Risk management and organizational anxiety

In practice risk management is a rational, means-end based process: risks are identified, analysed and then “solved” (or mitigated).  Although these steps seem to be objective, each of them involves human perceptions, biases and interests. Where Jill sees an opportunity, Jack may see only risks.

Indeed, the problem of differences in stakeholder perceptions is broader than risk analysis. The recognition that such differences in world-views may be irreconcilable is what led Horst Rittel to coin the now well-known term, wicked problem.   These problems tend to be made up of complex interconnected and interdependent issues which makes them difficult to tackle using standard rational- analytical methods of problem solving.

Most high-stakes risks that organisations face have elements of wickedness – indeed any significant organisational change is fraught with risk. Murphy rules; things can go wrong, and they often do. The current paradigm of risk management, which focuses on analyzing and quantifying risks using rational methods, is not broad enough to account for the wicked aspects of risk.

I had been thinking about this for a while when I stumbled on a fascinating paper by Robin Holt entitled, Risk Management: The Talking Cure, which outlines a possible approach to analysing interconnected risks. In brief, Holt draws a parallel between psychoanalysis (as a means to tackle individual anxiety) and risk management (as a means to tackle organizational anxiety).  In this post, I present an extensive discussion and interpretation of Holt’s paper. Although more about the philosophy of risk management than its practice, I found the paper interesting, relevant and thought provoking. My hope is that some readers might find it so too.

Background

Holt begins by noting that modern life is characterized by uncertainty. Paradoxically, technological progress which should have increased our sense of control over our surroundings and lives has actually heightened our personal feelings of uncertainty. Moreover, this sense of uncertainty is not allayed by rational analysis. On the contrary, it may have even increased it by, for example, drawing our attention to risks that we may otherwise have remained unaware of. Risk thus becomes a lens through which we perceive the world. The danger is that this can paralyze.  As Holt puts it,

…risk becomes the only backdrop to perceiving the world and perception collapses into self-inhibition, thereby compounding uncertainty through inertia.

Most individuals know this through experience: most of us have at one time or another been frozen into inaction because of perceived risks.  We also “know” at a deep personal level that the standard responses to risk are inadequate because many of our worries tend to be inchoate and therefore can neither be coherently articulated nor analysed. In Holt’s words:

..People do not recognize [risk] from the perspective of a breakdown in their rational calculations alone, but because of threats to their forms of life – to the non-calculative way they see themselves and the world. [Mainstream risk analysis] remains caught in the thrall of its own ‘expert’ presumptions, denigrating the very lay knowledge and perceptions on the grounds that they cannot be codified and institutionally expressed.

Holt suggests that risk management should account for the “codified, uncodified and uncodifiable aspects of uncertainty from an organizational perspective.” This entails a mode of analysis that takes into account different, even conflicting, perspectives in a non-judgemental way. In essence, he suggests “talking it over” as a means to increase awareness of the contingent nature of risks rather than a means of definitively resolving them.

Shortcomings of risk analysis

The basic aim of risk analysis (as it is practiced) is to contain uncertainty within set bounds that are determined by an organisation’s risk appetite.  As mentioned earlier, this process begins by identifying and classifying risks. Once this is done, one determines the probability and impact of each risk. Then, based on priorities and resources available (again determined by the organisation’s risk appetite) one develops strategies to mitigate the risks that are significant from the organisation’s perspective.

However, the messiness of organizational life makes it difficult to see risk in such a clear-cut way. We may  pretend to be rational about it, but in reality we perceive it through the lens of our background, interests , experiences.  Based on these perceptions we rationalize our action (or inaction!) and simply get on with life. As Holt writes:

The concept [of risk] refers to…the mélange of experience, where managers accept contingencies without being overwhelmed to a point of complete passivity or confusion, Managers learn to recognize the differences between things, to acknowledge their and our limits. Only in this way can managers be said to make judgements, to be seen as being involved in something called the future.

Then, in a memorable line, he goes on to say:

The future, however, lasts a long time, so much so as to make its containment and prediction an often futile exercise.

