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.

A fisher directed management system was describeded by Hart (2021). It was proposed that fishers should only be allowed to exploit a resource if they collaborated in a resource management system for which they would own and be collectively responsible for. As part of the system fishers would need to follow the rules of exploitation set by the group and provide a central unit with data with which to monitor the fishery. Any fisher not following the rules would at first be fined but eventually expelled from the fishery if he/she continued to act selfishly. This version of the model establishes the dynamics of a fleet of vessels and controls overfishing by imposing fines on fishers whose income is low and who are tempted to keep fishing beyond the set quota which is established each year depending on the abundance of the fish stock. This version will later be elaborated to have interactions between the fishers including pressure to comply with the norms set by the group and which could lead to a stable management system.

The Non-Deterministic model of affordable housing Negotiations (NoD-Neg) is designed for generating hypotheses about the possible outcomes of negotiating affordable housing obligations in new developments in England. By outcomes we mean, the probabilities of failing the negotiation and/or the different possibilities of agreement.
The model focuses on two negotiations which are key in the provision of affordable housing. The first is between a developer (DEV) who is submitting a planning application for approval and the relevant Local Planning Authority (LPA) who is responsible for reviewing the application and enforcing the affordable housing obligations. The second negotiation is between the developer and a Registered Social Landlord (RSL) who buys the affordable units from the developer and rents them out. They can negotiate the price of selling the affordable units to the RSL.
The model runs the two negotiations on the same development project several times to enable agents representing stakeholders to apply different negotiation tactics (different agendas and concession-making tactics), hence, explore the different possibilities of outcomes.
The model produces three types of outputs: (i) histograms showing the distribution of the negotiation outcomes in all the simulation runs and the probability of each outcome; (ii) a data file with the exact values shown in the histograms; and (iii) a conversation log detailing the exchange of messages between agents in each simulation run.

Sahelian transhumance is a type of socio-economic and environmental pastoral mobility. It involves the movement of herds from their terroir of origin (i.e., their original pastures) to one or more host terroirs, followed by a return to the terroir of origin.  According to certain pastoralists, the mobility of herds is planned to prevent environmental degradation, given the continuous dependence of these herds on their environment. However, these herds emit Greenhouse Gases (GHGs) in the spaces they traverse. Given that GHGs contribute to global warming, our long-term objective is to quantify the GHGs emitted by Sahelian herds. The determination of these herds’ GHG emissions requires: (1) the artificial replication of the transhumance, and (2) precise knowledge of the space used during their transhumance.
This article presents the design of an artificial replication of the transhumance through an agent-based model named MSTRANS. MSTRANS determines the space used by transhumant herds, based on the decision-making process of Sahelian transhumants.
MSTRANS integrates a constrained multi-objective optimization problem and algorithms into an agent-based model. The constrained multi-objective optimization problem encapsulates the rationality and adaptability of pastoral strategies. Interactions between a transhumant and its socio-economic network are modeled using algorithms, diffusion processes, and within the multi-objective optimization problem. The dynamics of pastoral resources are formalized at various spatio-temporal scales using equations that are integrated into the algorithms.
The results of MSTRANS are validated using GPS data collected from transhumant herds in Senegal. MSTRANS results highlight the relevance of integrated models and constrained multi-objective optimization for modeling and monitoring the movements of transhumant herds in the Sahel. Now specialists in calculating greenhouse gas emissions have a reproducible and reusable tool for determining the space occupied by transhumant herds in a Sahelian country. In addition, decision-makers, pastoralists, veterinarians and traders have a reproducible and reusable tool to help them make environmental and socio-economic decisions.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.

The goal of the paper is to propose an abstract but formalised model of how Schwartz higher order values may influence individual decisions on sharing an individual effort among alternative economic activities. Subsequently, individual decisions are aggregated into the total (collective) economic output, taking into account interactions between the agents. In particular, we explore the relationship between individual higher order values: Self–Enhancement, Self–Transcendence, Openness to Change, and Conservation – measured according to Schwartz’s universal human values theory – and individual and collective economic performance, by means of a theoretical agent based model. Furthermore, based on empirical observations, Openness to Change (measured by the population average in the case of collective output) is positively associated with individual and collective output. These relations are negative for Conservation. Self-Enhancement is positively associated with individual output but negatively with collective output. In case of Self–Transcendence, this effect is opposite. The model provides the potential explanations, in terms of individual and population differences in: propensity for management, willingness to change, and skills (measured by an educational level) for the empirically observed relations between Schwartz higher order values and individual and collective output. We directly calibrate the micro–level of the model using data from the ninth round of the European Social Survey (ESS9) and present the results of numerical simulations.

