Daniel Friedman, Barry Sinervo, Daniel Friedman, and Barry Sinervo
- Published in print:
- 2016
- Published Online:
- August 2016
- ISBN:
- 9780199981151
- eISBN:
- 9780190466657
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199981151.003.0008
- Subject:
- Economics and Finance, Behavioural Economics
The chapter sketches the many forces driving strategy share dynamics in human society, ranging from diploid genetics to individual learning. It then focuses on learning and imitation in strategic ...
More
The chapter sketches the many forces driving strategy share dynamics in human society, ranging from diploid genetics to individual learning. It then focuses on learning and imitation in strategic interaction. The overall goal is to identify empirically the adaptation processes of humans interacting with each other. After developing parametric models of learning rules and decision rules, the chapter shows how the models can be fit to laboratory data of profit-motivated human subjects playing matrix and bimatrix games. Belief learning models have fitted parameters describing how players’ beliefs respond to new and older evidence, and how strongly actions respond to beliefs. Models mentioned include noisy best response, quantal response equilibrium, weighted fictitious play, and experience-weighted attraction.Less
The chapter sketches the many forces driving strategy share dynamics in human society, ranging from diploid genetics to individual learning. It then focuses on learning and imitation in strategic interaction. The overall goal is to identify empirically the adaptation processes of humans interacting with each other. After developing parametric models of learning rules and decision rules, the chapter shows how the models can be fit to laboratory data of profit-motivated human subjects playing matrix and bimatrix games. Belief learning models have fitted parameters describing how players’ beliefs respond to new and older evidence, and how strongly actions respond to beliefs. Models mentioned include noisy best response, quantal response equilibrium, weighted fictitious play, and experience-weighted attraction.
Daniel Friedman and Barry Sinervo
- Published in print:
- 2016
- Published Online:
- August 2016
- ISBN:
- 9780199981151
- eISBN:
- 9780190466657
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199981151.001.0001
- Subject:
- Economics and Finance, Behavioural Economics
This book’s goal is to introduce evolutionary game theory to applied researchers in a manner accessible to graduate students and advanced undergraduates in biology, economics, engineering, and allied ...
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This book’s goal is to introduce evolutionary game theory to applied researchers in a manner accessible to graduate students and advanced undergraduates in biology, economics, engineering, and allied disciplines. Chapters 1 through 6 present the basic ideas and techniques of this field, including fitness, replicator dynamics, memes and genes, single- and multiple-population games, Nash equilibrium and evolutionarily stable states, noisy best response and other adaptive processes, the Price equation, cellular automata, and estimating payoff and choice parameters from the data. Chapters 7 through 14 collect exemplary applications from many fields, providing templates for applied work everywhere. These include a new co-evolutionary predator-prey learning model extending the rock-paper-scissors game; using human subject laboratory data to estimate models of learning in games; new approaches to plastic strategies and life cycle strategies, including estimates for male elephant seals; a comparison of machine-learning techniques for preserving diversity to those seen in the natural world; analyses of congestion in traffic networks (either Internet or highways)Less
This book’s goal is to introduce evolutionary game theory to applied researchers in a manner accessible to graduate students and advanced undergraduates in biology, economics, engineering, and allied disciplines. Chapters 1 through 6 present the basic ideas and techniques of this field, including fitness, replicator dynamics, memes and genes, single- and multiple-population games, Nash equilibrium and evolutionarily stable states, noisy best response and other adaptive processes, the Price equation, cellular automata, and estimating payoff and choice parameters from the data. Chapters 7 through 14 collect exemplary applications from many fields, providing templates for applied work everywhere. These include a new co-evolutionary predator-prey learning model extending the rock-paper-scissors game; using human subject laboratory data to estimate models of learning in games; new approaches to plastic strategies and life cycle strategies, including estimates for male elephant seals; a comparison of machine-learning techniques for preserving diversity to those seen in the natural world; analyses of congestion in traffic networks (either Internet or highways)
