Advancing Collective Decision-Making Theory with Integrated Agent-Based Modeling and Ethnographic Data Analysis: An Example in Ecological Restoration

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.