CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. Model authors can also request that their model code be peer reviewed to receive a DOI. All users of models published in the library must cite model authors when they use and benefit from their code.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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.