Nonlinear time series analysis
Nonlinear time series analysis
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
Modeling and managing collective cognitive convergence
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Understanding Collective Cognitive Convergence
Multi-Agent-Based Simulation IX
Exploiting scale invariant dynamics for efficient information propagation in large teams
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Docking agent-based simulation of collective emotion to equation-based models and interactive agents
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
An investigation of the vulnerabilities of scale invariant dynamics in large teams
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agents, pheromones, and mean-field models
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Between agents and mean fields
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
Modeling the emergence and convergence of norms
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Multi-agent systems are an attractive approach to modeling systems of interacting entities, but in some cases mathematical models of these systems can offer complementary benefits. We report a case study of how the two modeling methods can profitably engage one another. The system we study [12] is an agent-based simulation of how groups of interacting entities can come to think alike. Though formal analysis of most of the models in that paper is intractable, a mean field analysis can be performed for the simplest case. On the one hand, while the formal analysis captures some of the basic features of that model, other features remain analytically elusive, reinforcing the benefits of agent-based over equation-based modeling. On the other hand, the mathematical analysis draws our attention to certain interesting features of the model that we might not have considered if we had not performed it. Responsible modeling of a domain should include both approaches.