Ex Machina Coevolving Machines and the Origins of the Social Universe
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, John H. Miller introduces 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. Miller combines ideas from biology, computation, game theory, and the social sciences to evolve a set of interacting automata from asocial to social behavior.
Readers will learn how 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.
ComputersSocial AspectsData ScienceMachine LearningScience
RELEASED2022
PUBLISHERSanta Fe Institute of Science
LENGTH412
LANGUAGEEN
John H. Miller received his Ph.D. in Economics from the University of Michigan in 1988. He then joined the Santa Fe Institute as their first post doctoral fellow, followed by an appointment in the Department of Social and Decision Sciences at Carnegie Mellon University, where he served as Department Head from 2002 to 2014 and is currently Professor of Economics and Social Science. His research interests are in complex adaptive social systems and behavioral economics.-Santa Fe Institute
Ex Machina Coevolving Machines and the Origins of the Social Universe
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, John H. Miller introduces 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. Miller combines ideas from biology, computation, game theory, and the social sciences to evolve a set of interacting automata from asocial to social behavior.
Readers will learn how 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.
ComputersSocial AspectsData ScienceMachine LearningScience
RELEASED2022
PUBLISHERSanta Fe Institute of Science
LENGTH412
LANGUAGEEN
John H. Miller received his Ph.D. in Economics from the University of Michigan in 1988. He then joined the Santa Fe Institute as their first post doctoral fellow, followed by an appointment in the Department of Social and Decision Sciences at Carnegie Mellon University, where he served as Department Head from 2002 to 2014 and is currently Professor of Economics and Social Science. His research interests are in complex adaptive social systems and behavioral economics.-Santa Fe Institute
