Uncertainty and its discontents

20. June 2013 · Write a comment · Categories: Economics, Prediction, Regulation

There’s certainly a great deal of uncertainty about.

In Nate Silver’s book, The Signal and the Noise, there is a chapter on climate change (which has come in for some criticism – see Michael Mann’s blog on this) which contains a diagram on uncertainty supposedly sketched for him on a cocktail napkin by Gavin Schmidt. It occurred to me that this pattern of uncertainty at different timescales was more generally applicable (it describes very well, for instance, the different types of uncertainty in any projections of future mortality rates). In particular, I think it provides a good framework for considering the current arguments about economic growth, debt and austerity. Some of these arguments look to be at cross-purposes because they are focused on different timeframes.

uncertaintyIn the short term, looking less than 10 years ahead, initial condition uncertainty dominates. This means that in the short term we do not really understand what is currently going on (all our knowledge is to some extent historical) and trends which might seem obvious in a few years are anything but now. Politics operates in this sphere (long term thinking tends to look two parliaments ahead at most, ie 10 years). However, the market traders who by their activities move the markets and market indices on which we tend to base our forecasts and our economic policies are also working in the short term, the very short term (ie less than 3 months to close off a position and be able to compare your performance with your peers), even if they are trading in long term investments.

So both the politics and economics is very short term in its focus, and this is therefore where the debate about growth and austerity tends to be waged. The Austerians (which include the UK Government) claim to believe that debt deters growth, and that cutting spending in real terms is the only possible Plan A policy option. The Keynesians believe that, in a recession, and when interest rates cannot go any lower, demand can only be revived by Government spending. This argument is now well rehearsed, and is in my view shifting towards the Keynesians, but in the meantime austerian policies (with all the economic destruction they inevitably cause) continue in the UK.

However, there are other groups seemingly supportive of the UK Government’s position in this argument for altogether different reasons. Nassim Nicholas Taleb argues that high levels of debt increase an economy’s fragility to the inevitable large devastating economic events which will happen in the future and which we cannot predict in advance. He therefore dismisses the Keynesians as fragilistas, ie people who transfer more fragility onto the rest of us by their influence on policy. These concerns are focused on the structural uncertainty which is always with us and is difficult to reduce significantly. It is therefore important to reduce (or, if possible, reverse) your fragility to it.

At the longer term end are the groups who believe that we need to restrict future economic growth voluntarily before it is done for us, catastrophically rapidly, by a planet whose limits in many areas may now be very close to being reached. They are therefore implacably opposed to any policy which aims to promote economic growth. These concerns are focused where there are many possible future scenarios (ie scenario uncertainty), some of which involve apocalyptic levels of resource depletion and land degradation.

These different groups are tackling different problems. I do not believe that those concerned with the structural fragility of the economy really believe that the people paying for the restructure should be the disabled or those on minimum wage. Similarly, there is a big difference between encouraging people to consume less and move to more sustainable lifestyles and recalibrating down what is meant by a subsistence level of existence for those already there.

We do need to worry about too big to fail. Our economy houses too many institutions which appear to be too large to regulate effectively. We do need to reduce levels of debt when economic activity has returned to more normal levels. We will need to restructure our economy entirely for it to make long-term sense in a world where long term limits to growth seem inevitable. But none of these are our immediate concern. We need to save the economy first.

Skin in the right game

11. June 2013 · Write a comment · Categories: Actuarial, Economics, Prediction, Regulation

skin in the gameIn my previous post, I looked at some of the reasons why we are so useless at making economic predictions, and some of the ideas for what might be done about it. One of the key problems, raised by Nassim Nicholas Taleb most recently in his book Antifragile, is the absence of skin in the game, ie forecasters having something to lose if their forecasts are wildly off.

But what if all forecasters had to have something to lose before they were allowed to make forecasts? What if every IMF or OBR forecast came with a bill if it was seriously adrift? What if you knew whenever you read a forecast in a newspaper or on a television screen that the person making that forecast had invested something in their belief in their own forecast?

Betting on events dates back at least to the 16th century, but prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) have developed most strongly over the last 25 years or so (the University of Iowa launched its first electronic market as an experiment in 1988). Intrade, which describes itself as the world’s leading prediction market, is now a little smaller than it was following the news just before Christmas last year that it would no longer let Americans trade on its site. It had been sued by the US regulator of the commodities derivative markets for breaking a commitment not to allow trading on the constituents of those markets.

