13 perspectives to help see things as they really are, not as we expect them to be

Observe-Interpret-Design-Experiment

Observe-Interpret-Design-ExperimentWe are conditioned to see what we expect or are primed to see, not what is really there.  This is fast and efficient if our world is stable and predictable where the past is a good predictor of the future.  But what if it is unstable and unpredictable?  What if our expectations and experience are only partially useful?  The problem is that our expectations and experience are often deep within our subconscious mind.  It is difficult to switch them off even if we consciously realize they may be inadequate in a given situation.

How then do we see things as they really are, not as our subconscious would like or expects them to be?  One of the most effective things we can do is to develop the ability to step through a variety of perspectives when examining a situation.  In doing so we use our conscious brain to counter or supplement our subconscious brain.

FlorenceIn general, we need to adopt perspectives that allow us to be more empirical.  That is, we need to focus on more extensive observation and data gathering before drawing conclusions.  In most cases, we also need to recognise that the conclusions are only hypotheses that require further testing and validation.  We need to be open to revising, or even discarding, a hypothesis as we make more observations or collect further data.

I have categorised the various perspectives we can take under four broad categories – spatial, structural, psychological and effectual.

Spatial perspectives

Get close to the action to see actual behaviour and events rather than make assumptions based on past, prescribed or ‘normal’ behaviour.  This approach underpins the Genchi Genbutsu (“go and see”) principle of the Toyota Production System.

Move further away from the action so that you can see behaviour and events in a wider context (draw wider system boundaries) and therefore consider more structural influences and factors.  Moving further away is also likely to increase detachment and objectivity and therefore reduce any biases introduced by an emotional engagement to the situation.  The movement need only be psychological (for example, by imagining you are in another city), not necessarily physical.

Divide the situation into a comprehensive set of segments and examine each of them separately and then the relationships between them.  This approach reduces the risk that your focus quickly narrows on those aspects that you expect or assume to be the most salient.  It ensures that all aspects get at least some focused attention.  The segmentation basis could be physical, organisational or temporal (time based).

Examine the behaviour at the boundaries of the system or within the system.  New or changing influences and behaviours will often be first evident at the boundaries.  This is either because new external influences first interact with system at the boundaries, or because the more diverse interactions that occur at the boundaries generate new ideas and options.

Structural perspective

Consider the network aspects of the system or situation.  Are there any groupings or networks emerging?  If so, what are the ‘attractors’ for the groupings, or what is driving the linkages for the network?  Is there potential for these to be self-reinforcing, or are there likely to be fundamental constraints to their ongoing development?  On the other hand, what are the established networks and groupings?  Are they structured to facilitate or inhibit positive change?

Identify the constraints in the situation.  The constraints may relate to physical boundaries, information, expertise, resources, awareness and expectations.  If behaviour consistently occurs at or near a constraint, the behaviour is unlikely to change without first relaxing or modifying the constraint.

Look for any potentially significant decision points.   Major decisions create ‘forks in the road’ that influence the future development of the system or situation.  At the personal level, they include choice of a partner, a profession/career, a place to live, etc.  At the organisational level, these decision points are numerous and diverse and may not seem particularly significant initially.  However, they may stimulate or facilitate a self-reinforcing chain of events that eventually have game-changing impact.

Identify any emerging patterns of cause and effect.  Even though the situation appears essentially unpredictable, repeatable cause and effect patterns may be emerging.  Customers in a given segment may have begun to respond more predictably to on-line promotions.  Middle managers from one segment of the organisation may be exhibit similar resistance to a change initiative and offering suggestions for improvement that have a common underlying theme.  These emerging patterns provide early indications of what is working and what is not.  They provide potential guideposts for future action.

Behavioural/psychological perspectives

Look at the situation through the eyes of the key participants.  In other words, take a perspective of empathy.  Aspects of the situation that may be clear and positive to you may be unclear, uncertain and therefore threatening to some of the participants.  Benefits of a change initiative that you think are valuable may appear to be of marginal value when seen through the eyes of a stakeholder group.

Identify the dominant motivations and influences in the situation.  Look for the ‘why’ as well as the ‘what’?  Where do the energy and driving forces for action come from?  Are they derived from proactive aspirations or reactive defensiveness?  Is the source local or broadly based?  Is it likely to be enduring or short term?  In emergent and unpredictable situations, the driving influence is a potential source of consistency and coherence.  Although it will not enable future decisions and outcomes to be predicted with certainty, it will point to how they are likely to be biased.

