Randomness, Emergence and Causation: A Historical Perspective of Simulation in the Social Sciences

This chapter is a review of some simulation models, with special reference to social sciences. Three critical aspects are identified--i.e. randomness, emergence and causation--that may help understand the evolution and the main characteristics of these simulation models. Several examples illustrate the concepts of the paper.

How many times should one run a computational simulation?

This chapter is an attempt to answer the question "how many runs of a computational simulation should one do," and it gives an answer by means of statistical analysis. After defining the nature of the problem and which types of simulation are mostly affected by it, the chapter introduces statistical power analysis as a way to determine the appropriate number of runs. Two examples are then produced using results from an agent-based model. The reader is then guided through the application of this statistical technique and exposed to its limits and potentials.

Analytical Approaches to Agent-Based Models

The aim of this article is to present an approach to the analysis of simple systems composed of a large number of units in interaction. Suppose to have a large number of agents belonging to a finite number of different groups: as the agents randomly interact with each other, they move from a group to another as a result of the interaction. The object of interest is the stochastic process describing the number of agents in each group. As this is generally intractable, it has been proposed in the literature to approximate it in several ways. We review these approximations and we illustrate them with reference to a version of the epidemic model. The tools presented in the paper should be considered as a complement rather than as a substitute of the classical analysis of ABMs through simulation.