This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

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.

AncientS-ABM is an agent-based model for simulating and evaluating the potential social organization of an artificial past society, configured by available archaeological data. Unlike most existing agent-based models used in archaeology, our ABM framework includes completely autonomous, utility-based agents. It also incorporates different social organization paradigms, different decision-making processes, and also different cultivation technologies used in ancient societies. Equipped with such paradigms, the model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics; and to assess the influence of social organization on agents’ population growth, agent community numbers, sizes and distribution.

AncientS-ABM also blends ideas from evolutionary game theory with multi-agent systems’ self-organization. We model the evolution of social behaviours in a population of strategically interacting agents in repeated games where they exchange resources (utility) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time, while agents in stage games also receive non-static payoffs, in contrast to most games studied in the literature. To tackle this, we defined a novel formulation of the evolutionary dynamics via assessing agents’ rather than strategies’ fitness.

As a case study, we employ AncientS-ABM to evaluate the impact of the implemented social organization paradigms on an artificial Bronze Age “Minoan” society, located at different geographical parts of the island of Crete, Greece. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate better sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, where a “heterarchical” social structure emerges. Results also demonstrate that successful agent societies adopt an evolutionary approach where cooperation is an emergent strategic behaviour. In simulation scenarios where the natural disaster module was enabled, we observe noticeable changes in the settlements’ distribution, relating to significantly higher migration rates immediately after the modeled Theran eruption. In addition, the initially cooperative behaviour is transformed to a non-cooperative one, thus providing support for archaeological theories suggesting that the volcanic eruption led to a clear breakdown of the Minoan socio-economic system.

…

The various technologies used inside a Dutch greenhouse interact in combination with an external climate, resulting in an emergent internal climate, which contributes to the final productivity of the greenhouse. This model examines how differing technology development styles affects the overall ability of a community of growers to approach the theoretical maximum yield.

We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

This model looks at implications of author/referee interaction for quality and efficiency of peer review. It allows to investigate the importance of various reciprocity motives to ensure cooperation. Peer review is modelled as a process based on knowledge asymmetries and subject to evaluation bias. The model includes various simulation scenarios to test different interaction conditions and author and referee behaviour and various indexes that measure quality and efficiency of evaluation […]

This is a simulation model of communication between two groups of managers in the course of project implementation. The “world” of the model is a space of interaction between project participants, each of which belongs either to a group of work performers or to a group of customers. Information about the progress of the project is publicly available and represents the deviation Earned value (EV) from the planned project value (cost baseline).
The key elements of the model are 1) persons belonging to a group of customers or performers, 2) agents that are communication acts. The life cycle of persons is equal to the time of the simulation experiment, the life cycle of the communication act is 3 periods of model time (for the convenience of visualizing behavior during the experiment). The communication act occurs at a specific point in the model space, the coordinates of which are realized as random variables. During the experiment, persons randomly move in the model space. The communication act involves persons belonging to a group of customers and a group of performers, remote from the place of the communication act at a distance not exceeding the value of the communication radius (MaxCommRadius), while at least one representative from each of the groups must participate in the communication act. If none are found, the communication act is not carried out. The number of potential communication acts per unit of model time is a parameter of the model (CommPerTick).

The managerial sense of the feedback is the stimulating effect of the positive value of the accumulated communication complexity (positive background of the project implementation) on the productivity of the performers. Provided there is favorable communication (“trust”, “mutual understanding”) between the customer and the contractor, it is more likely that project operations will be performed with less lag behind the plan or ahead of it.
The behavior of agents in the world of the model (change of coordinates, visualization of agents’ belonging to a specific communicative act at a given time, etc.) is not informative. Content data are obtained in the form of time series of accumulated communicative complexity, the deviation of the earned value from the planned value, average indicators characterizing communication - the total number of communicative acts and the average number of their participants, etc. These data are displayed on graphs during the simulation experiment.
The control elements of the model allow seven independent values to be varied, which, even with a minimum number of varied values (three: minimum, maximum, optimum), gives 3^7 = 2187 different variants of initial conditions. In this case, the statistical processing of the results requires repeated calculation of the model indicators for each grid node. Thus, the set of varied parameters and the range of their variation is determined by the logic of a particular study and represents a significant narrowing of the full set of initial conditions for which the model allows simulation experiments.
…

Perpetual Motion Machine - A simple economy that operates at both a biophysical and economic level, and is sustainable. The goal: to determine the necessary and sufficient conditions of sustainability, and the attendant necessary trade-offs.

The TechNet_04 is an abstract model that embeds a simple cultural tranmission process in an environment where interaction is structured by spatially-situated networks.