Educational attainment and student retention in higher education are two of the main focuses of higher education research. Institutions in the U.S. are constantly looking for ways to identify areas of improvement across different aspects of the student experience on university campuses. This paper combines Department of Education data, U.S. Census data, and higher education theory on student retention, to build an agent-based model of student behavior.

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:

…

We present a socio-epistemic model of science inspired by the existing literature on opinion dynamics. In this model, we embed the agents (or scientists) into social networks - e.g., we link those who work in the same institutions. And we place them into a regular lattice - each representing a unique mental model. Thus, the global environment describes networks of concepts connected based on their similarity. For instance, we may interpret the neighbor lattices as two equivalent models, except one does not include a causal path between two variables.

Agents interact with one another and move across the epistemic lattices. In other words, we allow the agents to explore or travel across the mental models. However, we constrain their movements based on absorptive capacity and cognitive coherence. Namely, in each round, an agent picks a focal point - e.g., one of their colleagues - and will move towards it. But the agents’ ability to move and speed depends on how far apart they are from the focal point - and if their new position is cognitive/logic consistent.

Therefore, we propose an analytical model that examines the connection between agents’ accumulated knowledge, social learning, and the span of attitudes towards mental models in an artificial society. While we rely on the example from the General Theory of Relativity renaissance, our goal is to observe what determines the creation and diffusion of mental models. We offer quantitative and inductive research, which collects data from an artificial environment to elaborate generalized theories about the evolution of science.

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 agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory

We establish a double-layer network for China’s financial system, consisting of an interbank lending network and a cross-shareholding network. The loss of diffusion in an interbank lending channel independently, a cross-shareholding channel independently and a double-layer contagion channel after one of the financial institutions goes bankrupt with an initial shock are simulated to explore the nonlinear evolution mechanism of financial risk and impact factors of financial systemic risk in China.

This Netlogo replication of Kollman, K., J.H. Miller and S.E. Page (1997) Political Institutions and Sorting in a Tiebout Model, American Economic Review 87(5): 977-992. The model consists of of citizens who can vote for partie and move to other jurisdictions if they expect their preferences are better served. Parties adjust their positions to increase their share in the elections.

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.

This model examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.

Dynamic bipartite network model of agents and games in which agents can participate in multiple public goods games.