Search Results

All animals, including humans, search for a variety of different things in their natural environment, from food to mates to a suitable place to live. Most types of search can be represented as stopping problems of varying complexity, in which the animal has to decide when to stop searching and accept the current option. All forms of search take time, and in solving a stopping problem the animal has to trade off this time cost against the expected benefits of continuing to search. This chapter discusses two main approaches to predicting search behavior: the optimality approach and the heuristics approach. The optimality approach identifies the best possible solution to a search problem and thereby sets an upper bound to what natural selection can achieve. The heuristics approach considers simple decision algorithms, or “rules of thumb,” which animals may use to implement efficient search behavior. Although few studies have tried to integrate these functional and mechanistic perspectives, they are likely to provide complementary insights. Often, the form of an optimal strategy suggests which kinds of heuristics might be expected to evolve. Stopping problems may be simple, repeated, or embedded in other stopping problems. For example, if searchers assess the value of each encountered option by examining a series of cues, the assessment process can be considered as another stopping problem. When the searcher is uncertain about the environment it is in, its previous experiences during search can strongly influence the optimal behavior. Where a limited number of items can be accepted, as in mate search, a key constraint is whether the searcher can return to previously encountered items. Some search problems are complicated by the fact that the encountered items are themselves searching. The chapter concludes with a discussion of some open questions for future research.

Search is a common and crucial behavior for most organisms. It requires individuals to achieve an adaptive trade-off between exploration for new resources distributed in space or time and exploitation of those resources once they are found. Common to so many aspects of our lives, search behavior has been studied in a diverse range of scientific disciplines and paradigms: theoretical biologists study the characteristics of evolutionary search in high-dimensional spaces; behavioral ecologists analyze animals foraging for food; experimental psychologists investigate search in vision, memory, decision making, and problem solving; neuroscientists study the neural mechanisms of goal-directed behavior in humans and other animals; psychiatrists and clinical neuroscientists analyze aberrant volition such as drug-seeking behavior in addiction and attentional control in attention deficit hyperactivity disorder; computer scientists develop information search algorithms for mining large-scale databases and for individual navigation of the World Wide Web; social psychologists investigate how people seek and choose mates and friends; and political scientists study how groups look for solutions to problems. The need to integrate these insights further has led to the current book, which provides a cross-cutting perspective on the commonalities of cognitive search in different search domains.

This chapter focuses on Downsian party competition, a behavioral model of elections based on satisficing coupled to the Schattschneider-Schumpeter-Downs macrohypothesis that major parties in vigorous democracies are structured to win elections. The discussion is based on central premises about decision making that closely follow Simon’s analysis: winners satisfice while losers search. After a review of relevant literature, the chapter describes the model and several implications. It then models political parties as adaptive organizations that compete in a sequence of elections. It also considers alternative specifications of the challenger’s search behavior by endowing him with different degrees of sophistication and certain kinds of knowledge about the political terrain. Finally, it examines whether the results are sensitive to changes in key assumptions.