A paper on prediction markets earlier this year called Prediction Markets for Economic Forecasting by Snowberg, Wolfers and Zitzewitz suggests there are 3 main types:

·         Winner takes all. If the event you have bet on happens you win. If not, you lose your stake. Intrade is this type of prediction market: you pay a proportion of £10 a share based on the average probability of the event happening (according to the market participants) and get £10 back if it happens, nothing if it doesn’t. The Iowa Electronic Market current offerings, on congressional elections and US federal monetary policy, are also winner takes all. The price of the bet at any time should reflect the market’s view of the probability of the event happening.

·         Index. The amount paid out is unknown but tied to the variable you are betting on, eg the number of seats won by a party in a particular parliamentary election, or the value of an index at close of business on a particular date. The price of the bet at any time should reflect the market’s view of the expected value of the outcome.

·         Spread betting. Most commonly found on sports betting sites, the amount bet and the amount paid out are fixed, but the event that leads to a pay out (eg number of goals scored in a match more than x) changes until the numbers of buys and sells match (a “buy” in this example is betting the number of goals will be above x, a “sell” is betting the number of goals will be below y, a number less than x. The spread, on which the betting site makes its money, is the difference between x and y. This could equally be applied to the values of an index at a particular date (eg Spreadex offer just such bets on several major share indices as well as currency exchange rates). Depending on the relationship between the pay out and the bet, the value of the spread points at any time should reflect the market’s view of a particular point in the probability distribution of the event, eg if the pay out is twice the bet, this would be the median (ie a 50% chance of the outcome being higher or lower).

As we saw in my previous post, currently economic predictions are largely blown off course by either:

  • Over-confidence in the information used to make them; or
  • The difficulty in standing out against a market which is making everyone a lot of money (buying has limited downside, selling limited upside); or (another possibility I haven’t mentioned before)
  • Bias in individual “expert” judgements, eg those with reputations at stake may want to keep their assumptions somewhere in the middle of the pack rather than standing out most of the time as this is less risky (hence the obsession with “benchmarking” assumptions in the actuarial world for instance).

Prediction markets can help with all of these problems:

  • Having to bet on your opinion should cause you to weigh the evidence backing it more carefully. Also, once a market is established and attracting a lot of bets, the range of evidence on which opinions are being based should expand. Prediction markets also appear to be quite difficult to manipulate or arbitrage.
  • Prediction markets can respond very rapidly to changes in the information available. As an example, within 25 minutes of Donald Rumsfeld’s former chief of staff tweeting about the death of Osama Bin Laden, the market view of the probability of this event on a prediction market rose from 7% to 99%.
  • Betting can take place anonymously. So, although the betting site knows who you are, no one else does, and the data from the voting therefore gets out into the public domain without any individual being accused of talking down a market or risking their reputation.

For these reasons, amongst others, forecast accuracy for established prediction markets might be expected to outperform that of professional forecasters. The paper of Wolfers et al suggests that this is the case.

There are still problems. The markets need to be popular to be much use for predictions, so the questions need to be interesting enough for mass participation. Secondly, a market could theoretically be undermined (although not necessarily to the detriment of its predictive ability) by traders with inside information. However, there are quite a few safeguards in place against this type of activity. Intrade, for instance, requires a photo ID and two proofs of address before it registers anyone to trade on their site. And Spreadex are regulated by the Financial Conduct Authority. A third problem is referred to as the “longshot bias”, which is observed on all types of betting markets. People tend to over-bet on events with long odds against them and under-bet on events which are fairly likely (which explains the narrowing of the odds as the starting gun approached on that horse you bet on just because of its name in the Grand National a couple of months ago). This is a problem of the winner takes all type of market, seemingly related to behavioural factors around the difference between how we view winning and losing, and it is difficult to see how it could be avoided completely. Care may need to be taken therefore when interpreting prediction markets on events which are seen as having fairly low probabilities.

But overall, prediction markets would seem to offer a way of significantly improving economic predictions, so why not make them compulsory for people who want to make such forecasts? By putting a cost on such predictions (a minimum bet could be set based on the size of the organisation making it), it would remove the casual forecasting we currently see too much of, and encourage people to review their beliefs rather more carefully. It would also ensure that the markets were popular enough to be effective. It may be that economic forecasting will always be far from perfect, but this seems a good place to start if we want, in Nate Silver’s words, to be “less and less and less wrong”.

