Robert Axelrod. 2003. Advancing the Art of Simulation in the Social Sciences. Japanese Journal for Management Information System 12 (2):16-22.

This paper provides a theoretical background on agent-based modeling (with a focus on social sciences). It describes simulation as a third way of doing science, in contrast to both induction and deduction. It finally offers advices for doing simulation research, focusing on programming, analyzing and sharing the results.

Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs. One purpose of simulation is to be used as a scientific methodology (prediction, proof and discovery). Using simulation for prediction can help validate or improve the model upon which the simulation is based. However, even highly complicated simulation models can rarely prove completely accurate (it does not aim to provide an accurate representation of a particular empirical application). While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid intuition. Its goal is to enrich our understanding of funcademtal processes that may appear in a variety of application.

Emergent properties
Large-scale effects of locally interacting agents

Adpative rather than rational strategies
When agents use adaptive rather than optimizing strategies (rational), deducing the consequences is often impossible. Thus, simulation is often the only viable way to study populations of agents who are adaptive rather than fully rational. While people my try to be rational, they can rarely meet the requirements of information, or foresight that rational modle impose (Simon, 1955; March 1978)

Complexity
The complexity of agent-based modeling should be in the simulated results, not in the assumptions of the model.

Three apsects of the research process need to be taken care of once the conceptual model is developed:

Programming
The programming of a simulation model should achive three goals: validity, usability, and extendibility

Analyzing the results
Despite the purity and clarity of simulation data, the analysis poses real challenges. Understanding the results often means understanding the details of the history of a given run (results are path-dependent). In order to determine whetheer the conclusion from a given run are typical, it is necessary to do seveal dozen simutation runs using identical parameters (using different randome number seeds) to determine which results are typical and which are unusual. The statistical method for studying the effects of the changes will be regression if the changes are quantitative, and analysis of variance if the changes are qualitative. As always in statistical analysis, two questions need to be distinguished and addressed separately: are the difference statistically significant (meaning not likely to have been caused by chance), and are the difference substantively significant (meaning large enough in magnitude to be important)

Sharing the results
The basic problem is that it is hard to present a social science simulation briefly. It may be necessary to explain very carefully both the power and the limitations of the methodology each time a simulation report is published

Robert Axelrod, and Leigh Tesfatsion. 2005. A Guide for Newcomers to Agent-Based Modeling in the Social Sciences. In Handbook of Computational Economics: Agent-Based Computational Economics , edited by K. L. Judd and L. Tesfatsion: North-Holland.

This guides gives a short introduction to Agent-based modeling and the social sciences and suggests a list of introductory readings to help newcomers become acquainted with agent-based modeling (ABM)

Social sciences seeks the understanding how the individuals interact with each other, and how the results can be more than the sum of the parts (how large-scall effects arise from the micro-processes of interactions among many agents). ABM is a method for studying the following 2 properties:

• the system is composed of interacting agents
• the system exhibits emergent properties. When the interaction of the agents is contingent on past experience, and especially when the agents continually adapt to that experience, mathematical analysis is typically very limited in its ability to derive the dynamic consequences.

The 3rd Way
ABM (and simulation in general) is a third way of doing science in additiona to deduction and induction. Simulation, like deduction, starts with a set of explicit assumption. But unlike deduction, simulation does not prove theorems with generality. Instead, simulation generates data suitable for analysis by induction.

ABM is a methodological approach that can be used to pursue the following goals:

Empirical understanding
Can particular types of observed global regularities can be reliably generated from particular types of agent-based models.

Normative understanding
Evaluating whether designs proposed for social policies, institutions, or processes will result in socially desirable system performance over time.

Heuristic
The way a greater insight can be attained about the fundamental causal mechanisms in social systems? The large-scale effects of interacting agents are often surprising because ti can be hard to anticipate the full consequences of event simple forms of interaction.

The best example to depict heurisitc in agent-based models is the city segregation model developed by Thomas Schelling: Schelling, Thomas C. (1978), Micromotives and Macrobehavior, Norton, New York, pp. 137-57.

This classic work demonstrates what can happen when behavior in the aggregate is more than the simple summation of individual behaviors. The highlighted pages present an agent-based model that shows how a high degree of residential segregation can emerge from the location choices of fairly tolerant individuals.

Methodological advancement
Provide methods and tool necessary to undertake study of social systems through controlled computational experiments.

The suggested readings are categorized in:

• Complexity and ABM
• Emergence of collective behavior
• Evolution
• Learning
• Norms
• Markets
• Institutional design
• Networks
• Modeling techniques