From the Publisher

Social learning involves individuals learning from the behavior of others and may lead to spectacular outcomes such as herding, fads, frenzies, crashes, and booms. Providing a synthesis of the theoretical literature of the last ten years, Christophe Chamley demonstrates how these pathologies may occur in a society of rational individuals. His book provides informal as well as technical analyses for readers interested in social behavior, and more advanced researchers in economics, finance, and other social sciences. A key feature is the inclusion of exercises in each chapter, with proofs written out for students.

Review by Victor Niederhoffer, Daily Speculations

The beautiful and extraordinarily important book Rational Herds: Economic Models of Social Learning by Christophe Chamley treats topics important to decision-makers in life and markets, including how fast, delayed and divergent our learning is, herding, the convergence of beliefs, booms, bubbles and crashes, entries into business, diffusion of innovations, investments in knowledge, searching costs, the cost of information, the value of isolation versus joining (e.g., to stag hunt or not), regime switching, avalanches, variations in prices, limit orders versus market orders, and information delays in financial markets.

Each chapter starts out with examples of social learning, simple models that illumine how rational agents might act in comparable situations, and extensions into more realistic and technical models that apply to special cases considered in the literature. The more familiar the reader is with probability theory, simulations, economics of utility, and normal distribution theory, the easier it will be for him to gain knowledge from the book, but a hard-working layman with a pencil and paper can gain insights into many fruitful areas even without this background.

The book begins with an example of social learning among penguins typical of its many suggestive examples for market decision-makers. The penguins, after much hard winter work without food, move in a group to the edge of a cliff overlooking an ocean full of food. Possibly lurking in the water are killer sharks, whales and seals. The penguins as a group would be much better served if one of them would test the waters. But for the individual penguin, death might result. Action must be taken or else they'll all starve to death. The penguins solve the problem by pushing a particularly vigorous individual off the cliff. If he survives, they all jump in.

Comparable situations occur whenever danger or discomfort threatens a group and the first person to test the water might risk harm or, worse yet, social disapproval for being a non-conformist. We all face this, for example, when we see a fight and know that we can break it up if only group action can be taken, or when we are in a crowded room where the heating or ventilation isn't proper, or when we're in an establishment where the service is poor. We face this in the market when we have to test the waters before a trend is set, before an announcement has been released, before a new industry or market has come into favor, or before a change in interest rates has occurred. The book will help you decide how long to wait in such situations, and to analyze the attempts of others to substitute for you or coordinate with you.

Many of the basic models in the book start with binary choices such as the identification of a taxi as red or yellow, or the prediction of a market move as up or down. Consider the following situation: 70% of all months are up. Now, a study is made, e.g. of times when interest rates were very high, that shows the market is more likely that other months to decline. However, only 60% of similar predictions are accurate. Now there are two likelihoods that can result: The prediction applied to the 70% base can be wrong. This combined event has a 70% x 40% = 28% likelihood. The prediction can be accurately applied to the 30% of months that are declines. This combined event has a 30% x 60% = 18% likelihood. Thus, the probability of a rise is now 28/46 = 61%, down from the original 70%. But despite the forecast based on interest rates, the correct prediction is still for a rise. No change in decision will be forthcoming.

But now, suppose that a second study is made, and it also predicts a decline. But such a study also has only a 60% chance of being accurate. Applying this to the new 61% base probability of a rise, the likelihood of a rise is 61% x 40% = 24% and likelihood of a decline is 39% x 60% = 23%. Now it's about 50 /50 as to whether the market will rise or not. And with a third prediction based on another study forecasting a decline with better than a 50% probability of being accurate, the weights would swing to a decline forecast. Everyone who followed such a decision strategy, or learned of the studies, or believed that others were acting on the basis of correct decision-making, would follow the third forecast and change his action to selling Thus, depending on the delay and decisiveness of action, an avalanche, cascade or crash might occur.

There are hundreds of fascinating examples of group decision making given in Chamley's excellent book that will illuminate all your thinking about social behavior. Some of my favorites involve studies of the value of conformity, when to go out in the rain, when to get out of a booming market, when to follow an expert, the value of the risky market order or the conforming limit order, the value of experience (old-timers versus beginners), when to attack a currency, how much credibility to place on initial reports (the white van problem), the accuracy of group behavior, when it pays to coordinate or go it alone. and the path of truth. The book will change the way you think about groups and will have you rereading to to better appreciate these key areas.

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