Predicting 2009

Over the holiday period the media offer us plenty of fodder to discuss the past year’s events and what the future may hold. Whether it’s current affairs, music, sport, economics or any other aspect of human activity, most media outlets have something to say about what people did that was good, what they did that was bad, and what they’ll do next, in the hope that they can keep their sales up over the holiday period.

Every year The Economist publishes a collection of forecasts and predictions for the year ahead. The views and and opinions of journalists, politicians and business people accompany interactive maps and graphs that provide numerical analysis. But how good are these forecasts and predictions? And what use are they? This year The Economist stopped to look back on how well it performed:

“Who would have thought, at the start of 2008, that the year would see crisis engulf once-sturdy names from Freddie Mac and Fannie Mae to AIG, Merrill Lynch, HBOS, Wachovia and Washington Mutual (WaMu)?

Not us. The World in 2008 failed to predict any of this. We also failed to foresee Russia’s invasion of Georgia (though our Moscow correspondent swears it was in his first draft). We said the OPEC cartel would aim to keep oil prices in the lofty range of $60-80 a barrel (the price peaked at $147 in July)…”

And on the list goes. Not that any of us are particularly surprised, are we? So why should we bother to read their predictions for the next year? In its defence, The Economist offers a couple of points. First, the usual tactic (for anyone defending their predictions) of pointing out what they actually did get right (slumping house prices, interest-rate cuts, etc). But then they highlight a perspective which I think is almost essential when thinking about predictions of future social or economic activity:

“The second reason to carry on reading is that, oddly enough, getting predictions right or wrong is not all that matters. The point is also to capture a broad range of issues and events that will shape the coming year, to give a sense of the global agenda.”

Such a view is inherently realist. Given the multitudes of interacting elements and potential influences affecting economic systems, given that it is an ‘open’ historical system, producing a precise prediction about future system states is nigh-on impossible. Naomi Oreskes has highlighted the difference between ‘logical prediction’ (if A and B then C) and ‘temporal prediction’ (event C will happen at time t + 10), and this certainly applies here [I’m surprised I haven’t written about this distinction on this this blog before – I’ll try to remedy that soon]. Rather than simply developing models or predictions with the hope of accurately matching the timing and magnitude of future empirical events, I argue that we will be better placed (in many circumstances related to human social and economic activity) to use models and predictions as discussants to lead to better decision-making and as means to develop an understanding of the relevant causal structures and mechanisms at play.

In a short section of his recent book and TV series, The Ascent of Money, Niall Ferguson talks about the importance of considering history in economic markets and decision-making. He presents the example of Long Term Capital Management (LTCM) and their attempt to use mathematical models of the global economic system to guide their trading decision-making. In Ferguson’s words, their model was based on the following set of assumptions about how the system worked:

“Imagine another planet – a planet without all the complicating frictions caused by subjective, sometimes irrational human beings. One where the inhabitants were omniscient and perfectly rational; where they instantly absorbed all new information and used it to maximise profits; where they never stopped trading; where markets were continuous, frictionless and completely liquid. Financial markets on this plan would follow a ‘random walk’, meaning that each day’s prices would be quite unrelated to the previous day’s but would reflect all the relevant information available.” p.320

Using these assumptions about how the world works, the Nobel prize-winning mathematicians Myron Scholes and Robert C. Merton derived a mathematical model. Initially the model performed wonderfully, allowing returns of 40% on investments for the first couple of years. However, crises in the Asian and Russian financial systems in 1997 and 1998 – not accounted for in the assumptions of the mathematical model – resulted in LTCM losing $1.85 billion through the middle of 1998. The model assumptions were unable to account for these events, and subsequently its predictions were inaccurate. As Ferguson puts it:

“…the Nobel prize winners had known plenty of mathematics, but not enough history. They had understood the beautiful theory of Planet finance, but overlooked the messy past of Planet Earth.” p.329

When Ferguson says ‘not enough history’, his implication is that the mathematical model was based on insufficient empirical data. Had the mathematicians used data that covered the variability of the global economic system over a longer period of time it may have included a stock market downturn similar to that caused by Asian and Russian economic crises. But a data set for a longer time period would likely have been characterised by greater overall variability, requiring a greater number of parameters and variables to account for that variability. Whether such a model would have performed as well as the model they did produce is questionable, as is the potential to predict the exact timing and magnitude of any ‘significant’ event (e.g. a market crash).

But further, Ferguson also points out that the problem with the LTCM model wasn’t just that they hadn’t used enough data to develop their model, but that their assumptions (i.e. their understanding of Planet Finance) just aren’t realistic enough to accurately predict Planet Earth over ‘long’ periods of time. Traders and economic actors are not perfectly rational and do not have access to all the data all the time. Such a situation has led (more realistic) economists to develop ideas like bounded rationality.

