by Judith Curry

. . . this “crisp number” mode of thinking has promoted the use of over-simplistic models and masking of uncertainties that can in turn lead to incomplete understanding of problems and bad decisions. – Peter Taylor and Jerome Ravetz

It’s been a while since we’ve had an uncertainty post. Previously we’ve covered a lot of territory on that topic, but I think this talk provides some fresh insights and some great quotes.

Last March, Peter Taylor and Jerome Ravetz gave a presentation at the University of Oxford Centre for Practical Ethics [link to ppt, audio].

Title: The Value of Uncertainty

Abstract: The faith that truth lies in numbers goes back to the Pythagorean attempt to unify both practical and theoretical sciences. Its current manifestation is the idolisation of pre-Einsteinian physics in the quantification of social, economic, and behavioural sciences. The talk will explain how this “crisp number” mode of thinking has promoted the use of over-simplistic models and masking of uncertainties that can in turn lead to incomplete understanding of problems and bad decisions. The quality of a model in terms of its fitness for purpose can be ignored when convenience, especially computerised convenience, offers more easily calculated crisp numbers. Yet these inadequacies matter when computerised models generate pseudo-realities of their own through structures such as financial derivatives and processes such as algorithmic trading. Like Frankenstein’s monster, we have already seen financial market pseudo-reality take on an uncontrolled, unstable and dangerous life of its own, all the more beguiling when it generated income for all parties in the merry-go-round. Despite its manifest failings, it is still going on.

We believe the urgent task is to integrate uncertainty and quality into the quantitative sciences of complex systems, and we will offer some practical techniques that illustrate how this could be accomplished.

Below are some excerpts from the text of the .ppt:

The Value of Uncertainty

Perceived need to eliminate uncertainty

–Confusing science with removing uncertainty

–Delusional certitude

–Wilful blindness

Recognition of uncertainty can have value

–Appreciation of possibilities

–Adaptation to circumstances

–Better decisions

The Ethics of Uncertainty

Uncertainty mostly seen as undesirable, yet

–False certainties of “Useless Arithmetic”

–Consequences of authority metrics

Is recognising uncertainty good or bad?

–Prevents or delays crucial action? (overuse of “precautionary principle” or on the other hand tobacco “manufacturing doubt”)

–Confuses the public or causes loss of trust?

–Makes us feel uncomfortable?

–Surely someone must know the “truth”?

Aristotle on appropriate precision:

“It is the mark of an educated man to look for precision in each class of things just so far as the nature of the subject admits”

Delusional certitude

“It is hard to overstate the damage done in the recent past by people who thought they knew more about the world than they really did.” – John Kay in “Obliquity” 2010

“Understanding the models, particularly their limitations and sensitivity to assumptions, is the new task we face. “Many of the banking and financial institution problems and failures of the past decade can be directly tied to model failure or overly optimistic judgements in the setting of assumptions or the parameterization of a model.” – Tad Montross, 2010, Chairman and CEO of GenRe in “Model Mania”

Seek transparency and ease of interrogation of any model, with clear expression of the provenance of assumptions. Communicate the estimates with humility, communicate the uncertainty with confidence. Fully acknowledge the role of judgement.” – D. J. Spiegelhalter and H. Riesch

Tools For Judgement

Blobograms

Decision Portraits

Nomograms

The combination of these tools will enable us to reason rigorously about uncertain quantities

In particular, the use of blobs with nomograms would enable the identification of models that are strictly nonsensical: GIGO

That is, where uncertainties in inputs must be suppressed lest outputs become indeterminate

The Logic of Failure in Human Decision Making

Losers:

Acted without prior analysis

Didn’t test against evidence

Assumed absence of negative meant correct decisions made

Blind to emerging circumstances

Focused on the local not the global

Avoided uncertainty

‘‘good participants differed from the bad ones … in how often they tested their hypotheses. The bad participants failed to do this. For them, to propose a hypothesis was to understand reality; testing that hypothesis was unnecessary. Instead of generating hypotheses, they generated “truths” ’

“ … we do not feel it is generally appropriate to respond to limitations in formal analysis by increasing the complexity of the modelling. Instead, we feel a need to have an external perspective on the adequacy and robustness of the whole modelling endeavour, rather than relying on within-model calculations of uncertainties, which are inevitably contingent on yet more assumptions that may turn out to be misguided. ” – D. J. Spiegelhalter and H. Riesch

Whilst uncertainty is not to be glorified

We should not disguise our ignorance with delusionally certain models

We can take advantage of the greater scope uncertainty offers

Tools are needed to support judgement