Monday, 26 October 2015

SWOT in focus

There are some highly-effective, simple, low tech, structured methodologies that are commonplace in intelligence and business analysis, such as SWOT, Cause-Effect diagrams (aka Fishbone diagrams), Mind Mapping and Concept Mapping.  However, sometimes these ‘analytical tools’ are wielded by users without giving much thought to exactly what kinds of problems they are designed to be used on. It can help analysts to think about these techniques in a more-fundamental way.  This helps them understand the possibilities and limitations of various common methods, and with sufficient command of these principles lets them customise and adapt them to be more appropriate for a particular question.

In a series of posts we're going to take a closer look at some commonly-used analytical tools. We don’t intend to provide a detailed description of how to use them as there are many excellent guides out there already. Instead, we'll de-construct them a little and consider why these techniques work, and what is going on when they are used. We'll suggest some ways they can be extended and adapted to suit particular analytical problems. We'll start with one of the most faithful tools in the toolbox of analysts from all sectors of business: the 'SWOT' (strengths, weaknesses, opportunities and threats) analysis.

What is SWOT?

SWOT is a simple but effective method of delineating and categorising the factors that will lead to the success or failure of an objective or set of objectives. We've found it particularly useful in the early stages of (for example) a technical delivery project but it can also be used when thinking about how third parties might behave. Although it's probably best done in a group (using ideas on Post-its around a whiteboard) it be just as effective done privately, or asynchronously and remotely via electronic means.

What’s going on in a SWOT?

SWOT analysis involves three types of judgement or input:
  • Specifying the actor and objective
  • Generating and capturing 'factors' influencing the actor's achievement of their objective
  • Sorting into one of the four areas on the SWOT 
Let’s look at these in turn.

1. Specifying the Actor and Objectives:  Failure to precisely define and agree on these will usually lead to woolly, unactionable output.  When we define the actor - the person or organisation whose strengths, weaknesses etc. we are interested in, who in most cases will be ourself - and objectives - the things that the actor is trying to achieve - we are choosing a scope for the question. It isn't possible to do a SWOT without having both an actor and an objective, since a feature of an actor or their environment can only be a strength, weakness etc. with reference to something they're trying to achieve.

Generally the narrower the scope of the actor or objectives the ‘easier’ the analysis will be (that is to say, it is easier to exhaust possibilities or at least the imagination of the participants), but there is limited scope sensitivity and usually a SWOT analysis will take about the same amount of time (30 minutes to an hour) regardless of how strategic or tactical its scale.  But the narrower the scope, the easier it will be to map the output to particular, actionable decisions or policies.  For example, in decreasing breadth, and increasing actionability:
  • Who: OurCompany Inc. Objective: Increase profits
  • Who: OurCompany Inc.'s sales division. Objective: increase sales to financial sector clients
  • Who: OurCompany Inc.'s 'BuyMeNow' sales team. Objective: increase sales of the 'BuyMeNow' product to financial sector clients by 20% in the next financial year.
As the scope narrows, the more relevant the outputs of the latter are going to be to the 'BuyMeNow' product developers and managers. By removing possible actions and factors from the possibility-space, narrowing the scope helps participants focus, and hence reduces cognitive burden. But it increases the risk of failure of imagination - of missing a potentially important factor that falls outside the SWOT's scope.

2. Generating and Capturing 'Factors': The most intensive component of a SWOT analysis is the generation of suggested 'factors' that actually or potentially help or hinder the actor's achievement of the identified objectives. Generating these factors in a group, and exposing them, has a number of benefits. First, there is the creation of a consensus and the identification of specific differences. Being explicit and open encourages focus on the relevance of particular factors with respect to the objective and, for example, challenges supposed 'strengths' that aren't clearly linked to any desirable outcomes.

Ideally, this element of the SWOT will yield lots of ideas pertinent to the problem, without introducing too much noise. It may also be the case that group workshops introduce a subconscious sense of competition that encourages people to generate more and more-creative ideas: this artificially-generated stress can be a powerful motivator. Finally, the simple act of recording ideas on paper (real or virtual) provides a useful audit trail for the subsequent work, and by doing it in a open group, accountability is shared.

3. Sorting ideas:  The SWOT process invites you to post a concept in one of four mutually exclusive areas. Categorisation is the placing of a concept within a framework of ‘higher’ concepts, which provide conceptually convenient groupings. Although in one sense categorical groupings are a gross simplification, they are also essential for understanding. The architecture of human cognition appears to involve the sorting of concepts into higher categories, and their division into lower ones, based on similar characteristics within each class.