Assuming that financial traders try to be rational is likely not a bad assumption. But it has been pointed out that “[r]ationality is not tantamount to optimality”, and that in situations where information, memory or computing resources are not complete (as is usually the case in the real world) the principle of bounded rationality is a more worthwhile approach. For example, Herbert Simon recognised that rarely do actors in the real world optimise their behaviour, but rather they merely try to do ‘well enough’ to satisfy their goal(s). Simon termed this non-optimal behaviour ‘satisficing’, the basis for much of bounded rationality theory since. Thus, satisficing is essentially a cost-benefit tradeoff, establishing when the utility of an option exceeds an aspiration level.

Thinking along the same lines George Soros has developed his own ‘Human Uncertainty Principle’. This principle “holds that people’s understanding of the world in which they live cannot correspond to the facts and be complete and coherent at the same time. Insofar as people’s thinking is confined to the facts, it is not sufficient to reach decisions; and insofar as it serves as the basis of decisions, it cannot be confined to the facts. The human uncertainty principle applies to both thinking and reality. It ensures that our understanding is often incoherent and always incomplete and introduces an element of genuine uncertainty – as distinct from randomness – into the course of events.

The human uncertainty principle bears a strong resemblance to Heisenberg’s uncertainty principle, which holds that the position and momentum of quantum particles cannot be measured at the same time. But there is an important difference. Heisengberg’s uncertainty principle does not influence the behavior of quantum particles one iota; they would behave the same way if the principle had never been discovered. The same is not true of the human uncertainty principle. Theories about human behavior can and do influence human behavior. Marxism had a tremendous impact on history, and market fundamentalism is having a similar influence today.” Soros (2003) Preface

This final point has been explored in more detail by Ian Hacking and his discussion of the issue of the differences between interactive and indifferent kinds. Both of these views (satisficing and the uncertainy principle) implicitly understand that the context in which an actor acts is important. In the perfect world of Planet Finance and associated mathematical models context is non-existent.

In response to the problems encountered by LTCM, “Merrill Lynch observed in its annual reports that mathematical risk models, ‘may provide a greater sense of security than warranted; therefore, reliance on these models should be limited’“. I think it is clear that humans need to make decisions (whether they be social, economic, political, or about any resource) based on human understanding derived from empirical observation. Quantitative models will help with this but cannot be used alone, partly because (as numerous examples have shown) it is very difficult to make (accurate) predictions about future human activity. Likely there are general behaviours that we can expect and use in models (e.g. aim of traders to make profit). But how those behaviours play out in the different contexts provided by the vagaries of day-to-day events and changes in global economic, political and physical conditions will require multiple scenarios of the future to be examined.

My personal view is one of the primary benefits of developing quantitative models of human social and economic activity is that they allow us to make explicit our implicitly held models. Developing quantitative models forces us to be structured about our worldview – writing it down (often in computer code) allows other to scrutinise that model, something that is not possible if the model remains implicit. In some situations, such a private financial strategy-making, the publication this approach may not be welcome (because it is not beneficial for a competitor to know your model of the world). But in other decision-making situations, for example about environmental resources, this approach will be useful to foster greater understanding about how the ‘experts’ think the world works.

By writing down their expectations for the forthcoming year the experts at The Economist are making explicit their understanding of the world. It’s not terribly important that that they don’t get everything right – there’s very little possibility that will happen. What is important is that it helps us to think about potential alternative futures, what factors are likely to be most important in determining future events, how these factors and events are (inter)related, and what the current state of the world implies for the likelihood of different future states. This information might then be used to shape the future as we would like it to be, based on informed expectations. Quantitative models of human social and economic activity also offer this type of opportunity.

2 thoughts on “Predicting 2009

  1. One of the US’s top economic policy makers earned his wings developing a model of financial crises:”Bernanke’s 1980s Computer Model Predicts Crisis”http://www.npr.org/templates/story/story.php?storyId=95076198Their model doesn’t appear to have been designed for acuity in “temporal prediction” so they didn’t presage the current gloabl financial situation. Rather, they focus on the cascading behavior (some might say scalar dynamics) of ripple effects. A working paper on the model is at:http://www.nber.org/papers/w6455

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  2. Thanks for your comment and highlighting that model. I wasn’t able to access the paper to read it directly, but the news story certainly implies that Bernanke and his colleagues were (are?) focused on understanding the understanding of the causal mechanisms and structures causing the observed system behaviour. I find this sort of modelling much more satisfying than the ‘temporal prediction’ stuff I spoke about in my post. Combining understanding from this sort of macroeconomic modelling with human behavioural research and understanding (nearer the level of the individual actor) seems to me the best avenue for really improving understanding about such a complex system as the global economy.

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