Is the situation characterised by a few dominant emotions?  Emotions have a fundamental influence on behaviour and decision making.  We may be able to bring some clarity to what appears to unpredictable and illogical behaviour when we understand their emotional underpinning.  Looking at a situation through a lens of emotions may provide insights that we may find it difficult to discover otherwise.

Effectual (effects and results) perspectives

Notice what seems to be working.  Where are successes happening?  What activities and behaviour are being reinforced?  Where are people making progress in spite of their constraints and context?  What have they done to overcome the constraints?  Is this repeatable and scalable? In unpredictable and emergent situations we cannot rely primarily on what has worked in the past or has worked somewhere else.  We need to discover new principles for success.

Be alert to the surprising and the unusual.  Often, much ‘information’ is found in the unexpected events, behaviours, relationships and achievements.  These may be weak signals of the emergent direction of the situation, or they may be just random noise.  If it is random noise, and the surprise is a positive one, can the random conditions be identified and repeated consistently?  The earlier the small surprises can be identified, the more likely it is that they can be purposely enhanced if they are positive or suppressed if they are negative.

What do viral videos and ‘10 year overnight successes’ have in common? – they are both non-linear

Observe-Interpret-Design-Experiment

Observe-Interpret-Design-ExperimentViral videos and ’10 year overnight successes’ are both examples of non-linear behaviour – future performance is not a simple linear extrapolation of past performance.  In the case of both viral videos and ’10 year overnight successes’ performance suddenly grows rapidly after a (sometimes lengthy) period of relatively low success.  Both the timing and extent of this rapid change is difficult to predict and plan for.  Non-linear behaviour is not always positive.  In modern cultures, a rapid fall from popularity when something unexpectedly loses its trendiness is a negative example.  A rapid loss of confidence in a business leader or politician is another.

Non-linearWhy is non-linear behaviour so interesting and important?  Because it is helps explain why certain situations change so rapidly and unpredictably.  What might appear to be just small inconsequential changes or random ‘noise’ may in fact be the early weak signs of success (or failure).  We may think that our new initiative is not working and prematurely abandon or significantly change it.  Alternatively, we may see quick success and commit further resources to an initiative or change effort only to find that it expectedly slows down or stagnates.  A deeper awareness of the nature and types of non-linear behaviour will sensitise us to the potential for these types of situations and help us look for indicators of non-linear patterns and mechanisms.  It will also help us set expectations accordingly.

Sources of non-linear behaviour relevant to organisational and business situations include:

  • Self-reinforcing feedback (snowball effect)
  • Preferential attachment
  • Percolation/connectivity
  • Threshold response
  • Synergistic effects
  • Pressure build-up – Catastrophic failure
  • Decision points
  • Diversity

A whitepaper that summarises each of these sources can be found here.

Even though the onset of non-linear behaviour is often difficult to predict, we can recognize when the pre-conditions for non-linear behaviour are starting to emerge.  Just as geologists know that the preconditions for earthquakes exist in various regions of the world, we can develop expertise in recognizing the development of preconditions for non-linear behaviour in our organization, economy, industry, etc.  I suggest that the following are some indicators of such preconditions:

  • Periods of (disruptive) transition
  • Emergence of imitation based perspectives and decision making
  • Emergence of numerous separate but related events, technologies, perspectives:
  • Emergence of new enabling technologies
  • Broad based constrained pressure for or resistance to change
  • Complex initiatives that require broad based integration and coherence for success

In many cases, non-linear behaviour is an emergent property of the situation, not something we can directly engineer and control.  In situations such as synergy and diversity, it is possible to take a leading role.  In most others, we need to read the dynamics of the situation and find ways to influence, adapt and take advantage of the emerging situation.  The following list of potential responses focuses primarily on responding to emergent non-linear behaviour.

  • Experiment/probe
  • Promote desirable and suppress undesirable trends by influencing the constraints and attractors
  • Practise ‘planful opportunism’
  • Ride the wave or get out of the way
  • Observe, observe, observe

The whitepaper mentioned above explores these preconditions and potential responses.  I will also discuss specific examples of non-linear behaviour in more detail in future blog posts.

Why experts are partially blind – and 5 ways to ‘restore sight’

Partially blind

Partially blindHave you ever sat in a meeting and heard two attendees discuss the same topic on entirely parallel tracks?  Or perhaps you have been in meetings where an attendee keeps bringing the conversation back to a clearly inappropriate or irrelevant perspective.  Both situations are confusing and embarrassing for the other attendees.  Why do intelligent and competent people have such conversations?  Why do they miss the point so badly?  Why do they continue, even when the disconnect is obvious to others?

