Chapter III. Overview of Methods for Building a Simulation

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

Chapter III. Overview of Methods for Building a Simulation

3.1 Introduction

The following sections overview different methods that were considered for this simulation. Ultimately, object-oriented programming, fuzzy logic, and genetic algorithms were accepted. Conversely, game theory, cellular automata, neural networks, and genetic programming were rejected. Lastly, the issue of simple and complicated data is discussed.

Computer Science Thesis Index

2.5 Computerized Simulations with Agents

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

2.5 Computerized Simulations with Agents

By combining the world of previous models of group behavior with the principles of the new field of AI, many social scientists began crafting social science models employing both. In Simulation by the Social Scientist, Gilbert and Troitzsch note “Using computer simulation in the social sciences is a rather new idea – although the first examples data from the 1960s, simulation only began to be used widely in the 1990s…”

Quoted by plum, Troitzsch states, “Computer simulation in the social sciences had a difficult birth.” The models were all based on discrete event simulations or systems dynamics. Further, “The system dynamics approach makes use of large systems of difference equations to plot the trajectories of variables over time.”

The first major use of this socio-historical approach was the Club of Rome simulation of the global economy (Franz). However, the Club of Rome simulation gave the entire field a poor reputation, and its prediction of “global environmental catastrophe” revealed shortcomings of this approach.

“This early work also suffered in another respect: it was focused on prediction, while social scientists tend to be more concerned with understanding and explanation. This is partly due to scepticism [sic] about the possibility of making social predictions, based on both the inherent difficulty of doing so and also the possibility, peculiar to social and economic forecasting, that the forecast itself will affect the outcome.”

However, by the 1980s agent-based modeling had opened up new doors. Agents are self-contained programs that control their own actions based on their view of the world around them. Gilbert and Troitzsch note that agency models are increasingly influenced by the social sciences.

An impetus for the adoption of agent-based simulations by the social sciences was the need to provide more evidence for theories. Writing about the development of civic traditions in Italy, Bhavnani notes “Historical processes are notoriously difficult to study, and their findings equally difficult to validate empirically.” Historical analysis is a valuable tool, but without being able to “rerun” the past except in one’s mind any analysis is necessarily limited. Agent-based simulations provide a way around this.

Davidsson calls this emerging field Agent-Based Social Simulation and defines it as the intersection of social science, agent-based computing, and computer simulation. Davidson gives several justifications for this new field, including:

“[Social sciences] are very messy. They have ill-defined or unknown boundaries and individuals comprising the systems face constraints that are beyond their information processing capacities to define or to use in reaching decisions to act.

“The formal structure of agent based computing clearly provides a supportive environment for the application of logical formalisms and the formalisms developed with a view to a new social theory are frequently found useful in specifications of agents for purposes of engineering multi-agent systems.

“Social scientists have begun to convert social theories to computer programs. It is then possible to simulate social processes and carry out “experiments” that would otherwise be impossible “

Computer Science Thesis Index