This model simulates different trade dynamics in shellmound (sambaqui) builder communities in coastal Southern Brazil. It features two simulation scenarios, one in which every site is the same and another one testing different rates of cooperation. The purpose of the model is to analyze the networks created by the trade dynamics and explore the different ways in which sambaqui communities were articulated in the past.

How it Works?
There are a few rules operating in this model. In either mode of simulation, each tick the agents will produce an amount of resources based on the suitability of the patches inside their occupation-radius, after that the procedures depend on the trade dynamic selected. For BRN? the agents will then repay their owed resources, update their reputation value and then trade again if they need to. For GRN? the agents will just trade with a connected agent if they need to. After that the agents will then consume a random amount of resources that they own and based on that they will grow (split) into a new site or be removed from the simulation. The simulation runs for 1000 ticks. Each patch correspond to a 300x300m square of land in the southern coast of Santa Catarina State in Brazil. Each agent represents a shellmound (sambaqui) builder community. The data for the world were made from a SRTM raster image (1 arc-second) in ArcMap. The sites can be exported into a shapefile (.shp) vector to display in ArcMap. It uses a UTM Sirgas 2000 22S projection system.

Identifying how organisms respond to environmental stressors remains of central importance as human impacts continue to shift the environmental conditions for countless species. Some mammals are able to mitigate these environmental stressors at the cellular level, but the mechanisms by which cells are able to do this and how these strategies vary among species is not well understood. At the cellular level, it is difficult to identify the temporal dynamics of the system through empirical data because fine-grained time course samples are both incomplete and limited by available resources. To help identify the mechanisms by which animal cells mitigate extreme environmental conditions, we propose an agent-based model to capture the dynamics of the system. In the model, agents are regulatory elements and genes, and are able to impact the behaviors of each other. Rather than imposing rules for these interactions among agents, we will begin with randomized sets of rules and calibrate the model based on empirical data of cellular responses to stress. We will apply a common-garden framework to cultured cells from 16 mammalian species, which will yield genomic data and measures of cell morphology and physiology when exposed to different levels of temperature, glucose, and oxygen. These species include humans, dolphins, bats, and camels, among others, which vary in how they respond to environmental stressors, offering a comparative approach for identifying mechanistic rules whereby cells achieve robustness to environmental stressors. For calibration of the model, we will iteratively select for rules that best lead to the emergent outcomes observed in the cellular assays. Our model is generalized for any species, any cell type, and any environmental stressor, offering many applications of the model beyond our study. This study will increase our understanding of how organisms mitigate environmental stressors at the cellular level such that we can better address how organisms are impacted by and respond to extreme environmental conditions.

SeaROOTS ABM is a quite generic agent-based modeling system, for simulating and evaluating potential terrestrial and maritime mobility of artificial hominin groups, configured by available archaeological data and hypotheses. Necessary bathymetric, geomorphological and paleoenvironmental data are combined in order to reconstruct paleoshorelines for the study area and produce an archaeologically significant agent environment. Paleoclimatic and archaeological data are incorporated in the ABM in order to simulate maritime crossings and assess the emergent patterns of interaction between human agency and the sea.

SeaROOTS agent-based system includes completely autonomous, utility-based agents (Chliaoutakis & Chalkiadakis 2016), representing artificial hominin groups, with partial knowledge of their environment, for simulating their evolution and potential maritime mobility, utilizing alternative Least Cost Path analysis modeling techniques (Gustas & Supernant 2017, Gravel-Miguel & Wren 2021). Two groups of hominins, Neanderthals and Homo sapiens, are chosen in order to study the challenges and actions employed as a response to the fluctuating sea-levels, as well as probability scenarios with respect to sea-crossings via buoyant vessels (rafting) or the human body itself (swimming). SeaROOTS ABM aims to simulate various scenarios and investigate the degree climatic fluctuations influenced such activities and interactions in the Middle Paleolithic period.

The model focuses on simulating potential terrestrial and maritime routes, explore the interactions and relations between autonomous agents and their environment, as well as to test specific research questions; for example, when and under what conditions would Middle Paleolithic hominins be more likely to attempt a crossing and successfully reach the islands? By which agent type (Sapiens or Neanderthals) and how (e.g. swimming or by sea-vessels) could such short sea crossings be (mostly) attempted, and which (sea) routes were usually considered by the agents? When does a sea-crossing become a choice and when is it a result of forced migration, i.e. disaster- or conflict-induced displacement? Results show that the dynamic marine environment of the Inner Ionian, our case study in this work, played an important role in their decision-making process.