An Efficient Simulation Algorithm for Continuous-Time Agent-Based Linked Lives Models

Reinhardt, Oliver and Uhrmacher, Adelinde M. (2017) An Efficient Simulation Algorithm for Continuous-Time Agent-Based Linked Lives Models. In: 2017 Spring Simulation Multi-Conference, 23-26 Apr 2017, Virginia Beach, Virginia, USA. Proceedings, published by Society for Computer Simulation International, San Diego, CA, USA, 9:1-9:12.

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The efficient continuous-time simulation of linked lives in demography implies specific challenges. The resulting agent-based models constitute time-inhomogeneous Markov chains which require stochastic simulation algorithms. Each agent is characterized by diverse attributes, including a specific position in a dynamically evolving social network which influences the agent's behavior. This hampers the application of population-based approaches in implementing the stochastic simulation algorithm. However, as events are locally constrained by the social network, many events will happen independently of each other. We develop a stochastic simulation algorithm that maintains a dependency structure to realize lazy re-calculation of events. In case study on a Susceptible-Infected-Recovered-Model with social network and age-dependent susceptibility we evaluate the performance of the algorithm in comparison to an earlier version. The evaluation shows the improved scalability and a significant speedup of up to 150 times that can be achieved by taking dependencies into account when executing linked, continuous-time agent-based models.

Item Type: Conference or Workshop Item (Paper)
Projects: MoSiLLDe