The primary purpose of this model is to explain the dynamic processes within university-centered collaboration networks, with a particular focus on the complex transformation of academic knowledge into practical projects. Based on investigations of actual research projects and a thorough literature review, the model integrates multiple drivers and influencing factors to explore how these factors affect the formation and evolution of collaboration networks under different parameter scenarios. The model places special emphasis on the impact of disciplinary attributes, knowledge exchange, and interdisciplinary collaboration on the dynamics of collaboration networks, as well as the complex mechanisms of network structure, system efficiency, and interdisciplinary interactions during project formation.
Specifically, the model aims to:
- Simulate how university research departments drive the formation of research projects through knowledge creation.
- Investigate how the dynamics of collaboration networks influence the transformation of innovative hypotheses into matured projects.
- Examine the critical roles of knowledge exchange and interdisciplinary collaboration in knowledge production and project formation.
- Provide both quantitative and qualitative insights into the interactions among academia, industry, and project outputs.

AgModel is an agent-based model of the forager-farmer transition. The model consists of a single software agent that, conceptually, can be thought of as a single hunter-gather community (i.e., a co-residential group that shares in subsistence activities and decision making). The agent has several characteristics, including a population of human foragers, intrinsic birth and death rates, an annual total energy need, and an available amount of foraging labor. The model assumes a central-place foraging strategy in a fixed territory for a two-resource economy: cereal grains and prey animals. The territory has a fixed number of patches, and a starting number of prey. While the model is not spatially explicit, it does assume some spatiality of resources by including search times.

Demographic and environmental components of the simulation occur and are updated at an annual temporal resolution, but foraging decisions are “event” based so that many such decisions will be made in each year. Thus, each new year, the foraging agent must undertake a series of optimal foraging decisions based on its current knowledge of the availability of cereals and prey animals. Other resources are not accounted for in the model directly, but can be assumed for by adjusting the total number of required annual energy intake that the foraging agent uses to calculate its cereal and prey animal foraging decisions. The agent proceeds to balance the net benefits of the chance of finding, processing, and consuming a prey animal, versus that of finding a cereal patch, and processing and consuming that cereal. These decisions continue until the annual kcal target is reached (balanced on the current human population). If the agent consumes all available resources in a given year, it may “starve”. Starvation will affect birth and death rates, as will foraging success, and so the population will increase or decrease according to a probabilistic function (perturbed by some stochasticity) and the agent’s foraging success or failure. The agent is also constrained by labor caps, set by the modeler at model initialization. If the agent expends its yearly budget of person-hours for hunting or foraging, then the agent can no longer do those activities that year, and it may starve.

Foragers choose to either expend their annual labor budget either hunting prey animals or harvesting cereal patches. If the agent chooses to harvest prey animals, they will expend energy searching for and processing prey animals. prey animals search times are density dependent, and the number of prey animals per encounter and handling times can be altered in the model parameterization (e.g. to increase the payoff per encounter). Prey animal populations are also subject to intrinsic birth and death rates with the addition of additional deaths caused by human predation. A small amount of prey animals may “migrate” into the territory each year. This prevents prey animals populations from complete decimation, but also may be used to model increased distances of logistic mobility (or, perhaps, even residential mobility within a larger territory).

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This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.

This is an empirical model described in http://dx.doi.org/10.1016/j.landurbplan.2010.05.001. The objective of the model is to simulate how the decision-making of farmers/agents with different strategies can affect the landscape structure in a region in the Netherlands.

We provide an agent-based model of collective action, informed by Granovetter (1978) and its replication model by Siegel (2009). We use the model to examine the role of ICTs in collective action under different cultural and political contexts.

Designed to capture the evolutionary forces of global society.

A logging agent builds roads based on the location of high-value hotspots, and cuts trees based on road access. A forest monitor sanctions the logger on observed infractions, reshaping the pattern of road development.

An agent-based model which explores Creativity and Urban Development

A model to show the effects of flood risk on a housing market; the role of flood protection for risk reduction; the working of the existing public-private flood insurance partnership in the UK, and the proposed scheme ‘Flood Re’.

The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.