A tax on economists? Not at all. But it might mean that we all have skin in the right game.

Skin in the wrong game

10. June 2013 · Write a comment · Categories: Economics, Prediction

The main reason I set up this website was to draw more attention to the fact that we are fairly useless at making economic predictions. The graph I use as a logo is just one of many comparisons of economic projections with reality which could be shown to demonstrate just how useless. The problem is that there is then a bit of a void when we are looking for ways to support economic decision making. Nassim Nicholas Taleb and Nate Silver are, in different ways, exploring ways to fill this void.

Taleb, in his book Antifragile, links the fragility of the economic system, amongst other things, to an absence of “skin in the game”, ie something to lose if things do not go to plan. Something is fragile if it is vulnerable to volatility, robust if it is relatively unaffected by it, and antifragile if it profits from volatility. If decision makers gain an upside when things go right, but are effectively bailed out from the downside when they don’t by others who then suffer the downside instead, they will have no real incentive to be sufficiently careful about the decisions they take. This makes the chance of a downside for everyone else consequently greater. Taleb refers to people in this position, championing risky strategies (or, more often, strategies where the risks are not really known) without facing the risks personally, as “fragilistas”. He suggests instead a system of “nonpredictive decision making under uncertainty” on the basis that “it is far easier to figure out if something is fragile than to predict the occurrence of an event that may harm it”.

Silver, in his book The Signal and the Noise, suggests the main reason why economic predictions are routinely so awful, even when the forecasters are trying to be accurate rather than making a splash, is that the events forecasters are considering are often out of sample, ie the historical data they are considering to make their forecasts do not include the circumstances currently being faced, but that the forecasters are confident enough to make predictions despite this. This explains, for instance, the failure in the US to predict the housing crash (there had never been such a boom before), the failure of the ratings agencies to understand how risky mortgage-backed securities were (they were in new more complex forms) and the failure to predict that the housing market would take the rest of the economy down with it (much more trading betting on house price rises that did not materialise than had ever been seen before).

Silver cites the economist Terence Odean of the University of California at Berkeley, whose paper Do Investors Trade Too Much shows that equity traders trade excessively in the sense that their returns are, on average, reduced through trading (due to transaction costs), and suggests that this is partly due to overconfidence in the constant stream of information in the media drawing their attention from one possible model to another. This effect can be modelled to show that markets behave irrationally in the presence of overconfident decision making, even when decision-makers are otherwise completely rational.

However, when we are looking at the calls made by traders there are other forces at work that make things even worse. Silver’s book advances the theory that it is not so much an absence of skin in the game that leads traders to continue betting on rising markets when bubbles have started to develop, but skin in the wrong game (at least from the point of view of the rest of us fragile people). He gives the example of a trader looking a year ahead to make a call on whether the market will crash or not. If he buys and the market rises everyone is happy. If he buys and the market crashes, he will be in the same boat as everyone else and will probably keep his job if not his bonus. If he sells and the market crashes he will be seen as a genius, but the tangible rewards of this beyond his current position might not be life changing. However, if he sells and the market rises he may never work in the industry again.

For a trader who wants to keep his job and remain popular with his colleagues, selling therefore has limited upside and unlimited downside, to analyse it in a Talebian way. Buying on the other hand has potentially unlimited upside while the music is playing and limited downside when it stops. So these traders do have skin in the game, which keeps them buying even when they can see the iceberg approaching, and does not particularly reward accuracy in forecasting. It’s just a different game to the one the rest of us are playing.

For these amongst many other reasons, economic forecasting (Silver differentiates forecasting – meaning planning under conditions of uncertainty – from predicting – meaning calling the future correctly) is unlikely ever to be very accurate. But whereas Taleb believes that planning for the future must be nonpredictive to avoid making suckers of us all, Silver believes there may be some scope for improvement. This brings us to the idea of prediction markets and their ability to introduce some of the right skin in the right game, which I will discuss in my next post.

