Computational Model Library

Displaying 10 of 124 results for "Iván Briz I Godino" clear search

Using webs of replicas of Atwood’s Machine, we explore implications of the Maximum Power Principle. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.

We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.

Model of Rental Evictions in Phoenix During the Covid-19 Pandemic

Sean Bergin J M Applegate | Published Saturday, July 31, 2021 | Last modified Friday, October 15, 2021

The purpose of this model is to explore the dynamics of residency and eviction for households renting in the greater Phoenix (Arizona) metropolitan area. The model uses a representative population of renters modified from American Community Survey (ACS) data that includes demographic, housing and economic information. Each month, households pay their subsistence, rental and utility bills. If a household is unable to pay their monthly rent or utility bill they apply for financial assistance. This model provides a platform to understand the impact of various economic shock upon households. Also, the model includes conditions that occurred as a result of the Covid-19 pandemic which allows for the study of eviction mitigation strategies that were employed, such as the eviction moratorium and stimulus payments. The model allows us to make preliminary predictions concerning the number of households that may be evicted once the moratorium on evictions ends and the long-term effects on the number of evicted households in the greater Phoenix area going forward.

We build a computational model to investigate, in an evolutionary setting, a series of questions pertaining to happiness.

The agent-based simulation of innovation diffusion is based on the idea of the Bass model (1969).

The adoption of an agent is driven two parameters: its innovativess p and its prospensity to conform with others. The model is designed for a computational experiment building up on the following four model variations:

(i) the agent population it fully connected and all agents share the same parameter values for p and q
(ii) the agent population it fully connected and agents are heterogeneous, i.e. individual parameter values are drawn from a normal distribution
(iii) the agents population is embeded in a social network and all agents share the same parameter values for p and q

An Agent-Based Model of Flood Risk and Insurance

J Dubbelboer I Nikolic K Jenkins J Hall | Published Monday, July 27, 2015 | Last modified Monday, October 03, 2016

A model to show the effects of flood risk on a housing market; the role of flood protection for risk reduction; the working of the existing public-private flood insurance partnership in the UK, and the proposed scheme ‘Flood Re’.

Health and social public information office (SPUN) simulation

Emilio Sulis Manuela Vinai | Published Friday, November 06, 2015 | Last modified Saturday, November 07, 2015

The program simulate the functioning of an italian health and social public information office (SPUN) on the basis of the real data collected in the first five years of functioning.

Exploring Transitions towards Sustainable Construction

Jesus Rosales-Carreon César García-Díaz | Published Wednesday, October 30, 2013 | Last modified Saturday, January 31, 2015

This model illustrates actor interaction in the construction sector, according to information gathered in NL. It offers a simple frame to represent diverse interests, interdependencies and effects on the number of built sustainable houses.

FilterBubbles_in_Carley1991

Benoît Desmarchelier | Published Wednesday, May 21, 2025

The model is an extension of: Carley K. (1991) “A theory of group stability”, American Sociological Review, vol. 56, pp. 331-354.

The original model from Carley (1991) works as follows:
- Agents know or ignore a series of knowledge facts;
- At each time step, each agent i choose a partner j to interact with at random, with a probability of choice proportional to the degree of knowledge facts they have in common.
- Agents interact synchronously. As such, interaction happens only if the partnert j is not already busy interacting with someone else.

Forager mobility and interaction

L S Premo | Published Thursday, January 10, 2013 | Last modified Saturday, April 27, 2013

This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

Displaying 10 of 124 results for "Iván Briz I Godino" clear search

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