Injecting data into simulation. Can agent-based modelling learn from microsimulation

Abstract

Most agent-based models use uniform random distributions to configure the values of initial conditions in simulations. Moreover, ran- dom values are often used to distribute ob jects spatially, to determine unmeasured exogenous factors, and sometimes to determine aspects of the agents’ behaviour. An alternative approach to the design and ini- tialisation of an agent-based simulation is to adapt the principles of mi- crosimulation using external sources of contextual data. In this approach, quantitative data are used in several ways: sample surveys for the initial conditions, calculated regression equations for the evolution of variables, and empirically based distributions for the calculation of new values. In this paper, we consider some of the advantages and difficulties of this alternative approach.

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