Uncertainty is not just probability

I have just had published my paper, based on the discussion paper referred to in a previous post. In Facebook it is described as:

An understanding of Keynesian uncertainties can be relevant to many contemporary challenges. Keynes was arguably the first person to put probability theory on a sound mathematical footing. …

So it is not just for economists. I could be tempted to discuss the wider implications.

Comments are welcome here, at the publisher’s web site or on Facebook. I’m told that it is also discussed on Google+, Twitter and LinkedIn, but I couldn’t find it – maybe I’ll try again later.

Dave Marsay


Instrumental Probabilities

Reflecting on my recent contribution to the economics ejournal special issue on uncertainty (comments invited), I realised that from a purely mathematical point of view, the current mainstream mathematical view, as expressed by Dawid, could be seen as a very much more accessible version of Keynes’. But there is a difference in expression that can be crucial.

In Keynes’ view ‘probability’ is a very general term, so that it always legitimate to ask about the probability of something. The challenge is to determine the probability, and in particular whether it is just a number. In some usages, as in Kolmogorov, the term probability is reserved for those cases where certain axioms hold. In such cases the answer to a request for a probability might be to say that there isn’t one. This seems safe even if it conflicts with the questioner’s presuppositions about the universality of probabilities. In the instrumentalist view of Dawid, however, suggests that probabilistic methods are tools that can always be used. Thus the probability may exist even if it does not have the significance that one might think and, in particular, it is not appropriate to use it for ‘rational decision making’.

I have often come across seemingly sensible people who use ‘sophisticated mathematics’ in strange ways. I think perhaps they take an instrumentalist view of mathematics as a whole, and not just probability theory. This instrumentalist mathematics reminds me of Keynes’ ‘pseudo-mathematics’. But the key difference is that mathematicians, such as Dawid, know that the usage is only instrumentalist and that there are other questions to be asked. The problem is not the instrumentalist view as such, but the dogma (of at last some) that it is heretical to question widely used instruments.

The financial crises of 2007/8 were partly attributed by Lord Turner to the use of ‘sophisticated mathematics’. From Keynes’ perspective it was the use of pseudo-mathematics. My view is that if it is all you have then even pseudo-mathematics can be quite informative, and hence worthwhile. One just has to remember that it is not ‘proper’ mathematics. In Dawid’s terminology  the problem seems to be that the instrumental use of mathematics without any obvious concern for its empirical validity. Indeed, since his notion of validity concerns limiting frequencies, one might say that the problem was the use of an instrument that was stunningly inappropriate to the question at issue.

It has long seemed  to me that a similar issue arises with many miscarriages of justice, intelligence blunders and significant policy mis-steps. In Keynes’ terms people are relying on a theory that simply does not apply. In Dawid’s terms one can put it blunter: Decision-takers were relying on the fact that something had a very high probability when they ought to have been paying more attention to the evidence in the actual situation, which showed that the probability was – in Dawid’s terms – empirically invalid. It could even be that the thing with a high instrumental probability was very unlikely, all things considered.

Decision-making under uncertainty: ‘after Keynes’

I have a new discussion paper. I am happy to take comments here, on LinkedIn, at the more formal Economics e-journal site or by email (if you have it!), but wish to record substantive comments on the journal site while continuing to build up a site of whatever any school of thought may think is relevant, with my comments, here.

Please do comment somewhere.


I refer to Keynes’ ‘weights of argument’ mostly as something to be taken into account in addition to probability. For example, if one has two urns each with a mix of 100 otherwise identical black and white balls, where the first urn is known to have equal number of each colour, but the mix for the other urn is unknown, then conventionally one has equal probability of drawing a black ball form each urn, but the weight of argument is greater for the first than the second.

Keynes does fully develop his notion of weights and it seems not to be well understood, and I wanted my overview of Keynes’ views to be non-contentious. But from some off-line comments I should clarify.

Ch. VI para 8 is worth reading, followed by Ch. III para 8. Whatever the weight may be, it is ‘strengthened by’:

  • Being more numerous.
  • Having been obtained with a greater variety of conditions.
  • Concerning a greater generalisation.

