This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The real estate market allows families to move to dwellings with higher quality or lower price when the families capitalize property values. The goods market allows consumers to search on a flexible number of firms choosing by price and proximity. The labor market entails a matching process between firms (given its location) and candidates, according to their qualification. The government may be configured into one, four or seven distinct sub-national governments, which are all economically conurbated. The role of government is to collect taxes on the value added of firms in its territory and invest the taxes into higher levels of quality of life for residents. The results suggest that the configuration of administrative boundaries is relevant to the levels of quality of life arising from the reversal of taxes. The model with seven regions is more dynamic, but more unequal and heterogeneous across regions. The simulation with only one region is more homogeneously poor. The study seeks to contribute to a theoretical and methodological framework as well as to describe, operationalize and test computer models of public finance analysis, with explicitly spatial and dynamic emphasis. Several alternatives of expansion of the model for future research are described. Moreover, this study adds to the existing literature in the realm of simple microeconomic computational models, specifying structural relationships between local governments and firms, consumers and dwellings mediated by distance.

This version adds a Maslowian entropy to each agent decision based on Kendrick et. al. Rudimentary implementation assumes agents with lower scores are more likely to make decisions autonomously rather than sociotropically.

Merger and acquisition (M&A) activity has many strategic and operational objectives. One operational objective is to develop common and efficient information systems that maybe the source of creating

Model implemented in Lammers, W., Pattyn, V., Ferrari, S. et al. Evidence for policy-makers: A matter of timing and certainty?. Policy Sci 57, 171–191 (2024). https://doi.org/10.1007/s11077-024-09526-9

Pandemic (pip install pandemic)

An agent model in which commuting, compliance, testing and contagion parameters drive infection in a population of thousands of millions. Agents follow Ornstein-Uhlenbeck processes in the plane and collisions drive transmission. Results are stored at SwarmPrediction.com for further analysis, and can be retrieved by anyone.

This is a very simple simulation that in a special case can be shown to be approximated by a compartmental model with time varying infection rate.

The model uses opinion dynamics to test a simple and ecient but empirically based approach for generating social networks in spatial agent-based models which explicitly takes into account restrictions and opportunities imposed by effects of baseline homophily and considers the probability of links that depends on geographical distance between potential partners.

This model simulates the form and function of an idealised estuary with associated barrier-spit complex on the north east coast of New Zealand’s North Island (from Bream Bay to central Bay of Plenty) during the years 2010 - 2050 CE. It combines variables from social, ecological and geomorphic systems to simulate potential directions of change in shallow coastal systems in response to external forcing from land use, climate, pollution, population density, demographics, values and beliefs. The estuary is over 1000Ha, making it a large estuary according to Hume et al. (2007) - there are 12 large estuaries in the Auckland region alone (Suyadi et al., 2019). The model was developed as part of Andrew Allison’s PhD Thesis in Geography from the School of Environment and Institute of Marine Science, University of Auckland, New Zealand. The model setup allows for alteration of geomorphic, ecological and social variables to suit the specific conditions found in various estuaries along the north east coast of New Zealand’s North Island.
This model is not a predictive or forecasting model. It is designed to investigate potential directions of change in complex shallow coastal systems. This model must not be used for any purpose other than as a heuristic to facilitate researcher and stakeholder learning and for developing system understanding (as per Allison et al., 2018).

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.

This model represnts an unique human-aquifer interactions model for the Li-extraction in Salar de Atacama, Chile. It describes the local actors’ experience of mining-induced changes in the socio-ecological system, especially on groundwater changes and social stressors. Social interactions are designed specifically according to a long-term local fieldwork by Babidge et al. (2019, 2020). The groundwater system builds on the FlowLogo model by Castilla-Rho et al. (2015), which was then parameterized and calibrated with local hydrogeological inputs in Salar de Atacama, Chile. The social system of the ABM is defined and customozied based on empirical studies to reflect three major stressors: drought stress, population stress, and mining stress. The model reports evolution of groundwater changes and associated social stress dynamics within the modeled time frame.

The purpose of this curricular model is to teach students the basics of modeling complex systems using agent-based modeling. It is a simple SIR model that simulates how a disease spreads through a population as its members change from susceptible to infected to recovered and then back to susceptible. The dynamics of the model are such that there are multiple emergent outcomes depending on the parameter settings, initial conditions, and chance.

The curricular model can be used with the chapter Agent-Based Modeling in Mixed Methods Research (Moritz et al. 2022) in the Handbook of Teaching Qualitative & Mixed Methods (Ruth et al. 2022).

The instructional videos can be accessed on YouTube: Video 1 (https://youtu.be/32_JIfBodWs); Video 2 (https://youtu.be/0PK_zVKNcp8); and Video 3 (https://youtu.be/0bT0_mYSAJ8).