ReMoTe-S is an agent-based model of the residential mobility of Swiss tenants. Its goal is to foster a holistic understanding of the reciprocal influence between households and dwellings and thereby inform a sustainable management of the housing stock. The model is based on assumptions derived from empirical research conducted with three housing providers in Switzerland and can be used mainly for two purposes: (i) the exploration of what if scenarios that target a reduction of the housing footprint while accounting for households’ preferences and needs; (ii) knowledge production in the field of residential mobility and more specifically on the role of housing functions as orchestrators of the relocation process.

The model is based on Swann and Buhrmester’s Identity Fusion behavioural theory, which seeks to explain why an individual puts the group’s priorities above their personal expectations. In order to observe the theory and validate group behaviour, a case study was carried out focusing on scenarios of group violence in football stadiums in Brazil. For the modelling, each agent has a distribution of levels of identification with the group to which they belong, with their level of fusion varying between 1 and 5. According to behavioural theory, an individual’s degree of fusion with the group directly interferes with their behaviour of replicating actions and absorbing group beliefs.

We built an agent-based model to foster the understanding of homeowners’ insulation activity.

The model’s purpose is to provide a potential explanation for the emergence, sustenance and decline of unpopular norms based on pluralistic ignorance on a social network.

This model simulates a group of farmers that have encounters with individuals of a wildlife population. Each farmer owns a set of cells that represent their farm. Each farmer must decide what cells inside their farm will be used to produce an agricultural good that is self in an external market at a given price. The farmer must decide to protect the farm from potential encounters with individuals of the wildlife population. This decision in the model is called “fencing”. Each time that a cell is fenced, the chances of a wildlife individual to move to that cell is reduced. Each encounter reduces the productive outcome obtained of the affected cell. Farmers, therefore, can reduce the risk of encounters by exclusion. The decision of excluding wildlife is made considering the perception of risk of encounters. In the model, the perception of risk is subjective, as it depends on past encounters and on the perception of risk from other farmers in the community. The community of farmers passes information about this risk perception through a social network. The user (observer) of the model can control the importance of the social network on the individual perception of risk.

The purpose of the Credit and debt market of low-income families model is to help the user examine how the financial market of low-income families works.

The model is calibrated based on real-time data which was collected in a small disadvantaged village in Hungary it contains 159 households’ social network and attributes data.
The simulation models the households’ money liquidity, expenses and revenue structures as well as the formal and informal loan institutions based on their network connections. The model forms an intertwined system integrated in the families’ local socioeconomic context through which families handle financial crises and overcome their livelihood challenges from one month to another.
The simulation-based on the abstract model of low-income families’ financial survival system at the bottom of the pyramid, which was described in following the papers:

…

The intention of this model is to create an universal basis on how to model change in value prioritizations within social simulation. This model illustrates the designing of heterogeneous populations within agent-based social simulations by equipping agents with Dynamic Value-based Cognitive Architectures (DVCA-model). The DVCA-model uses the psychological theories on values by Schwartz (2012) and character traits by McCrae and Costa (2008) to create an unique trait- and value prioritization system for each individual. Furthermore, the DVCA-model simulates the impact of both social persuasion and life-events (e.g. information, experience) on the value systems of individuals by introducing the innovative concept of perception thermometers. Perception thermometers, controlled by the character traits, operate as buffers between the internal value prioritizations of agents and their external interactions. By introducing the concept of perception thermometers, the DVCA-model allows to study the dynamics of individual value prioritizations under a variety of internal and external perturbations over extensive time periods. Possible applications are the use of the DVCA-model within artificial sociality, opinion dynamics, social learning modelling, behavior selection algorithms and social-economic modelling.

The Pampas Model is an Agent-Based Model intended to explore the dynamics of structural and land use changes in agricultural systems of the Argentine Pampas in response to climatic, technological economic, and political drivers.

A letter sending model with historically informed initial positions to reconstruct communication and archiving processes in the Republic of Letters, the 15th to 17th century form of scholarship.

The model is aimed at historians, willing to formalize historical assumptions about the letter sending process itself and allows in principle to set heterogeneous social roles, e.g. to evaluate the role of gender or social status in the formation of letter exchange networks. The model furthermore includes a pruning process to simulate the loss of letters to critically asses the role of biases e.g. in relation to gender, geographical regions, or power structures, in the creation of empirical letter archives.

Each agent has an initial random topic vector, expressed as a RGB value. The initial positions of the agents are based on a weighted random draw based on data from [2]. In each step, agents generate two neighbourhoods for sending letters and potential targets to move towards. The probability to send letters is a self-reinforcing process. After each sending the internal topic of the receiver is updated as a movement in abstract space by a random amount towards the letters topic.

…

This is based off my previous Profiler tutorial model, but with an added tutorial on converting it into a model usable with BehaviorSpace, and creating a BehaviorSpace experiment.