Keynes argues that this weight cannot be reduced to a single number, and so weights can be incomparable. He uses the term ‘strength’ to indicate that something is increased while recognizing that it may not be measurable. This can be confusing, as in Ch. III para 7, where he refers to ‘the strength of argument’. In simple cases this would just be the probability, not to be confused with the weight.

It seems to me that Keynes’ concerns relate to Mayo’s:

Severity Principle: Data x provides a good evidence for hypothesis H if and only if x results from a test procedure T which, taken as a whole, constitutes H having passed a severe test – that is, a procedure which would have, with very high probability, uncovered the discrepancies from H, and yet no such error is detected.

In cases where one has performed a test, severity seems to roughly correspond to have a strong weight, at least in simpler cases. Keynes’ notion applies more broadly. Currently, it seems to me, care needs taking in applying either to particular cases. But that is no reason to ignore them.



Dave Marsay



Paul Romer has recently attracted attention by his criticism of what he terms ‘mathiness’ in economic growth theory. As a mathematician, I would have thought that economics could benefit from more mathiness, not less. But what he seems to be denigrating is not mathematics as I understand it, but what Keynes called ‘pseudomathematics’. In his main example the problem is not inappropriate mathematics as such, but a succession of symbols masquerading as mathematics, which Paul unmasks using – mathematics. Thus, it seems to me the paper that he is criticising would have benefited from more (genuine) mathiness and less pseudomathiness.

I do agree with Paul, in effect, that bad (pseudo) mathematics has been crowding out the good, and that this should be resisted and reversed. But, as a mathematician, I guess I would think that.

I also agree with Paul that:

We will make faster scientific progress if we can continue to rely on the clarity and precision that math brings to our shared vocabulary, and if, in our analysis of data and observations, we keep using and refining the powerful abstractions that mathematical theory highlights … .

But more broadly some of Paul’s remarks suggest to me that we should be much clearer about the general theoretical stance and the role of mathematics within it. Even if an economics paper makes proper use of some proper mathematics, this only ever goes so far in supporting economic conclusions, and I have the impression that Paul is expecting too much, such that any attempt to fill his requirement with mathematics would necessarily be pseudo-mathematics. It seems to me that economics can never be a science like the hard sciences, and as such it needs to develop an appropriate logical framework. This would be genuinely mathsy but not entirely mathematical. I have similar views about other disciplines, but the need is perhaps greatest for economics.


Bloomberg (and others) agree that (pseudo)-mathiness is rife in macro-economics and that (perhaps in consequence) there has been a shift away from theory to (naïve) empiricism.

Tim Harford, in the ft, discusses the related misuse of statistics.

… the antidote to mathiness isn’t to stop using mathematics. It is to use better maths. … Statistical claims should be robust, match everyday language as much as possible, and be transparent about methods.

… Mathematics offers precision that English cannot. But it also offers a cloak for the muddle-headed and the unscrupulous. There is a profound difference between good maths and bad maths, between careful statistics and junk statistics. Alas, on the surface, the good and the bad can look very much the same.

Thus, contrary to what is happening, we might look for a reform and reinvigoration of theory, particularly macroeconomic.


Romer adds an analogy between his mathiness, which has actual formulae and a description on the one hand, and computer code, which typically has both the actual code and some comments. Romer’s mathiness is like when the code is obscure and the comments are wrong, as when the code does a bubble sort but the comment says it does a prime number sieve. He gives the impression that in economics this may often be deliberate. But a similar phenomenon is when the coder made the comment in good faith, so that the code appears to do what it says in the comment, but that there is some subtle, technical, flaw. A form of pseudo-mathiness is when one is heedless to such a possibility. The cure is more genuine mathiness. Even in computer code, it is possible to write code that is more or less obscure, and the less obscure code is typically more reliable. Similarly in economics, it would be better for economists to use mathematics that is within their competence, and to strive to make it clear. Maybe the word Romer is looking for is obscurantism?

Dave Marsay 

Artificial Intelligence?

