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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Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the Health Department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively.

CoDMER v. 2.0 was parameterized with ethnographic data from organizations dealing with prescribed fire and seeding native plants, to advance theory on how collective decisions emerge in ecological restoration.

This is a social trust model for investigating the social relationships and social networks in the real world and in social media.

We demonstrate how Repast Simphony statecharts can efficiently encapsulate the deep classification hierarchy of the U.S. Air Force for manpower life cycle costing.

This model simulates the lithic raw material use and provisioning behavior of a group that inhabits a permanent base camp, and uses stone tools.

BEGET Classic includes previous versions used in the classroom and for publication. Please check out the latest version of B3GET here, which has several user-friendly features such as directly importing and exporting genotype and population files.

The classic versions of B3GET include: version one and version three were used in undergraduate labs at the University of Minnesota to demonstrate principles in primate behavioral ecology; version two first demonstrated proof of concept for creating virtual biological organisms using decision-vector algorithms; version four was presented at the 2017 annual meeting at the American Association of Physical Anthropologists; version five was presented in a 2019 publication from the Journal of Human Evolution (Crouse, Miller, and Wilson, 2019).

This model is an agent-based simulation written in Python 2.7, which simulates the cost of social care in an ageing UK population. The simulation incorporates processes of population change which affect the demand for and supply of social care, including health status, partnership formation, fertility and mortality. Fertility and mortality rates are drawn from UK population data, then projected forward to 2050 using the methods developed by Lee and Carter 1992.

The model demonstrates that rising life expectancy combined with lower birthrates leads to growing social care costs across the population. More surprisingly, the model shows that the oft-proposed intervention of raising the retirement age has limited utility; some reductions in costs are attained initially, but these reductions taper off beyond age 70. Subsequent work has enhanced and extended this model by adding more detail to agent behaviours and familial relationships.

The version of the model provided here produces outputs in a format compatible with the GEM-SA uncertainty quantification software by Kennedy and O’Hagan. This allows sensitivity analyses to be performed using Gaussian Process Emulation.

HOW IT WORKS

This model consists of three agents, and each agent type operates per business theories as below.
a. New technologies(Tech): It evolves per sustaining or disruptive technology trajectory with the constraint of project management triangle (Scope, Time, Quality, and Cost).
b. Entrepreneurs(Entre): It builds up the solution by combining Tech components per its own strategy (Exploration, Exploitation, or Ambidex).
c. Consumer(Consumer): It selects the solution per its own preference due to Diffusion of innovation theory (Innovators, Early Adopters, Early Majority, Late Majority, Laggards)
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The model provides instruments for the simulation of interbank network evolution. There are tools for dynamic network analysis, allowing to evaluate graph topological invariants, thermodynamic network features and combinational node-based features.