Round-based Super-Individuals - Balancing Speed and Accuracy

Wilsdorf, Pia and Pierce, Maria E. and Hillston, Jane and Uhrmacher, Adelinde M. (2019) Round-based Super-Individuals - Balancing Speed and Accuracy. In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2019), 03-05 Jun 2019, Chicago, IL, USA. Proceedings, published by ACM, New York, USA, pp. 95-98.

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Agent- or individual-based models which are based on a continuous-time Markov chain semantics are increasingly receiving attention in simulation. To reduce computational cost, model aggregation techniques based on Markov chain lumping can be leveraged. However, for models with nested, attributed agents, and arbitrary functions determining their dynamics it is not trivial to find a partition that satisfies the lumpability conditions. Thus, we exploit the potential of the so-called super-individual approaches where sub-populations of agents are approximated by representatives based on some criteria for similarity, and propose a round-based execution scheme to balance speed and accuracy of the simulations. For realization we use an expressive rule-based modeling and simulation framework, evaluate the performance using a fish habitat model, and discuss open questions for future research.

Item Type: Conference or Workshop Item (Paper)
Additional Information: DOI: 10.1145/3316480.3322894
Projects: ESCeMMo