About Predictiveness

That participants or practitioners in a particular domain fail to frequently make accurate predictions does not necessarily mean knowledge and understanding in that field is wholly worthless. But when encountering predictions we should bear in mind to what extent we are likely to be able to rely upon a given prediction.

Amongst the considerations we might weigh are:

• comparisons: how accurate have similar predictions been by those reputed to be experts, for example, a Harvard or Princeton Economics professor, or a Wall Street analyst interviewed on television about recent happenings on world markets and the future outlook, say, for oil prices or a stock index – what good comparisons from the applicable historical record can we make use of to guide our judgements?
• expertise: do the actors in the field looked to as possessing relevant expertise exhibit the necessary intellectual discipline such as taking base rates into account and taking pains to properly qualify their prediction statements, and where diverging from base rates, advancing first-rate evidence and arguments which demonstrates any “strong” prediction is warranted?
• prospects: if the current level of predictive accuracy in the field we are concerned with is generally quite modest, in the near term does there seem to be any substantial likelihood of a real “breakthrough” which might significantly increase accuracy – is it the case, for instance, that the particular decision task shows little or no promise of being tractable to accurate prediction? This is information which is surely very much worth knowing.

Predictiveness

Does the claim or conclusion contain or imply a framework or means to predict future events or outcomes? By fairly generating what these clear predicted outcomes are in advance, and then following-up to see if the results are borne out, we can overcome hindsight biases, ad+hoc excuses, and overconfidence, providing an effective way to assess the claim or conclusion.

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