Generating and sorting ideas is usually an iterative process, both cognitively and in collaboration with others. Ideas already on the board provide cognitive shortcuts by allowing comparison with other potential categorical siblings rather than on first principles: it is often easier to consider “does this idea I have feel more like the things in the S area that it does those things in the W area?” rather than to consider if the idea is a strength or a weakness in isolation.

In the end, you will have packaged up ideas in exclusive buckets which provide additional meaning to the ideas.  For example, there is an obvious implication that you should use strengths and exploit opportunities, while reducing the effects of our weaknesses and mitigating  threats.  So by simply classifying ideas, you have already decided the fate of them within a wider framework of action. If the SWOT analysis is about a third party, then you will have an idea of the kinds of things that party is likely to pursue, assuming they are broadly rationally self-interested.

Playing with the SWOT Analysis

Clearly, personal preference and problem-specifics will partly determine the best way to conduct a SWOT, but it can be easier to separate the generation and sorting of factors (steps 2 and 3) from one another. Switching between generating an idea and then immediately judging it and assigning it to a box involves transitioning between two different cognitive styles can be tiring, and may take longer. It's sometimes easier either to take each quadrant in turn, and use it to further narrow the scope of thinking, or to generate ideas until they ‘dry up’ and judge each in turn.

It's not necessarily worth investing too much energy in thinking about whether a factor should be in one quadrant or another. If in doubt ‘split’ the idea into more narrowly specified sub-ideas and then they will be easier to categorise. Nevertheless, categorisation can be harder than it feels like it should be, and so it sometimes helps to exploit the 2x2 nature of the grid by thinking not about which quadrant a factor belongs in, but where on each axis it should be placed.

If instead of strengths, weaknesses, opportunities and threats being primary categories, the axes are considered to represent first scales of control from ‘in your control’ (strengths and weaknesses) to ‘outside your control’ (opportunities and threats) and from ‘good’ (i.e. they do or would tend to help achieve your objectives) and ‘bad’ (they tend to work against them) then a new type of diagram can be generated.  This represents a more generic 2x2 matrix.
Instead of 'inside / outside control', 'actual / potential' or 'present / future' work well to help distinguish opportunities and threats from strengths and weaknesses. Another added advantage is that these axes can be considered continuous scales rather than discrete categories.  This invites an optional bolt-on judgement where individual ideas can be compared to see how relatively strong they are on each axis (e.g. is X being more helpful than Y) and their position on the scale adjusted appropriately.  This can, if used carefully, begin to indicate which ideas are the priority.

Through this substitution of the categories for continuous axes we can see that the SWOT may be considered a highly specific instance of a much (infinitely?) broader family of analytical tools: the 2x2 (matrix).  Very briefly, you could judge ideas on any set of axes which are conceptually orthogonal - whether these provide enlightenment is a up to the analyst. For example, here is a manifestation of the Dunning-Kruger effect for stakeholder analysis and communication strategies, based on the two axes 'understands' and 'thinks they understand'.
2x2 matrices are helpful in general because they force us to consider combinations of features that are sometimes overlooked (e.g. 'knowing' but 'unaware') and they encourage us to decompose high-level concepts into their lower-level determinants (e.g. 'strength' being a combination of 'in our control' and 'helpful to an objective').

Turning it into Action

The point of a SWOT analysis is not, of course, just situational awareness. Its primary function is to inform our action planning. If the analysis has been done properly, then action planning should flow very naturally from the output. One way explicitly to transition from a SWOT to a set of actions is to vote on the top (say) three factors in each quadrant, and then identify a specific action to take with each one: either to exploit (strengths and opportunities) or mitigate (weaknesses and threats).

Finally, if a SWOT is from the perspective of a third party, and particularly if their objectives are inimical to ours (e.g. if they are an enemy group or rival firm), we can plan our own actions using a trick called the 'Jolly Inversion', which broadly observes that their strengths are likely to map to our threats, their weaknesses to our opportunities, their opportunities to our weaknesses, and their threats to our strengths.


A SWOT analysis is a straightforward but powerful precursor to action planning, if done with a modicum of care and precision. It's part of a wider family of analytical methods that help minimise the cognitive load associated with generating and classifying ideas into higher-level families that invite similar responses. It forms a useful bridge between your objective and the actions that can help achieve it, and provides an audit trail for future reference.

Monday, 19 October 2015

Confidence and Probability: Summary

(This post concludes a series that begins here.)

Why is defining confidence so hard?

