Cognitive biases as project meta-risks

Introduction and background

A  comment by John Rusk on this post got me thinking about the effects of  cognitive  biases on the perception and analysis of project  risks.  A cognitive bias is a human tendency to base a judgement or decision on a flawed perception or understanding of data or events.  A recent paper suggests that cognitive biases may have played a role in some high profile project failures.   The author of the paper, Barry Shore, contends that the failures were caused by poor  decisions which could be traced back to specific biases.  A direct implication is that  cognitive biases can have a significant negative  effect on how project risks are perceived and acted upon.  If true, this has consequences for the practice of risk management in projects (and other areas, for that matter). This essay discusses the role of cognitive biases in risk analysis, with a focus on project environments.

Following the pioneering work of Daniel Kahneman and Amos Tversky, there has been a lot of applied research on the role of cognitive biases in various areas of social sciences (see Kahneman’s Nobel Prize lecture for a very readable account of his work on cognitive biases).  A lot of this research highlights the fallibility of intuitive decision making.  But even judgements ostensibly based on data are subject to cognitive biases.  An example of this is when data is misinterpreted to suit the decision-maker’s preconceptions (the so-called confirmation bias). Project risk management is largely about making decisions regarding uncertain events that might impact a project. It involves, among other things, estimating the likelihood of these events occurring and the resulting impact on the project. These estimates and the decisions based on them can be erroneous for a host of reasons.  Cognitive biases are an often overlooked, yet universal,  cause of error.

Cognitive biases as project meta-risks

So, what role do cognitive biases play in project risk analysis? Many researchers have considered specific cognitive biases as project risks:  for example, in this paper, Flyvbjerg describes how the risks posed by optimism bias can be addressed using reference class forecasting (see my post on improving project forecasts for more on this).  However, as suggested in the introduction, one can go further. The first point to note is that biases are part and parcel of the mental make up of humans, so any aspect of risk management that involves human judgment is subject to bias. As such, then, cognitive biases may be thought of as meta-risks: risks that affect risk analyses. Second, because they are a part of the mental baggage of all humans,  overcoming them involves an understanding of the thought processes that govern decision-making,  rather than externally-directed analyses (as in the case of risks).  The analyst has to understand how his or her perception of risks may be affected by these meta-risks.

The publicly available research and professional literature on meta-risks in business and organisational contexts is sparse. One relevant reference is a paper by Jack Gray on meta-risks in financial portfolio management.  The first few lines of the paper state,

“Meta-risks are qualitative, implicit risks that pass beyond the scope of explicit risks. Most are born out the complex interaction between the behaviour pattern of individuals and those of organizational structures” (italics mine).

Although he doesn’t use the phrase, Gray seems to be referring to cognitive biases – at least in part. This is confirmed by a reading of the paper. It describes, among other things, hubris (which roughly corresponds to the  illusion of control) and discounting evidence that conflicts with one’s views (which corresponds to confirmation bias) as meta-risks. From this (admittedly small) sampling of the literature, it seems that the notion of cognitive biases as meta-risks has some precedent.

Next, let’s look at how biases can manifest themselves as meta-risks in a project environment. To keep the discussion manageable, I’ll focus on a small set of biases:

Anchoring: This refers to the tendency of humans to rely on a single piece of information when making a decision. I have seen this manifest itself in task duration estimation – where “estimates plucked out of thin air” by management serve as an anchor for subsequent estimation by the project team. See this post for more on anchoring in project situations. Anchoring is a meta-risk because the over-reliance on a single piece of information about a risk can have an adverse effect on decisions relating to that risk.

Availability: This refers to the tendency of people to base decisions on information that can be easily recalled, neglecting potentially more important information. As an example, a project manager might give undue weight to his or her most recent professional experiences when analysing project risks. Here availability is a meta-risk because it is a barrier to an objective consideration of risks that are not immediately apparent to the analyst.

Representativeness: This refers to the tendency to make judgements based on  seemingly representative, known samples . For example, a project team member  might base a task estimate based on another (seemingly) similar task, ignoring important differences between the two. Another manifestation of representativeness is when probabilities of events are estimated based on those of comparable, known events. An example of this is the gambler’s fallacy. This is clearly a meta-risk, especially where “expert judgement” is used as a technique to assess risk (Why? Because such judgements are invariably based on comparable tasks that the expert has encountered before.).

Selective perception: This refers to the tendency of individuals to give undue importance to data that supports their own views. Selective perception is a bias that we’re all subject to; we hear what we want to hear, see what we choose to see, and remain deaf  and blind to the rest. This is a meta-risk because it results in a skewed (or incomplete) perception of risks.

Loss Aversion: This refers to the tendency of people to give preference to avoiding losses (even small losses) over making gains. In risk analysis this might manifest itself as overcautiousness. Loss aversion is a meta-risk because it might, for instance, result in the assignment of an unreasonably large probability of occurrence to a risk.

A particularly common manifestation of loss aversion in project environments is the sunk cost bias. In situations where significant investments have been made in projects, risk analysts might be biased towards downplaying risks.

Information bias: This is the tendency of some analysts to seek as much data as they can lay their hands on prior to making a decision.  The danger here is of being swamped by too much irrelevant information. Data by itself does not improve the quality of decisions (see this post by Tim van Gelder for more on the dangers of data-centrism). Over-reliance on data – especially when there is no way to determine the quality and relevance of data as is often the case – can hinder risk analyses. Information bias is a meta-risk for two reasons already alluded to above; first, the data may not capture important qualitative factors and second, the data may not be relevant to the actual risk.

I could work my way through a few more of the biases listed here, but I think I’ve already made my point: projects encompass a spectrum of organisational and technical situations, so just about any cognitive bias is a potential meta-risk.

Conclusion

Cognitive biases are meta-risks because they can affect decisions pertaining to risks – i.e. they are risks of risk analysis.  Shore’s research suggests that the risks posed by these meta-risks are very real;  they can cause project failure  So, at a practical level, project managers need to understand  how cognitive biases could affect their own  risk-related judgements (or any other judgements for that matter).   The previous section provides illustrations of how selected cognitive biases  can affect risk analyses;  there are, of course,  many more.  Listing examples is illustrative, and helps make the point that cognitive biases are meta-risks. However, it is more useful and interesting to understand how biases operate and what we can do to overcome them.   As I have mentioned above, overcoming biases requires an understanding of the thought processes through which humans make decisions in the face of uncertainty.  Of particular interest is  the role of  intuition and rational thought in forming judgements, and the common mechanisms that underlie judgement-related cognitive biases.   A knowledge and awareness of these mechanisms  might help project managers in consciously countering the operation of cognitive biases in their own decision making.  I’m currently making some notes on these topics, with the intent of publishing them in a forthcoming essay – please stay tuned.

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Note

Part II of this post published here.