What is stable: the large but coordinated change during a diffusion or the small but constant and uncoordinated changes during a dynamic equilibrium? This agent-based model of a diffusion creates output that reveal insights for system stability.

This is an agent-based model, simulating wolf (Canis Lupus) reappearance in the Netherlands. The model’s purpose is to allow researchers to investigate the reappearance of wolves in the Netherlands and the possible effect of human interference. Wolf behaviour is modelled according to the literature. The suitability of the Dutch landscape for wolf settlement has been determined by Lelieveld (2012) [1] and is transformed into a colour-coded map of the Netherlands. The colour-coding is the main determinant of wolf settlement. Human involvement is modelled through the public opinion, which varies according to the size, composition and behaviour of the wolf population.

[1] Lelieveld, G.: Room for wolf comeback in the Netherlands, (2012).

This code simulates individual-level, longitudinal substance use patterns that can be used to understand how cross-sectional U-shaped distributions of population substance use emerge. Each independent computational object transitions between two states: using a substance (State 1), or not using a substance (State 2). The simulation has two core components. Component 1: each object is assigned a unique risk factor transition probability and unique protective factor transition probability. Component 2: each object’s current decision to use or not use the substance is influenced by the object’s history of decisions (i.e., “path dependence”).

Model explains both the final state and the dynamics of the development process of the wine sector in the Małopolska region in Poland. Model admits heterogeneous agents (regular farms,large and small vineyards).

This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.

Experiments performed with this population extension and substantive interpretations derived from them are published in:

Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.

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We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.

The purpose of this model is to explore the effects of different power structures on a cross-functional team’s prosocial decision making. Are certain power distributions more conducive to the team making prosocial decisions?

This model inspects the performance of firms as the product attribute space changes, which evolves as a consequence of firms’ actions. Firms may create new product variants by dragging demand from other existing variants. Firms decide whether to open new product variants, to invade existing ones, or to keep their variant portfolio. At each variant there is a Cournot competition each round. Competition is nested since many firms compete at many variants simultaneously, affecting firm composition at each location (variant).

After the Cournot outcomes, at each round firms decide whether to (i) keep their existing product variant niche, (ii) invade an existing variant, (iii) create a new variant, or (iv) abandon a variant. Firms’ profits across their niche take into consideration the niche-width cost and the cost of opening a new variant.

This model was utilized for the simulation in the paper titled Effect of Network Homophily and Partisanship on Social Media to “Oil Spill” Polarizations. It allows you to examine whether oil spill polarization occurs through people’s communication under various conditions.

・Choose the network construction conditions you’d like to examine from the “rewire-style” chooser box.
・Select the desired strength of partisanship from the “partisanlevel” chooser box. You can also set the strength manually in the code tab.
・You can set the number of dynamic topics using the “number-of-topics” slider.
・Use the “divers-of-opinion” slider to set the number of preference types for each dynamic topic.
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This model illustrates how to apply a simple cluster-analysis on points distributed around 5 centers. The result can be displayed in shades of a color or a spectacular colored pattern.