The purpose of the model is to simulate the spatial dynamics of potato late blight to analyse whether resistant varieties can be used effectively for sustainable disease control. The model represents an agricultural landscape with potato fields and data of a Dutch agricultural region is used as input for the model. We simulated potato production, disease spread and pathogen evolution during the growing season (April to September) for 36 years. Since late blight development and crop growth is weather dependent, measured weather data is used as model input. A susceptible and late blight resistant potato variety are distinguished. The resistant variety has a potentially lower yield but cannot get infected with the disease. However, during the growing season virulent spores can emerge as a result of mutations during spore production. This new virulent strain is able to infect the resistant fields, resulting in resistance breakdown. The model shows how disease severity, resistance durability and potato yield are affected by the fraction of fields across a landscape with a disease-resistant potato variety.

We develop an IBM that predicts how interactions between elephants, poachers, and law enforcement affect poaching levels within a virtual protected area. The model is theoretical at this stage and is not meant to provide a realistic depiction of poaching, but instead to demonstrate how IBMs can expand upon the existing modelling work done in this field, and to provide a framework for future research. The model could be further developed into a useful management support tool to predict the outcomes of various poaching mitigation strategies at real-world locations. The model was implemented in NetLogo version 6.1.0.

We first compared a scenario in which poachers have prescribed, non-adaptive decision-making and move randomly across the landscape, to one in which poachers adaptively respond to their memories of elephant locations and where other poachers have been caught by law enforcement. We then compare a situation in which ranger effort is distributed unevenly across the protected area to one in which rangers patrol by adaptively following elephant matriarchal herds.

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).

The purpose of this model is the simulation of social care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. The model simulates care provision as a negotiation process conducted between agents across their kinship networks, with agents with stronger familial relationships to the recipient being more likely to attempt to allocate time to care provision. The model also simulates demographic change, the impact of socioeconomic status, and allows agents to relocate and change jobs or reduce working hours in order to provide care.
Despite the relative lack of empirical data in this model, the model is able to reproduce plausible patterns of social care provision. The inclusion of detailed economic and behavioural mechanisms allows this model to serve as a useful policy development tool; complex behavioural interventions can be implemented in simulation and tested on a virtual population before applying them in real-world contexts.

The model is about customers going to a restaurant when they are hungry. They wait in the queue if no tables are available. Customers can leave the restaurant and got upset and decide to never return to the restaurant. The model tries to show 2 things: 1.the main caracteristics of the people that decided to never return to the restaurant and 2.the main factors that can impact the total number of customers that decided to never return to the restaurant.

Studies on word-of-mouth identify two behaviors leading to transmission of information between individuals: proactive transmission of information, and information seeking. Individuals who are aware might be curious of it and start seeking for information; they might find around them the expertise held by another individual. Field studies indicate individuals do not adopt an innovation if they don’t hold the corresponding expertise. This model describes this information seeking behavior, and enables the exploration of the dynamics which emerges out of it.

The study goes back to a model created in the 1990s which successfully tried to replicate the changes of the percentages of female teachers among the teaching staff in high schools (“Gymnasien”) in the German federal state of Rheinland-Pfalz. The current version allows for additional validation and calibration of the model and is accompanied with the empirical data against which the model is tested and with an analysis program especially designed to perform the analyses in the most recent journal article.

The model is a microsimulation, where the agents don’t Interact with each other. It simulates income distribution, unemployment dynamics, education, and Family grant in Brazil, focusing on the impact on social inequality. It tracks the indicators Gini index, Lorenz curve, and Palma ratio. The objective is to explore how these factors influence wealth distribution and social inequality over time.
This work was developed in partnership with the Graduate Program in Computational Modeling, in the Universidade Federal do Rio Grande - FURG, in Brazil.

ViSA simulates the decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. The lack of sufficient supply of ESSs triggers stakeholders to apply different management options to increase their supply. However, while attempting to reduce the supply-demand gap, conflicts arise among stakeholders due to the tradeoff nature of some ESS. ViSA investigates conditions and scenarios that can minimize such supply-demand gap while reducing the risk of conflicts by suggesting different mixes of management options and decision rules.

SONG is a simulator designed for simulating the process of transportation network growth.