Although one may well argue that this is not the case for many organizational risks, it is undeniable that certain mitigation strategies (for example, accepting risks that turn out to be significant later) may have significant consequences in the not-so-near future.

Advice from a politician-scholar

So how can one address the slippery aspects of risk – the things people sense intuitively, but find difficult to articulate?

Taking inspiration from Machiavelli, Holt suggests reframing risk management as a means to determine wise actions in the face of the contradictory forces of fortune and necessity.  As Holt puts it:

Necessity describes forces that are unbreachable but manageable by acceptance and containment—acts of God, tendencies of the species, and so on. In recognizing inevitability, [one can retain one’s] position, enhancing it only to the extent that others fail to recognize necessity. Far more influential, and often confused with necessity, is fortune. Fortune is elusive but approachable. Fortune is never to be relied upon: ‘The greatest good fortune is always least to be trusted’; the good is often kept underfoot and the ridiculous elevated, but it provides [one] with opportunity.

Wise actions involve resolve and cunning (which I interpret as political nous). This entails understanding that we do not have complete (or even partial) control over events that may occur in the future. The future is largely unknowable as are people’s true drives and motivations. Yet, despite this, managers must act.  This requires personal determination together with a deep understanding of the social and political aspects of one’s environment.

And a little later,

…risk management is not the clear conception of a problem coupled to modes of rankable resolutions, or a limited process, but a judgemental  analysis limited by the vicissitudes of budgets, programmes, personalities and contested priorities.

In short: risk management in practice tends to be a far way off from how it is portrayed in textbooks and the professional literature.

The wickedness of risk management

Most managers and those who work under their supervision have been schooled in the rational-scientific approach of problem solving. It is no surprise, therefore, that they use it to manage risks: they gather and analyse information about potential risks, formulate potential solutions (or mitigation strategies) and then implement the best one (according to predetermined criteria). However, this method works only for problems that are straightforward or tame, rather than wicked.

Many of the issues that risk managers are confronted with are wicked, messy or both.  Often though, such problems are treated as being tame.   Reducing a wicked or messy problem to one amenable to rational analysis invariably entails overlooking  the views of certain stakeholder groups or, worse, ignoring key  aspects of the problem.  This may work in the short term, but will only exacerbate the problem in the longer run. Holt illustrates this point as follows:

A primary danger in mistaking a mess for a tame problem is that it becomes even more difficult to deal with the mess. Blaming ‘operator error’ for a mishap on the production line and introducing added surveillance is an illustration of a mess being mistaken for a tame problem. An operator is easily isolated and identifiable, whereas a technological system or process is embedded, unwieldy and, initially, far more costly to alter. Blaming operators is politically expedient. It might also be because managers and administrators do not know how to think in terms of messes; they have not learned how to sort through complex socio-technical systems.

It is important to note that although many risk management practitioners recognize the essential wickedness of the issues they deal with, the practice of risk management is not quite up to the task of dealing with such matters.  One step towards doing this is to develop a shared (enterprise-wide) understanding of risks by soliciting input from diverse stakeholders groups, some of who may hold opposing views.

The skills required to do this are very different from the analytical techniques that are the focus of problem solving and decision making techniques that are taught in colleges and business schools.  Analysis is replaced by sensemaking – a collaborative process that harnesses the wisdom of a group to arrive at a collective understanding of a problem and thence a common  commitment to a course of action. This necessarily involves skills that do not appear in the lexicon of rational problem solving: negotiation, facilitation, rhetoric and those of the same ilk that are dismissed as being of no relevance by the scientifically oriented analyst.

In the end though, even this may not be enough: different stakeholders may perceive a given “risk” in have wildly different ways, so much so that no consensus can be reached.  The problem is that the current framework of risk management requires the analyst to perform an objective analysis of situation/problem, even in situations where this is not possible.

To get around this Holt suggests that it may be more useful to see risk management as a way to encounter problems rather than analyse or solve them.

What does this mean?

He sees this as a forum in which people can talk about the risks openly:

To enable organizational members to encounter problems, risk management’s repertoire of activity needs to engage their all too human components: belief, perception, enthusiasm and fear.

