The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
A multiagent reinforcement learning algorithm using extended optimal response
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Support to decision makers: the use of recursive simulation to support decisionmaking
Proceedings of the 35th conference on Winter simulation: driving innovation
MASON: A Multiagent Simulation Environment
Simulation
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
Learning about other agents in a dynamic multiagent system
Cognitive Systems Research
Agent interaction, multiple perspectives, and swarming simulation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
The influence of call graph topology on the dynamics of telecommunication markets
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Simulating human-like decisions in a memory-based agent model
Computational & Mathematical Organization Theory
Pheromones, probabilities, and multiple futures
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
On pricing strategies of boundedly rational telecommunication operators
Transactions on Compuational Collective Intelligence VI
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Current trends in model construction in the field of agent-based computational economics base behavior of agents on either game theoretic procedures (e.g. belief learning, fictitious play, Bayesian learning) or are inspired by artificial intelligence (e.g. reinforcement learning). Evidence from experiments with human subjects puts the first approach in doubt, whereas the second one imposes significant computational and memory requirements on agents. In this paper, we introduce an efficient computational implementation of n-th order rationality using recursive simulation. An agent is n-th order rational if it determines its best response assuming that other agents are (n-1)-th order rational and zero-order agents behave according to a specified, non-strategic, rule. In recursive simulations, the simulated decision makers use simulation to inform their own decision making (search for best responses). Our goal is to provide agent modelers with an off-the-shelf implementation of n-th order rationality that leads to model-consistent behaviors of agents, without requiring a learning phase. We extend two classic games (Shapley's fictitious play and Colonel Blotto) to illustrate aspects of the n-th order rationality concept as implemented in our framework.