The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).

AgModel is an agent-based model of the forager-farmer transition. The model consists of a single software agent that, conceptually, can be thought of as a single hunter-gather community (i.e., a co-residential group that shares in subsistence activities and decision making). The agent has several characteristics, including a population of human foragers, intrinsic birth and death rates, an annual total energy need, and an available amount of foraging labor. The model assumes a central-place foraging strategy in a fixed territory for a two-resource economy: cereal grains and prey animals. The territory has a fixed number of patches, and a starting number of prey. While the model is not spatially explicit, it does assume some spatiality of resources by including search times.

Demographic and environmental components of the simulation occur and are updated at an annual temporal resolution, but foraging decisions are “event” based so that many such decisions will be made in each year. Thus, each new year, the foraging agent must undertake a series of optimal foraging decisions based on its current knowledge of the availability of cereals and prey animals. Other resources are not accounted for in the model directly, but can be assumed for by adjusting the total number of required annual energy intake that the foraging agent uses to calculate its cereal and prey animal foraging decisions. The agent proceeds to balance the net benefits of the chance of finding, processing, and consuming a prey animal, versus that of finding a cereal patch, and processing and consuming that cereal. These decisions continue until the annual kcal target is reached (balanced on the current human population). If the agent consumes all available resources in a given year, it may “starve”. Starvation will affect birth and death rates, as will foraging success, and so the population will increase or decrease according to a probabilistic function (perturbed by some stochasticity) and the agent’s foraging success or failure. The agent is also constrained by labor caps, set by the modeler at model initialization. If the agent expends its yearly budget of person-hours for hunting or foraging, then the agent can no longer do those activities that year, and it may starve.

Foragers choose to either expend their annual labor budget either hunting prey animals or harvesting cereal patches. If the agent chooses to harvest prey animals, they will expend energy searching for and processing prey animals. prey animals search times are density dependent, and the number of prey animals per encounter and handling times can be altered in the model parameterization (e.g. to increase the payoff per encounter). Prey animal populations are also subject to intrinsic birth and death rates with the addition of additional deaths caused by human predation. A small amount of prey animals may “migrate” into the territory each year. This prevents prey animals populations from complete decimation, but also may be used to model increased distances of logistic mobility (or, perhaps, even residential mobility within a larger territory).

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Presented here is a socioeconomic agent-based model (ABM) to examine the Hollywood labor system as a network within a simulated movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie year. Using the ABM, I test the role slight individual preference for racial and ethnic similarity within one’s own network at the microlevel and find that it is insufficient to explain the phenomena of racial and ethnic underrepresentation at the macrolevel. The ABM also includes the ability to test alternative explanations, such as overt opportunity loss as a possible explanation.

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.

An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.