This gets to the root of the problem: risk matters because it increases anxiety and generally affects peoples’ sense of wellbeing. Given this, it is no surprise that Holt’s proposed solution draws on psychoanalysis.

The analogy between psychoanalysis and risk management

Any discussion of psychoanalysis –especially one that is intended for an audience that is largely schooled in rational/scientific methods of analysis – must begin with the acknowledgement that the claims of psychoanalysis cannot be tested. That is, since psychoanalysis speaks of unobservable “objects” such as the ego and the unconscious, any claims it makes about these concepts cannot be proven or falsified.

However  as Holt suggests, this is exactly what makes it a good fit for encountering (as opposed to  analyzing) risks. In his words:

It is precisely because psychoanalysis avoids an overarching claim to produce testable, watertight, universal theories that it is of relevance for risk management. By so avoiding universal theories and formulas, risk management can afford to deviate from pronouncements using mathematical formulas to cover the ‘immanent indeterminables’ manifest in human perception and awareness and systems integration.

His point is that there is a clear parallel between psychoanalysis and the individual, and risk management and the organisation:

We understand ourselves not according to a template but according to our own peculiar, beguiling histories. Metaphorically, risk management can make explicit a similar realization within and between organizations. The revealing of an unconscious world and its being in a constant state of tension between excess and stricture, between knowledge and ignorance, is emblematic of how organizational members encountering messes, wicked problems and wicked messes can be forced to think.

In brief, Holt suggests that what psychoanalysis does for the individual, risk management ought to do for the organisation.

Talking it over – the importance of conversations

A key element of psychoanalysis is the conversation between the analyst and patient. Through this process, the analyst attempts to get the patient to become aware of hidden fears and motivations. As Holt puts it,

Psychoanalysis occupies the point of rupture between conscious intention and unconscious desire — revealing repressed or overdetermined aspects of self-organization manifest in various expressions of anxiety, humour, and so on

And then, a little later,   he makes the connection to organisations:

The fact that organizations emerge from contingent, complex interdependencies between specific narrative histories suggests that risk management would be able to use similar conversations to psychoanalysis to investigate hidden motives, to examine…the possible reception of initiatives or strategies from the perspective of inherently divergent stakeholders, or to analyse the motives for and expectations of risk management itself. This fundamentally reorients the perspective of risk management from facing apparent uncertainties using technical assessment tools, to using conversations devoid of fixed formulas to encounter questioned identities, indeterminate destinies, multiple and conflicting aims and myriad anxieties.

Through conversations involving groups of stakeholders who have different risk perceptions,   one might be able to get a better understanding of a particular risk and hence, may be, design a more effective mitigation strategy.   More importantly, one may even realise that certain risks are not risks at all or others that seem straightforward have implications that would have remained hidden were it not for the conversation.

These collective conversations would take place in workshops…

…that tackle problems as wicked messes, avoid lowest-denominator consensus in favour of continued discovery of alternatives through conversation, and are instructed by metaphor rather than technical taxonomy, risk management is better able to appreciate the everyday ambivalence that fundamentally influences late-modern organizational activity. As such, risk management would be not merely a rationalization of uncertain experience but a structured and contested activity involving multiple stakeholders engaged in perpetual translation from within environments of operation and complexes of aims.

As a facilitator of such workshops, the risk analyst provokes stakeholders to think about their feelings and motivations that may be “out of bounds” in a standard risk analysis workshop.  Such a paradigm goes well beyond mainstream risk management because it addresses the risk-related anxieties and fears of individuals who are affected by it.

Conclusion

This brings me to the end of my not-so-short summary of Holt’s paper. Given the length of this post, I reckon I should keep my closing remarks short. So I’ll leave it here paraphrasing the last line of the paper, which summarises its main message:  risk management ought to be about developing an organizational capacity for overcoming risks, freed from the presumption of absolute control.

