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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The Palaeo-Agulhas Plain formed an important habitat exploited by Pleistocene hunter-gatherer populations during periods of lower sea level. This productive, grassy habitat would have supported numerous large-bodied ungulates accessible to a population of skilled hunters with the right hunting technology. It also provided a potentially rich location for plant food collection, and along its shores a coastline that moved with the rise and fall of sea levels. The rich archaeological and paleontological records of Pleistocene sites along the modern Cape south coast of South Africa, which would have overlooked the Palaeo-Agulhas Plain during Pleistocene times of lower sea level, provides a paleoarchive of this extinct ecosystem. In this paper, we present a first order illustration of the “palaeoscape modeling” approach advocated by Marean et al. (2015). We use a resourcescape model created from modern studies of habitat productivity without the Palaeo-Agulhas Plain. This is equivalent to predominant Holocene conditions. We then run an agent-based model of the human foraging system to investigate several research questions. Our agent-based approach uses the theoretical framework of optimal foraging theory to model human foraging decisions designed to optimize the net caloric gains within a complex landscape of spatially and temporally variable resources. We find that during the high sea-levels of MIS 5e (+5-6 m asl) and the Holocene, the absence of the Plain left a relatively poor food base supporting a much smaller population relying heavily on edible plant resources from the current Cape flora. Despite high species diversity of plants with edible storage organs, and marine invertebrates, encounter rates with highly profitable resources were low. We demonstrate that without the Palaeo-Agulhas Plain, human populations must have been small and low density, and exploited plant, mammal, and marine resources with relatively low caloric returns. The exposure and contraction of the Palaeo-Agulhas Plain was likely the single biggest driver of behavioral change during periods of climate change through the Pleistocene and into the transition to the Holocene.

Agent-based model of hunting behavior of Ache hunter-gatherers from Paraguay. We evaluate the effect of group size and cooperative hunting

The Megafaunal Hunting Pressure Model (MHPM) is an interactive, agent-based model designed to conduct experiments to test megaherbivore extinction hypotheses. The MHPM is a model of large-bodied ungulate population dynamics with human predation in a simplified, but dynamic grassland environment. The overall purpose of the model is to understand how environmental dynamics and human predation preferences interact with ungulate life history characteristics to affect ungulate population dynamics over time. The model considers patterns in environmental change, human hunting behavior, prey profitability, herd demography, herd movement, and animal life history as relevant to this main purpose. The model is constructed in the NetLogo modeling platform (Version 6.3.0; Wilensky, 1999).

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.

Many archaeological assemblages from the Iberian Peninsula dated to the Last Glacial Maximum contain large quantities of European rabbit (Oryctolagus cuniculus) remains with an anthropic origin. Ethnographic and historic studies report that rabbits may be mass-collected through warren-based harvesting involving the collaborative participation of several persons.

We propose and implement an Agent-Based Model grounded in the Optimal Foraging Theory and the Diet Breadth Model to examine how different warren-based hunting strategies influence the resulting human diets.

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Many archaeological assemblages from the Iberian Peninsula dated to the Last Glacial Maximum contain large quantities of European rabbit (Oryctolagus cuniculus) remains with an anthropic origin. Ethnographic and historic studies report that rabbits may be mass-collected through warren-based harvesting involving the collaborative participation of several persons.

We propose and implement an Agent-Based Model grounded in the Optimal Foraging Theory and the Diet Breadth Model to examine how different warren-based hunting strategies influence the resulting human diets.

Particularly, this model is developed to test the following hypothesis: What if an age and/or gender-based division of labor was adopted, in which adult men focus on large prey hunting, and women, elders and children exploit warrens?

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Zooarchaeological evidences indicate that rabbit hunting became prevalent during the Upper Palaeolithic in the Iberian Peninsula.

The purpose of the ABM is to test if warren hunting using nets as a collective strategy can explain the introduction of rabbits in the human diet in the Iberian Peninsula during this period. It is analyzed whether this hunting strategy has an impact on human diet breadth by affecting the relative abundance of other main taxa in the dietary spectrum.
Model validity is measured by comparing simulated diet breadth to the observed diet breadth in the zooarchaeological record.

The agent-based model is explicitly grounded on the Diet Breadth Model (DBM), from the Optimal Foraging Theory (OFT).
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This NetLogo model represents hunters and forestry road development in a spatial landscape. The cumulative effects of multiple resource use is explored.

The original Ache model is used to explore different distributions of resources on the landscape and it’s effect on optimal strategies of the camps on hunting and camp movement.