An agent-based model of how public space conditions shape the collective formation of local sports communities in high-mobility cities. A small gathering is already exercising in an abstract, systematically configurable public space; the model asks how strangers join them and whether the loose gathering consolidates into a stable community. Three conditions are manipulated, each acting through exactly one channel: spatial legibility governs whether a passer-by notices the activity, routine compatibility governs whether a member attends in a given week, and social anchor availability governs how much one episode of co-presence builds a tie. Ties carry a continuous strength with two thresholds, so that relationships of differing strength cross their thresholds at different condition levels. Because the model does not represent the origin of an activity, dissolution is irreversible and survival is a clean binary outcome. Across 32,790 runs in eight experiments the model yields two well separated thresholds along routine compatibility — one for survival, one for consolidation — with a wide band between them in which a space is used continuously but no community forms. The tie submodel is adapted from Jin, Girvan and Newman (2001) and verified against it by docking. Robustness is established against the seeded gathering, the pre-existing social network, a joint ±20 per cent perturbation of the behavioural parameters, and the parameters of the tie machinery.

The model presented here is extensively described in the paper ‘Talk less to strangers: How homophily can improve collective decision-making in diverse teams’ (forthcoming at JASSS). A full replication package reproducing all results presented in the paper is accessible at https://osf.io/76hfm/.

Narrative documentation includes a detailed description of the model, including a schematic figure and an extensive representation of the model in pseudocode.

The model develops a formal representation of a diverse work team facing a decision problem as implemented in the experimental setup of the hidden-profile paradigm. We implement a setup where a group seeks to identify the best out of a set of possible decision options. Individuals are equipped with different pieces of information that need to be combined to identify the best option. To this end, we assume a team of N agents. Each agent belongs to one of M groups where each group consists of agents who share a common identity.
The virtual teams in our model face a decision problem, in that the best option out of a set of J discrete options needs to be identified. Every team member forms her own belief about which decision option is best but is open to influence by other team members. Influence is implemented as a sequence of communication events. Agents choose an interaction partner according to homophily h and take turns in sharing an argument with an interaction partner. Every time an argument is emitted, the recipient updates her beliefs and tells her team what option she currently believes to be best. This influence process continues until all agents prefer the same option. This option is the team’s decision.

We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

Objective of our model is to simulate the emergence and operation of a technological niches (TN) in terms of actors’ interaction. A TN can be conceived as protected socio-economic space where radical innovations are developed and tested

A modelling system to simulate Neanderthal demography and distribution in a reconstructed Western Europe for the late Middle Paleolithic.

This generic model simulates climate change adaptation in the form of resistance, accommodation, and retreat in coastal regions vulnerable to sea level rise and flooding. It tracks how population changes as households retreat to higher ground.

While the world’s total urban population continues to grow, not all cities are witnessing such growth, some are actually shrinking. This shrinkage causes several problems to emerge including population loss, economic depression, vacant properties and the contraction of housing markets. Such problems challenge efforts to make cities sustainable. While there is a growing body of work on study shrinking cities, few explore such a phenomenon from the bottom up using dynamic computational models. To overcome this issue this paper presents an spatially explicit agent-based model stylized on the Detroit Tri-county area, an area witnessing shrinkage. Specifically, the model demonstrates how through the buying and selling of houses can lead to urban shrinkage from the bottom up. The model results indicate that along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also captured (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to achieve urban sustainability.

This model uses ’satisficing’ as a model for farmers’ decision making to learn about influences of alternative decision-making models on simulation results and to exemplify a way to transform a rather theoretical concept into a feasible decision-making model for agent-based farming models.

How does the world population adapt its policies on energy when it is confronted with a climate change? This model combines a climate-economy model with adaptive agents.

This thesis presents an abstract spatial simulation model of the Maya Central Lowlands coupled human and natural system from 1000 BCE to the present day. It’s name is the Climatically Heightened but Anothropogenically Achieved Historical Kerplunk model (CHAAHK). The simulation features features virtual human groups, population centers, transit routes, local resources, and imported resources. Despite its embryonic state, the model demonstrates how certain anthropogenic characteristics of a landscape can interact with externally induced trauma and result in a prolonged period of relative sociopolitical uncomplexity. Analysis of batch simulation output suggests decreasing empirical uncertainties about ancient wetland modification warrants more investment. This first submission of CHAAHK’s code represents the simulation’s implementation that was featured in the author’s master’s thesis.