Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
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Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
Displaying 10 of 272 results for "Alexis Comber" clear search
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 NetLogo model implements the Walk Away strategy in a spatial public goods game, where individuals have the ability to leave groups with insufficient levels of cooperation.
Status-power dynamics on a playground, resulting in a status landscape with a gender status gap. Causal: individual (beauty, kindness, power), binary (rough-and-tumble; has-been-nice) or prior popularity (status). Cultural: acceptability of fighting.
A curious aspect of the Covid-19 pandemic is the clustering of outbreaks. Evidence suggests that 80\% of people who contract the virus are infected by only 19% of infected individuals, and that the majority of infected individuals faile to infect another person. Thus, the dispersion of a contagion, $k$, may be of more use in understanding the spread of Covid-19 than the reproduction number, R0.
The Virus Transmission with Super-spreaders model, written in NetLogo, is an adaptation of the canonical Virus Transmission on a Network model and allows the exploration of various mitigation protocols such as testing and quarantines with both homogenous transmission and heterogenous transmission.
The model consists of a population of individuals arranged in a network, where both population and network degree are tunable. At the start of the simulation, a subset of the population is initially infected. As the model runs, infected individuals will infect neighboring susceptible individuals according to either homogenous or heterogenous transmission, where heterogenous transmission models super-spreaders. In this case, k is described as the percentage of super-spreaders in the population and the differing transmission rates for super-spreaders and non super-spreaders. Infected individuals either recover, at which point they become resistant to infection, or die. Testing regimes cause discovered infected individuals to quarantine for a period of time.
This Agent-Based Model is designed to simulate how similarity-based partner selection (homophily) shapes the formation of co-offending networks and the diffusion of skills within those networks. Its purpose is to isolate and test the effects of offenders’ preference for similar partners on network structure and information flow, under controlled conditions.
In the model, offenders are represented as agents with an individual attribute and a set of skills. At each time step, agents attempt to select partners based on similarity preference. When two agents mutually select each other, they commit a co-offense, forming a tie and exchanging a skill. The model tracks the evolution of network properties (e.g., density, clustering, and tie strength) as well as the spread of skills over time.
This simple and theoretical model does not aim to produce precise empirical predictions but rather to generate insights and test hypotheses about the trade-off between network stability and information diffusion. It provides a flexible framework for exploring how changes in partner selection preferences may lead to differences in criminal network dynamics. Although the model was developed to simulate offenders’ interactions, in principle, it could be applied to other social processes involving social learning and skills exchange.
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This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.
How do households alter their spending patterns when they experience changes in income? This model answers this question using a random assignment scheme where spending patterns are copied from a household in the new income bracket.
This model is designed to show the effects of personality types and student organizations have on ones chance to making friendships in a university setting. As known from psychology studies, those that are extroverted have an easier chance making friendships in comparison to those that are introverted.
Once every tick a pair of students (nodes) will be randomly selected they will then have the chance to either be come friends or not (create an edge or not) based on their personality type (you are able to change what the effect of each personality is) and whether or not they are in the same club (you can change this value) then the model triggers the next tick cycle to begin.
The purpose of this model is to better understand the dynamics of a multihost pathogen in two host system comprising of high densities of domestic hosts and sympatric wildlife hosts susceptible to the pathogen.
Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. To explore such organizational dynamics, the agent-based model integrates a cognitive model, dynamic social networks, and a physical environment.
Displaying 10 of 272 results for "Alexis Comber" clear search