The basic idea behind developing MIXTRUST was to represent a network of agricultural stakeholders composed of farmers and a cooperative in a mixed landscape to test its performances in response to risks. A mixed landscape here is a landscape where crop and livestock systems interact by the intermediary of material flows of agricultural products. It can be within mixed farms, or between farms, often specialized, (e.g. straw-manure).

We provide a theory-grounded, socio-geographic agent-based model to present a possible explanation for human movement in the Adriatic region within the Cetina phenomenon.

Focusing on ideas of social capital theory from Piere Bordieu (1986), we implement agent mobility in an abstract geography based on cultural capital (prestige) and social capital (social position). Agents hold myopic representations of social (Schaff, 2016) and geographical networks and decide in a heuristic way on moving (and where) or staying.

The model is implemented in a fork of the Laboratory for Simulation Development (LSD), appended with GIS capabilities (Pereira et. al. 2020).

3spire is an ABM where farming households make management decisions aimed at satisficing along the aspirational dimensions: food self-sufficiency, income, and leisure. Households decision outcomes depend on their social networks, knowledge, assets, household needs, past management, and climate/market trends

Grasslands have a large share of the world’s land cover and their sustainable management is important for the protection and provisioning of grassland ecosystem services. The question of how to manage grassland sustainably is becoming increasingly important, especially in view of climate change, which on the one hand extends the vegetation period (and thus potentially allows use intensification) and on the other hand causes yield losses due to droughts. Fertilization plays an important role in grassland management and decisions are usually made at farm level. Data on fertilizer application rates are crucial for an accurate assessment of the effects of grassland management on ecosystem services. However, these are generally not available on farm/field scale. To close this gap, we present an agent-based model for Fertilization In Grasslands (FertIG). Based on animal, land-use, and cutting data, the model estimates grassland yields and calculates field-specific amounts of applied organic and mineral nitrogen on grassland (and partly cropland). Furthermore, the model considers different legal requirements (including fertilization ordinances) and nutrient trade among farms. FertIG was applied to a grassland-dominated region in Bavaria, Germany comparing the effects of changes in the fertilization ordinance as well as nutrient trade. The results show that the consideration of nutrient trade improves organic fertilizer distribution and leads to slightly lower Nmin applications. On a regional scale, recent legal changes (fertilization ordinance) had limited impacts. Limiting the maximum applicable amount of Norg to 170 kg N/ha fertilized area instead of farm area as of 2020 hardly changed fertilizer application rates. No longer considering application losses in the calculation of fertilizer requirements had the strongest effects, leading to lower supplementary Nmin applications. The model can be applied to other regions in Germany and, with respective adjustments, in Europe. Generally, it allows comparing the effects of policy changes on fertilization management at regional, farm and field scale.

This model demonstrates how different psychological mechanisms and network structures generate various patterns of cultural dynamics including cultural diversity, polarization, and majority dominance, as explored by Jung, Bramson, Crano, Page, and Miller (2021). It focuses particularly on the psychological mechanisms of indirect minority influence, a concept introduced by Serge Moscovici (1976, 1980)’s genetic model of social influence, and validates how such influence can lead to social change.

The aim of our model is to investigate the team dynamics through two types of task allocation strategies, with a focus on the dynamic interplay between individual needs and group performance. To achieve this goal, we have formulated an agent-based model (ABM) to formalize Deci & Ryan’s self-determination theory (SDT) and explore the social dynamics that govern the relationship between individual and group levels of team performance.

The Soy2Grow ABM aims to simulate the adoption of soybean production in Flanders, Belgium. The model primarily considers two types of agents as farmers: 1) arable and 2) dairy farmers. Each farmer, based on its type, assesses the feasibility of adopting soybean cultivation. The feasibility assessment depends on many interrelated factors, including price, production costs, yield, disease, drought (i.e., environmental stress), social pressure, group formations, learning and skills, risk-taking, subsidies, target profit margins, tolerance to bad experiences, etc. Moreover, after adopting soybean production, agents will reassess their performance. If their performance is unsatisfactory, an agent may opt out of soy production. Therefore, one of the main outcomes to look for in the model is the number of adopters over time.

The main agents are farmers. Generally, factors influencing farmers’ decision-making are divided into seven main areas: 1) external environmental factors, 2) cooperation and learning (with slight differences depending on whether they are arable or dairy farmers), 3) crop-specific factors, 4) economics, 5) support frameworks, 6) behavioral factors, and 7) the role of mobile toasters (applicable only to dairy farmers).
Moreover, factors not only influence decision-making but also interact with each other. Specifically, external environmental factors (i.e., stress) will result in lower yield and quality (protein content). The reducing effect, identified during participatory workshops, can reach 50 %. Skills can grow and improve yield; however, their growth has a limit and follows different learning curves depending on how individualistic a farmer is. During participatory workshops, it was identified that, contrary to cooperative farmers, individualistic farmers may learn faster and reach their limits more quickly. Furthermore, subsidies directly affect revenues and profit margins; however, their impact may disappear when they are removed. In the case of dairy farmers, mobile toasters play an important role, adding toasting and processing costs to those producing soy for their animal feed consumption.
Last but not least, behavioral factors directly influence the final adoption decision. For example, high risk-taking farmers may adopt faster, whereas more conservative farmers may wait for their neighbors to adopt first. Farmers may evaluate their success based on their own targets and may also consider other crops rather than soy.

This model proposes a new approach analyzing to the doctrinal paradox by considering a deliberative process (which can be represented by an agent-based model) in comparison with classical (binary) majority voting and an aggregation of (continuous) degrees of belief prior to majority voting. This model is a multivariate extension of the Hegselmann–Krause opinion dynamics model.

This agent-based model explores the dynamics between human behavior and vaccination strategies during COVID-19 pandemics. It examines how individual risk perceptions influence behaviors and subsequently affect epidemic outcomes in a simulated metropolitan area resembling New York City from December 2020 to May 2021.

Agents modify their daily activities—deciding whether to travel to densely populated urban centers or stay in less crowded neighborhoods—based on their risk perception. This perception is influenced by factors such as risk perception threshold, risk tolerance personality, mortality rate, disease prevalence, and the average number of contacts per agent in crowded settings. Agent characteristics are carefully calibrated to reflect New York City demographics, including age distribution and variations in infection probability and mortality rates across these groups. The agents can experience six distinct health statuses: susceptible, exposed, infectious, recovered from infection, dead, and vaccinated (SEIRDV). The simulation focuses on the Iota and Alpha variants, the dominant strains in New York City during the period.

We simulate six scenarios divided into three main categories:
1. A baseline model without vaccinations where agents exhibit no risk perception and are indifferent to virus transmission and disease prevalence.
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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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