COLLECTIVE BEHAVIOR MODLES

Colective Behavior models

Collective Behavior Models

Cognitive scientists tend to focus on the behavior of single individuals thinking and perceiving on their own. However, interacting groups of people also create emergent organizations at a higher level than the individual. Interacting ants create colony architectures that no single ant intends. Populations of neurons create structured thought, permanent memories and adaptive responses that no neuron can comprehend by itself. Similarly, people create group-level behaviors that are beyond the ken of any single person. The emergence of higher-level organizations from the interactions of lower-level units is surprising in the case of group behavior because we are the lower-level units, and the higher-level organizations typically emerge spontaneously, without our knowledge.

Social phenomena such as rumors, the emergence of a standard currency, transport systems, the World Wide Web, resource harvesting, crowds, and scientific establishments arise because of individuals' beliefs and goals, but the eventual form that these phenomena take is rarely dictated by any individual. There is a growing realization across the social sciences that one of the best ways to build useful theories of group phenomena is to create working computational models of social units (e.g. individuals, households, firms or nations) and their interactions, and to observe the global structures that these interactions produce. In the past few years, the use of computational models of collective behavior has grown tremendously in sociology, economics, psychology, and anthropology. This approach is relevant to cognitive science because it integrates computational modeling and understanding of human behavior.

This relevance is timely because these models provide balance to cognitive science's bias to view cognition as a property of an individual mind rather than as resulting from interactions among people and their environments. We will focus on computational models called Agent- Based Models (ABMs), which build social structures from the 'bottom-up', by simulating individuals by virtual agents, and creating emergent organizations out of the operation of rules that govern interactions among agents. ABMs have several attractive features that supplement traditional methods for exploring group behavior.

First, they are expressed with unambiguous mathematical and computational formalisms so that once they have been fully described, their predictions are clear, quantitative and objective. Second, they provide true bridging explanations that link two distinct levels of analysis: the properties of individual agents (e.g. their attributes and interactions), and the emergent group-level behavior. When successful, agent-based models are particularly satisfying models because they show how coherent, grouplevel structures can spontaneously emerge without leaders ordering the organization, and sometimes despite leaders' effort. Third, because the models are typically either simple or informed by real-world data, they are appropriately constrained and cannot fit any conceivable pattern of data. The self-organization process itself exerts strong constraints on the kinds of patterns likely to be observed .

In this review of ABMs, we will characterize the approach; describe crucial decisions that a modeler must make; present case studies of ABMs from literatures on organization, contagion and cooperation, and assess the future opportunities and challenges for ...