Journal Article
No. 2015-41 | November 20, 2015
The Economics of Radical Uncertainty

Abstract

In situations of what we now describe as radical uncertainty, the core model of agent behaviour, of rational autonomous agents with stable preferences, is not useful. Instead, a different principle, in which the decisions of an agent are based directly on the decisions and strategies of other agents, is offered as a relevant core model. Preferences are not stable, but evolve. It is not a special case in such circumstances, but the general one. The author provides empirical evidence to suggest that as a description of behaviour in the modern world, economic rationality is applicable in a declining number of situations. He discusses models drawn from the modern literature on cultural evolution in which imitation of others is the basic strategy, and suggests a heuristic way of classifying situations in which the different models are relevant. The key point is that in situations where radical uncertainty is present, we require theoretical ‘null’ models of agent behaviour which are different from those of economic rationality. Under uncertainty, fundamentally different behavioural rules are ‘rational’. The author gives an example of a very simple pure sentiment model of the business cycle, in which agents use very simple heuristic decision rules. It is nevertheless capable of approximating a number of deep features of output growth over the cycle.

JEL Classification:

D81, E14, E32

Assessment

  • Downloads: 2231 (Discussion Paper: 1172)

Links

Cite As

Paul Ormerod (2015). The Economics of Radical Uncertainty. Economics: The Open-Access, Open-Assessment E-Journal, 9 (2015-41): 1–20. http://dx.doi.org/10.5018/economics-ejournal.ja.2015-41


Comments and Questions


Romar Correa - article by Paul Ormerod
November 21, 2015 - 05:14

A pleasure to read you again, Paul Ormerod! I have two responses. May I suggest you add the following reference to your deliberations? I have found the book Rational Herds … by Christophe Chamley (2004) a sophisticated and fecund source for developing themes like imitation that you engage with. Secondly, ...[more]

... since the stochastic error terms, epsilon and eta, in your equations (1) and (2), respectively, are ‘private’ and ‘present’ should they not be distinguished from the ‘public’ and ‘past’ terms in the two equations? They can either be additively separate at the end or be attached to the first terms in the two equations. Also, I gather that the decision to have weights (alpha) in (1) and, thus, an addition operator there and coefficients (beta and gamma) and a negative sign connecting the two terms in (2) is deliberate.


Peter Smith - Comment
December 04, 2015 - 08:20

The paper sets out the case for treating imitation as a rational response to an uncertain environment very clearly. However, a couple of questions arise from this.

(i) Imitation has to start somewhere. Can we identify typical points of initiation that become centres of imitation? My money would be ...[more]

... on modelling case studies from the business literature as fuzzy systems: fuzzy logic enables us to reason rigorously with partial or ambiguous information. It needs a narrative account of how the situation we face works (what are the main factors, and how they interact), but the method has advantages over conventional econometric approaches (particularly in the fairly common situation – in economic time series – of probability distributions that are very far from gaussian [1]). Fuzzy logic is the more general version, embracing conventional 2-valued (true/false) logics as a special case. (Avid fans of Aristotle will, of course, be aware that he had identified the issue of fuzziness in logic, although he did not develop it [2]). For a worked example in an economics context, with a basic introduction to the theory, see [3].

(ii) What does uncertainty do to our concept of rational action, as developed by, for example, Hausman [4]? If we take ‘being rational’ as finding ways to extract satisfaction from one’s situation in an effective way, we may find ourselves adjusting our original goals (and possibly even our values, i.e., those states of the world that we would like to see brought nearer), in the light of both experience and reflective analysis. Simple and uncertain situations correspond to two cells of a classification of problem structures originally developed by Ian Mitroff [5]. Mainstream economics forces everything into the well-structured cell, which should be reserved exclusively for choices where it is possible to identify a unique best solution – one that all will accept as such. (There are, of course, few well-structured problems in management, and virtually none in policy-making.) Ill-structured situations are uncertain and ambiguous, so that there can be dispute about the ‘true nature’ of the problem, and how it is to be addressed. Here, effective, rational action depends on our abilities: to make potent diagnoses; to manage the invention of new solutions; and to manage implementation vigilantly – because of the unforeseen consequences our initiatives may set off. (Despite the James-Watt-and-his-Granny’s-Kettle model of invention that is so deeply embedded in our culture, creativity can be managed [6]; and unforeseen consequences can, of course, be positive as well as negative, see, for example, [7].) Theory may help us understanding the range and variety of such consequences; it will rarely offer us simple prescriptions for action.
Unfortunately, things can get a lot stickier than this (when the role of prescriptive theory and our ability to forecast outcomes are even more attenuated). In Mitroff’s wickedly-structured domain, there is conflict among the parties as to what values and which paradigm should direct our choices. (I am using ‘paradigm’ in something approaching Kuhn’s original sense of a conviction-carrying image of reality that makes alternatives look like nonsense [8] – but compare this with [9].) Here, rationality consists in skilled political action: negotiating compromises, building coalitions, neutralizing opponents, gaining control of resources, gaining control of critical posts and functions, moving disputes into more congenial forums, and moulding public opinion [10].

References

[1] Mandelbrot, B. and Hudson, R.L. (2005). The Mis(Behaviour) of Markets: a Fractal View of Risk, Ruin, and Reward. Profile Books.
[2] Barnes, J. (ed.) (2003). Aristotle: Ethics. Folio Society.
[3] Smith, PJ (2011). The Reform of Economics. (Chapters 3-5). Taw Books.
[4] Hausman, D. (1992). The Inexact and Separate Science of Economics. (Chapter 8.) Cambridge.
[5] Mitroff, II (1974). The Subjective Side of Science. (Chapter 7.) Elsevier.
[6] Gordon, W. (1961). Synectics: The Development of Creative Capacity. Harper & Row.
[7] Stacey, R.D. (1992). Managing Chaos: Dynamic Business Strategies in an Unpredictable World. (Chapter 4.) Pitman.
[8] Kuhn, T. (1970). The Structure of Scientific Revolutions. University of Chicago Press.
[9] Haack, S. (2007). Defending Science: Between Scientism and Cynicism. (p 51 ff) Prometheus Books.
[10] Mintzberg, H. (1983). Power in and around Organizations. (Chapter 13.) Prentice-Hall.
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Peter Smith, 30 November 2015.
Springcott, Chittlehamholt, Devon EX37 9PD.
Email: manindev@yahoo.com
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