We all understand the idea of analytical confidence. Analyst and non-analyst alike, we have a clear sense that some probabilistic judgements are dodgy - rough guesses, stabs-in-the-dark, first impressions, better than nothing but not by much - while others are gold-standard - well-evidenced, based on robust modelling, the judgements of experts, the results of controlled experiments with solid protocols. We also feel like this distinction is important - legally, perhaps, with regard to situations such as the L'Aquila earthquake - but also morally, connected to the concept of 'epistemic responsibility', the notion that we ought to exercise diligence in ensuring our beliefs are justified and that it is wrong to make decisions based on dubious evidential foundations. 

Given the force of our intuition regarding confidence, why does it seem so hard for us to say what 'confidence' actually means? And if it's important for decision-making, how then can we communicate our sense of confidence in anything other than vague, circular, and possibly incoherent terms?

Theories of confidence

We identified seven theories, each of which provides an alternative interpretation of the concept of confidence. For each theory, we looked at three factors: how coherent it was, how much it accorded with analysts' usage, and whether it was relevant to a decision (and therefore whether it was worth communicating without redundancy). The conclusions are summarised in the table below.

One salient feature is that none of the theories gets top marks on all three criteria. The theories that are both coherent and decision-relevant - those that relate to the expected value and expected cost of new information - are not ones that analysts themselves subscribe to. The 'quality' theory, which suggests that confidence relates to the general soundness of the available information, looks the best overall but it is only tangentially decision-relevant. We are faced with two problems that may have different answers: what does 'confidence' mean, and what should 'confidence' mean? In order to answer them we need to take a short philosophical detour. First, we'll look at why we may not be able to trust intuition and usage as a guide to finding a coherent definition of 'confidence'.

Naive realism

Naive realism is the approach to understanding the world that we are born with. We open our eyes, and the world is just there - we experience it directly, and what we experience is how it actually is. It is only through philosophical and scientific investigation that we find the demarcation between 'us' and 'the world' is fuzzier than it first appears. Our ability to perceive things at all depends on complex cognitive software, and some of the elements of our experience, such as the differences between colours, represent features that despite having significance in our evolutionary environment, do not form a qualitative distinction from a physics standpoint. Red things and green things are different, but the redness and the greenness, if it can be said to be anywhere, is in us. Our natural inclination, though, is to perceive the redness and greenness as 'out there', as a property of the world that we are merely passively perceiving. But the idea that 'redness' and 'greenness' are properties of things is not as coherent a theory as the idea that 'redness' and 'greenness' are instead properties of the way that we and things interact.

Our 'default setting' of naive realism may also apply to our concepts of probability and confidence. The view that probability is 'out there' is an intuitive one, as we saw when looking at the 'uncertain probability' theory of confidence, but it doesn't survive careful examination. It may be the same with 'confidence' itself. We might intuitively believe that confidence statements are statements about the system we're looking at, but this doesn't mean that a coherent theory can be made along these lines. Confidence might instead be better explained as a feature of us - of the payoffs from our decisions, or our personal information-set. Intuition cannot be relied on as a guide to what drives our intuitive sense of confidence.

Could our sense of 'confidence' be innate?

Questions of confidence generate strong and consistent intuitive responses, but as we've seen, the drivers of those responses are opaque to us. This is a clue that the notion of 'confidence' might be something rather fundamental for which we have evolved specialised computational software. By analogy, we have evolved to see red things as distinct from green things. As eye users, we don't need to know that the distinction in fact corresponds to differences in photic wavelengths. It is experienced as fundamental. This is because the distinction is so useful - for, say, finding fruit or spotting dangerous animals - that evolution has shaped our cognitive hardware around the distinction. The problem-space - the evolutionary environment and the decisions we need to make within it - has shaped our perception of the world, so animals with different problems see the world differently

But how can ascription of analytical confidence possibly correspond to any problem faced by ancestral creatures? Isn't the application of a confidence judgement to a probabilistic statement an extremely-artificial, rarefied kind of problem, faced by desk-bound analysts but surely not by tree-dwelling primates? 

Well, perhaps not. There's no reason to believe that ancestral primates played cricket, but the skills involved - motor control, perception, throwing projectiles - all have plausible mappings to the evolutionary environment. If there is a common theme to the foregoing discussions about confidence, it is one that relates to the justification for a decision: the extent to which we should act on the information, rather than continuing to refine our judgements. This certainly is a fundamental problem, one faced not just by humans but by all animals and indeed systems in general that need to interact with their environment.

The universal analytical problem

James Schlesinger
"Seldom if ever does anyone ask if a further reduction
in uncertainty, however small, is worth the cost..."

Other than in the most simplistic environments, animals do not face the kind of static decision-problems, with a fixed information set, that decision-theory primers tend to focus on. For most organisms, information is a flow that both affects and is affected by their behaviour. There is usually a tension between gathering information and acting on it. Collecting information is normally risky, costly and time-consuming. But the benefit of more information is reduced decision-risk. This means there is a practical engineering trade-off to be made, and one that is expressible in a simple question: "when do I stop collecting information, and act on it?"

