Computational Model Library

Displaying 10 of 317 results for "Chelsea E Hunter" clear search

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Romulus-Catalin Damaceanu | Published Friday, May 04, 2012 | Last modified Saturday, April 27, 2013

An algorithm implemented in NetLogo that can be used for searching resources.

Peer reviewed An Agent-Based Model of Status Construction in Task Focused Groups

Andreas Flache Rafael Wittek André Grow | Published Sunday, May 18, 2014 | Last modified Tuesday, June 16, 2015

The model simulates interactions in small, task focused groups that might lead to the emergence of status beliefs among group members.

Peer reviewed lgm_ecodynamics

Colin Wren | Published Monday, April 22, 2019

This is a modification of a model published previous by Barton and Riel-Salvatore (2012). In this model, we simulate six regional populations within Last Glacial Maximum western Europe. Agents interact through reproduction and genetic markers attached to each of six regions mix through subsequent generations as a way to track population dynamics, mobility, and gene flow. In addition, the landscape is heterogeneous and affects agent mobility and, under certain scenarios, their odds of survival.

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Platas López Alejandro Guerra-Hernández | 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..

The model is intended to simulate visitor spatial and temporal dynamics, encompassing their numbers, activities, and distribution along a coastline influenced by beach landscape design. Our primary focus is understanding how the spatial distribution of services and recreational facilities (e.g., beach width, entrance location, recreational facilities, parking availability) impacts visitation density. Our focus is not on tracking the precise visitation density but rather on estimating the areas most affected by visitor activity. This comprehension allows for assessing the diverse influences of beach layouts on spatial visitor density and, consequently, on the landscape’s biophysical characteristics (e.g., vegetation, fauna, and sediment features).

An Agent-Based Model of Corruption: Micro Approach

Valery Dzutsati | Published Friday, January 30, 2015 | Last modified Sunday, September 27, 2015

Endogenous social transition from a high-corruption state to a low-corruption state, replication of Hammond 2009

This NetLogo model simulates trait-based biotic responses to climate change in an environmentally heterogeneous continent in an evolving clade, the species of which are each represented by local populations that disperse and interbreed; they also are subject to selection, genetic drift, and local extirpation. We simulated mammalian herbivores, whose success depends on tooth crown height, vegetation type, precipitation and grit. This model investigates the role of dispersal, selection, extirpation, and other factors contribute to resilience under three climate change scenarios.

Peer reviewed Horse population dynamics

Nika Galic | Published Tuesday, November 12, 2013 | Last modified Wednesday, October 29, 2014

This model investigates the link between prescribed growth in body size, population dynamics and density dependence through population feedback on available resources.

Societal Simulator v203 fertility graph fix

Tim Gooding | Published Wednesday, November 26, 2014

This is the same model as used in the article ‘Modelling Society’s Evolutionary Forces’ except the Fertility graph has been corrected. The Fertility graph was not used in the published article.

This is an agent-based model of a simple insurance market with two types of agents: customers and insurers. Insurers set premium quotes for each customer according to an estimation of their underlying risk based on past claims data. Customers either renew existing contracts or else select the cheapest quote from a subset of insurers. Insurers then estimate their resulting capital requirement based on a 99.5% VaR of their aggregate loss distributions. These estimates demonstrate an under-estimation bias due to the winner’s curse effect.

Displaying 10 of 317 results for "Chelsea E Hunter" clear search

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