The model simulates seven agents engaging in collective action and inter-network social learning. The objective of the model is to demonstrate how mental models of agents can co-evolve through a complex relationship among factors influencing decision-making, such as access to knowledge and personal- and group-level constraints.

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 simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).

This Repast Simphony model simulates genomic admixture during the farming expansion of human groups from mainland Asia into the Papuan dominated islands of Southeast Asia during the Neolithic period.

Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.

This generic individual-based model of a bird colony shows how the influence neighbour’s stress levels synchronize the laying date of neighbours and also of large colonies. The model has been used to demonstrate how this form of simulation model can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions.

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.

New theoretical agent-based model of population-wide adoption of prosocial common-pool behavior with four parameters (initial percent of adopters, pressure to change behavior, synergy from behavior, and population density); dynamics in behavior, movement, freeriding, and group composition and size; and emergence of multilevel group selection. Theoretical analysis of model’s dynamics identified six regions in model’s parameter space, in which pressure-synergy combinations lead to different outcomes: extinction, persistence, and full adoption. Simulation results verified the theoretical analysis and demonstrated that increases in density reduce number of pressure-synergy combinations leading to population-wide adoption; initial percent of contributors affects underlying behavior and final outcomes, but not size of regions or transition zones between them; and random movement assists adoption of prosocial common-pool behavior.

SimAdapt: An individual-based genetic model for simulating landscape management impacts on populations

The model simulates the diffusion of four low-carbon energy technologies among households: photovoltaic (PV) solar panels, electric vehicles (EVs), heat pumps, and home batteries. We model household decision making as the decision marking of one person, the agent. The agent decides whether to adopt these technologies. Hereby, the model can be used to study co-adoption behaviour, thereby going beyond traditional diffusion models that focus on the adop-tion of single technologies. The combination of these technologies is of particular interest be-cause (1) using the energy generated by PV solar panels for EVs and heat pumps can reduce emissions associated with transport and heating, respectively, and (2) EVs, heat pumps, and home batteries can help to integrate PV solar panels in local electricity grids by offering flexible demand (EVs and heat pumps) and energy storage (home batteries and EVs), thereby reducing grid impacts and associated upgrading costs.

The purpose of the model is to represent realistic adoption and co-adoption behaviour. This is achieved by grounding the decision model on the risks-as-feelings model (Loewenstein et al., 2001), theory from environmental and social psychology, and empirically informing agent be-haviour by survey-data among 1469 people in the Swiss region Romandie.

The model can be used to construct scenarios for the diffusion of the four low-carbon energy technologies depending on different contexts, and as a virtual experimentation environment for ex ante evaluation of policy interventions to stimulate adoption and co-adoption.