The Risk – a dialogue mapping vignette

Foreword

Last week, my friend Paul Culmsee conducted an internal workshop in my organisation on the theme of collaborative problem solving. Dialogue mapping is one of the tools he introduced during the workshop.  This piece, primarily intended as a follow-up for attendees,  is an introduction to dialogue mapping via a vignette that illustrates its practice (see this post for another one). I’m publishing it here as I thought it might be useful for those who wish to understand what the technique is about.

Dialogue mapping uses a notation called Issue Based Information System (IBIS), which I have discussed at length in this post. For completeness, I’ll begin with a short introduction to the notation and then move on to the vignette.

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A crash course in IBIS

The IBIS notation consists of the following three elements:

1. Issues(or questions): these are issues that are being debated. Typically, issues are framed as questions on the lines of “What should we do about X?” where X is the issue that is of interest to a group. For example, in the case of a group of executives, X might be rapidly changing market condition whereas in the case of a group of IT people, X could be an ageing system that is hard to replace.
2. Ideas(or positions): these are responses to questions. For example, one of the ideas of offered by the IT group above might be to replace the said system with a newer one. Typically the whole set of ideas that respond to an issue in a discussion represents the spectrum of participant perspectives on the issue.
3. Arguments: these can be Pros (arguments for) or Cons (arguments against) an issue. The complete set of arguments that respond to an idea represents the multiplicity of viewpoints on it.

Compendium is a freeware tool that can be used to create IBIS maps– it can be downloaded here.

In Compendium, IBIS elements are represented as nodes as shown in Figure 1: issues are represented by blue-green question marks; positions by yellow light bulbs; pros by green + signs and cons by red – signs.  Compendium supports a few other node types, but these are not part of the core IBIS notation. Nodes can be linked only in ways specified by the IBIS grammar as I discuss next.

Figure 1: IBIS node types

The IBIS grammar can be summarized in three simple rules:

1. Issues can be raised anew or can arise from other issues, positions or arguments. In other words, any IBIS element can be questioned.  In Compendium notation:  a question node can connect to any other IBIS node.
2. Ideas can only respond to questions– i.e. in Compendium “light bulb” nodes can only link to question nodes. The arrow pointing from the idea to the question depicts the “responds to” relationship.
3. Arguments  can only be associated with ideas– i.e. in Compendium “+” and “–“  nodes can only link to “light bulb” nodes (with arrows pointing to the latter)

The legal links are summarized in Figure 2 below.

Figure 2: Legal links in IBIS

…and that’s pretty much all there is to it.

The interesting (and powerful) aspect of IBIS is that the essence of any debate or discussion can be captured using these three elements. Let me try to convince you of this claim via a vignette from a discussion on risk.

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 The Risk – a Dialogue Mapping vignette

“Morning all,” said Rick, “I know you’re all busy people so I’d like to thank you for taking the time to attend this risk identification session for Project X.  The objective is to list the risks that we might encounter on the project and see if we can identify possible mitigation strategies.”

He then asked if there were any questions. The head waggles around the room indicated there were none.

“Good. So here’s what we’ll do,”  he continued. “I’d like you all to work in pairs and spend 10 minutes thinking of all possible risks and then another 5 minutes prioritising.  Work with the person on your left. You can use the flipcharts in the breakout area at the back if you wish to.”

Twenty minutes later, most people were done and back in their seats.

“OK, it looks as though most people are done…Ah, Joe, Mike have you guys finished?” The two were still working on their flip-chart at the back.

“Yeah, be there in a sec,” replied Mike, as he tore off the flip-chart page.

“Alright,” continued Rick, after everyone had settled in. “What I’m going to do now is ask you all to list your top three risks. I’d also like you tell me why they are significant and your mitigation strategies for them.” He paused for a second and asked, “Everyone OK with that?”

Everyone nodded, except Helen who asked, “isn’t it important that we document the discussion?”

“I’m glad you brought that up. I’ll make notes as we go along, and I’ll do it in a way that everyone can see what I’m writing. I’d like you all to correct me if you feel I haven’t understood what you’re saying. It is important that  my notes capture your issues, ideas and arguments accurately.”

Rick turned on the data projector, fired up Compendium and started a new map.  “Our aim today is to identify the most significant risks on the project – this is our root question”  he said, as he created a question node. “OK, so who would like to start?”

