Science advice and the management of risk

Science advice and the management of risk in government and business

The foundation for science and technology, 10 November 2010

An authoritative summary of the UK governments position on risk, with talks and papers.

  •  Beddington gives a good overview. He discusses probability versus impact ‘heat maps’, the use of ‘worst case’ scenarios, the limitations of heat maps and Blackett reviews. He discusses how management strategy has to reflect both the location on the heat map and the uncertainty in the location.
  • Omand discusses ‘Why wont they (politicians) listen (to the experts)?’  He notes the difference between secrets (hard to uncover) and secrets (hard to make sense of), and makes ‘common cause’ between science and intelligence in attempting to communicate with politicians. Presents a familiar type of chart in which probability is thought of as totally ordered (as in Bayesian probability) and seeks to standardise on the descriptors of ranges of probability, such as ‘highly probable’.
  • Goodman discusses economic risk management and the need to cope with ‘irrational cycles of exuberance’, focussing on ‘low probability high impact’ events. Only some risks can be quantified. Recommends ‘generalised Pareto distribution’.
  • Spielgelhalter introduced the discussion with some important insights:

The issue ultimately comes down to whether we can put numbers on these events.  … how can a figure communicate the enormous number of assumptions which underlie such quantifications? … The … goal of a numerical probability … becomes much more difficult when dealing with deeper uncertainties. … This concerns the acknowledgment of indeterminacy and ignorance.

Standard methods of analysis deal with recognised, quantifiable uncertainties, but this is only part of the story, although … we tend to focus at this level. A first extra step is to be explicit about acknowledged inadequacies – things that are not put into the analysis such as the methane cycle in climate models. These could be called ‘indeterminacy’. We do not know how to quantify them but we know they might be influential.

Yet there are even greater unknowns which require an essential humility. This is not just ignorance about what is wrong with the model, it is an acknowledgment that there could be a different conceptual basis for our analysis, another way to approach the problem.

There will be a continuing debate  about the process of communicating these deeper uncertainties.

  • The discussion covered the following:
    • More coverage of the role of emotion and group think is needed.
    • “[G]overnments did not base policies on evidence; they proclaimed them because they thought that a particular policy would attract votes. They would then seek to find evidence that supported their view. It would be more realistic to ask for policies to be evidence tested [rather than evidence-based.]”
    • “A new language was needed to describe uncertainty and the impossibility of removing risk from ordinary life … .”
    •  Advisors must advise, not covertly subvert decision-making.

Comments

If we accept that there is more to uncertainty than  can be reflected in a typical scale of probability, then it is no wonder that organisational decisions fail to take account of it adequately, or that some advisors seek to subvert such poor processes. Moreover, this seems to be a ‘difference that makes a difference’.

From a Keynesian perspective conditional probabilities, P(X|A), sometimes exist but unconditional ones, P(X), rarely do. As Spielgelhalter notes it is often the assumptions that are wrong: the estimated probability is then irrelevant. Spielgelhalter mentioned the common use of ‘sensitivity analysis’, noting that it is unhelpful. But what is commonly done is to test the sensitivity of P(X|y,A) to some minor variable y while keeping the assumptions, A. fixed. What is more often (for these types of risk) needed is a sensitivity to assumptions. Thus, if P(X|A) is high:

  • one needs to identify possible alternatives, A’, to A for which P(X|A’) is low, no matter how improbable A’ may be regarded.

Here:

  • ‘Possible’ means consistent with the evidence rather than anything psychological.
  • The criteria for what is regarded as ‘low’ or ‘high’ will be set by the decision context.

The rationale is that everything that has ever happened was, with hind-sight, possible: the things that catch us out are those that we overlooked, perhaps because we thought them improbable.

A conventional analysis would overlook emergent properties, such as booming cycles of ‘irrational’ exuberance. Thus in considering alternatives one needs to consider potential emotions and other emergencies and epochal events.

This suggests a typical ‘risk communication’ would consist of an extrapolated ‘main case’ probability together with a description of scenarios in which the opposite probability would hold.

See also

mathematicsheat maps, extrapolation and induction

Other debates, my bibliography.

Dave Marsay

 

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About Dave Marsay
Mathematician with an interest in 'good' reasoning.

One Response to Science advice and the management of risk

  1. Norma Hollister says:

    David,
    This is exactly what I see in investment protocols and rationalizations. There are assumptions based on what we can imagine–quite limited, and popular opinion is the wild card. Unpredictability of the masses.

    It really is a fun and somewhat helpful exercise to proclaim one’s wildest predictions because, at their core, there is some correlation to reality, albeit often intuited and not describable.

    A frightening thought regarding the wishful prophecy issued by economists and governments, is how they plan to soothe our populations when the certainty of their affirmations is revealed to be…nothing. I would imagine the behavior of fractals has a key role. We do tend to repeat evn the most stupid behaviors over aand over. A self-similarity mistake.
    Norma

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