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, Distinguished Professor of Anthropology, UCLA, Los Angeles, CA 90095 (firstname.lastname@example.org)
In a multi-site study of Welsh-American identity, informants were asked to rate the "Welshness" and "Americanness" of the behavior in a set of 21 scenarios, or brief narratives designed to exemplify Welsh and American personhood concepts. In addition, consultants were asked to rate how desirable or ideal the behaviors were, in their opinion. The Welsh-American population in the two sites, one in Iowa and the other on the Vermont/New York border, varied from low to high social visibility. Using consensus analysis of the scenario data, we test of a series of hypotheses concerning the perceived differences between "Welsh" and "American" personhood in high and low visibility sites and between the diaspora populations and the homeland of Wales.
Following the publication of the letter from Dwight Read, (see “New Results: The Logic of Older/Younger Sibling Terms in Classificatory Terminologies” in MACT Letters, November 9 2004) Kris Lehman (F. K. L. Chit Hlaing) responded to that letter. Together Professors Read and Lehman then agreed to compile an exchange, including previous discussions, and have submitted the sequence of letters below to MACT. They offer the exchange both to record some important developments in the mathematical theory of kinship category systems as reflected in their joint work in progress, and to record the way such work develops through technical exchanges.
Intelligibility and Unintelligibility: Response to Professor Mithen’s Review of Human Thought and Social Organization: Anthropology on a New Plane by Murray Leaf and Dwight Read
Mithen describes our book, Human Thought and Social Organization, as unintelligible. Since a previous review by Bojka Milicic showed an excellent grasp of the full range of implications of the argument and another by Radu Umbres showed a good understanding of it, we are confident that Mithen's description is wrong as a matter of fact. In our reply we address what led him astray.
Starting from a reflection by Jean Pouillon, it is shown - both deductively and on the basis of experimental data - that consciousness is deprived of any decisional power. Consciousness' role is reduced to transmitting to the body instructions based on the emotional response to percepts. Language allows human beings to generate a self-justifying narrative of their deeds. Such an account does not reflect, however, the actual psychological mechanism at work. Consciousness' actual effectiveness resides in influencing on the one hand the affect of the speaker (as speech or as “inner speech”), and on the other hand the affect of any listener. The pair “body” and “soul” gets thus validated, but their traditionally assigned responsibilities need reassigning between one body that decides and acts and one soul whose feedback operates at the affective level only.
Comments on: Lyman, Lee R. 2009. “Review of Artifact Classification: A Conceptual and Methodological Approach, by Dwight Read” Journal of Anthropological Research 65:111-113
A good book review provides documentation for its evaluations, especially when they are either very positive or very negative. A good review is also faithful to what the author has written and bases criticisms or praise on accurate paraphrasing or quotes from the book. This review by Lyman fails on both accounts. Critical comments are not documented and the review is based on what Lyman imagines Read to have written, not what Read actually wrote.
The present manuscript is intended to informally elucidate my ideas on a general theory of collective behavior and structure formation, with a resulting architecture that can be broadly applied. The proposed model represents a decomposition of intent, based on the idea that an agent’s behavior, whether it represents an individual or a group, can be seen as an emergent property of a collection of intertwined aims and constraints. I consider a disentangled agent that is formed by multiple and relatively independent components. Part of the resulting agent’s task is to present alternatives, or ‘fields of action’ to its component selves. Correspondingly, the composed agent is itself constrained by a field of action that the superstructure to which it belongs presents. The superstructure of agents possesses a certain amount of cohesion, and can thus be ascribed agency and modeled as a unit; its independent parts could be consciously or evolutionally constructed and aligned.
“Cultural Models” (CM) is a term that has come to apply to culturally standardized and shared/distributed cognitive structures for explaining or structuring action. They contrast with more cultural conceptual systems (such as kinship or ethnobiological terminological systems) and more general procedures analyzing and imposing initial structure on new problems. They are functionally a little like Schank and Abelson’s “scripts”. CMs combine motives, emotions, goals, mechanisms, classificatory information, etc.--in each case, perhaps, cross-linking to separate cognitive structures within which these separate entities are organized, structured, and classified--into possible actions. CMs can be used by individual actors to generate behavior--often after some consideration of the downstream implications of the choice of one model over another--but are not themselves the individual internal cognitive schemas that actually generate behavior. Different CMs are cross-linked with one another in a variety of ways. One area of cross-linkage includes models held by members of a given community in responsse to similar situations (as in overlap among models for doing similar things, modes for use in similar situations, models involving similar attidudes or goals, and so forth). Another kind of cross-linkage involves models for more or less the same thing that are held by members of different communities--expecially where membership overlaps in one way or another. CMs have to be easily learned, productive, and systematic.
I want to discuss the implications of these kinds of overlap for the shape of cultural models and the way in which they are learned, held, and applied. Illustrative examples will be utilized, but no systematic formal description or model of CMs will be offered--it’s too soon.
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.
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.