The ForagerNet3_Demography model is a non-spatial ABM designed to serve as a platform for exploring several aspects of hunter-gatherer demography.

This model simulates networking mechanisms of an empirical social network. It correlates event determinants with place-based geography and social capital production.

In order to test how prosocial strategies (compassionate altruism vs. reciprocity) grow over time, we developed an evolutionary simulation model where artificial agents are equipped with different emotionally-based drivers that vary in strength. Evolutionary algorithms mimic the evolutionary selection process by letting the chances of agents conceiving offspring depend on their fitness. Equipping the agents with heritable prosocial strategies allows for a selection of those strategies that result in the highest fitness. Since some prosocial attributes may be more successful than others, an initially heterogeneous population can specialize towards altruism or reciprocity. The success of particular prosocial strategies is also expected to depend on the cultural norms and environmental conditions the agents live in.

The purpose of this curricular model is to teach students the basics of modeling complex systems using agent-based modeling. It is a simple SIR model that simulates how a disease spreads through a population as its members change from susceptible to infected to recovered and then back to susceptible. The dynamics of the model are such that there are multiple emergent outcomes depending on the parameter settings, initial conditions, and chance.

The curricular model can be used with the chapter Agent-Based Modeling in Mixed Methods Research (Moritz et al. 2022) in the Handbook of Teaching Qualitative & Mixed Methods (Ruth et al. 2022).

The instructional videos can be accessed on YouTube: Video 1 (https://youtu.be/32_JIfBodWs); Video 2 (https://youtu.be/0PK_zVKNcp8); and Video 3 (https://youtu.be/0bT0_mYSAJ8).

The model represents urban commuters’ transport mode choices among cars, public transit, and motorcycles—a mode highly prevalent in developing countries. Using an agent-based modeling approach, it simulates transport dynamics and serves as a testbed for evaluating policies aimed at improving mobility.

The model simulates an ecosystem of human agents who decide, at each time step, which mode of transportation to use for commuting to work. Their decision is based on a combination of personal satisfaction with their most recent journey—evaluated across a vector of individual needs—the information they crowdsource from their social network, and their personal uncertainty regarding trying new transport options.

Agents are assigned demographic attributes such as sex, age, and income level, and are distributed across city neighborhoods according to their socioeconomic status. To represent social influence in decision-making, agents are connected via a scale-free social network topology, where connections are more likely among agents within the same socioeconomic group, reflecting the tendency of individuals to form social ties with similar others.

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Riparian forests are one of the most vulnerable ecosystems to the development of biological invasions, therefore limiting their spread is one of the main challenges for conservation. The main factors that explain plant invasions in these ecosystems are the capacity for both short- and long-distance seed dispersion, and the occurrence of suitable habitats that facilitate the establishment of the invasive species. Large floods constitute an abiotic filter for invasion.

This model simulates the spatio-temporal spread of the woody invader Gleditsia. triacanthos in the riparian forest of the National Park Esteros de Farrapos e Islas del Río Uruguay, a riparian system in the coast of the Uruguay river (South America). In this model, we represent different environmental conditions for the development of G. triacanthos, long- and short-distance spread of its fruits, and large floods as the main factor of mortality for fruit and early stages.

Field results show that the distribution pattern of this invasive species is limited by establishment, i.e. it spreads locally through the expansion of small areas, and remotely through new invasion foci. This model recreates this dispersion pattern. We use this model to derive management implications to control the spread of G. triacanthos

The current rate of production and consumption of meat poses a problem both to peoples’ health and to the environment. This work aims to develop a simulation of peoples’ meat consumption behaviour in Britain using agent-based modelling. The agents represent individual consumers. The key variables that characterise agents include sex, age, monthly income, perception of the living cost, and concerns about the impact of meat on the environment, health, and animal welfare. A process of peer influence is modelled with respect to the agents’ concerns. Influence spreads across two eating networks (i.e. co-workers and household members) depending on the time of day, day of the week, and agents’ employment status. Data from a representative sample of British consumers is used to empirically ground the model. Different experiments are run simulating interventions of application of social marketing campaigns and a rise in price of meat. The main outcome is the average weekly consumption of meat per consumer. A secondary outcome is the likelihood of eating meat.

This is an agent-based model constructed in Netlogo v6.2.2 which seeks to provide a simple but flexible tool for researchers and dog-population managers to help inform management decisions.

It replicates the basic demographic processes including:
* reproduction
* natural death
* dispersal
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This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.

The rapid environmental changes currently underway in many dry regions of the world, and the deep uncertainty about their consequences, underscore a critical challenge for sustainability: how to maintain cooperation that ensures the provision of natural resources when the benefits of cooperating are variable, sometimes uncertain, and often limited. We present an agent-based model that simulates the economic decisions of households to engage, or not, in labor-sharing agreements under different scenarios of water supply, water variability, and socio-environmental risk. We formulate the model to investigate the consequences of environmental variability on the fate of labor-sharing agreements between farmers. The economic decisions were implemented in the framework of prospect theory.