This project uses agent-based models to develop a deeper understanding of fundamental aspects of in-group favoritism and out-group hostility, in all their manifestations including ethnocentrism, ethnic conflict, and discrimination based on factors such as skin color, religion or national origin. The importance of these problems is manifest. In the US, racial discrimination is a major cause of economic inequality. At the global level, ethnic conflict is endemic, with almost one hundred ethnic conflicts active at the same time. Previous models have assumed that the existence and membership of groups are fixed, and that shared membership entails partiality. The agent-based models in this project are able to drop these restrictive assumptions, and thereby allow the investigation of how individuals form their social identity, how in-groups become coherent, and why perceived similarity often becomes the basis of favoritism. Agent-based modeling starts with specific assumptions about individuals (called agents), how they interact, and how the population changes over time. An agent-based model is run as a computer simulation to generate artificial histories. The artificial histories are then analyzed to see what happens over time and why. In this project, the models emphasize simplicity for the sake of insight, rather than try for a completely accurate depiction of any one case. The project is built on the premise that every person has some observable and relatively stable characteristics such as skin color, language, and religion. These characteristics can then be used by someone else to determine whether that person will be treated as one of "us" or one of "them". The first model is designed to investigate (1) the conditions under which in-group favoritism is likely to arise and persist, (2) why hostility between ethnic groups is so common, (3) what are the likely effects of making discrimination more costly, and (4) how settlement patterns can affect tolerance for immigrants. A more advanced model, called the Multi-Trait Model, is used to investigate (1) why some characteristics are emphasized more than others in discrimination, (2) what social categories agents tend to emphasize in drawing boundaries between groups, and (3) what temporary interventions can have lasting beneficial effects on tolerance, cooperation, and equality, even in a world with scarce resources. The project uses facts, concepts and theories from a broad range of social science disciplines, especially political science and sociology. The project also draws on concepts and theories from evolutionary biology and computer science. The findings provide new opportunities for interdisciplinary research, new perspectives for analyzing in-group/out-group dynamics, and new theoretically grounded hypotheses for later empirical testing. The broader impacts for society will be twofold. 1. The insights from the theoretical models will provide a sounder basis on which to make public policies to prevent, inhibit or correct the problems caused by in-group favoritism and out-group hostility. While a theoretical model can never by itself provide useful guidance, the insights of a simple formal model can be helpful to both analysts and policy makers in providing a framework in which to ask potentially fruitful questions. In particular, the results will suggest new ways of thinking about how limited resources might be most effectively focused to reduce discrimination, lessen social tensions from immigration, increase tolerance through targeted education, and intervene in ethnic conflicts. 2. The project will integrate teaching and research by providing a web site for students in high school through graduate school. The web site will have all the resources needed for a student with little mathematical training to run agent-based models to explore the dynamics of in-group favoritism and out-group hostility. Students and researchers will also be able to modify the Java source code to conduct new experiments of their own design, and to see QuickTime movies showing how their population evolves over time and space. The web site will also include archived data, suggested exercises for students, and a list of unsolved problems.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
Standard Grant (Standard)
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Brian D. Humes
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University of Michigan Ann Arbor
Ann Arbor
United States
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