Agent-based Modeling and Simulation in Archaeology by Gabriel Wurzer Kerstin Kowarik & Hans Reschreiter

Author:Gabriel Wurzer, Kerstin Kowarik & Hans Reschreiter
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

6.4.3 Solving the Dilemma of Self-fulfilling Prophecies

The two previous topics proof that some models cannot be simplified or split in simpler simulations. As a consequence, the results can be more complex to understand and justify. Parallel to these issues there is another one that, in our opinion, seems to be the most important issue of state of the art archaeological simulations: the definition of behaviors.

The vast majority of published ABMs are based on the classic SugarScape model by Epstein and Axtell (1996). The model consists of a discrete world, defined as a finite bi-dimensional matrix, where a set of agents interact between them as well as with the environment. This behavior is defined as a list of rules; if one condition applies, a simple behavior is executed (e.g. if there is no food the agent will move to the adjacent place with more resources). In other words, the decision-making process of the agent is wired, as its choices are completely predefined. As a consequence it is incapable of finding solutions and reacting to conditions not devised by the modeler. This is a weakness of the methodology if we think that the interest on ABMs is precisely the emergence of large scale behaviors from the interaction of these simple rules. In theory these behaviors should not be explicitly defined in the original model, but as we said behavior is predefined, so there exist an important thread of circular explanations (Macal and North 2010).

Moreover, if everything in an ABM is related to the agent’s behavior, why is it not analyzed like a parameter? Behavior is arbitrarily defined based on archaeological assumptions, so it should also be explored to ensure scientific quality. Most models do not face this issue, so we are not really sure if different behaviors (even slight variations) affect the observed outcome as no sensitivity analysis is performed to study it.

The consequence of this problem is that critiques against ABM focus on the fact that they are self-fulfilling prophecies: emergent traits are not related to the problem, but to the way it was programmed, and if we can take a look at the code, we will learn the implicit or explicit assumptions the modeler introduced in order to achieve the final outcome.

The solution of this issue is critical for the future of ABM. For simple models it can be argued that the assumptions are less important, because such models can be replicated and understood without problems. Theory building models guarantee the scientific quality, as the number of parameters is small and behavior is so simple that the emergence of non-expected behavior can be understood with proper analysis.

The same cannot be said about hypothesis testing simulations. They are usually more complex because their goal is to understand realistic scenarios. For this reason these models create agents with several traits and a large list of conditions and rules, as simpler agents would not be able to take decisions based on the amount of data being used. In the end it will become

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