Figure 3: The root question

“Sure,” we’ll start, said Joe easily. “Our top risk is that the schedule is too tight. We’ll hit the deadline only if everything goes well, and everyone knows that they never do.”

“OK,” said Rick, “as he entered Joe and Mike’s risk as an idea connecting to the root question. “You’ve also mentioned a point that supports your contention that this is a significant risk – there is absolutely no buffer.” Rick typed this in as a pro connecting to the risk. He then looked up at Joe and asked,  “have I understood you correctly?”

“Yes,” confirmed Joe.

Figure 4: Map in progress

“That’s pretty cool,” said Helen from the other end of the table, “I like the notation, it makes reasoning explicit. Oh, and I have another point in support of Joe and Mike’s risk – the deadline was imposed by management before the project was planned.”

Rick began to enter the point…

“Oooh, I’m not sure we should put that down,” interjected Rob from compliance. “I mean, there’s not much we can do about that can we?”

…Rick finished the point as Rob was speaking.

Figure 5: Two pros for the idea

“I hear you Rob, but I think  it is important we capture everything that is said,” said Helen.

“I disagree,” said Rob. “It will only annoy management.”

“Slow down guys,” said Rick, “I’m going to capture Rob’s objection as “this is a management imposed-constraint rather than risk. Are you OK with that, Rob?”

Rob nodded his assent.

Fig 6: A con enters the picture

I think it is important we articulate what we really think, even if we can’t do anything about it,” continued Rick. There’s no point going through this exercise if we don’t say what we really think. I want to stress this point, so I’m going to add honesty  and openness  as ground rules for the discussion. Since ground rules apply to the entire discussion, they connect directly to the primary issue being discussed.”

Figure 7: A “criterion” that applies to the analysis of all risks

“OK, so any other points that anyone would like to add to the ones made so far?” Queried Rick as he finished typing.

He looked up. Most of the people seated round the table shook their heads indicating that there weren’t.

“We haven’t spoken about mitigation strategies. Any ideas?” Asked Rick, as he created a question node marked “Mitigation?” connecting to the risk.

Figure 8: Mitigating the risk

“Yeah well, we came up with one,” said Mike. “we think the only way to reduce the time pressure is to cut scope.”

“OK,” said Rick, entering the point as an idea connecting to the “Mitigation?” question. “Did you think about how you are going to do this? He entered the question “How?” connecting to Mike’s point.

Figure 9: Mitigating the risk

“That’s the problem,” said Joe, “I don’t know how we can convince management to cut scope.”

“Hmmm…I have an idea,” said Helen slowly…

“We’re all ears,” said Rick.

“…Well…you see a large chunk of time has been allocated for building real-time interfaces to assorted systems – HR, ERP etc. I don’t think these need to be real-time – they could be done monthly…and if that’s the case, we could schedule a simple job or even do them manually for the first few months. We can push those interfaces to phase 2 of the project, well into next year.”

There was a silence in the room as everyone pondered this point.

“You know, I think that might actually work, and would give us an extra month…may be even six weeks for the more important upstream stuff,” said Mike. “Great idea, Helen!”

“Can I summarise this point as – identify interfaces that can be delayed to phase 2?” asked Rick, as he began to type it in as a mitigation strategy. “…and if you and Mike are OK with it, I’m going to combine it with the ‘Cut Scope’ idea to save space.”

“Yep, that’s fine,” said Helen. Mike nodded OK.

Rick deleted the “How?” node connecting to the “Cut scope” idea, and edited the latter to capture Helen’s point.

Figure 10: Mitigating the risk

“That’s great in theory, but who is going to talk to the affected departments? They will not be happy.” asserted Rob.  One could always count on compliance to throw in a reality check.

“Good point,”  said Rick as he typed that in as a con, “and I’ll take the responsibility of speaking to the department heads about this,” he continued entering the idea into the map and marking it as an action point for himself. “Is there anything else that Joe, Mike…or anyone else would like to add here,” he added, as he finished.

