Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Norms and time in agent-based systems
Proceedings of the 8th international conference on Artificial intelligence and law
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
Infrastructure for forensic analysis of multi-agent based simulations
ProMAS'09 Proceedings of the 7th international conference on Programming multi-agent systems
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Validating ambient intelligence based ubiquitous computing systems by means of artificial societies
Information Sciences: an International Journal
An integrated approach for the validation of emergence in component-based simulation models
Proceedings of the Winter Simulation Conference
Post-mortem analysis of emergent behavior in complex simulation models
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
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We introduce a method for analysing emergent behaviours in multi-agent simulations using complex events. Complex events are composed of interrelated events, and they can be defined at any level of spatio-temporal abstraction (equal to or above the lowest level of abstraction given by the model). Minimal types of complex events define sets, which are equated with particular emergent behaviours and can be detected in simulation. Since complex events are derived from the agent-based model itself, they provide significant benefits when compared with traditional state-aggregation methods. First, they provide a method of specifying emergent behaviour, so that such behaviour can be monitored. Second, they provide a mechanism that retains the underlying structure of that behaviour. This latter property supports analysis of the mechanisms at lower levels that give rise to emergent behaviours, and identification of patterns between levels. In other words, multi-agent simulations become less 'opaque' [1].