Search Results

This chapter examines the role that networks play in facilitating or inhibiting search for solutions to problems at both the individual and collective levels. At the individual level, search in networks enables individuals to transport themselves to a very different location in the solution space than they could likely reach through isolated experimental or cognitive search. Research on networks suggests that (a) ties to diverse others provide a wider menu of choices and insights for individuals, and (b) strong ties will be relatively more useful for complex information, and weak ties for simple information. At the collective level, these conclusions become less clear. The key question is how the collective operates to coordinate within the group versus beyond it so as to balance experimentation and convergence toward a solution. Collective coordination of search and collective evaluation of potential solutions may significantly influence the optimal network structure for collective problem-solving search.

This chapter reports the discussion of a group of mostly behavioral biologists, who attempt to put research on search from their own discipline into a framework that might help identify parallels with cognitive search. Essential components of search are a functional goal, uncertainty about goal location, the adaptive varying of position, and often a stopping rule. The chapter considers a diversity of cases where search is in domains other than spatial and lists other important dimensions in which search problems differ. One dimension examined in detail is social interactions between searchers and searchers, targets and targets, and targets and searchers. The producer-scrounger game is presented as an example; despite the extensive empirical and theoretical work on the equilibrium between the strategies, it is largely an open problem how animals decide when to adopt each strategy, and thus how real equilibria are attained. Another dimension that explains some of the diversity of search behavior is the modality of the information utilized (e.g., visual, auditory, olfactory). The chapter concludes by highlighting further parallels between search in the external environment and cognitive search. These suggest some novel avenues of research.

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.