The simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behaviour. Then, agents compete in order to increase their portfolio playing the ultimatum game with a random-stranger matching.

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.

This model studies the emergence and dynamics of generalized trust. It does so by modeling agents that engage in trust games and, based on their experience, slowly determine whether others are, in general, trustworthy.

Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives.

The purpose of the model is to simulate the cultural hitchhiking hypothesis to explore how neutral cultural traits linked with advantageous traits spread together over time

The model is intended to simulate visitor spatial and temporal dynamics, encompassing their numbers, activities, and distribution along a coastline influenced by beach landscape design. Our primary focus is understanding how the spatial distribution of services and recreational facilities (e.g., beach width, entrance location, recreational facilities, parking availability) impacts visitation density. Our focus is not on tracking the precise visitation density but rather on estimating the areas most affected by visitor activity. This comprehension allows for assessing the diverse influences of beach layouts on spatial visitor density and, consequently, on the landscape’s biophysical characteristics (e.g., vegetation, fauna, and sediment features).

Although beneficial to scientific development, data sharing is still uncommon in many research areas. Various organisations, including funding agencies that endorse open science, aim to increase its uptake. However, estimating the large-scale implications of different policy interventions on data sharing by funding agencies, especially in the context of intense competition among academics, is difficult empirically. Here, we built an agent-based model to simulate the effect of different funding schemes (i.e., highly competitive large grants vs. distributive small grants), and varying intensity of incentives for data sharing on the uptake of data sharing by academic teams strategically adapting to the context.

The purpose of the model is to examine whether and how mobile pastoralists are able to achieve an Ideal Free Distribution (IFD).

The model explores the emergence of inequality in cognitive and socio-emotional skills at the societal level within and across generations that results from differences in parental investment behavior during childhood and adolescence.

This model aims to replicate the evolution of opinions and behaviours on a communal plan over time. It also aims to foster community dialogue on simulation outcomes, promoting inclusivity and engagement. Individuals (referred to as agents), grouped based on Sinus Milieus (Groh-Samberg et al., 2023), face a binary choice: support or oppose the plan. Motivated by experiential, social, and value needs (Antosz et al., 2019), their decision is influenced by how well the plan aligns with these fundamental needs.