Social Dynamics

Brian Skyrms presents eighteen essays which apply adaptive dynamics (of cultural evolution and individual learning) to social theory. Altruism, spite, fairness, trust, division of labor, and signaling are treated from this perspective. Correlation is seen to be of fundamental importance. Interactions with neighbors in space, on static networks, and on co-evolving dynamics networks are investigated. Spontaneous emergence of social structure and of signaling systems are examined in the context of learning dynamics.

1 - Evolution and the Social Contract
2 - Trust, Risk, and the Social Contract
3 - Bargaining with Neighbors: Is Justice Contagious?
4 - Stability and Explanatory Significance of Some Simple Evolutionary Models
5 - Dynamics of Conformist Bias
6 - Chaos and the Explanatory Significance of Equilibrium: Strange Attractors in Evolutionary Game Dynamics
7 - Evolutionary Dynamics of Collective Action in N-person Stag Hunt Dilemmas
8 - Learning to Take Turns
9 - Evolutionary Considerations in the Framing of Social Norms
10 - Learning to Network
11 - A Dynamic Model of Social Network Formation
12 - Network Formation by Reinforcement Learning: The Long and the Medium Run
13 - Time to Absorption in Discounted Reinforcement Models
14 - Learning to Signal: Analysis of a Micro-Level Reinforcement Model
15 - Inventing New Signals
16 - Signals, Evolution and the Explanatory Power of Transient Information
17 - Co-Evolution of Pre-Play Signaling and Cooperation
18 - Evolution of Signaling Systems with Multiple Senders and Receivers

Brian Skyrms is Distinguished Professor of Logic and Philosophy of Science and Economics at the University of California, Irvine. His interests cover a range of topics, including the evolution of conventions, the social contract, inductive logic, decision theory, rational deliberation, the metaphysics of logical atomism, causality, and truth. He is the author of Signals: Evolution, Learning, and Information (OUP, 2010) and From Zeno to Arbitrage: Essays on Quantity. Coherence, and Induction (OUP, 2012).