The Clocks and the Wind

12. May 2013 · Write a comment · Categories: Birmingham, Economics, Prediction

There has been much discussion over the past few months over whether high levels of debt cause low growth (the “austerian” camp, eg Britain, Canada and Germany within the G7) or whether instead low growth causes high levels of debt to accumulate (the “Keynesian” camp, to which Japan appears to be providing leadership currently). There has been relatively little discussion about the possibility that neither is the case.

We are compulsive pattern spotters. That explains to a large extent our dominance as a species, and completely explains the dominant position that mathematics and its applications holds in our culture.

I was reminded most stirringly of this a few years ago, on a lunch break. The Ikon Gallery in Birmingham was hosting an exhibition by Japanese sound artist Yukio Fujimoto called The Tower of Time. However, instead of siting it at their gallery space in Brindley Place, it had instead been staged at Perrott’s Folly, just around the corner from my office at the time.

Yukio Fujimoto. The Tower of Time
Installation view – Perrott’s Folly, Birmingham, UK 2009  Photo: Stuart Whipps

Perrott’s Folly was built in 1758 by John Perrott. It is a building 94 feet high, with one room on each of its six octagonal floors, and no obvious purpose (hence “folly”). It may have been somewhere to spy on his wife from, while she was alive or dead, or it may have been a gambling den for him and his mates. Or it may have been something else entirely. I think we are unlikely to ever know for sure.

After a brief introduction on the ground floor, I climbed the stairs to the first floor to find one little black square alarm clock with a red second hand ticking in the middle of the wooden floor. The next floor had ten such clocks, in a row. The next 100, in a square, the fifth floor had 1,000.

A curious thing happened to me as I moved up the tower. The clocks’ mechanisms appeared to alter with altitude. I put it that way as an example of an obviously false causality, ie that the height above sea level in some way affected how the clocks worked (and before I get complaints, I mean effects that could be detected within a matter of a few tens of feet and with no measuring equipment other than my eyes and ears). Because what I saw did change. I looked at one clock and I could see that the battery was powering the gear mechanism that kept the second hand, minute hand and hour hand in their required relative motion. I looked at ten clocks in a row and I could see the same, although I also noticed the second hands were not all at the same point along the row and that there was an order in which each piece of red plastic reached the top before beginning the next circuit. I found myself having to watch the clocks for several minutes to see the pattern confirmed. But was this “pattern” anything which had any meaning, or was it just a way for my brain to store the images it was collecting in an easily fileable format?

When I moved to 100 clocks, the relevance of the gear mechanism became secondary. I could “see” lines of second hands moving together in the way that lines of plants in a cornfield move with the breeze. This, combined with the swooshing of 100 clocks (as the ticking of each individual clock combined to make a different noise – this change in sound was I believe the artist’s main reason for constructing the installation in the first place), made me need to check several times that one of the strange pointed windows in the tower had not been opened and let in a stray breeze. At 1,000 clocks it was just pure cornfield, the individual clocks now as hard to imagine as it had been to imagine anything else four floors below.

I can “see” that the “wind” is blowing a pattern through the second hands of the clocks and yet I “know” that this is not happening. Now transfer that wind I can see to a situation where I do not readily have a theory for what is happening to individual elements within a system. Suddenly what anyone with eyes can see becomes so much more powerful than what we might know. Returning to the austerity debate for instance, perhaps the individual growth clocks have no relationship with the patterns of debt I can see being blown through them. Perhaps if I just arranged the clocks differently I would see the wind blowing from a different direction. Perhaps the clocks and the wind have nothing to do with each other outside my head, despite the “evidence” of my eyes.

Why does it matter? Because if we cannot prevent ourselves from seeing patterns and then extending them via models where we have to make some things depend on other things, even in the face of weak and conflicting evidence, then we need to know this about ourselves. Because if giving a person the wrong map is worse than not giving him one at all, our natural instinct to construct these maps is likely to keep getting us into trouble.

Needles and Pins 3 May 2013

03. May 2013 · Write a comment · Categories: Cartoons, Prediction

 

Hopes over Experience

19. April 2013 · Write a comment · Categories: Economics, Prediction

“IMF slashes UK growth forecast”. Does this sound familiar? It should. Every 9 or 10 months the headline seems to return to the newspapers in an almost identical form. September 2011, July 2012 and now “IMF slashes” is back this month. This occurs every time the IMF’s world economic output report (full reports every April, updates every October) happens to adjust down one of its predictions for UK growth.The latest is entitled Hopes, Realities, and Risks and is notable for its Oxford comma.

