The purpose of this model is to explore the dynamics of residency and eviction for households renting in the greater Phoenix (Arizona) metropolitan area. The model uses a representative population of renters modified from American Community Survey (ACS) data that includes demographic, housing and economic information. Each month, households pay their subsistence, rental and utility bills. If a household is unable to pay their monthly rent or utility bill they apply for financial assistance. This model provides a platform to understand the impact of various economic shock upon households. Also, the model includes conditions that occurred as a result of the Covid-19 pandemic which allows for the study of eviction mitigation strategies that were employed, such as the eviction moratorium and stimulus payments. The model allows us to make preliminary predictions concerning the number of households that may be evicted once the moratorium on evictions ends and the long-term effects on the number of evicted households in the greater Phoenix area going forward.

The model is a stylized representation of a social-ecological system of agents moving and harvesting a renewable resource. The purpose is to analyze how mobility affects sustainability. Experiments changing agents’ mobility, landscape and information governments have can be run.

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.

Indirect reciprocity can be evolved by the shared information among the people of small subgroups in the population.

The Nice Musical Chairs (NMC) model represent the competition for space between groups of stakeholders of farming and herding activities in the arid Afro-Eurasia.

This model is an application of Brantingham’s neutral model to a real landscape with real locations of potential sources. The sources are represented as their sizes during current conditions, and from marine geophysics surveys, and the agent starts at a random location in Mossel Bay Region (MBR) surrounding the Archaeological Pinnacle Point (PP) locality, Western Cape, South Africa. The agent moves at random on the landscape, picks up and discards raw materials based only upon space in toolkit and probability of discard. If the agent happens to encounter the PP locality while moving at random the agent may discard raw materials at it based on the discard probability.

The purpose of this model is to examine equity and efficiency in crop production across a system of irrigated farms, as a function of maintenance costs, assessed water fees, and the capacity of farmers to trade water rights among themselves.

The purpose of this agent-based model is to compare different variants of crowdworking in a general way, so that the obtained results are independent of specific details of the crowdworking platform. It features many adjustable parameters that can be used to calibrate the model to empirical data, but also when not calibrated it yields essential results about crowdworking in general.
Agents compete for contracts on a virtual crowdworking platform. Each agent is defined by various properties like qualification and income expectation. Agents that are unable to turn a profit have a chance to quit the crowdworking platform and new crowdworkers can replace them. Thus the model has features of an evolutionary process, filtering out the ill suited agents, and generating a realistic distribution of agents from an initially random one. To simulate a stable system, the amount of contracts issued per day can be set constant, as well as the number of crowdworkers. If one is interested in a dynamically changing platform, the simulation can also be initialized in a way that increases or decreases the number of crowdworkers or number of contracts over time. Thus, a large variety of scenarios can be investigated.

Like many developing countries, Nigeria is faced with a number of tradeoffs that pit rapid economic development against environmental preservation. Environmentally sustainable, “green” economic development is slower, more costly, and more difficult than unrestricted, unregulated economic growth. The mathematical model that we develop in this code suggests that widespread public awareness of environmental issues is insufficient to prevent the tendency towards sacrificing the environment for the sake of growth. Even if people have an understanding of negative impacts and always choose to act in their own self-interest, they may still act collectively in such a way as to bring down the quality of life for the entire society. We conclude that additional actions must be taken besides raising public awareness of the environmental problem.

EiLab - Model I - is a capital exchange model. That is a type of economic model used to study the dynamics of modern money which, strangely, is very similar to the dynamics of energetic systems. It is a variation on the BDY models first described in the paper by Dragulescu and Yakovenko, published in 2000, entitled “Statistical Mechanics of Money”. This model demonstrates the ability of capital exchange models to produce a distribution of wealth that does not have a preponderance of poor agents and a small number of exceedingly wealthy agents.

This is a re-implementation of a model first built in the C++ application called Entropic Index Laboratory, or EiLab. The first eight models in that application were labeled A through H, and are the BDY models. The BDY models all have a single constraint - a limit on how poor agents can be. That is to say that the wealth distribution is bounded on the left. This ninth model is a variation on the BDY models that has an added constraint that limits how wealthy an agent can be? It is bounded on both the left and right.

EiLab demonstrates the inevitable role of entropy in such capital exchange models, and can be used to examine the connections between changing entropy and changes in wealth distributions at a very minute level.

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