Computational Model Library

Our aim is to show effects of group living when only low-level cognition is assumed, such as pattern recognition needed for normal functioning, without assuming individuals have knowledge about others around them or warn them actively.
The model is of a group of vigilant foragers staying within a patch, under attack by a predator. The foragers use attentional scanning for predator detection, and flee after detection. This fleeing action constitutes a visual cue to danger, and can be received non-attentionally by others if it occurs within their limited visual field. The focus of this model is on the effectiveness of this non-attentional visual information reception.
A blind angle obstructing cue reception caused by behaviour can exist in front, morphology causes a blind angle in the back. These limitations are represented by two visual field shapes. The scan for predators is all-around, with distance-dependent detection; reception of flight cues is limited by visual field shape.
Initial parameters for instance: group sizes, movement, vision characteristics for predator detection and for cue reception. Captures (failure), number of times the information reached all individuals at the same time (All-fled, success), and several other effects of the visual settings are recorded.

Peer reviewed MGA - Minimal Genetic Algorithm

Cosimo Leuci | Published Tue Sep 3 07:52:29 2019 | Last modified Thu Jan 30 08:42:08 2020

Genetic algorithms try to solve a computational problem following some principles of organic evolution. This model has educational purposes; it can give us an answer to the simple arithmetic problem on how to find the highest natural number composed by a given number of digits. We approach the task using a genetic algorithm, where the possible answers to solve the problem are represented by agents, that in logo programming environment are usually known as “turtles”.

Peer reviewed Flibs'NLogo - An elementary form of evolutionary cognition

Cosimo Leuci | Published Thu Jan 30 08:34:19 2020

Flibs’NLogo implements in NetLogo modelling environment, a genetic algorithm whose purpose is evolving a perfect predictor from a pool of digital creatures constituted by finite automata or flibs (finite living blobs) that are the agents of the model. The project is based on the structure described by Alexander K. Dewdney in “Exploring the field of genetic algorithms in a primordial computer sea full of flibs” from the vintage Scientific American column “Computer Recreations”
As Dewdney summarized: “Flibs […] attempt to predict changes in their environment. In the primordial computer soup, during each generation, the best predictor crosses chromosomes with a randomly selected flib. Increasingly accurate predictors evolve until a perfect one emerges. A flib […] has a finite number of states, and for each signal it receives (a 0 or a 1) it sends a signal and enters a new state. The signal sent by a flib during each cycle of operation is its prediction of the next signal to be received from the environment”

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Platas López | Published Tue Jan 14 17:04:32 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..

Demand planning requires processing of distributed information. In this process, individuals, their properties and interactions play a crucial role. This model is a computational testbed to investigate these aspects with respect to forecast accuracy.

Kiss Nightclub simulation

Mathieu Bourgais | Published Fri Apr 27 14:34:57 2018 | Last modified Fri Apr 5 13:06:18 2019

Model for the simulation of the Kiss Nightclub fevacuations with agents featring cognition, emotions, emotonal contagion, personality, social relations and norms.

Cooperation Under Resources Pressure (CURP)

María Pereda José Manuel Galán Ordax José Ignacio Santos Martín | Published Mon Nov 21 10:47:02 2016 | Last modified Wed Apr 25 16:56:11 2018

This is an agent-based model designed to explore the evolution of cooperation under changes in resources availability for a given population

Agent-Based Model for the Evolution of Ethnocentrism

Max Hartshorn | Published Sat Mar 24 21:34:18 2012 | Last modified Sat Apr 27 20:18:21 2013

This is an implementation of an agent based model for the evolution of ethnocentrism. While based off a model published by Hammond and Axelrod (2006), the code has been modified to allow for a more fine-grained analysis of evolutionary dynamics.

This generic model simulates climate change adaptation in the form of resistance, accommodation, and retreat in coastal regions vulnerable to sea level rise and flooding. It tracks how population changes as households retreat to higher ground.

Perceived Scientific Value and Impact Factor

Davide Secchi Stephen J Cowley | Published Wed Apr 12 03:29:15 2017 | Last modified Mon Jan 29 09:45:53 2018

The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.

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