On Incentives and Updating in Agent Based Models
Computational Economics
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
The Use of Models - Making MABS More Informative
MABS '00 Proceedings of the Second International Workshop on Multi-Agent-Based Simulation-Revised and Additional Papers
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The effects of distinct agent interaction and activation structures are compared and contrasted in several multi-agent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with so-called 'small-world' networks being an intermediate form between these two extremes. A model of retirement behavior is studied with each network type, resulting in important differences in key model outputs. Then, in the context of a model of multi-agent firm formation it is demonstrated that the medium of interaction--whether through individual agents or through firms--affects the qualitative character of the results. Finally, alternative agent activation 'schedules' are studied. In particular, two activation modes are compared: (1) all agents being active exactly once each period, and (2) each agent having a random number of activations per period with mean 1. In some models these two regimes produce indistinguishable results at the aggregate level, but in certain cases the differences between them are significant.