We represent commuters and their preferences for transportation cost, time and safety. Agents assess their options via their preferences, their environment, and the modes available. The model has policy levers to test impact on last-mile problem.

A model of attitudinal dynamics based on the cognitive mechanism of emotional coherence. The code is written in Java. For initialization an additional dataset is required.

This model extends Schelling’s segregation model with mobility sharing. Agents of two groups occupy cells of a 32 × 32 grid and repeatedly choose activity destinations subject to a preference threshold, the minimum share of ingroup neighbours they accept. Trips are matched into shared rides between members of different groups, using one of four matching algorithms on a shareability network (maximal cross-group matching, maximum weight matching on contact time, maximum cardinality matching, or random greedy matching) with a maximum detour constraint. After each trip, an agent’s preference threshold changes with the time spent in contact with outgroup members.

Starting from complete segregation, the model examines under which conditions contact during shared trips leads to integration. Besides the main scenario, in which contact always lowers the threshold, it includes two extensions. In the first, a share of adversarial agents becomes less tolerant with contact, and this share grows or shrinks with the current level of segregation, governed by a branching dissimilarity index and a conversion rate. In the second, each contact raises or lowers the threshold with a probability that depends on the agent’s current attitude relative to a compatibility threshold.

Segregation is measured with the index of dissimilarity and the Gini coefficient. The model file includes BehaviorSpace experiments for each analysis in the associated article. The matching step uses the NetworkX library through the Python extension of NetLogo (requires Python 3 with numpy and networkx 3.x).

This model simulates the behaviour of the agents in 3 wine markets parallel trading systems: Liv-ex, Auctions and additionally OTC market (finally not used). Behavioural aspects (impatience) is additionally modeled. This is an extention of parallel trading systems model with technical trading (momentum and contrarian) and noise trading.

The simulation model SimPLS shows an application of the PLS agent concept, using SEM as empirical basis for the definition of agent architectures. The simulation model implements the PLS path model TAM about the decision of using innovative products.

This study separates operational interoperability, fare integration and revenue-sharing governance in a stylised multi-operator bus network. An agent-based model implemented in NetLogo 7.0.3 represents passengers, buses and stations across 14 stations, nine routes and three operators. A 2 × 2 institutional design is complemented by financial clearing comparisons and 43,200 robustness runs: behavioural sensitivity (E1), a 243-configuration operational factorial (E2), and targeted joint behavioural–operational stress testing (E3). The audited experiment records show favourable interoperability effects on waiting, total travel time, transfers and realised generalised cost in all 1,944 E2 and 768 E3 configuration-level primary comparisons. Fare integration substantially reduces passenger expenditure but produces an aggregate deficit under the assumed accounting coefficients. Revenue-sharing rules redistribute operator-level outcomes without changing aggregate profit or passenger trajectories by construction. The model is intended to identify mechanisms and test conditional robustness, not predict city-specific policy effects. The paper contributes an explicit separation of access, pricing and settlement mechanisms coupled with reproducible factorial and stress-test comparisons.

AncientS-ABM is an agent-based model for simulating and evaluating the potential social organization of an artificial past society, configured by available archaeological data. Unlike most existing agent-based models used in archaeology, our ABM framework includes completely autonomous, utility-based agents. It also incorporates different social organization paradigms, different decision-making processes, and also different cultivation technologies used in ancient societies. Equipped with such paradigms, the model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics; and to assess the influence of social organization on agents’ population growth, agent community numbers, sizes and distribution.

AncientS-ABM also blends ideas from evolutionary game theory with multi-agent systems’ self-organization. We model the evolution of social behaviours in a population of strategically interacting agents in repeated games where they exchange resources (utility) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time, while agents in stage games also receive non-static payoffs, in contrast to most games studied in the literature. To tackle this, we defined a novel formulation of the evolutionary dynamics via assessing agents’ rather than strategies’ fitness.

As a case study, we employ AncientS-ABM to evaluate the impact of the implemented social organization paradigms on an artificial Bronze Age “Minoan” society, located at different geographical parts of the island of Crete, Greece. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate better sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, where a “heterarchical” social structure emerges. Results also demonstrate that successful agent societies adopt an evolutionary approach where cooperation is an emergent strategic behaviour. In simulation scenarios where the natural disaster module was enabled, we observe noticeable changes in the settlements’ distribution, relating to significantly higher migration rates immediately after the modeled Theran eruption. In addition, the initially cooperative behaviour is transformed to a non-cooperative one, thus providing support for archaeological theories suggesting that the volcanic eruption led to a clear breakdown of the Minoan socio-economic system.

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Juan Castilla-Rho et al. (2015) developed a platform, named FLowLogo, which integrates a 2D, finite-difference solution of the governing equations of groundwater flow with agent-based simulation. We used this model for Rafsanjan Aquifer, which is located in an arid region in Iran. To use FLowLogo for a real case study, one needs to add GIS shapefiles of boundary conditions and modify the code written in NetLogo a little bit. The FlowLogo model used in our research is presented here.

We compare three model estimates for the time and treatment requirements to eliminate HCV among HIV-positive MSM in Victoria, Australia: a compartmental model; an ABM parametrized by surveillance data; and an ABM with a more heterogeneous population.

In the “World of Cows”, dairy farmers run their farms and interact with each other, the surrounding agricultural landscape, and the economic and political framework. The model serves as an exemplary case of an interdependent human-environment system.

With the model, users can analyze the influence of policies and markets on land use decisions of dairy farms. The land use decisions taken by farms determine the delivered ecosystem services on the landscape level. Users can choose a combination of five policy options and how strongly market prices fluctuate. Ideally, the choice of policy options fulfills the following three “political goals” 1) dairy farming stays economically viable, 2) the provision of ecosystem services is secured, and 3) government spending on subsidies is as low as possible.

The model has been designed for students to practice agent-based modeling and analyze the impacts of land use policies.