The old saying, “when all you have is a hammer, everything looks like a nail”, gives a clue to one of the key causes of the ‘parallel conversation’ problem.  Our professional training and experience colours the perspective we bring to any related context.  This is particularly so for deep and successful experts.  Their narrowness of perspective is reinforced not only by confidence gained from success but also by the need to defend and preserve their reputation and ego.

Such expertise based narrowness of perspective is a significant problem during the Observe stage of Adaptive Iteration.   Adaptive Iteration is applicable when the situation is unpredictable and emergent, precisely the situations where preconceptions and narrow perspectives are most risky.  During the Observe stage we need to be open-minded, empathetic and sensitive to both detail and trends.

Does this mean that experts should not be involved in the Observe stage of Adaptive Iteration?  Not necessarily.  However, it does mean that we need to think explicitly about how we organise for and go about observing our experiments, not only to reduce the potential for expertise bias, but also to reduce the risk of other forms of unconscious biases (more on these in later blog entries).

The types of things we can do to reduce the potential for observation biases include:

  • T-shaped people:  Include people on the team who have not only deep expertise but also broad interests and knowledge and the ability to collaborate with people with other types of expertise.  These are becoming known as ‘T-shaped people.
  • Multiple perspectives:  Explicitly observe the situation (experiment) from multiple points of view.  The objective here is to quieten our unconscious biases by adopting one or more conscious biases.  I will explore a variety of possible perspectives in a future blog post.
  • Diverse team:  Include people on the team with a range of expertise and from a range of backgrounds.  Diversity reduces the potential for observational bias only if the team dynamics enables the diverse observations to be surfaced, discussed and synthesized.  The ability to do this depends on a mixture of structure, process and personality.
  • Prepared mind:  Train (prepare) your team to be a better observers.  As Louis Pasteur is reported to have said, “in the field of observation, chance favours the prepared mind”.  Techniques for preparing the mind include learning how to suspend judgement, to implicitly adopt multiple perspectives, to appreciate the impact and role of context, and to see the underlying systems dynamics.  Interestingly, research is starting to suggest that our ability to have empathy (critical when observing many human interactions) can be increased by reading emotionally engaging fiction.
  • Focus on data:  Where possible capture rich data to lead, inform or validate human observations.  The complex, unpredictable and emergent nature of situations where Adaptive Iteration is applicable means that is usually not possible to rely solely on data for our observations.  Nevertheless, some situations are amenable to supplementing observations with techniques such as video recordings or with the analysis of (often large) data sets to reveal emerging patterns, trends and relationships.

What is a design hypothesis and when is it required?

Observe-Interpret-Design-Experiment

Observe-Interpret-Design-ExperimentThe concept of a design hypothesis is central to Adaptive Iteration.  But what is it and when is one required?

All designs have some degree of freedom and therefore involve choice by the designer.  How does the designer make that choice?  It depends on the stability and predictability of the context in which the design is to be used and on the extent to which past practice has evolved optimal designs or design variants for similar situations.

If there is substantial precedence, it is likely that design choices will be determined by past best practice.  Past best practice may exist in a number of forms, including:  formal design rules, modular components or established design heuristics.

In the organizational context, best practice is often promulgated by consultants and the management literature.  However, quite often not enough attention is given to describing the context for the supposed best practice or to assessing the contextual factors critical to the success of the ‘best practice’.  As a result, many ‘best practice’ organizational improvement initiatives fail to achieve expectations because of significant contextual differences between the best practice context and that of the implementing organization.  As I will discuss in a future blog post, adaptive iteration should be used to tailor and refine the design and implementation of many such initiatives.

If there is limited relevant precedence and the context is stable and predictable, it is likely that the design choices will be made by experts through a combination of their experience and analysis based on existing information and codified knowledge.  If the context is complex and unpredictable, experience and past practice are significantly less valuable in determining optimal design choices.  In such cases, the designer should consider an iterative hypothesis based approach.  The initial design choices are recognised as informed predictions, usually involving input from experts, that need to be evaluated and refined through repeated research and testing (experiments).  In a sense, the designers have a dialogue with the context.

In comparison with expert driven design, hypothesis driven design involves greater use of multi-disciplinary and multi-perspective teams.  It favours early action (prototypes, concept outlines, story boards, ‘sighter’ trials, etc) to generate feedback and learning.

Use ‘thought experiments’ to reduce emergent risk

Observe-Interpret-Design-Experiment

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.