Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and 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 additional 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 91 results for "Ernest Aigner" clear search
We construct an agent-based model to investigate and understand the roles of green attachment, engagement in local ecological investment (i.e., greening), and social feedback.
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.
The model combines the two elements of disorganization and motivation to explore their impact on teams. Effects of disorganization on team task performance (problem solving)
The original Ache model is used to explore different distributions of resources on the landscape and it’s effect on optimal strategies of the camps on hunting and camp movement.
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 adaptation of the Relative Agreement model of opinion dynamics (Deffuant et al. 2002) extends the Meadows and Cliff (2012) implementation of this model in a manner that explores the effect of the network structure among the agents.
The model studies the dynamics of risk-sharing cooperatives among heterogeneous farmers. Based on their knowledge on their risk exposure and the performance of the cooperative farmers choose whether or not to remain in the risk-sharing agreement.
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?
Motivated by the emergence of new Peer-to-Peer insurance organizations that rethink how insurance is organized, we propose a theoretical model of decision-making in risk-sharing arrangements with risk heterogeneity and incomplete information about the risk distribution as core features. For these new, informal organisations, the available institutional solutions to heterogeneity (e.g., mandatory participation or price differentiation) are either impossible or undesirable. Hence, we need to understand the scope conditions under which individuals are motivated to participate in a bottom-up risk-sharing setting. The model puts forward participation as a utility maximizing alternative for agents with higher risk levels, who are more risk averse, are driven more by solidarity motives, and less susceptible to cost fluctuations. This basic micro-level model is used to simulate decision-making for agent populations in a dynamic, interdependent setting. Simulation results show that successful risk-sharing arrangements may work if participants are driven by motivations of solidarity or risk aversion, but this is less likely in populations more heterogeneous in risk, as the individual motivations can less often make up for the larger cost deficiencies. At the same time, more heterogeneous groups deal better with uncertainty and temporary cost fluctuations than more homogeneous populations do. In the latter, cascades following temporary peaks in support requests more often result in complete failure, while under full information about the risk distribution this would not have happened.
Displaying 10 of 91 results for "Ernest Aigner" clear search