This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).

The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

An agent model is presented that aims to capture the impact of cheap talk on collective action in a commons dilemma. The commons dilemma is represented as a spatially explicit renewable resource. Agent’s trust in others impacts the speed and harvesting rate, and trust is impacted by observed harvesting behavior and cheap talk. We calibrated the model using experimental data (DeCaro et al. 2021). The best fit to the data consists of a population with a small frequency of altruistic and selfish agents, and mostly conditional cooperative agents sensitive to inequality and cheap talk. This calibrated model provides an empirical test of the behavioral theory of collective action of Elinor Ostrom and Humanistic Rational Choice Theory.

Hierarchical problem-solving model
The model simulates a hierarchical problem-solving process in which a manager delegates parts of a problem to specialists, who attempt to solve specific aspects based on their unique skills. The goal is to examine how effectively the hierarchical structure works in solving the problem, the total cost of the process, and the resulting solution quality.

Problem-solving random network model
The model simulates a network of agents (generalists) who collaboratively solve a fixed problem by iterating over it and using their individual skills to reduce the problem’s complexity. The goal is to study the dynamics of the problem-solving process, including agent interactions, work cycles, total cost, and solution quality.

Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for DMASON parallel ABMS platform, and we use it for comparing the applications generated by these platforms.

A general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft-used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners.

The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.

The modeling includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The model is spatial and dynamic.

CPNorm is a model of a community of harvesters using a common pool resource where adhering to the optimal extraction level has become a social norm. The model can be used to explore the robustness of norm-driven cooperation in the commons.

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.

AGENTS model is an agent-based computational framework designed to explore the socio-ecological and economic dynamics of agricultural production in the Byzantine Negev Highlands, with a focus on viticulture. It integrates historical, environmental, and social factors to simulate settlement sustainability, crop yields, and the impacts of varying climate conditions. The model is built in NetLogo and incorporates GIS-based topographical and hydrological data. Key features include the ability to assess climate impacts on crop profitability and settlement strategies, evaluate economic outputs of ancient vineyards, and simulate agent decision-making processes under diverse scenarios.

The AGENTS model is highly flexible, enabling users to simulate agricultural regimes with any two crops: one cash crop (a crop grown for profit, e.g., grapevines) and one staple crop (a crop grown for subsistence, e.g., wheat). While the default setup models viticulture and wheat cultivation in the Byzantine Negev Highlands, users can adapt the model to different environmental and socio-ecological contexts worldwide—both past and present.

Users can load external files to customize precipitation, evaporation, topography, and labor costs (measured as man-days per 0.1ha, converted to kg of wheat per model patch size area), and can also edit key parameters related to yield calculations. This includes modifying crop-specific yield formulas, soil and runoff indices, and any factors influencing crop performance. The model inherently simulates cash crops grown in floodplain regions and staple crops cultivated along riverbanks, providing a powerful tool to investigate societal resilience and responses to climate stressors across diverse environments.

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