by From Lars Syll The incorporation of new information makes sense only if the future is to be similar to the past. Any kind of empirical test, whatever form it adopts, will not make sense, however, if the world is uncertain because in such a world induction does not work. Past experience is not a useful guide to guess the future in these conditions (it only serves when the future, somehow, is already implicit in the present) … I believe the only way to use past experience is to assume that the world is repetitive. In a non-repetitive world in which relevant novelties unexpectedly arise testing is irrelevant … These considerations are applicable to decisions in conditions of radical uncertainty. If the actions that I undertake in t0 will have very different consequences according to the
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from Lars Syll
The incorporation of new information makes sense only if the future is to be similar to the past. Any kind of empirical test, whatever form it adopts, will not make sense, however, if the world is uncertain because in such a world induction does not work. Past experience is not a useful guide to guess the future in these conditions (it only serves when the future, somehow, is already implicit in the present) … I believe the only way to use past experience is to assume that the world is repetitive. In a non-repetitive world in which relevant novelties unexpectedly arise testing is irrelevant …
These considerations are applicable to decisions in conditions of radical uncertainty. If the actions that I undertake in t0 will have very different consequences according to the eventual state of the world in t1, it is crucial to gather reliable knowledge about these states. But how could I evaluate in t0 my beliefs about the state of the world in t1? If the world were repetitive (governed by immutable laws) and these laws were known, I could assume that what I find out about the present state is relevant to determine how the future state (the one that will prevail) will be. It would make then sense to apply a strategy for gathering empirical evidence (a sequence of actions to collect new data). But if the world is not repetitive, what makes me think that the new information may be at all useful regarding future events? …
Conceiving economic processes like sequences of events in which uncertainty reigns, where consequently there are “no laws”, nor “invariants” or “mechanisms” to discover, the kind of learning that experiments or last experience provide is of no use for the future, because it eliminates innovation and creativity and does not take into account the arboreal character and the open-ended nature of the economic process … However, as said before, we can gather precise information, restricted in space and time (data). But, what is the purpose of obtaining this sort of information if uncertainty about future events prevails? … The problem is that taking uncertainty seriously puts in question the relevance the data obtained by means of testing or experimentation has for future situations.
To yours truly, Marqués’ book is especially important since it shows how far-reaching the effects of taking Keynes’ concept of genuine uncertainty really are.
Almost a hundred years after John Maynard Keynes wrote his seminal A Treatise on Probability(1921), it is still very difficult to find economics textbooks that seriously try to incorporate his far-reaching and incisive analysis of uncertainty, inductive inference and evidential weight.
The standard view in economics and statistics — and the axiomatic probability theory underlying it — is to a large extent based on the rather simplistic idea that ‘more is better.’ But as Keynes argues – ‘more of the same’ is not what is important when making inductive inferences. It’s rather a question of ‘more but different.’
Variation, not replication, is at the core of induction. Finding that p(x|y) = p(x|y & w) doesn’t make w ‘irrelevant.’ Knowing that the probability is unchanged when w is present gives p(x|y & w) another evidential weight (‘weight of argument’). Running 10 replicative experiments do not make you as ‘sure’ of your inductions as when running 10 000 varied experiments – even if the probability values happen to be the same.
According to Keynes we live in a world permeated by unmeasurable uncertainty – not quantifiable stochastic risk – which often forces us to make decisions based on anything but ‘rational expectations.’ Keynes rather thinks that we base our expectations on the confidence or ‘weight’ we put on different events and alternatives. To Keynes, expectations are a question of weighing probabilities by ‘degrees of belief,’ beliefs that often have preciously little to do with the kind of stochastic probabilistic calculations made by the rational agents as modelled by ‘modern’ social sciences. And often we “simply do not know.” As Keynes writes in Treatise:
If different wholes were subject to different laws qua wholes and not simply on account of and in proportion to the differences of their parts, knowledge of a part could not lead, it would seem, even to presumptive or probable knowledge as to its association with other parts … In my judgment, the practical usefulness of those modes of inference … on which the boasted knowledge of modern science depends, can only exist … if the universe of phenomena does in fact present those peculiar characteristics of atomism and limited variety which appears more and more clearly as the ultimate result to which material science is tending.
Science according to Keynes should help us penetrate to “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts.” Models can never be more than a starting point in that endeavour. He further argued that it was inadmissible to project history on the future. Consequently, we cannot presuppose that what has worked before, will continue to do so in the future. That statistical models can get hold of correlations between different ‘variables’ is not enough. If they cannot get at the causal structure that generated the data, they are not really ‘identified.’
How strange that writers of economics textbooks do not even touch upon these aspects of scientific methodology that seems to be so fundamental and important for anyone trying to understand how we learn and orient ourselves in an uncertain world. An educated guess on why this is a fact would be that Keynes concepts are not possible to squeeze into a single calculable numerical ‘probability.’ In the quest for quantities one puts a blind eye to qualities and looks the other way — but Keynes ideas keep creeping out from under the carpet.
Robert Lucas once wrote — in Studies in Business-Cycle Theory — that “in cases of uncertainty, economic reasoning will be of no value.” Now, if that was true, it would put us in a tough dilemma. If we have to consider — as Lucas — uncertainty incompatible with economics being a science, and we actually know for sure that there are several and deeply important situations in real-world contexts where we — both epistemologically and ontologically — face genuine uncertainty, well, then we actually would have to choose between reality and science.
That can’t be right. We all know we do not know very much about the future. We all know the future harbours lots of unknown unknowns. Those are ontological facts we just have to accept. But — I still think it possible we can go for both reality and science, and develop a realist, relevant, non-ergodic economic science.