Computational Model Library

Displaying 10 of 896 results for "Jan Buurma" clear search

The model is a combination of a spatially explicit, stochastic, agent-based model for wild boars (Sus scrofa L.) and an epidemiological model for the Classical Swine Fever (CSF) virus infecting the wild boars.

The original model (Kramer-Schadt et al. 2009) was used to assess intrinsic (system immanent host-pathogen interaction and host life-history) and extrinsic (spatial extent and density) factors contributing to the long-term persistence of the disease and has further been used to assess the effects of intrinsic dynamics (Lange et al. 2012a) and indirect transmission (Lange et al. 2016) on the disease course. In an applied context, the model was used to test the efficiency of spatiotemporal vaccination regimes (Lange et al. 2012b) as well as the risk of disease spread in the country of Denmark (Alban et al. 2005).

References: See ODD model description.

Landscape connectivity and predator–prey population dynamics

Jacopo A. Baggio | Published Thursday, November 10, 2011 | Last modified Saturday, April 27, 2013

A simple model to assess the effect of connectivity on interacting species (i.e. predator-prey type)

Knowledge Space Model for Opinion Dynamics

Shane Mueller | Published Thursday, September 28, 2017 | Last modified Thursday, September 20, 2018

Knowledge Space model of Opinion Dynamics.

Peer reviewed Routes & Rumours 0.1.1

Jakub Bijak Martin Hinsch Oliver Reinhardt | Published Tuesday, July 12, 2022

Routes & Rumours is an agent-based model of (forced) human migration. We model the formation of migration routes under the assumption that migrants have limited geographical knowledge concerning the transit area and rely to a large degree on information obtained from other migrants.

This code simulates the WiFi user tracking system described in: Thron et al., “Design and Simulation of Sensor Networks for Tracking Wifi Users in Outdoor Urban Environments”. Testbenches used to create the figures in the paper are included.

WWHW is an agent-based model designed to allow the exploration of the emergence, resilience and evolution of cooperative behaviours in hunter-fisher-gatherer societies.

Agent based approach to the class of the Integrated Assessment Models. An agent-based model (ABM) that focuses on the energy sector and climate relevant facts in a detailed way while being complemented with consumer goods, labour and capital markets to a minimal necessary extent.

Wolf-sheep predation Netlogo model, extended, with foresight

Guido Fioretti andreapolicarpi | Published Wednesday, September 16, 2020 | Last modified Tuesday, April 13, 2021

This model is an extension of the Netlogo Wolf-sheep predation model by U.Wilensky (1997). This extended model studies several different behavioural mechanisms that wolves and sheep could adopt in order to enhance their survivability, and their overall impact on global equilibrium of the system.

The SIM-VOLATILE model is a technology adoption model at the population level. The technology, in this model, is called Volatile Fatty Acid Platform (VFAP) and it is in the frame of the circular economy. The technology is considered an emerging technology and it is in the optimization phase. Through the adoption of VFAP, waste-treatment plants will be able to convert organic waste into high-end products rather than focusing on the production of biogas. Moreover, there are three adoption/investment scenarios as the technology enables the production of polyhydroxyalkanoates (PHA), single-cell oils (SCO), and polyunsaturated fatty acids (PUFA). However, due to differences in the processing related to the products, waste-treatment plants need to choose one adoption scenario.

In this simulation, there are several parameters and variables. Agents are heterogeneous waste-treatment plants that face the problem of circular economy technology adoption. Since the technology is emerging, the adoption decision is associated with high risks. In this regard, first, agents evaluate the economic feasibility of the emerging technology for each product (investment scenarios). Second, they will check on the trend of adoption in their social environment (i.e. local pressure for each scenario). Third, they combine these two economic and social assessments with an environmental assessment which is their environmental decision-value (i.e. their status on green technology). This combination gives the agent an overall adaptability fitness value (detailed for each scenario). If this value is above a certain threshold, agents may decide to adopt the emerging technology, which is ultimately depending on their predominant adoption probabilities and market gaps.

This simulation model is to simulate the emergence of technological innovation processes from the hypercycles perspective.

Displaying 10 of 896 results for "Jan Buurma" clear search

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