Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model

TL;DR

This paper introduces a method to learn discrete-event simulation models of complex systems from data, focusing on local interactions and assuming multivariate normal distributions, enabling interpretable and data-efficient modeling.

Contribution

It proposes a novel approach to model complex system dynamics using discrete-event simulation based on local interactions and normal distribution assumptions.

Findings

Successfully captures network dynamics in various fields

Data-efficient learning of complex interactions

Produces interpretable simulation models

Abstract

The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret. In this paper, we will explore the possibility of learning a discrete-event simulation representation of complex system dynamics assuming multivariate normal distribution of the state variables, based on the observation that many complex system dynamics can be decomposed into a sequence of local interactions, which individually change the system state only minimally but in sequence generate complex and diverse dynamics. Our results show that the algorithm can data-efficiently capture complex network dynamics in several fields with meaningful events.