The subject of ‘Artificial Intelligence’ (AI) has long provided ample scope for long and inconclusive debates. Wikipedia seems to have settled on a view, that we may take as straw-man:

Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it. [Dartmouth Conference, 1956] The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds. [John Searle’s straw-man hypothesis]

Readers of my blog will realise that I agree with Searle that his hypothesis is wrong, but for different reasons. It seems to me that mainstream AI (mAI) is about being able to take instruction. This is a part of learning, but by no means all. Thus – I claim – mAI is about a sub-set of intelligence. In many organisational settings it may be that sub-set which the organisation values. It may even be that an AI that ‘thought for itself’ would be a danger. For example, in old discussions about whether or not some type of AI could ever act as a G.P. (General Practitioner – first line doctor) the underlying issue has been whether G.P.s ‘should’ think for themselves, or just apply their trained responses. My own experience is that sometimes G.P.s doubt the applicability of what they have been taught, and that sometimes this is ‘a good thing’. In effect, we sometimes want to train people, or otherwise arrange for them to react in predictable ways, as if they were machines. mAI can create better machines, and thus has many key roles to play. But between mAI and ‘superhuman intelligence’  there seems to be an important gap: the kind of intelligence that makes us human. Can machines display such intelligence? (Can people, in organisations that treat them like machines?)

One successful mainstream approach to AI is to work with probabilities, such a P(A|B) (‘the probability of A given B’), making extensive use of Bayes’ rule, and such an approach is sometimes thought to be ‘logical’, ‘mathematical, ‘statistical’ and ‘scientific’. But, mathematically, we can generalise the approach by taking account of some context, C, using Jack Good’s notation P(A|B:C) (‘the probability of A given B, in the context C’). AI that is explicitly or implicitly statistical is more successful when it operates within a definite fixed context, C, for which the appropriate probabilities are (at least approximately) well-defined and stable. For example, training within an organisation will typically seek to enable staff (or machines) to characterise their job sufficiently well for it to become routine. In practice ‘AI’-based machines often show a little intelligence beyond that described above: they will monitor the situation and ‘raise an exception’ when the situation is too far outside what it ‘expects’. But this just points to the need for a superior intelligence to resolve the situation. Here I present some thoughts.

When we state ‘P(A|B)=p’ we are often not just asserting the probability relationship: it is usually implicit that ‘B’ is the appropriate condition to consider if we are interested in ‘A’. Contemporary mAI usually takes the conditions a given, and computes ‘target’ probabilities from given probabilities. Whilst this requires a kind of intelligence, it seems to me that humans will sometimes also revise the conditions being considered, and this requires a different type of intelligence (not just the ability to apply Bayes’ rule). For example, astronomers who refine the value of relevant parameters are displaying some intelligence and are ‘doing science’, but those first in the field, who determined which parameters are relevant employed a different kind of intelligence and were doing a different kind of science. What we need, at least, is an appropriate way of interpreting and computing ‘probability’ to support this enhanced intelligence.

The notions of Whitehead, Keynes, Russell, Turing and Good seem to me a good start, albeit they need explaining better – hence this blog. Maybe an example is economics. The notion of probability routinely used would be appropriate if we were certain about some fundamental assumptions. But are we? At least we should realise that it is not logical to attempt to justify those assumptions by reasoning using concepts that implicitly rely on them.

Dave Marsay

Distilling the Science from the Art

Geoff Evatt (U o Manchester, UK) gave a ‘Mathematics in the Workplace’ talk at the recent Manchester Festival of Mathematics and its Applications, printed in the Oct 2014 Mathematics Today.

He showed how the Mathematical modeller could turn their hand to diverse subjects of financial regulation and … .

He is critical of the view that ‘Mathematical Modelling is like an Art’ and advocates the prescriptive teaching of best-practice. His main motivation seems to be to attract more students and the up-take by industry (etc).

This … will be achieved by academics from a variety of universities agreeing in what is ‘best practice’ in teaching modelling is … .


Taking the title, I accept that the term ‘art’ may be misleading, but I am not convinced that there is much science in, for example, finance, or that those funding the mathematics really care, so the term ‘science’ could be equally misleading and more dangerous. I would say that mathematical modelling is often a craft. Where it is part of a proper scientific endeavour, I would think that this would be because of the domain experts and ought to be certified from a scientific rather than mathematical point of view. To me ‘best practice’ is to work closely with domain experts, to give them what they need, and to make sure that they understand what they do – and don’t have. It is good to seek to be scientific and objective, but not to misrepresent what has actually been achieved.