Figure 11: Completed discussion of first risk (click to view larger image)

“Nope,” said Mike, “I’m good with that.”

“Yeah me too,” said Helen.

“I don’t have anything else to say about this point,” said Rob, “ but it would be great if you could give us a tutorial on this technique. I think it could be useful to summarise the rationale behind our compliance regulations. Folks have been complaining that they don’t understand the reasoning behind some of our rules and regulations. ”

“I’d be interested in that too,” said Helen, “I could use it to clarify user requirements.”

“I’d be happy to do a session on the IBIS notation and dialogue mapping next week. I’ll check your availability and send an invite out… but for now, let’s focus on the task at hand.”

The discussion continued…but the fly on the wall was no longer there to record it.

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Afterword

I hope this little vignette illustrates how IBIS and dialogue mapping can aid collaborative decision-making / problem solving by making diverse viewpoints explicit. That said, this is a story, and the problem with stories is that things  go the way the author wants them to.  In real life, conversations can go off on unexpected tangents, making them really hard to map. So, although it is important to gain expertise in using the software, it is far more important to practice mapping live conversations. The latter is an art that requires considerable practice. I recommend reading Paul Culmsee’s series on the practice of dialogue mapping or <advertisement> Chapter 14 of The Heretic’s Guide to Best Practices</advertisement> for more on this point.

That said, there are many other ways in which IBIS can be used, that do not require as much skill. Some of these include: mapping the central points in written arguments (what’s sometimes called issue mapping) and even decisions on personal matters.

To sum up: IBIS is a powerful means to clarify options and lay them out in an easy-to-follow visual format. Often this is all that is required to catalyse a group decision.

Three types of uncertainty you (probably) overlook

Introduction – uncertainty and decision-making

Managing uncertainty – deciding what to do in the absence of reliable information – is a significant part of project management and many other managerial roles. When put this way, it is clear that managing uncertainty is primarily a decision-making problem. Indeed, as I will discuss shortly, the main difficulties associated with decision-making are related to specific types of uncertainties that we tend to overlook.

Let’s begin by looking at the standard approach to decision-making, which goes as follows:

1. Define the decision problem.
2. Identify options.
3. Develop criteria for rating options.
4. Evaluate options against criteria.
5. Select the top rated option.

As I have pointed out in this post, the above process is too simplistic for some of the complex, multifaceted decisions that we face in life and at work (switching jobs, buying a house or starting a business venture, for example). In such cases:

1. It may be difficult to identify all options.
2. It is often impossible to rate options meaningfully because of information asymmetry – we know more about some options than others. For example, when choosing whether or not to switch jobs, we know more about our current situation than the new one.
3. Even when ratings are possible, different people will rate options differently – i.e. different people invariably have different preferences for a given outcome. This makes it difficult to reach a consensus.

Regular readers of this blog will know that the points listed above are characteristics of wicked problems.  It is fair to say that in recent years, a general awareness of the ubiquity of wicked problems has led to an appreciation of the limits of classical decision theory. (That said,  it should be noted that academics have been aware of this for a long time: Horst Rittel’s classic paper on the dilemmas of planning, written in 1973, is a good example. And there are many others that predate it.)

In this post  I look into some hard-to-tackle aspects of uncertainty by focusing on the aforementioned shortcomings of classical decision theory. My discussion draws on a paper by Richard Bradley and Mareile Drechsler.

This article is organised as follows: I first present an overview of the standard approach to dealing with uncertainty and discuss its limitations. Following this, I elaborate on three types of uncertainty that are discussed in the paper.

Background – the standard view of uncertainty

The standard approach to tackling uncertainty was  articulated by Leonard Savage in his classic text, Foundations of Statistics. Savage’s approach can be summarized as follows:

1. Figure out all possible states (outcomes)
2. Enumerate actions that are possible
3. Figure out the consequences of actions for all possible states.
4. Attach a value (aka preference) to each consequence
5. Select the course of action that maximizes value (based on an appropriately defined measure, making sure to factor in the likelihood of achieving the desired consequence)

(Note the close parallels between this process and the standard approach to decision-making outlined earlier.)

