Abstract
The references on the principles and methodology for developing agent-based models of social phenomena usually describe general principles and illustrate the process using worked examples, but seldom focus on the pitfalls and errors that make practical model building a tortuous and difficult task. This chapter contains a discussion of the positive and negative aspects of my personal experience in a PhD work on simulation of large scale social conflict. The purpose will be to describe the process from the initial plan to the final dissertation, analyze the pitfalls and their overcoming in terms of principles of model development, and summarize the ideas that I found useful for practical development of agent-based models of social phenomena. The most serious pitfalls usually occur at the conception and design stages, when seemingly trivial points can be easily overlooked. These include starting with excessive ambition but unclear ideas on whether the purpose is understanding or prediction (i.e. what is the level of abstraction), poor knowledge of the relevant theories, and failure to identify which entities, variables and mechanisms must be considered. Several practical hints for avoiding these issues are presented, such as writing a reduced version of the “Overview, Design Concepts and Details” template that includes the bare minimum of items for a first working version, and devising efficient strategies for exploring the parameter space. This chapter will be of interest to MSc and PhD students working on social simulation, and to researchers developing projects on agent-based modeling of social phenomena, either individually or in teamwork.
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Notes
- 1.
The large protest of September 15th, 2011 forced the Government to step back in the application of a controversial decision about the “Taxa Social Única” (a charge of the companies to the Social Security System based on the workers’ monthly salaries).
- 2.
Media coverage of protests by either TV stations or activists trying to catch hot spots of confrontation and posting videos on YouTube has a significant impact, and was thought to be essential for coupling the two models.
- 3.
- 4.
The motifs were related to subindices in the Fragile States Index indicator and included e.g. low salaries, bad governance, loss of independence and subordination to foreign interests, and illegitimacy of the government to impose austerity measures.
- 5.
The “critical” level was introduced in an attempt to avoid respondents giving biased answers towards the top level. That attempt was not very successful, though!
- 6.
This was far more realistic than the results reported by Torrens and McDaniel (2013), in which a large proportion of the virtual crowd ended up arrested.
- 7.
The “combinatorial explosion problem” arises because the outcome of applying multiple rules depends on their sequence, which in turn may depend on the number of contexts. Therefore, programming the agents using exhaustive lists of rule sequences usually becomes unfeasible.
- 8.
In Epstein’s model and in most ABM of “abstract” type based on spatial grids agents have random movement and the grid has torus geometry. This ensures that the probability of interactions between agents is the same for all grid cells.
- 9.
In Epstein’s ABM “cops” have an extremely simple behavior, and it is in fact possible to implement an ABM with just “citizen” agents which has the same qualitative behavior as Epstein’s ABM. The improved “cop” behavior in “Protestlab” included implementing goal-directed movement, context rules to keep cordons and avoid encirclement, and in later stages the protection of specific cells according to orders by a “command” agent.
- 10.
- 11.
If the level of repression is high and peacefully manifesting opposition to the government can lead to serious consequences, as occurs in authoritarian regimes, the decision to rebel or not can be described by the same basic mechanism of joining a riot, although the conditions (threshold and estimated arrest probability) may be different in both cases.
- 12.
According to Epstein, bounding outcomes to plausible ranges and illuminating core uncertainties are two goals of modeling (Epstein 2008).
- 13.
“Multi-role” and “offensive” cops tended to relinquish perimeter protection to pursue and arrest protesters; simulations with “cops” with these personalities (or mission profiles) resembled police charges to disperse protesters.
- 14.
Mobilization is a complicated multi-stage process (Klandermans 1997), and it is very doubtful that it can be modeled as simple or complex contagion (see e.g. Dodds and Watts 2005 and Jackson 2010). It would also have been very difficult to determine the relative importance of each context (network layer) and come up with a plausible model for the agents’ decision of joining a protest.
- 15.
The elements of the ODD protocol are summarized in Railsback and Grimm, Figure 3.1, page 37, and explained in pages 37–44 of Railsback and Grimm (2011). In pages 47–48 of the same reference, Railsback and Grimm present an example of a “summary ODD description” of a very simple ABM, with slightly different items than the ones suggested herein.
- 16.
The need of modeling collectives even with ABMs of “abstract” type is one of the major differences between modeling ethnic conflict and conflict against a central authority. Also, since ethnic conflicts tend to be more violent, it is necessary to consider killing of agents, not just “jailing” them, and to introduce some form of population dynamics (Epstein 2002; Epstein et al. 2001).
- 17.
In the development of “ProtestLab” this was provided by knowledge about the characteristics of the events to be simulated, and in the ABM described in the dissertation by the settings in several previous works. However, in many other situations it may not be possible to devise a “reference case.”
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Acknowledgements
Funding by the Research Council of Norway (grant #250449) is gratefully acknowledged. I also wish to acknowledge the comments of three reviewers, which contributed significantly to the improvement of the manuscript.
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Lemos, C.M. (2019). Pitfalls in the Development of Agent-Based Models in Social Sciences: Avoiding Them and Learning from Them. In: Diallo, S., Wildman, W., Shults, F., Tolk, A. (eds) Human Simulation: Perspectives, Insights, and Applications. New Approaches to the Scientific Study of Religion , vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-17090-5_3
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