But what if I’m wrong?

This phrase should be emblazoned on a plaque placed on every risk underwriter’s wall, and used as a mantra to guard against the complacency and the smugness that can come from being far too comfortable with one’s intellectual prowess, and the knowledge which one has gained through long experience.

As Socrates is supposed to have said (as attributed in Plato’s Apologia): “…he thinks he knows something when he does not, whereas when I do not know, neither do I think I know”.

We seem to live in a world in which it is essential to have an answer (the answer- and immediately) to every issue, problem or question; with any uncertainty or hesitation being seen as a sign of weakness. All too often, underwriters and senior executives can become caught by the fear of seeming weak and indecisive if they do not project certainty and confidence, when everyone else thinks “X” is a good risk. After all, if “everyone” thinks so, it must be, mustn’t it?

It takes courage to admit doubt and to go “contra the herd”.

So, we have a paradox. On the one hand, being self-questioning is a key attribute in developing sound judgement; yet, on the other, one is expected to move rapidly towards an effective decision.

In this context, it helps to establish a framework of mental models and techniques that one can apply to overcome both the hubris of certainty and the spectre of decision paralysis, while minimizing the risks of a really poor outcome. Call it a form of mental triage.

The legendary Charlie Munger is a vocal proponent of the need to have a framework of mental models readily available in order to maximize one’s ability to make the right decision. And, as his partner Warren Buffett also said: “Outstanding long-term results are produced primarily by avoiding dumb decisions, rather than by making brilliant ones.”

Of course, the catalogue of such models is very extensive. However, at least in the realm of (re)insurance, a handful would appear particularly relevant.

Firstly, inversion. Rather than asking what is the best or most likely outcome; ask instead what would automatically produce the worst outcome and try to avoid it. One would hope that any risk manager considering aggregations would find this approach useful, in fact essential. A corollary of this is maintaining objectivity by challenging one’s initial opinion by seeking evidence that would disconfirm it.

Secondly, emergence. The interaction of lower-order factors or components can often lead to the emergence of an outcome that is non-linear, and not easily predictable from its component parts. One could, in a sense, refer to these as tipping points, where the rate of change rapidly increases with potentially disastrous consequences. So, one has to look for linkages that may not be obvious.

Thirdly, irreducibility. While the goal of simplification is an admirable one, at some point continuing the attempt becomes counter-productive and potentially misleading, because the model for a risk and its components have become irreducible.

Fourthly, Bayesian updating. This has a formal mathematical model; but, for practical purposes, requires one to take into account new information as it arrives in order to update one’s assessment of the probability of an outcome. Given that the world is largely non-deterministic, this is essential if one is not to become “stuck” in an outdated and potentially fatal model.

There are, as we said, many more, and we aim to return to the topic in a future post. None of this is exactly “rocket science”, and it will be seen that there are overlaps between the four models briefly described. Nevertheless, it is surprising how often individuals fail to analyze and reflect upon why they should or did make a certain decision. At Awbury, we debate constantly how we can make an effective and rational decision based upon the information available to us, being well aware that no single approach can or should be considered the “only way”. “Received wisdom” can easily become a trap.

The Awbury Team

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