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A road freight transport (RFT) operation involves the participation of several types of companies in its execution. The TRANSOPE model simulates the subcontracting process between 3 types of companies: Freight Forwarders (FF), Transport Companies (TC) and self-employed carriers (CA). These companies (agents) form transport outsourcing chains (TOCs) by making decisions based on supplier selection criteria and transaction acceptance criteria. Through their participation in TOCs, companies are able to learn and exchange information, so that knowledge becomes another important factor in new collaborations. The model can replicate multiple subcontracting situations at a local and regional geographic level.
The succession of n operations over d days provides two types of results: 1) Social Complex Networks, and 2) Spatial knowledge accumulation environments. The combination of these results is used to identify the emergence of new logistics clusters. The types of actors involved as well as the variables and parameters used have their justification in a survey of transport experts and in the existing literature on the subject.
As a result of a preferential selection process, the distribution of activity among agents shows to be highly uneven. The cumulative network resulting from the self-organisation of the system suggests a structure similar to scale-free networks (Albert & Barabási, 2001). In this sense, new agents join the network according to the needs of the market. Similarly, the network of preferential relationships persists over time. Here, knowledge transfer plays a key role in the assignment of central connector roles, whose participation in the outsourcing network is even more decisive in situations of scarcity of transport contracts.
We consider scientific communities where each scientist employs one of two characteristic methods: an “adequate” method (A) and a “superior” method (S). The quality of methodology is relevant to the epistemic products of these scientists, and generate credit for their users. Higher-credit methods tend to be imitated, allowing to explore whether communities will adopt one method or the other. We use the model to examine the effects of (1) bias for existing methods, (2) competence to assess relative value of competing methods, and (3) two forms of interdisciplinarity: (a) the tendency for members of a scientific community to receive meaningful credit assignment from those outside their community, and (b) the tendency to consider new methods used outside their community. The model can be used to show how interdisciplinarity can overcome the effects of bias and incompetence for the spread of superior methods.
This is an agent-based model that allows to test alternative designs for three model components. The model was built using the LUDAS design strategy, while each alternative is in line with the strategy. Using the model, it can be shown that alternative designs, though built on the same strategy, lead to different land-use patterns over time.
This is the code for the model described in an article in the International Journal of Microsimulation. Lawson (2013) ‘Modelling Household Spending Using a Random Assignment Scheme’, International Journal of Microsimulation, 6(2) Autumn 2013, 56-75.
This is an agent-based model that captures the dynamic processes related to moving from an educational system where the school a student attends is based on assignment to a neighborhood school, to one that gives households more choice among existing and newly formed public schools.
The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.
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 is the model for a paper that is based on a simulation model, programmed in Netlogo, that demonstrates changes in market structure that occur as marginal costs, demand, and barriers to entry change. Students predict and observe market structure changes in terms of number of firms, market concentration, market price and quantity, and average marginal costs, profits, and markups across the market as firms innovate. By adjusting the demand growth and barriers to entry, students can […]