Samuel Bowles

Research Professor, Santa Fe Institute
Director, Behavioral Sciences Program

Samuel Bowles is a Research Professor at the Santa Fe Institute where he heads the Behavioral Sciences Program. He is Professor of Economics at the University of Siena. He taught economics at Harvard from 1965 to 1973 and at the University of Massachusetts since then.

His recent studies on cultural evolution have challenged the conventional economic assumption that people are motivated entirely by self-interest. These have included the mathematical modeling and agent-based computer simulations of the evolution of altruistic behaviors by means of multi-level selection, a costly-signaling model of the evolution of pro-social behaviors, and behavioral experiments in 15 hunter-gather and other small-scale societies.

Bowles’ current research includes both theoretical and empirical studies of the role of incomplete contracts in labor markets and financial markets in explaining income inequality. Related topics on which he has recently published are: globalization and its impact on public policies to enhance economic security and alleviate poverty, personality as a determinant of labor market success, and the role of culture, genes, and schooling in intergenerational transmission of economic status. He is co-editor of four forthcoming books on these subjects, all based on interdisciplinary research workshops he organized at the Santa Fe Institute.

He has served as an economic advisor to the governments of South Africa and Greece, to presidential candidates Robert F. Kennedy and Jesse Jackson, and to the World Bank and the International Labor Organization.

His major work, A Cooperative Species: Human reciprocity and its evolution, was co-authored with Herbert Gintis and completed in 2006. Drawing on their recent research on cultural and genetic evolution and his empirical studies of behavior in small-scale societies, this work will seek to explain why humans, unlike other animals, engage in extensive forms of cooperation among large numbers of unrelated individuals.