The model reflects the predator-prey mustelid-vole population dynamics, typically observed in boreal systems. The goal of the model is to assess which intrinsic and extrinsic factors (or factor combinations) are needed for the generation of the cyclic pattern typically observed in natural vole populations. This goal is achieved by contrasting the alternative model versions by “switching off” some of the submodels in order to reflect the four combinations of the factors hypothesized to be driving vole cycles.
This model is an extended version of the matching problem including the mate search problem, which is the generalization of a traditional optimization problem. The matching problem is extended to a form of asymmetric two-sided matching problem.
This model is a replication of that described by Peterson (2002) and illustrates the ‘spread’ feedback loop type described in Millington (2013).
SimAdapt: An individual-based genetic model for simulating landscape management impacts on populations
Agent-based version of the simple search and barter economy conceived by Peter Diamond in 1982. The model is also known as Coconut Model.
A first version of a model that describes how coalitions are formed during open, networked innovation
This model is used to investigate the role of opinion leader. More specifically: the influence of ‘innovative behavior’, ‘weigth of normative influence’, ‘better product judgment’, ‘number of opinion
This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.
Experiments performed with this population extension and substantive interpretations derived from them are published in:
Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.