An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study.

World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with "neighbors" (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics.

Statistical Mechanics of Political Polarization.

Rapidly increasing political polarization threatens democracies around the world. Scholars from several disciplines are assessing and modeling polarization antecedents, processes, and consequences. Social systems are complex and networked. Their constant shifting hinders attempts to trace causes of observed trends, predict their consequences, or mitigate them. We propose an equivalent-neighbor model of polarization dynamics. Using statistical physics techniques, we generate anticipatory scenarios and examine whether leadership and/or external events alleviate or exacerbate polarization. We consider three highly polarized USA groups: Democrats, Republicans, and Independents. We assume that in each group, each individual has a political stance s ranging between left and right. We quantify the noise in this system as a "social temperature" T. Using energy E, we describe individuals' interactions in time within their own group and with individuals of the other groups. It depends on the stance s as well as on three intra-group and six inter-group coupling parameters. We compute the probability distributions of stances at any time using the Boltzmann probability weight exp(-E/T). We generate average group-stance scenarios in time and explore whether concerted interventions or unexpected shocks can alter them. The results inform on the perils of continuing the current polarization trends, as well as on possibilities of changing course.

Collaborative planning principles for disaster preparedness.

Natural and technological hazards can have consequences of a scale and severity far exceeding most human experience. Massive earthquakes predicted as imminent for some regions of the world, fires engulfing large tracts of land and the global COVID-19 pandemic of 2020 illustrate several key preparedness challenges. The hazards literature stresses the importance of involving communities in decisions before, during and after calamitous events occur. Currently, community planning and hazard risk management planning are largely carried out in separate tracks that seldom intersect. We propose that hazard risk managers may benefit from integrating in their approaches collaborative planning principles, especially at the pre-disaster stage. We further propose that community planners deliberately consider hazards and integrate the potential consequences of a disaster into routine plan-making, boosting communities' resilience. Finally, since citizen involvement is necessary but burdensome in both planning and hazards management, we suggest a set of criteria for considering who-from among the many community and public stakeholders-should be involved, when, and how.

Sociophysics Analysis of Multi-Group Conflicts.

We present our research on the application of statistical physics techniques to multi-group social conflicts. We identify real conflict situations of which the characteristics correspond to the model. We offer realistic assumptions about conflict behaviors that get factored into model-generated scenarios. The scenarios can inform conflict research and strategies for conflict management. We discuss model applications to two- and three-group conflicts. We identify chaotic time evolution of mean attitudes and the occurrence of strange attractors. We examine the role that the range of interactions plays with respect to the occurrence of chaotic behavior.

Evaluating participatory decision processes: which methods inform reflective practice?

Evaluating participatory decision processes serves two key purposes: validating the usefulness of specific interventions for stakeholders, interveners and funders of conflict management processes, and improving practice. However, evaluation design remains challenging, partly because when attempting to serve both purposes we may end up serving neither well. In fact, the better we respond to one, the less we may satisfy the other. Evaluations tend to focus on endogenous factors (e.g., stakeholder selection, BATNAs, mutually beneficial tradeoffs, quality of the intervention, etc.), because we believe that the success of participatory decision processes hinges on them, and they also seem to lend themselves to caeteris paribus statistical comparisons across cases. We argue that context matters too and that contextual differences among specific cases are meaningful enough to undermine conclusions derived solely from comparisons of process-endogenous factors implicitly rooted in the caeteris paribus assumption. We illustrate this argument with an environmental mediation case. We compare data collected about it through surveys geared toward comparability across cases to information elicited through in-depth interviews geared toward case specifics. The surveys, designed by the U.S. Institute of Environmental Conflict Resolution, feed a database of environmental conflicts that can help make the (statistical) case for intervention in environmental conflict management. Our interviews elicit case details - including context - that enable interveners to link context specifics and intervention actions to outcomes. We argue that neither approach can "serve both masters."