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Collective Decision-Making, Ethnographic Data, Ecological Restoration, Empirical Modeling
Journal of Artificial Societies and Social Simulation 13 (1) 2
Kyeywords: Empirical Modeling, Genetic Optimization, Falsification
Abstract: The pioneering works in Agent-Based Modeling (ABM) - notably Schelling (1969) and Epstein and Axtell (1996) - introduced the method for testing hypotheses in "complex thought experiments" (Cederman 1997, 55). Although purely theoretical experiments can be important, the empirical orientation of the social sciences demands that the gap between modeled "thought experiments" and empirical data be as narrow as possible. In an ideal setting, an underlying theory of real-world processes would be tested directly with empirical data, according to commonly accepted technical and methodological standards. A possible procedure for narrowing the gap between theoretical assumptions and empirical data comparison is presented in this paper. It introduces a two-stage process of optimizing a model and then reviewing it critically, both from a quantitative and qualitative point of view. This procedure systematically improves a model's performance until the inherent limitations of the underlying theory become evident. The reference model used for this purpose simulates air traffic movements in the approach area of JFK International Airport in New York. This phenomenon was chosen because it provides a testbed for evaluating an empirical ABM in an application of sufficient complexity. The congruence between model and reality is expressed in simple distance measurements and is visually contrasted in Google Earth. Context knowledge about the driving forces behind controlled approaches and genetic optimization techniques are used to optimize the results within the range of the underlying theory. The repeated evaluation of a model's 'fitness' - defined as the ability to hit a set of empirical data points - serves as a feedback mechanism that corrects its parameter settings. The successful application of this approach is demonstrated and the procedure could be applied to other domains.
Moira Zellner, Cristy Watkins, Dean Massey, Lynne Westphal, Jeremy Brooks and Kristen Ross
Journal of Artificial Societies and Social Simulation 17 (4) 11
Kyeywords: Collective Decision-Making, Ethnographic Data, Ecological Restoration, Empirical Modeling
Abstract: Ecological restoration actions generally result from collective decision-making processes and can involve diverse, at times contentious, views. As such, it is critical to understand these processes and the factors that might influence the resolution of diverse perspectives into a set of coordinated actions. This paper describes the adaptation and calibration of a stylized collective decision-making agent-based model using ethnographic data, to advance theory on how decisions emerge in the context of ecological restoration in the Chicago Wilderness. The prototypical model provided structure and organization of the empirical data of two Chicago Wilderness member groups and revealed organizational structures, patterns of interactions via formal and informal meetings, and parameter values for the various mechanisms. The organization of the data allowed us to identify where our original model mechanisms required adaptation. After model modifications were completed, baseline scenarios were contrasted with observations for final parameter calibration and to elaborate explanations of the study cases. This exercise allowed us to identify the components and mechanisms in the system to which the outputs are most sensitive. We constructed relevant hypothetical scenarios around these critical components, and found that key liaisons, agents with high interaction frequencies and high mutual respect values are useful in promoting efficient decision processes but are limited in their ability to change the collective position with respect to a restoration practice. Simulations suggest that final collective position can be changed when there is a more equitable distribution of agents across groups, or the key liaison is very persuasive (i.e. interacts frequently and is highly respected) but is non-reciprocal (i.e. does not respect others highly). Our work advances our understanding of key mechanisms influencing collective decision processes and illustrates the value of agent-based modeling and its integration with ethnographic data analysis to advance the theory of collective decision making.