Kahneman and Tversky’s work laid the foundations for behavioural economics (for example, Camerer (2003)). Part of the attraction of these approaches is that they fit the decision-making facts. In many real-world settings heuristics are the rule, not the exception. This is not confined to Frisbee-catching dogs (Gigerenzer (2007)) – though heuristic decision-making is found throughout the animal world, in complex decisions such as the choice of mate. It has also been found to be true in many aspects of human decision-making, from finding a soulmate to finding a checkmate (Gigerenzer (2007)). So why are such simple rules of thumb ubiquitous? And what are their implications? (a)  The costs of cognition  The simplest explanation is that collecting and processing the information necessary for complex decision-making is costly, perhaps punitively so. Fully defining future states of the world, and probability-weighting them, is beyond anyone’s cognitive limits. Even in relatively simple games, such as chess, cognitive limits are quickly breached. Chess grandmasters are unable to evaluate fully more than 5 chess moves ahead. The largest super-computers cannot fully compute much beyond 10 moves ahead (Gigerenzer (2007)). Most real-world decision-making is far more complex than chess – more moving pieces with larger numbers of opponents evaluated many more moves ahead. Simon coined the terms “bounded rationality” and “satisficing” to explain cost-induced deviations from rational decision-making (Simon (1956)). A generation on, these are the self-same justifications being used by behavioural economists today. For both, less may be more because more information comes at too high a price.

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