Lake Arrowhead Conference, 2007

The Center for Human Complex Systems incorporates a group of scholars whose research focuses on the interaction of heterogeneous individuals. We examine how culture and structure co-evolve to influence behavior and interaction, thereby affecting system performance. Conversely, we consider how individual choices and social interaction shape, and are shaped by, system structure. We place particular emphasis on the role of information processes (how information gets represented, processed, and communicated), methods of social order-creation (competition, coevolution, self-organization, autopoiesis, restructuring) and redefinition (rule generation and selection, boundary construction, institution of culturally based conceptual structures) of social systems. Methodologically we emphasize agent-based computational methods as a way to incorporate agent heterogeneity in the study of social behavior of individual actor/agents inhabiting complex social systems.

Contact person: Dwight Read, Professor of Anthropology, UCLA, Los Angeles, CA 90095 (dread@anthro.ucla.edu)

A Survey of Web-based Collective Decision Making Systems

(2008)

A collective decision making system uses an aggregation mechanism to combine the input of individuals to generate a decision. The decisions generated serve a variety of purposes from governance rulings to forecasts for planning. The Internet hosts a suite of collective decision making systems, some that were inconceivable before the web. In this paper, we present a taxonomy of collective decision making systems into which we place seven principal web-based tools. This taxonomy serves to elucidate the state of the art in web-based collective decision making as well as to highlight opportunities for innovation.

Prediction Markets as an Aggregation Mechanism for Collective Intelligence

Watkins, Jennifer H.

(2007)

Collective intelligence is the result of the proper aggregation of local information from many individuals to generate an optimal global solution to a problem. Often, these solutions are more optimal than what any individual could have provided. In this article, we focus on prediction markets as the aggregation mechanism for collective intelligence. Prediction markets, like commodity markets, channel inputs from all traders into a single dynamic stock price. Instead of determining the value of a particular good, a prediction market is used to determine the probability of a particular event occurring. We present and discuss five features of prediction markets that urge a collective toward optimal solutions. Through the combination of these features, prediction markets lend themselves to the systematic study of the promising phenomenon of collective intelligence.