In the run-up to the financial crash best practice included characterising mathematical modelling in this area as an ‘art’ and not a science, to prevent financiers and politicians from thinking that the ‘mathematical’ nature of the models somehow lent them the same credibility normally accorded to mathematics. A key part of the financial problem was that this was not well-enough understood.

A key part of economics is the concept of ‘uncertainty’. The classical mathematical models did not model uncertainty beyond mere probability, possibly because was not covered by contemporary mainstream courses.

Best practice would include ensuring that the mathematics used was appropriate to the domain, or at least in explaining any short-falls. I think that this requires more development than Evatt supposes. I also think that one would need to go beyond academics, to include people who understand the issues involved.

Dave Marsay

What should replace utility maximization in economics?

Mainstream economics has been based on the idea of people producing and trading in order to maximize their utility, which depends on their assigning values and conditional  probabilities to outcomes. Thus, in particular, mainstream economics implies that people do best by assigning probabilities to possible outcomes, even when there seems no sensible way to do this (such as when considering a possible crash). Ken Arrow has asked, if one rejects utility maximization, what should one replace it with?

The assumption here seems to be that it is better to have a wrong theory than to have no theory. The fear seems to be that economies would grind to a holt unless they were sanctioned by some theory – even a wrong one. But this fear seems at odds with another common view, that economies are driven by businesses, which are driven by ‘pragmatic’ men. It might be that without the endorsement of some (wrong) theory some practices, such as the development of novel technical instruments and the use of large leverages, would be curtailed. But would this be a bad thing?

Nonetheless, Arrow’s challenge deserves a response.

There are many variations in detail of utility maximization theories. Suppose we identity ‘utility maximization’ as a possible heuristic, then utility maximization theory claims that people use some specific heuristics, so an obvious alternative is to consider a wider  range. The implicit idea behind utility maximization theory seems to be under a competitive regime resembling evolution, the evolutionary stable strategies (‘the good ones’) do maximize some utility function, so that in time utility maximizers ought to get to dominate economies. (Maybe poor people do not maximize any utility, but they – supposedly – have relatively little influence on economies.) But this idea is hardly credible. If – as seems to be the case – economies have significant ‘Black Swans’ (low probability high impact events) then utility maximizers  who ignore the possibility of a Black Swan (such as a crash) will do better in the short-term, and so the economy will become dominated by people with the wrong utilities. People with the right utilities would do better in the long run, but have two problems: they need to survive the short-term and they need to estimate the probability of the Black Swan. No method has been suggested for doing this. An alternative is to take account of some notional utility but also take account of any other factors that seem relevant.

For example, when driving a hire-car along a windy road with a sheer drop I ‘should’ adjust my speed to trade time of arrival against risk of death or injury. But usually I simply reduce my speed to the point where the risk is slight, and accept the consequential delay. These are qualitative judgements, not arithmetic trade-offs. Similarly an individual might limit their at-risk investments (e.g. stocks) so that a reasonable fall (e.g. 25%) could be tolerated, rather than try to keep track of all the possible things that could go wrong (such as terrorists stealing a US Minuteman) and their likely impact.

More generally, we could suppose that people act according to their own heuristics, and that there are competitive pressures on heuristics, but not that utility maximization is necessarily ‘best’ or even that a healthy economy relies on most people having similar heuristics, or that there is some stable set of ‘good’ heuristics. All these questions (and possibly more) could be left open for study and debate. As a mathematician it seems to me that decision-making involves ideas, and that ideas are never unique or final, so that novel heuristics could arise and be successful from time to time. Or at least, the contrary would require an explanation. In terms of game theory, the conventional theory seems to presuppose a fixed single-level game, whereas – like much else – economies seem to have scope for changing the game and even for creating higher-level games, without limit. In this case, the strategies must surely change and are created rather than drawn from a fixed set?

See Also

Some evidence against utility maximization. (Arrow’s response prompted this post).

My blog on reasoning under uncertainty with application to economics.

Dave Marsay