Nonlinear time series analysis
Nonlinear time series analysis
Information-driven phase changes in multi-agent coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Universality in Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Modeling uncertain domains with polyagents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Real-time agent characterization and prediction
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Exploiting the environment for coordinating agent intentions
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
Digital pheromones for coordination of unmanned vehicles
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
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One motivation for many agent-based models is to predict the future. The nonlinearity of agent interactions in most non-trivial domains mean that the usefulness of such predictions will be limited beyond a certain point (the "prediction horizon"), due to unbounded divergence of their trajectories. The model's predictions are increasingly useful out to the prediction horizon, but become misleading beyond that point. We exhibit and characterize this behavior in a simple model, based on the polyagent modeling construct, which uses multiple ghost agents mediated through a shared environment to explore alternative futures concurrently for a domain entity. We also discuss how a single agent in such a model can estimate the prediction horizon based on locally available information, and use this estimate to modulate dynamically how far it seeks to look into the future.