Use ‘thought experiments’ to reduce emergent risk


Observe-Interpret-Design-ExperimentImagine that you are considering an initiative of some sort.  It could be that you are a manager and you need to respond to signs of growing tension between two of your staff.  Or perhaps you need to develop an approach to social media for your organization or business unit.  Both situations are emergent.  The details of how they will play out cannot be predicted with confidence.  Also, your actions will create responses that will further influence the dynamics of the situation and could generate unforeseen or unintended consequences.  Therefore, you need to be prepared and able to adaptively iterate your response as the situation emerges.

The potential for downside risk in such situations can be mitigated by initially conducting a series of thought experiments.  As with any real experiment these need to be planned and ‘observed’.  The thought experiment must have an intent – either to test a design hypothesis or to generate insights on which to develop a future design hypothesis.

Thought experiments rely on your ability to mentally immerse yourself in the situation.  In the case of tension between two of your staff, your design hypothesis might be that you need to get both of them together around a table and confront the issues that are creating the tension.  In your thought experiment you would play that meeting out in your mind in sufficient detail to pick up any aspect that is likely to influence the conduct and outcome of the meeting.  Your thought experiment may show that the tone of the meeting will be influenced by the expectations and frame of mind of the participants when they enter the meeting.  This will highlight that the way you invite both to the meeting will be important.  You will then need to revisit your design hypothesis to include a design for the initial setting up of the meeting.

You are now ready to rerun your thought experiment further into the meeting to see how it unfolds. You may get to a point where you simply to do not have a clear enough understanding of the context for the tension between the staff to be reasonably confident about how the meeting will progress.  Because this is just a thought experiment, you may decide to make an assumption about the nature of the context and continue your experiment. If you do, it is important to be aware that the rest of the thought experiment is based on an assumption, not a fact.  You may want to test several assumptions at this point to experimentally see how they affect the conduct and outcome of the meeting.  Once you do this, you might find that, with your current level of context knowledge, the meeting is too risky at this time.  You may then revise your design hypothesis to include some real information gathering before a joint meeting with both of your staff.  Your previous thought experiment about the conduct of the meeting will be valuable in directing the nature and focus of the information gathering.  Once you have gathered the information, you will probably rerun your thought experiment.

The social media example is more complex and strategic but still requires the same approach to a thought experiment: you need an initial design hypothesis; you need to be able to immerse yourself in the context in sufficient detail to recognise critical issues and decision points; you need to be aware of when you are making assumptions, and you need to be prepared to iterate the process and modify your design hypothesis on the basis of your learning.

I will flesh out the social media example in a future blog post.

Start-up stories of HP, Sony and Microsoft – Adaptive Iteration

Adaptive Iteration can be seen in the start-up stories of Hewlett Packard, Sony and Microsoft.  All three started with a high level purpose to start a business, but without specific constraints about the products and markets they would serve.  In essence, the founders only had the constraint that the business be built on their core expertise and interests.  Each adaptively iterated until they developed business designs (products, markets, competitive advantage, operating philosophy) that were both profitable and met their high level purpose.

For Hewlett-Packard, the founders iterated through a range of product and market combinations as they adapted to the rapidly growing and evolving electrical and electronics marketplace.  These included audio oscillators for Walt Disney Studios, electronic test, microwave and data printing equipment, medical electronics, electronic calculators, mini computers, inkjet printers and personal computers.  No doubt, Hewlett-Packard also used adaptive iteration to design, test and refine each of its products and product categories.  Not only was Hewlett-Packard a master of adaptive iteration of its business and products, it also excelled at the adaptive iteration of its people based practices.  These included profit sharing, flexible working hours, flexible work spaces and management by walking around (MBWA).

The start-up history of Sony Corporation, is a constant series of adaptive iterations in response to the founders’ objective to build a technology company and to the resource constraints in Japan following World War II.  Sony’s history involved failed ‘experiments’ with rice cookers and electrically heated cushions, extensive use of a network of contacts to ‘observe’ opportunities in the marketplace and improvisational design to address those opportunities and to overcome shortages of materials.

Before founding Microsoft, Bill Gates and Paul Allen had at least two failed experiments to exploit the business potential of their programming skills - a machine, called Traf-0-Data, that counted traffic and an offer to various large computer companies to write a BASIC computer program for the then new Intel 8080 microprocessor chip.  The next adaptive ‘experiment’ was an offer to a small company called MITS to write a BASIC computer program for their just announced Altair computer – the world’s first commercially available micro-computer.  MITS accepted this offer and Bill Gates and Paul Allen formed Microsoft.  Microsoft then began the adaptive iteration of its business model and software products.