To keep things concrete it is useful to see how this process would work in a simple real-life example. Bradley and Drechsler quote the following example from Savage’s book that does just that:

…[consider] someone who is cooking an omelet and has already broken five good eggs into a bowl, but is uncertain whether the sixth egg is good or rotten. In deciding whether to break the sixth egg into the bowl containing the first five eggs, to break it into a separate saucer, or to throw it away, the only question this agent has to grapple with is whether the last egg is good or rotten, for she knows both what the consequence of breaking the egg is in each eventuality and how desirable each consequence is. And in general it would seem that for Savage once the agent has settled the question of how probable each state of the world is, she can determine what to do simply by averaging the utilities (Note: utility is basically a mathematical expression of preference or value) of each action’s consequences by the probabilities of the states of the world in which they are realised…

In this example there are two states (egg is good, egg is rotten), three actions (break egg into bowl, break egg into separate saucer to check if it rotten, throw egg away without checking) and three consequences (spoil all eggs, save eggs in bowl and save all eggs if last egg is not rotten, save eggs in bowl and potentially waste last egg). The problem then boils down to figuring out our preferences for the options (in some quantitative way) and the probability of the two states.  At first sight, Savage’s approach seems like a reasonable way to deal with uncertainty.  However, a closer look reveals major problems.

Problems with the standard approach

Unlike the omelet example, in real life situations it is often difficult to enumerate all possible states or foresee all consequences of an action. Further, even if states and consequences are known, we may not what value to attach to them – that is, we may not be able to determine our preferences for those consequences unambiguously. Even in those situations  where we can,  our preferences for may be subject to change  – witness the not uncommon situation where lottery winners end up wishing they’d never won. The standard prescription works therefore works only in situations where all states, actions and consequences are known – i.e. tame situations, as opposed to wicked ones.

Before going any further, I should mention that Savage was cognisant of the limitations of his approach. He pointed out that it works only in what he called small world situations–  i.e. situations in which it is possible to enumerate and evaluate all options.  As Bradley and Drechsler put it,

Savage was well aware that not all decision problems could be represented in a small world decision matrix. In Savage’s words, you are in a small world if you can “look before you leap”; that is, it is feasible to enumerate all contingencies and you know what the consequences of actions are. You are in a grand world when you must “cross the bridge when you come to it”, either because you are not sure what the possible states of the world, actions and/or consequences are…

In the following three sections  I elaborate on the complications mentioned above emphasizing, once again, that many real life situations are prone to such complications.

State space uncertainty

The standard view of uncertainty assumes that all possible states are given as a part of the problem definition – as in the omelet example discussed earlier.  In real life, however, this is often not the case.

Bradley and Drechsler identify two distinct cases of state space uncertainty. The first one is when we are unaware that we’re missing states and/or consequences. For example, organisations that embark on a restructuring program are so focused on the cost-related consequences that they may overlook factors such as loss of morale and/or loss of talent (and the consequent loss of productivity). The second, somewhat rarer, case is when we are aware that we might be missing something but we don’t quite know what it is. All one can do here, is make appropriate contingency plans based on  guesses regarding possible consequences.

Figuring out possible states and consequences is largely a matter of scenario envisioning based on knowledge and practical experience. It stands to reason that this is best done by leveraging the collective experience and wisdom of people from diverse backgrounds. This is pretty much the rationale behind collective decision-making techniques such as Dialogue Mapping.

Option uncertainty

The standard approach to tackling uncertainty assumes that the connection between actions and consequences is well defined. This is often not the case, particularly for wicked problems.  For example, as I have discussed in this post, enterprise transformation programs with well-defined and articulated objectives often end up having a host of unintended consequences. At an even more basic level, in some situations it can be difficult to identify sensible options.

Option uncertainty is a fairly common feature in real-life decisions. As Bradley and Drechsler put it:

Option uncertainty is an endemic feature of decision making, for it is rarely the case that we can predict consequences of our actions in every detail (alternatively, be sure what our options are). And although in many decision situations, it won’t matter too much what the precise consequence of each action is, in some the details will matter very much.

