On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Self-stabilization
Machine Dreams: Economics Becomes a Cyborg Science
Machine Dreams: Economics Becomes a Cyborg Science
Analyzing Expected Time by Scheduler-Luck Games
IEEE Transactions on Software Engineering
Reachability problems for sequential dynamical systems with threshold functions
Theoretical Computer Science - Mathematical foundations of computer science
Convergence of the Iterated Prisoner's Dilemma Game
Combinatorics, Probability and Computing
Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Approach
Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Approach
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Simulation for the Social Scientist
Simulation for the Social Scientist
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
Coupling and self-stabilization
Distributed Computing - Special issue: DISC 04
An Introduction to Sequential Dynamical Systems
An Introduction to Sequential Dynamical Systems
Slow emergence of cooperation for win-stay lose-shift on trees
Machine Learning
Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)
Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)
Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity)
Evolution and Structure of the Internet: A Statistical Physics Approach
Evolution and Structure of the Internet: A Statistical Physics Approach
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In this paper we advocate the study of discrete models of social dynamics under adversarial scheduling. The approach we propose forms part of a foundational basis for a generative approach to social science (Epstein 2007). We highlight the feasibility of the adversarial scheduling approach by using it to study the Prisoners's Dilemma Game with Pavlov update, a dynamics that has already been investigated under random update in Kittock (1994), Dyer et al. (2002), Mossel and Roch (2006) and Dyer and Velumailum (2011). The model is specified by letting players at the nodes of an underlying graph G repeatedly play the Prisoner's Dilemma against their neighbours. The players adapt their strategies based on the past behaviour of their opponents by applying the so-called win-stay lose-shift strategy. With random scheduling, starting from any initial configuration, the system reaches the fixed point in which all players cooperate with high probability. On the other hand, under adversarial scheduling the following results hold: A scheduler that can select both game participants can preclude the system from reaching the unique fixed point on most graph topologies. A non-adaptive scheduler that is only allowed to choose one of the participants is no more powerful than a random scheduler. With this restriction, even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is 'reasonably fair'.