Computational Model Library

This model computes the guaranteed viability kernel of a model describing the evolution of a population submitted to successive floods.
The population is described by its wealth and its adaptation rate to floods, the control are information campaigns that have a cost but increase the adaptation rate and the expected successive floods belong to given set defined by the maximal high and the minimal time between two floods.

A minimal genetic algorithm was preliminarily developed to search for the solution of an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon is particularly important in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
Since a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.

Peer reviewed A Macroeconomic Model of a Closed Economy

Ian Stuart | Published Sat May 8 15:22:16 2021 | Last modified Wed Jun 23 14:33:37 2021

This model/program presents a “three industry model” that may be particularly useful for macroeconomic simulations. The main purpose of this program is to demonstrate a mechanism in which the relative share of labor shifts between industries.

Care has been taken so that it is written in a self-documenting way so that it may be useful to anyone that might build from it or use it as an example.

This model is not intended to match a specific economy (and is not calibrated to do so) but its particular minimalist implementation may be useful for future research/development.

Team Cognition

Iris Lorscheid | Published Sun May 23 14:25:17 2021

The teamCognition model investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. The agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions.

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 candidate solutions to the problem are represented by agents, that in logo programming environment are usually known as “turtles”.

The uFUNK Model

Davide Secchi | Published Mon Aug 31 11:35:44 2020

The agent-based simulation is set to work on information that is either (a) functional, (b) pseudo-functional, (c) dysfunctional, or (d) irrelevant. The idea is that a judgment on whether information falls into one of the four categories is based on the agent and its network. In other words, it is the agents who interprets a particular information as being (a), (b), (c), or (d). It is a decision based on an exchange with co-workers. This makes the judgment a socially-grounded cognitive exercise. The uFUNK 1.0.2 Model is set on an organization where agent-employee work on agent-tasks.

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Platas López Alejandro Guerra-Hernández | Published Tue Jan 14 17:04:32 2020 | Last modified Sun Jul 26 00:26:21 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..

Agent Based Integrated Assessment Model

Marcin Czupryna | Published Sat Jun 27 08:19:59 2020

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.

Inquisitiveness in ad hoc teams

Davide Secchi | Published Sun Oct 18 22:09:14 2015 | Last modified Thu Jun 11 19:53:09 2020

This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.

IOP 2.1.2 is an agent-based simulation model designed to explore the relations between (1) employees, (2) tasks and (3) resources in an organizational setting. By comparing alternative cognitive strategies in the use of resources, employees face increasingly demanding waves of tasks that derive by challenges the organization face to adapt to a turbulent environment. The assumption tested by this model is that a successful organizational adaptation, called plastic, is necessarily tied to how employees handle pressure coming from existing and new tasks. By comparing alternative cognitive strategies, connected to ‘docility’ (Simon, 1993; Secchi, 2011) and ‘extended’ cognition (Clark, 2003, Secchi & Cowley, 2018), IOP 2.1.2 is an attempt to indicate which strategy is most suitable and under which scenario.

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