This model simulates networking mechanisms of an empirical social network. It correlates event determinants with place-based geography and social capital production.

The application of a smartphone application to register physical encounters between individuals is considered by public health authorities, as a means to reduce the number of infections in the 2020 COVID-19 pandemic. The general idea is that continuous registration of all other smartphones in the vicinity of an individual’s smartphone potentially enables early warning of the owners of the other smartphones, in case the individual is tested positive as infected. Those other individuals can then go into isolation and be considered for testing. The purpose of the present simulation is to explore the potential effects of this application on frequencies of infection, isolation, and positive and negative infection test results.

NetPlop is a presentation editor built entirely in NetLogo, an agent-based modelling environment. The NetPlop Editor includes a variety of tools to design slide decks, and the Viewer allows these decks to be dis-played to an enraptured audience. A key feature of NetPlop is the ability to embed agent-based models. NetPlop was developed for SIGBOVIK 2021.

Simulations based on the Axelrod model and extensions to inspect the volatility of the features over time (AXELROD MODEL & Agreement threshold & two model variations based on the Social identity approach)
The Axelrod model is used to predict the number of changes per feature in comparison to the datasets and is used to compare different model variations and their performance.

Input: Real data

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Riparian forests are one of the most vulnerable ecosystems to the development of biological invasions, therefore limiting their spread is one of the main challenges for conservation. The main factors that explain plant invasions in these ecosystems are the capacity for both short- and long-distance seed dispersion, and the occurrence of suitable habitats that facilitate the establishment of the invasive species. Large floods constitute an abiotic filter for invasion.

This model simulates the spatio-temporal spread of the woody invader Gleditsia. triacanthos in the riparian forest of the National Park Esteros de Farrapos e Islas del Río Uruguay, a riparian system in the coast of the Uruguay river (South America). In this model, we represent different environmental conditions for the development of G. triacanthos, long- and short-distance spread of its fruits, and large floods as the main factor of mortality for fruit and early stages.

Field results show that the distribution pattern of this invasive species is limited by establishment, i.e. it spreads locally through the expansion of small areas, and remotely through new invasion foci. This model recreates this dispersion pattern. We use this model to derive management implications to control the spread of G. triacanthos

This code simulates individual-level, longitudinal substance use patterns that can be used to understand how cross-sectional U-shaped distributions of population substance use emerge. Each independent computational object transitions between two states: using a substance (State 1), or not using a substance (State 2). The simulation has two core components. Component 1: each object is assigned a unique risk factor transition probability and unique protective factor transition probability. Component 2: each object’s current decision to use or not use the substance is influenced by the object’s history of decisions (i.e., “path dependence”).

The model simulates interaction between internal physiological factors (e.g. energy balance) and external social factors (e.g. competition level) underlying feeding and social interaction behaviour of commercially group-housed pigs.

Three policy scenarios for urban expansion under the influences of the behaviours and decision modes of four agents and their interactions have been applied to predict the future development patterns of the Guangzhou metropolitan region.

PopComp by Andre Costopoulos 2020
[email protected]
Licence: DWYWWI (Do whatever you want with it)

I use Netlogo to build a simple environmental change and population expansion and diffusion model. Patches have a carrying capacity and can host two kinds of populations (APop and BPop). Each time step, the carrying capacity of each patch has a given probability of increasing or decreasing up to a maximum proportion.

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Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.