Research on Bounded Rationality

National Bureau Of Economic Research Inc, Cambridge MA

Investigators

Abstract

This research develops intelligent algorithms that mimic the cognitive processes of human decision-makers. Such algorithms allocate cognitive resources like other scarce resources. Attention is only allocated to tasks in which it will do the most good. The algorithms settle for approximate solutions and "educated guesses" when solving highly complex problems. The research develops models that predict the form of such educated guesses, providing an implementable model of artificial intelligence. To succeed such models must find a sensible middle ground between the extreme rationality of omniscience and the extreme myopia of mechanistic strategies. An intelligent machine that tried to think of everything (omniscience) would run out of time when making a decision. By contrast, a machine that acted myopically would quickly blunder into obvious mistakes that humans would never make. Finding a middle ground between the extremes of omniscience and myopia will require answers to numerous questions about cognition. What information do people use? How is that information manipulated? How do individuals decide when to stop working on a complex problem and act on their best guess? The "directed cognition model" that addresses these questions is expressed as a three-step algorithm. First, the algorithm evaluates the expected benefit of various cognitive operations. The expected benefit is related to the predicted likelihood that a cognitive operation will reveal useful information about an upcoming decision. Second, the algorithm executes the cognitive operation with the greatest expected benefit. Third, the algorithm repeatedly cycles through these first two steps, stopping when the cognitive costs of analysis outweigh the expected benefits. The directed cognition model realizes three goals. First, the model is psychologically plausible, predicting numerous observed psychological phenomena (e.g., salience, myopia, and anchoring) and matching the cognitive strategies that experimental subjects claim to use. Second the model generates precise quantitative predictions that can be empirically tested. Initial experimental data overwhelmingly rejects the perfectly rational model in favor of the directed cognition model. Third, because the model is general it can be applied to a wide class of problems. By extending the directed cognition model and integrating it with other models of cost-effective cognition, the research develops a general bounded rationality approach to optimization, including boundedly rational dynamic programming. At the core of this approach is a theory of endogenous approximation. Current applications include contract theory and consumption. Contract theory applications explain both the presence and form of contract incompleteness, including boilerplate contracts. Boundedly rational consumption models explain why households adjust too slowly to changes in their economic environment and why households simultaneously exhibit excessive sensitivity to salient variables like current income. The directed cognition model makes sharp predictions about how much time experimental subjects will choose to spend on each problem in a multi-part quiz. The model also predicts how the quality of respondents' answers will vary when the amount of time allowed for a given problem is fixed by the experimenter and varied across subjects. If the directed cognition model continues to be empirically validated, it will represent one of the first economic models that can formally predict the difficulty of a decision problem --- i.e., the model predicts the quantitative relationship between time spent analyzing a problem and optimality/accuracy of the resulting decision. Ultimately, a relatively general model of artificially intelligent decision-making may be developed, which can be applied and tested in a wide range of choice problems.