Computational Model Library

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Cultural Evolution of Sustainable Behaviours: Landscape of Affordances Model

Roope Oskari Kaaronen Nikita Strelkovskii | Published Wednesday, December 04, 2019 | Last modified Wednesday, December 04, 2019

This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

  1. The landscape of affordances provided by the material environment,
  2. Individual learning and habituation,
  3. Social learning and network structure,
  4. Personal states (such as habits and attitudes), and

This is a replication of the SequiaBasalto model, originally built in Cormas by Dieguez Cameroni et al. (2012, 2014, Bommel et al. 2014 and Morales et al. 2015). The model aimed to test various adaptations of livestock producers to the drought phenomenon provoked by climate change. For that purpose, it simulates the behavior of one livestock farm in the Basaltic Region of Uruguay. The model incorporates the price of livestock, fodder and paddocks, as well as the growth of grass as a function of climate and seasons (environmental submodel), the life cycle of animals feeding on the pasture (livestock submodel), and the different strategies used by farmers to manage their livestock (management submodel). The purpose of the model is to analyze to what degree the common management practices used by farmers (i.e., proactive and reactive) to cope with seasonal and interannual climate variations allow to maintain a sustainable livestock production without depleting the natural resources (i.e., pasture). Here, we replicate the environmental and livestock submodel using NetLogo.

One year is 368 days. Seasons change every 92 days. Each day begins with the growth of grass as a function of climate and season. This is followed by updating the live weight of cows according to the grass height of their patch, and grass consumption, which is determined based on the updated live weight. After consumption, cows grow and reproduce, and a new grass height is calculated. Cows then move to the patch with less cows and with the highest grass height. This updated grass height value will be the initial grass height for the next day.

Peer reviewed Dynamic Value-based Cognitive Architectures

Bart de Bruin | Published Tuesday, November 30, 2021

The intention of this model is to create an universal basis on how to model change in value prioritizations within social simulation. This model illustrates the designing of heterogeneous populations within agent-based social simulations by equipping agents with Dynamic Value-based Cognitive Architectures (DVCA-model). The DVCA-model uses the psychological theories on values by Schwartz (2012) and character traits by McCrae and Costa (2008) to create an unique trait- and value prioritization system for each individual. Furthermore, the DVCA-model simulates the impact of both social persuasion and life-events (e.g. information, experience) on the value systems of individuals by introducing the innovative concept of perception thermometers. Perception thermometers, controlled by the character traits, operate as buffers between the internal value prioritizations of agents and their external interactions. By introducing the concept of perception thermometers, the DVCA-model allows to study the dynamics of individual value prioritizations under a variety of internal and external perturbations over extensive time periods. Possible applications are the use of the DVCA-model within artificial sociality, opinion dynamics, social learning modelling, behavior selection algorithms and social-economic modelling.

Peer reviewed Dawkins Weasel

Kristin Crouse | Published Thursday, February 08, 2018 | Last modified Tuesday, February 04, 2020

Dawkins’ Weasel is a NetLogo model that illustrates the principle of evolution by natural selection. It is inspired by a thought experiment presented by Richard Dawkins in his book The Blind Watchmaker (1996).

Peer reviewed Population Genetics

Kristin Crouse | Published Thursday, February 08, 2018 | Last modified Wednesday, September 09, 2020

This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Guerra-Hernández Alejandro Platas López | Published Tuesday, January 14, 2020 | Last modified Sunday, July 26, 2020

Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..

Peer reviewed Modelling the Social Complexity of Reputation and Status Dynamics

André Grow Andreas Flache | Published Wednesday, February 01, 2017 | Last modified Wednesday, January 23, 2019

The purpose of this model is to illustrate the use of agent-based computational modelling in the study of the emergence of reputation and status beliefs in a population.

Peer reviewed MOOvPOPsurveillance

Aniruddha Belsare Matthew Gompper Joshua J Millspaugh | Published Tuesday, April 04, 2017 | Last modified Tuesday, May 12, 2020

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.

The emergence of tag-mediated altruism in structured societies

Shade Shutters David Hales | Published Tuesday, January 20, 2015 | Last modified Thursday, March 02, 2023

This abstract model explores the emergence of altruistic behavior in networked societies. The model allows users to experiment with a number of population-level parameters to better understand what conditions contribute to the emergence of altruism.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

Displaying 10 of 90 results R clear search

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