Does Complex Behavior Require Complex Representations ?

Models in cognitive science often postulate that individuals maintain complex representations of their environment when simpler explanations, based on simple behaviors interacting with each other and environmental constraints, would suffice. As an example, I consider representational approaches to animal behavior (e.g., Gallistel, 1990; Myerson and Miezin, 1980), which posit that complex group behavior results from complex representations of events within the central nervous systems of individual animals. For example, ducks feeding from two food sources distribute themselves proportionately to the density of food available at each source. This phenomenon, probability matching, i s typically explained by attributing representations of the density of food available at each source within the central nervous system (CNS) of each duck. Are such complex representations required to explain this phenomenon? I will compare the results of two simulations of probability matching in groups. In one, individuals maintain and update representations of food available at each source. Although probability matching emerges, the organisms exhibit various unrealistic behaviors. In the second, each individual follows simple behavioral rules but has no representation of the food density at each source. Probability matching emerges and the behavior observed is more realistic than that in the first simulation. This adds to demonstrations in other domains that complexity at one level of analysis need not result from complexity at lower levels (e.g., Resnick, 1994; Sigmund, 1993). Centralized versus decentralized analysis It used to be said that our ability to build arches distinguished humans from lesser species -arches require planning and engineering. It turns out that at least one lesser species -termites -builds arches. In fact, it appears that their arch building depends on highly organized cooperation. Scores of them deposit excrement, eventually forming several columns, which eventually join to form arches. The arches form the foundation for their nests. How is such an amazing joint activity organized? One possibility is that each insect is acting as part of a team. At first blush, it appears that each insect monitors and represents the activities of the others and their collective progress. As Braitenberg (1984) points out, we often prefer such explanations, even for simpler systems, and are quite willing to attribute complex representations and motivations when simpler explanations would suffice. Another possibility is that the complex group behavior emerges from the interactions of large numbers of insects acting on the same small behavioral repertoire. Indeed, this is the case. Each termite develops a seasonal sensitivity to a particular pheromone secreted in their waste. Where termites deposit their waste is guided by gradients of pheromone concentration. Eventually, columns form where the concentrations are strongest. Competition between adjacent columns leads to their being joined into (see Kugler et al., 1989, for a detailed analysis). Resnick (1994) describes these two ways of analyzing problems as centralized and decentralized thinking (for many more examples of decentralized analysis, see Resnick, 1994; Braitenberg, 1984; Sigmund, 1993; and Langton, 1989). Decentralized thinking is not new in cognitive science. Consider this list of demonstrations that decomposing a system into simpler components can reduce the complexity of representations required: Wiener’s (1948) treatment of self-organizing systems, Gibson’s analysis of optic flow (e.g., Gibson, Olum, and Rosenblatt, 1955), the “active” or “animate” approach to vision (e.g., Ballard, 1991), Reynolds’ (1987) treatment of flocking behavior, and the literature cited above. Despite these compelling demonstrations, decentralized analysis has not received the attention it warrants. Many models in cognitive science start out “representation-heavy. ” I do not wish to imply that there is no place for representation in models of behavior and cognition. My thesis is that as a starting point, models of complex systems need not postulate complexity at the lowest level. To demonstrate this point, I will apply decentralized analysis to the phenomenon of probability matching in groups of foraging animals. Probability matching Various species adapt quickly to changes in the probability of important stimuli in their environments. For example, when presented with multiple sources of food in an environment, individuals quickly divide into groups with sizes proportional to the density of food available from each source (see Gallistel, 1990, chapter 11, for an overview). It is important to note that the overall density of food is what determines the size of the groups. Given two sources dispersing morsels of equivalent magnitudes at equivalent rates, the animals divide into two groups of approximately equal numbers clustered around each of the sources. If the amount of food available from one source increases, due to an increase in either its rate of dispersal or the magnitude of