John H. Miller received a B.A. in Economics and B.S. in Finance from the University of Colorado in 1982, a M.A. and Ph.D. in Economics from the University of Michigan in 1988, where he worked with Ted Bergstrom and Hal Varian. In 1988 he joined the Santa Fe Institute as their first post doctoral fellow. He started as an Assistant Professor in the Department of Social and Decision Sciences at Carnegie Mellon University in 1990, becoming an Associate Professor in 1995, and Professor in 2000. He headed the Department of Social and Decision Sciences at Carnegie Mellon from 2002-2014 (and the Information Systems program from 1998-2001). He has been continually involved with the Santa Fe Institute since 1988, holding numerous appointments, and is currently on the external faculty and chair of the Institute's Science Steering Committee (along with ex officio appointments to its Science Board and Board of Trustees). His scientific interests surround complex adaptive social systems, behavioral economics, adaptive algorithms, and computational modeling. He has published articles on these topics in various literatures, including anthropology, complex systems, decision science, economics, law, management, medicine, physics, political science, as well as general science journals. He has written a number of books, including Ex Machina: Coevolving Machines and the Origins of the Social Universe (SFI Press), A Crude Look at the Whole (Basic), and Complex Adaptive Social Systems (with Scott Page, Princeton University Press). He was born and raised in Denver, Colorado, the fourth generation of a family of cattle ranchers.
If we could rewind the tape of the Earth’s deep history back to the beginning and start the world anew, would social behavior arise yet again?
While the study of origins is foundational to many scientific fields, such as physics and biology, it has rarely been pursued in the social sciences. Yet knowledge of something’s origins often gives us new insights into the present.
In Ex Machina: Coevolution and the Origins of the Social Universe (SFI Press), I introduce a methodology for exploring systems of adaptive, interacting, choice-making agents, and uses this approach to identify conditions sufficient for the emergence of social behavior. Using ideas from biology, computation, game theory, and the social sciences, I explore how evolving automata can go from asocial to social behavior, and find that systems of simple adaptive agents—seemingly locked into an asocial morass—can be rapidly transformed into a bountiful social world driven only by a series of small evolutionary changes. Such unexpected revolutions by evolution may provide an important clue to the emergence of social life.