As Kenneth Arrow, Nobel Laureate in Economics, said about his four-year experience as a weather officer in the US during World War II, “one thing I learned from meteorology is that being an actual science was no guarantee of exactness”. Economics may or may not be an actual science, but it certainly suffers when trying to predict the future. There are at least three explanations for this unpredictability that are common to economics and other scientific disciplines. First, assigning probabilities is particularly difficult for infrequent high-impact events such as financial crises, tsunamis, or climate change, for which there are few precedents. Because the samples of such events are so small, as we learn about these processes our assessment of their likelihood will change, possibly sharply. Second, even systems that are deterministic rather than stochastic can be very unpredictable. Small differences in starting conditions can imply very different outcomes. Weather systems display chaotic dynamics, so that small initial forecast errors may lead to large revisions to the forecast further out. Economists, in contrast, often rely on simple linear models, but with additive stochastic errors. In modelling any phenomenon there is a choice, and among the experts in the area often a live debate, about whether it is more useful to use deterministic but chaotic or stochastic models. 1 But the motivation in both cases is to bring out the importance of unpredictability. Another feature that hinders predictability is that a priori indistinguishable shocks can have very different effects in some systems.
This occurs because all firms assume a worst-case scenario – that they will be unable to access liquidity when required – and this causes them to hoard liquidity. Alternatively when information is dispersed across a large number of people, each with a noisy signal about economic fundamentals, outcomes may be determined by an “information cascade”, the outcome of which is hard to predict (Banerjee, 1992; Bikhchandani et al, 1992). 5  See May and Arinaminpathy (2010) for a model of the financial system which makes this distinction. 4  BIS Review 36/2010  There is strong experimental evidence that people are subject to biases when collecting and processing information under uncertainty, and that such processing occurs only intermittently. 6 The evidence suggests that people are also frequently incoherent in assessing, assigning and processing probabilities – even in highly artificial settings where information is complete – and find it particularly difficult to assess probabilities which are very low or very high, partly because there is less scope for learning in these cases. There is also evidence that, when making complex decisions or without full information, such as when deciding whether to have eye surgery, people often use rough heuristics, or go on first impressions, appearances, gut instinct or intuition. We want to highlight four main aspects of the behavioural view: (i)  Perceptions of risk have been shown to be unduly influenced by recent or personal experience.