According to Stephanie Flanders, the BBC Economics Editor, the IMF rarely gives direct advice on the back of these reports, preferring to give discreet prompts. However this time the report says about the UK: “Greater near-term flexibility in the path of fiscal adjustment should be considered in the light of lacklustre private demand.”

Olivier Blanchard, the IMF’s chief economist, even singled out the UK in response to a question while launching the latest report: “There are a few countries where there is enough fiscal space to go further – one example is the UK. In the face of weak demand it is really time to consider an adjustment to the initial fiscal consolidation plans.”

So there you are, we are all doomed unless we change policy. You would imagine that an institution would have a fairly solid track record of understanding countries’ economies and making reasonably accurate predictions on the back of this expert knowledge for it to feel able to lecture us all quite so authoritatively. Unfortunately, they don’t.

As you can see, compared to the stacks of predictions the IMF have given us over the last 4 years on growth in world output alone, the actual growth figures are unfortunately fairly clearly outliers. The one thing we can take from the latest report with any confidence is that the current projections for 2013, 2014 and 2018 will not only be wrong, but probably by miles.

So it would be very difficult to justify a change in economic policy on the basis of a world economic output report. Which is a pity, because I agree that many of us will be doomed to a life of fewer opportunities and less economic independence if the current contractionary policies continue, scrabbling around for our share of a crumbling welfare state while the few of us already immunised from society by money feel very little pain at all. For a proper description of why austerity is a very bad idea, read Paul Krugman’s End This Depression Now or read his blog. Read the account of the Great Capitol Hill Baby Sitting Co-op crisis on page 26, which originally appeared in a 1977 article by Joan and Richard Sweeney. The means for ending the double dip, soon to be triple dip and probably ultimately corrugated recession are in our hands and have been known about for decades. Your spending is my income, and my spending is your income, so we need to stop contracting our economy.

And we also need to stop reading IMF reports.

 

Actuaries and Science Fiction

18. April 2013 · Write a comment · Categories: Actuarial, Prediction, Science Fiction

Brian Aldiss told me a story the other week (at the Birmingham Science Fiction Group, where he is an honorary president) about Margaret Thatcher and her attitude towards science fiction. Kingsley Amis had been invited to a party at Downing Street and had decided to take along an inscribed copy of his latest book Russian Hide and Seek. Mrs Thatcher, a little suspicious about what she was being handed, had apparently asked what it was about.

Amis had explained that it was set in the future when the UK had been under Russian occupation for 50 years.

“Can’t you do any better than that?” the Prime Minister is reported to have said. “Get yourself another crystal ball.”

Aldiss recounted this story as he felt it illustrated how Mrs Thatcher totally misunderstood what science fiction was about. It was not about prediction of the future, but for people who “liked the disorientation” (the essence of science fiction in Aldiss’s view) of portraying an unfamiliar landscape and trying to work out what would hold true under different circumstances.

It seems to me that this is also what being an actuary is about. Actuaries are not about prediction either, but they are prepared to embrace the disorientation of asking what ifs and exploring maybes, and, by so doing, try to quantify what different currently unfamiliar landscapes might look like.

Science fiction has many forms but two main camps politically: the camp which believes a more enlightened form of society is possible (although what that means might vary considerably between different campers); and the camp which doesn’t but instead believes that all we can hope to do is survive a remorseless universe governed by nothing more than the laws of physics and evolutionary biology.

I think actuaries may have leaned more towards the second of these world views, particularly in fulfilling their statutory roles in recent years. We have worked within the remorseless universe of regulators and assumed that increasingly complex systems will make us safer in a Darwinian financial world. However the group think this has inevitably promoted has made us all less safe. As a result, we have heard many voices in the discussions about the financial crisis, including many what ifs and maybes, but few of these voices have been actuaries’. To quote Bob Godfrey (admittedly he was talking about animation at the time), the professionals are in a rut and the amateurs aren’t good enough.

Actuaries need to put themselves about as much as the amateurs do. Sometimes that will be uncomfortable. Sometimes we may look a little foolish for a while. But in my view it is the only way we are going to contribute meaningfully to the construction of a better society. And we might even produce some decent science fiction in the process.

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