Foreword

The publication of this Handbook, bringing together game theory and industrial organization, is an occasion worth celebrating. After all, industrial organization (IO) – the study of how firms in a given market behave – was game theory’s first systematic application to economics, and the success of that application had much to do with giving game-theoretic ideas the prominent place they now have in the economics profession more generally. Before game theory remade industrial organization in the 1970s, most IO analyses focused on two extreme but simple kinds of markets: perfectly competitive and monopolistic. In a perfectly competitive market, there are many small sellers (all selling the same kind of good) and many small buyers (“small” here means that the quantities sold by a seller and bought by a buyer are tiny compared with the totals for the market). One might guess that large numbers of traders would make analysis complicated, but they actually simplify matters. If each seller is small relative to the market, its own behavior can’t affect other sellers appreciably. So when figuring out what it should do, it needn’t worry about how the others anticipate it might behave – their anticipations aren’t relevant. In other words, a seller doesn’t have to be strategic (symmetrically, neither does a buyer). And consequently an economic analyst has a relatively easy job predicting the seller’s behavior as the solution to a simple profit-maximization problem. Indeed, because the seller is selling the same sort of good as all its competitors, it will take the good’s market price as given (if it chooses a higher price, it will have no customers – since they can get a perfect substitute for less; and it will be overwhelmed by customers if it chooses a lower price). In other words, the seller has no market power. In a monopolistic market, by contrast, there is just one seller (I shall continue to assume throughout that there are many small buyers). Thus, as with perfect competition, the seller doesn’t have to worry about what other sellers are thinking about it – this time because there are no other sellers. And so, again, the seller’s optimization exercise as well as the analyst’s prediction exercise are quite straightforward (although the seller now does have market power; its own behavior determines the market price). However, the intermediate case, oligopoly – where there is more than one seller, but not so many that a single seller has no effect on competitors – is more difficult. Think of the American automobile industry as it used to be, consisting primarily of General Motors (GM), Ford, and Chrysler. When GM worked out which models to manufacture, how many units of each model to produce, and what prices to set, it had to take into account what it anticipated Ford and Chrysler would do, and their actions depended on their forecasts about GM. Clearly, grappling with these anticipatory interactions between firms is essential to understanding the automobile industry. Yet such interactions are potentially very complex. Specifically, when an oligopolistic firm A tries to predict what its rival, firm B, will do, it must anticipate what B anticipates A will do, and what B anticipates A anticipates B will do, and so on. That’s why Nash equilibrium (Nash, 1950) was such a breakthrough: it cuts through this potentially infinite sequence of mutual anticipations.