• How should we make decisions so as to maximize payoff?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in the future?
The science of economics got its start in 1776, when Scottish philosopher Adam Smith (1723–1790) published An Inquiry into the Nature and Causes of the Wealth of Nations. While the ancient Greeks and others had made contributions to economic thought, Smith was the first to treat it as a science, using the idea that economies can be thought of as consisting of individual agents maximizing their own economic well-being. Most people think of economics as being about money, but economists will say that they are really studying how people make choices that lead to preferred outcomes. When McDonald’s offers a hamburger foradollar, they areasserting that theywould preferthe dollarand hoping thatcustomers will prefer the hamburger. The mathematical treatment of “preferred outcomes” or utility was first formalized by L´eon Walras (pronounced “Valrasse”) (1834-1910) and was improved by Frank Ramsey (1931) and later by John von Neumann and Oskar Morgenstern in their book The Theory of Games and Economic Behavior (1944). Decision theory, which combines probability theory with utility theory, provides a for-malandcompleteframeworkfordecisions (economicorotherwise)madeunderuncertainty— that is, in cases where probabilistic descriptions appropriately capture the decision maker’s environment. This is suitable for “large” economies where each agent need pay no attention to the actions of other agents as individuals. For “small” economies, the situation is much more like a game: the actions of one player can significantly affect the utility of another (either positively or negatively). Von Neumann and Morgenstern’s development of game theory (see also Luce and Raiffa, 1957) included the surprising result that, for some games a rational agent should adopt policies that are (or least appear to be) randomized. Unlike decision theory, game theory does not offer an unambiguous prescription for selecting actions. For the most part, economists did not address the third question listed above, namely, how to make rational decisions when payoffs from actions are not immediate but instead result from several actions taken in sequence. This topic was pursued in the field of operations research, which emerged in World War II from efforts in Britain to optimize radar installations, and later found civilian applications in complex management decisions. The work of Richard Bellman (1957) formalized a class of sequential decision problems called Markov decision processes, which we study in Chapters 17 and 21.
Work in economics and operations research has contributed much to our notion of rational agents, yet for many years AI research developed along entirely separate paths. One reason was the apparent complexity of making rational decisions. The pioneering AI researcher Herbert Simon (1916–2001) won the Nobel Prize in economics in 1978 for his early work showing that models based on satisficing—making decisions that are “good enough,” rather than laboriously calculating an optimal decision—gave a better description of actual human behavior (Simon, 1947). Since the 1990s, there has been a resurgence of interest in decision-theoretic techniques for agent systems (Wellman, 1995).
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