In his book ‘Strategic Intuition‘, William Duggan argues that it was Gates and Allen’s strategic intuition that enabled them to see the opportunity for personal computer programs.  I believe that strategic intuition is another name for highly developed observation and interpretation skills. Gates and Allen were able to recognise the weak signals that personal computing was about to emerge as a significant technology and that it would create a self-reinforcing cycle of hardware and software developments.

What do we mean by Adaptive Iteration?

Prepared Mind quote

AI Cycle - OIDE(P)At Discerning Action we believe that Adaptive Iteration is a valuable approach to working with unpredictability and uncertainty in organisations.  What do we mean by Adaptive Iteration?  What are the core elements and how do they fit together?

For the purposes of this overview I will begin with the Design step in the Adaptive Iteration cycle (although that may not always be the case in practice).

A design is a hypothesis about the most appropriate response to a complex and unpredictable problem or opportunity.  The design hypothesis could relate to a physical product, a service, a plan of action, a strategy, a business model, an organisational development initiative, etc.  A design is a hypothesis because until it has been successfully tested in context through one or more experiments, its suitability as a response is not proven.

Experiments can take various forms, including a thought experiment, a prototype, a simulation, a trial or a pilot.  A unique natural occurrence in a business or organisational context could also be considered a form of unplanned or natural experiment.  In such a case, the Adaptive Iteration cycle would start with the Experiment step.

An experiment could be predominantly confirmatory or predominantly exploratory. In the latter case, the primary objective would be to stimulate information or insights (learning) on which the next iteration of design is based.  I believe the expertise to formulate and execute various types of experiments is a critical aspect of Adaptive Iteration, and is not well developed in many organisations.

Prepared Mind quoteAn experiment is of little value unless it is supported by accurate observation.  However, the observer’s challenge is that in observation our brains are heavily influenced by what we expect to see.  Our observations are significantly biased by our, often subconscious, experience and expectations.  If the context for our observations is stable and predictable, this bias provides us with significant cognitive processing advantage by making it fast and efficient.  But it is a major weakness if the context is complex and unpredictable.  In such cases, the Observe step requires explicit consideration and the development of specific observational expertise.  As Louis Pasteur once said, “in the field of observation, chance favours the prepared mind”.  When observing in complex and unpredictable contexts we need to “prepare our mind”.

Experimental observations are useful only if they lead to relevant learning through interpretation.  The learning may relate directly to the initial design hypothesis, or to unexpected observations that arose because they were made with an open mind and/or from multiple perspectives.

In complex and unpredictable contexts the types of things we seek to interpret from our observations include: critical decision points and options; weak signals that may indicate an emerging coherence; the early stages of a reinforcing feedback loop that may trigger rapid change; indicators of the underlying driving forces and motivators in the system; and the nature and impact of the constraints and boundaries of the system.

Based on the results of the experiment, the design will be further modified or refined.  These changes are, in effect, another hypothesis to be tested and evaluated by further Adaptive Iteration.

At the center of Adaptive Iteration is a clear and shared understanding of overall intent or purpose.  This creates coherence for the myriad of decisions that must be made when undertaking Adaptive Iteration.  A shared understanding of overall Purpose is especially important given the complex and unpredictable nature of the context.  The Purpose should be at a reasonably high level otherwise it will constrain the nature and scope of the design hypotheses that are generated.  If it is at too low a level, it will already have significant design decisions built in and will reduce adaptive scope.


Why develop agility and adaptive capacity?

AI Cycle - OIDE(P)

Many of the issues and trends facing organisations today are complex and, to a large degree, unpredictable in terms of their specific impact and timing.  They include climate change, globalisation, social media, big data, internet of things, rise of China and other industrialising countries, global financial crisis fallout, and the retirement of the baby boomer generation.  These issues and trends offer opportunities for many but also have the potential to disrupt existing business and organisational models through rapid and unpredictable change.  As a result, agility and adaptability are, and will increasingly be, critical organisational capabilities.

However, the current dominant approach for solving problems and realising opportunities in organisations is based on the underlying assumption that the context for the problem or opportunity is well described and amenable to analysis and prediction. This assumption, in turn, leads to an essentially linear ‘analyse-plan-implement’ approach to creating or responding to change.  In addition, this approach underpins almost all teaching in professions such as engineering and commerce.  By itself, the ‘analyse-plan-implement’ approach will not be sufficient to address the emergent and unpredictable issues and trends typified by the list above.  Nor will it be sufficient to create the level of agility and adaptive capability necessary to sustain organisational health and success.

The ‘analyse-plan-implement’ approach needs to be supplemented by capabilities, methods and techniques that iteratively create solutions and responses based on a Purpose-driven adaptive cycle of Design, Experimentation, Observation and Interpretation (which I will refer to as Adaptive Iteration).

AI Cycle - OIDE(P)

Our purpose at Discerning Action is to develop and demonstrate Adaptive Iteration as a practical approach for building agility and adaptive capacity in organisations, and to present techniques for and examples of its application.