Even in very simplistic models, 'solving' this trade-off - finding the optimal level of information - is often mathematically intractable (because probability has a broadly linear impact on payoffs but is affected logarithmically by information). Our response to dynamic information problems is therefore likely to be heuristic in nature, and could well depend on ancient cognitive architecture. 

This means that if confidence is (as seems likely) something to do with the relative justifications for 'act now' versus 'wait and see', then it is entirely plausible that in making confidence judgements we are using the same cognitive machinery that we might use to, for example, decide whether to continue eating an unfamiliar fruit, to keep fishing in a particular stream, to make camp here or press on to the next valley, to go deeper into the cave, to hide in the grass or pounce. However it is experienced, optimal decision-making must involve consideration of the relative value, and relative cost, of further information-gathering, and of the risks associated with accepting our current level of uncertainty and acting now. A theoretical description of optimal decision-making in a dynamic information environment will utilise exactly these concepts, and so it shouldn't be surprising if we have cognitive software that appears to take them into account. Whether or not this is where our notion of 'confidence' comes from can only be a guess, but it's a satisfying theory with a robust theoretical foundation.

Should our definition of confidence capture intuition, or replace it?

Broadly speaking, there is a significant gulf between our intuitive concepts of probability and confidence, and what we might consider a coherent theoretical treatment of them. Crudely, we tend to think of probability as inherent in systems, whereas it is more-coherently thought of as a property of evidence. But we tend to see 'confidence' as a feature of the evidence - of its quantity or quality, say - whereas it might more-coherently be thought of as a function of the evidence we don't (yet) have, and plausibly of the importance of the decision that it informs. What, then, should we do in designing a metric for capturing confidence, and communicating it to customers? 

This is not by any means a unique problem. Many precise concepts start out pre-scientifically as messy and intuitive. In time, they are replaced by neater, more-coherent concepts that are then capable of supplanting and contradicting our naive beliefs. Through such a process, we now say that graphite and diamond are the same thing, that whales are mammals, that radiant heat and light are degrees on a scale, that Pluto is not a planet and so on. 

Photo: Steve Johnson

A similar thing has happened to the concept of probability. Seventeenth-century attempts to quantify uncertainty wrestled with the mathematical laws governing the outcomes of games of chance, but over several centuries these have been refined into a small but powerful set of axioms that have little superficial relevance to games involving dice and playing cards. The interpretation of probability has undergone a parallel journey, from the 'intuitive' concept that uncertainty resides in (for example) dice and coins, to the more-coherent notion that it is a feature of information. The intuition is a stepping-stone on the path to scientific precision.

How shall we define 'confidence'?

Over the course of the last few posts, we've reviewed a number of proposed systems for defining confidence. Mostly these are theoretically-weak, and often simply provide an audit trail for a probability rather than delineating a separate, orthogonal concept of the kind that we strongly believe 'confidence' must consist of. But they have intuitive appeal, probably because they were designed and drafted by working analysts rather than by information theorists. We have a choice to make, roughly between the following definitions:

Confidence-I: 'Confidence' measures the quality and quantity of the information available when the probabilistic judgement was formed. High-confidence probabilistic judgements will be founded on large bodies of evidence, repeatable experiments, direct experience, or extensive expertise. Low-confidence probabilistic judgements will be founded on only cursory evidence, anecdotes, indirect reports, or limited expertise.
Confidence-II: 'Confidence' captures the expected value of further information. High-confidence probabilistic judgements are associated with a high cost of information-collection, low-risk decisions, or low levels of uncertainty. Low-confidence probabilistic judgements are associated with a low cost of information-collection, high-risk decisions or high levels of uncertainty.

'Confidence-I' is easy for analysts to understand and to apply. But it adds little to what is already captured in the probability, and it doesn't directly tell the reader what they should do with the information. 'Confidence-II' will make little sense to most analysts, and will require training, and the development of robust heuristics, to understand and apply. But it says something meaningfully distinct from the probability of the judgement to which it applies, and carries the immanent implication that low-confidence judgements ought to be refined further while high-confidence ones can be acted on.

We hope that this series of posts has demonstrated a number of things: that 'confidence' is an important concept distinct from probability, that intuition is not a reliable guide as to its meaning, and that it can be made meaningful but only counter-intuitively. Is an attempt to move towards a meaningful Confidence-II-type definition worth the cost? Will it add more than the effort of getting there? To an analytical organisation with enough vision, capability, and commitment to rigour, perhaps it would at least be worth experimenting with.