This model is designed to show the effects of personality types and student organizations have on ones chance to making friendships in a university setting. As known from psychology studies, those that are extroverted have an easier chance making friendships in comparison to those that are introverted.
Once every tick a pair of students (nodes) will be randomly selected they will then have the chance to either be come friends or not (create an edge or not) based on their personality type (you are able to change what the effect of each personality is) and whether or not they are in the same club (you can change this value) then the model triggers the next tick cycle to begin.

This model simulations social and childcare provision in the UK. Agents within simulated households can decide to provide for informal care, or pay for private care, for their loved ones after they have provided for childcare needs. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. This model extends our previous simulations of social care by simulating the impact of childcare demand on social care availability within households, which is known to be a significant constraint on informal care provision.

Results show that our model replicates realistic patterns of social and child care provision, suggesting that this framework can be a valuable aid to policy-making in this area.

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.

Pastoralscape is a model of human agents, lifestock health and contageous disease for studying the impact of human decision making in pastoral communities within East Africa on livestock populations. It implements an event-driven agent based model in Python 3.

The agent-based model WEEM (Woodlot Establishment and Expansion Model) as described in the journal article, has been designed to make use of household socio-demographics (household status, birth, and death events of households), to better understand the temporal dynamics of woodlot in the buffer zones of Budongo protected forest reserve, Masindi district, Uganda. The results contribute to a mechanistic understanding of what determines the current gap between intention and actual behavior in forest land restoration at farm level.

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

This is model that explores how a few farmers in a Chinese village, where all farmers are smallholders originally, reach optimal farming scale by transferring in farmland from other farmers in the context of urbanization and aging.

MELBIS-V1 is a spatially explicit agent-based model that allows the geospatial simulation of the decision-making process of newcomers arriving in the bilingual cities and boroughs of the island of Montreal, Quebec in CANADA, and the resulting urban segregation spatial patterns. The model was implemented in NetLogo, using geospatial raster datasets of 120m spatial resolution.

MELBIS-V2 enhances MELBIS-V1 to implement and simulate the decision-making processes of incoming immigrants, and to analyze the resulting spatial patterns of segregation as immigrants arrive and settle in various cities in Canada. The arrival and segregation of immigrants is modeled with MELBIS-V2 and compared for three major Canadian immigration gateways, including the City of Toronto, Metro Vancouver, and the City of Calgary.

The teamCognition model investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. The agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions.

This model implements a combined Protective Action Decision Model (PADM) and Protection Motivation Theory (PAM) model for human decision making regarding hazard mitigations. The model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It was validated against FEMA damage assessments and post-storm surveys (O’Neil 2017).