstrate these is to show you examples. I use some examples as shown by John Brandon in a slide presentation.

heatmap1

Marketing people created the map above. By using eye-tracking devices, a heat map is created showing where attention of the user is focused on display pages. The map above is a sample heat map of a Google search result.

A dominant pattern for search engine results is the “F” pattern showing the eye being drawn to the upper left (hot colors) and then moving down and across from there (shown as the blue colors). There are, however, factors (such as the inclusion of images, graphics, and additional columns) that can significantly alter this pattern.

This heat map displays risk by location. It was created by RMS (risk management company) to show risks related to catastrophic events: Earthquakes, hurricanes, severe storms (including tornados and hail), wind storms, wildfires and volcanoes. An insurance company might use it to determine the “probability of loss” related to such an event.

Screen Shot 2014-06-10 at 9.39.20 am

This heat map shows the reality of fraud attempts in real-time using live data. A red dot pops up to show a fraud attempt. ThreatMatrix culls the data from 1,950 customers, which includes about 9,000 websites, and tracks about 360,000 cyberattacks per day.

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This heat map from MarketProphit shows buzz and sentiment around specific stocks. The larger blocks indicate the most buzz (or discussion) around a stock as culled from Twitter. The colors show sentiment; the red blocks denote negative comments and the green denotes positive comments. In an instant, financial planners can see general trends with stocks based on social media posts.

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This heat map shows the movements of customers in a retail store aisle. Red areas represent the spots where most customers shop. Retailers can use the heat map for product placements and to see whether a sales campaign was successful.

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This heat map shows the age of buildings in the Portland, Ore. area. About 544,000 structures are represented, including about 4,500 erected in the 1800s and 10,265 buildings constructed in 1978 alone.

Justin Palmer created this heat maps based on public data released by the City of Portland. It shows the age of buildings. This can help municipalities see which neighborhoods hold the greatest concentration of structures that may need repairs.

Click on the link (http://citysdk.waag.org/buildings/) to see the map in large scale, iit s beautiful. It shows the nearly 10 million buildings in the Netherlands; some in central Amsterdam are more than 1,000 years old.

Some would argue that heat maps are very specific – “the heat map is a treemap-like graphical technique used to represent a two-dimensional array of data” as shown in the example below.

treeheatmap

Many techniques illustrated in this post, as argued by the purists, are not heat maps. I agree and I disagree. The principle of using color to very quickly highlight the issues of concern, attention or importance is extremely valuable and it is a perfect example of how we work better with visuals than with coded jargon. The fact that it is called a heat map – well, does it matter? Notionally, it is a perfect description even though it might not be as it was originally labeled – I think this is a good and natural evolution of the principles. I will call these techniques ‘heat maps’ – it works and it is powerful.