…and unfortunately, the cases in which the details matter are precisely those problems in which they are the hardest to figure out – i.e. in wicked problems.

Preference uncertainty

An implicit assumption in the standard approach is that once states and consequences are known, people will be able to figure out their relative preferences for these unambiguously. This assumption is incorrect, as there are at least two situations in which people will not be able to determine their preferences. Firstly, there may be  a lack of factual information about one or more of the states. Secondly, even when one is able to get the required facts, it is hard to figure out how we would value the consequences.

A common example of the aforementioned situation is the job switch dilemma. In many (most?) cases in which one is debating whether or not to switch jobs, one lacks enough factual information about the new job – for example, the new boss’ temperament, the work environment etc. Further, even if one is able to get the required information, it is impossible to know how it would be to actually work there.  Most people would have struggled with this kind of uncertainty at some point in their lives. Bradley and Drechsler term this ethical uncertainty. I prefer the term preference uncertainty, as it has more to do with preferences than ethics.

Some general remarks

The first point to note is that the three types of uncertainty noted above map exactly on to the three shortcomings of classical decision theory discussed in the introduction.  This suggests a connection between the types of uncertainty and wicked problems. Indeed, most wicked problems are exemplars of one or more of the above uncertainty types.  For example, the paradigm-defining super-wicked problem of climate change displays all three types of uncertainty.

The three types of uncertainty discussed above are overlooked by the standard approach to managing uncertainty.  This happens in a number of ways. Here are two common ones:

1. The standard approach assumes that all uncertainties can somehow be incorporated into a single probability function describing all possible states and/or consequences. This is clearly false for state space and option uncertainty: it is impossible to define a sensible probability function when one is uncertain about the possible states and/or outcomes.
2. The standard approach assumes that preferences for different consequences are known. This is clearly not true in the case of preference uncertainty…and even for state space and option uncertainty for that matter.

In their paper, Bradley and Dreschsler arrive at these three types of uncertainty from considerations different from the ones I have used above. Their approach, while more general, is considerably more involved. Nevertheless, I would recommend that readers who are interested should take a look at it because they cover a lot of things that I have glossed over or ignored altogether.

Just as an example, they show how the aforementioned uncertainties can be reduced. There is a price to be paid, however: any reduction in uncertainty results in an increase in its severity. An example might help illustrate how this comes about. Consider a situation of state space uncertainty. One can reduce- or even, remove – this by defining a catch-all state (labelled, say, “all other outcomes”). It is easy to see that although one has formally reduced state space uncertainty to zero, one has increased the severity of the uncertainty because the catch-all state is but a reflection of our ignorance and our refusal to do anything about it!

There are many more implications of the above. However, I’ll point out just one more that serves to illustrate the very practical implications of these uncertainties. In a post on the shortcomings of enterprise risk management, I pointed out that the notion of an organisation-wide risk appetite is problematic because it is impossible to capture the diversity of viewpoints through such a construct. Moreover,  rule or process based approaches to risk management tend to focus only on those uncertainties that can be quantified, or conversely they assume that all uncertainties can somehow be clumped into a single probability distribution as prescribed by the standard approach to managing uncertainty. The three types of uncertainty discussed above highlight the limitations of such an approach to enterprise risk.

Conclusion

The standard approach to managing uncertainty assumes that all possible states, actions and consequences are known or can be determined. In this post I have discussed why this is not always so.  In particular, it often happens that we do not know all possible outcomes (state space uncertainty), consequences (option uncertainty) and/or our preferences for consequences (preference or ethical uncertainty).

As I was reading the paper, I felt the authors were articulating issues that I had often felt uneasy about but chose to overlook (suppress?).  Generalising from one’s own experience is always a fraught affair, but  I reckon we tend to deny these uncertainties because they are inconvenient – that is, they are difficult if not impossible to deal with within the procrustean framework of the standard approach.  What is needed as a corrective is a recognition that the pseudo-quantitative approach that is commonly used to manage uncertainty may not the panacea it is claimed to be. The first step towards doing this is to acknowledge the existence of the uncertainties that we (probably) overlook.