The search for MH370: uncertainty

There is an interesting podcast about the search for MH370 by a former colleague. I think it illustrates in a relatively accessible form some aspects of uncertainty.

According to the familiar theory, if one has an initial probability distribution over the globe for the location of MH370’s flight recorder, say, then one can update it using Bayes’ rule to get a refined distribution. Conventionally, one should search where there is a higher probability density (all else being equal). But in this case it is fairly obvious that there is no principled way of deriving an initial distribution, and even Bayes’ rule is problematic. Conventionally, one should do the best one can, and search accordingly.

The podcaster (Simon) gives examples of some hypotheses (such as the pilot being well, well-motivated and unhindered throughout) for which the probabilistic approach is more reasonable. One can then split one’s effort over such credible hypotheses, not ruled out by evidence.

A conventional probabilist would note that any ‘rational’ search would be equivalent to some initial probability distribution over hypotheses, and hence some overall distribution. This may be so, but it is clear from Simon’s account that this would hardly be helpful.

I have been involved in similar situations, and have found it easier to explain the issues to non-mathematicians when there is some severe resource constraint, such as time. For example, we are looking for a person. The conventional approach is to maximise our estimated probability of finding them based on our estimated probabilities of them having acted in various ways (e.g., run for it, hunkered down). An alternative is to consider the ways they may ‘reasonably’ be thought to have acted and then to seek to maximize the worst case probability of finding them. Then again, we may have a ranking of ways that they may have acted, and seek to maximize the number of ways for which the probability of our success exceeds some acceptable amount (e.g. 90%). The key point here is that there are many reasonable objectives one might have, for only one of which the conventional assumptions are valid. The relevant mathematics does still apply, though!

Dave Marsay

More to Uncertainty than 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.

(Actually, my paper was published jan 2016,but somehow this request for comments got stuck in a limbo somewhere. Better late than never?)

Which rationality?

We often suppose that rationality is ‘a good thing’, but is it always?

Rationality is variously defined as being in accord with reason, logic or ‘the facts’. Here ‘reason’ may mean one’s espoused or actual reasons, or it may mean in accord with some external standards. Thus in its broadest interpretation, it seems that anything that has a reason for being the way that it is may be considered broadly rational. But the notion of rationality derives from ‘reason’, one aspect of which is ‘sound judgement, good sense’. This suggests some external standard.

If we use the term ‘simple’ to denote a situation in which there are definite ‘objective’ standards of soundness and goodness, then rationality in simple situations is behaviour that accords with those standards. Philosophers can argue endlessly about whether any such situations exist, so it seems sensible to define rationality more generally as being relative to some set of standards. The question then being: What standards?

My natural inclination as a mathematician is that those standards should always include the best relevant logics, including mathematics. Yet I have witnessed many occasions on which the use of mathematics has tended to promote disasters, and the advocates of such approaches (apart from those few who think like me) have seemed culpable. I have a great deal of respect and sympathy for the view that mathematics is harmful in complex situations. Yet intellectually it seems quite wrong, and I cannot accept it.

In each case there seems to be some specific failing, which many of my colleagues have attributed to some human factor, such as hubris or the need to keep a job or preserve an institution. But the perpetrators do not seem to me to be much different from the rest of us, and I have long thought that there is some more fundamental common standard that is incompatible with the use of reason. The financial crises of 2007/8/9 are cases where it is hard to believe that most of those pushing the ‘mathematical’ view that turned out to be harmful were either irrational or rationally harmful.

Here I want to suggest a possible explanation.

From a theoretical perspective, there are no given ‘facts’, ‘logics’ or ‘reasons’ that we can rely on.This certainly seems to be true of finance and economics. For example, in economics the models used may be mathematical and in this sense beyond criticism, but the issue of their relevance to a particular situation is never purely logical, and ought to be questioned. Yet it seems that many institutions, including businesses,  rely on having absolute beliefs: questioning them would be wasteful in the short-run. So individual actors tend not only to be rational, but also to be narrowly rational ‘in the short run’, which normally goes with acting ‘as if’ it had narrow facts.

For example, it seems to me to be  a fact that according to the best contemporary scientific theories, the earth is not stationary. It is generally expedient to for me to act ‘as if’ I knew that the earth moved. But unless we can be absolutely sure that the earth moves, the tendency to suppose that it is a fact that the earth moves could be dangerous. (You could try substituting other facts, such as that economies always tend to a healthy equilibrium.)

In a healthy society there would be a competition of ideas,  such that society as a whole could be said to be being more broadly rational, even while its actors were being only narrowly rational. For example, a science would consist of various schools, each of which would be developing its own theories, consistent with the facts, which between them would be exploring and developing the space of all such credible theories. At a practical level, an engineer would appreciate the difference between building a bridge based on a theory that had been tested on similar bridges, and building a novel type of bridge where the existing heuristics could not be relied upon.

I do not think that human society as a whole is healthy in this sense. Why not? In evolutionary theory separate niches, such as islands, promote the development of healthy diversity. Perhaps the rise of global communications and trade, and even the spread of the use of English, is eliminating the niches in which ideas can be explored and so is having a long-run negative impact that needs to be compensated for?

Thus I think we need to distinguish between short and long-run rationalities, and to understand and foster both. It seems to me that most of the time, for most areas of life, short-run rationality is adequate, and it is this that is familiar. But this needs to be accompanied by an understanding of the long-run issues, and an appropriate balance achieved. Perhaps too much (short-run) rationality can be harmful (in the long-run). And not only in economies.

Dave Marsay

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