This model demonstrates how different psychological mechanisms and network structures generate various patterns of cultural dynamics including cultural diversity, polarization, and majority dominance, as explored by Jung, Bramson, Crano, Page, and Miller (2021). It focuses particularly on the psychological mechanisms of indirect minority influence, a concept introduced by Serge Moscovici (1976, 1980)’s genetic model of social influence, and validates how such influence can lead to social change.

System Narrative
How do rebel groups control territory and engage with the local economy during civil war? Charles Tilly’s seminal War and State Making as Organized Crime (1985) posits that the process of waging war and providing governance resembles that of a protection racket, in which aspiring governing groups will extort local populations in order to gain power, and civilians or businesses will pay in order to ensure their own protection. As civil war research increasingly probes the mechanisms that fuel local disputes and the origination of violence, we develop an agent-based simulation model to explore the economic relationship of rebel groups with local populations, using extortion racket interactions to explain the dynamics of rebel fighting, their impact on the economy, and the importance of their economic base of support. This analysis provides insights for understanding the causes and byproducts of rebel competition in present-day conflicts, such as the cases of South Sudan, Afghanistan, and Somalia.

Model Description
The model defines two object types: RebelGroup and Enterprise. A RebelGroup is a group that competes for power in a system of anarchy, in which there is effectively no government control. An Enterprise is a local civilian-level actor that conducts business in this environment, whose objective is to make a profit. In this system, a RebelGroup may choose to extort money from Enterprises in order to support its fighting efforts. It can extract payments from an Enterprise, which fears for its safety if it does not pay. This adds some amount of money to the RebelGroup’s resources, and they can return to extort the same Enterprise again. The RebelGroup can also choose to loot the Enterprise instead. This results in gaining all of the Enterprise wealth, but prompts the individual Enterprise to flee, or leave the model. This reduces the available pool of Enterprises available to the RebelGroup for extortion. Following these interactions the RebelGroup can choose to AllocateWealth, or pay its rebel fighters. Depending on the value of its available resources, it can add more rebels or expel some of those which it already has, changing its size. It can also choose to expand over new territory, or effectively increase its number of potential extorting Enterprises. As a response to these dynamics, an Enterprise can choose to Report expansion to another RebelGroup, which results in fighting between the two groups. This system shows how, faced with economic choices, RebelGroups and Enterprises make decisions in war that impact conflict and violence outcomes.

The code for the paper “Social norms and the dominance of Low-doers”

Spatial explicit model of a rangeland system, based on Australian conditions, where grass, woody shrubs and fire compete fore resources. Overgrazing can cause the system to flip from a healthy state to an unproductive shrub state. With the model one can explore the consequences of different movement rules of the livestock on the resilience of the system.

The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

Pandemic (pip install pandemic)

An agent model in which commuting, compliance, testing and contagion parameters drive infection in a population of thousands of millions. Agents follow Ornstein-Uhlenbeck processes in the plane and collisions drive transmission. Results are stored at SwarmPrediction.com for further analysis, and can be retrieved by anyone.

This is a very simple simulation that in a special case can be shown to be approximated by a compartmental model with time varying infection rate.

This model allows for the investigation of the effect spatial clustering of raw material sources has on the outcome of the neutral model of stone raw material procurement by Brantingham (2003).

A more complete description of the model can be found in Appendix I as an ODD protocol. This model is an expansion of the Hemelrijk (1996) that was expanded to include a simple food seeking behavior.

The NIER model is intended to add qualitative variables of building owner types and peer group scales to existing energy efficiency retrofit adoption models. The model was developed through a combined methodology with qualitative research, which included interviews with key stakeholders in Cleveland, Ohio and Detroit and Grand Rapids, Michigan. The concepts that the NIER model adds to traditional economic feasibility studies of energy retrofit decision-making are differences in building owner types (reflecting strategies for managing buildings) and peer group scale (neighborhoods of various sizes and large-scale Districts). Insights from the NIER model include: large peer group comparisons can quickly raise the average energy efficiency values of Leader and Conformist building owner types, but leave Stigma-avoider owner types as unmotivated to retrofit; policy interventions such as upgrading buildings to energy-related codes at the point of sale can motivate retrofits among the lowest efficient buildings, which are predominantly represented by the Stigma-avoider type of owner; small neighborhood peer groups can successfully amplify normal retrofit incentives.

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.

Butterflies (turtles) goes through metamorphism and moves to corresponding patches each season of the year. The number of years and seasons are monitored.