Computational Model Library

Displaying 10 of 1121 results for "Clint A Penick" clear search

Peer reviewed Vigilant sharing in a small-scale society

Marcos Pinheiro | Published Wednesday, July 22, 2020 | Last modified Wednesday, July 29, 2020

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

This model examines an important but underappreciated mechanism affecting urban segregation and integration: urban venues. The venue- an area where urbanites interact- is an essential aspect of city life that tends to influence how satisfactory any location is. We study the venue/segregation relationship by installing venues into Schelling’s classic agent-based segregation model.

ICARUS is a multi-agent compliance inspection model (ICARUS - Inspecting Compliance to mAny RUleS). The model is applicable to environments where an inspection agency, via centrally coordinated inspections, examines compliance in organizations which must comply with multiple provisions (rules). The model (ICARUS) contains 3 types of agents: entities, inspection agency and inspectors / inspections. ICARUS describes a repeated, simultaneous, non-cooperative game of pure competition. Agents have imperfect, incomplete, asymmetric information. Entities in each move (tick) choose a pure strategy (comply/violate) for each rule, depending on their own subjective assessment of the probability of the inspection. The Inspection Agency carries out the given inspection strategy.

A more detailed description of the model is available in the .nlogo file.
Full description of the model (in line with the ODD+D protocol) and the analysis of the model (including verification, validation and sensitivity analysis) can be found in the attached documentation.

Peer reviewed A financial market with zero intelligence agents

edgarkp | Published Wednesday, March 27, 2024

The model’s aim is to represent the price dynamics under very simple market conditions, given the values adopted by the user for the model parameters. We suppose the market of a financial asset contains agents on the hypothesis they have zero-intelligence. In each period, a certain amount of agents are randomly selected to participate to the market. Each of these agents decides, in a equiprobable way, between proposing to make a transaction (talk = 1) or not (talk = 0). Again in an equiprobable way, each participating agent decides to speak on the supply (ask) or the demand side (bid) of the market, and proposes a volume of assets, where this number is drawn randomly from a uniform distribution. The granularity depends on various factors, including market conventions, the type of assets or goods being traded, and regulatory requirements. In some markets, high granularity is essential to capture small price movements accurately, while in others, coarser granularity is sufficient due to the nature of the assets or goods being traded

Replication of a Social-Links-Evolution-Model

Sascha Holzhauer | Published Wednesday, December 01, 2010 | Last modified Saturday, April 27, 2013

ReSolEvo File output version

Human mate choice is a complex system

Paul Smaldino Jeffrey C Schank | Published Friday, February 08, 2013 | Last modified Saturday, April 27, 2013

A general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft-used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners.

This is the R code of the mathematical model that includes the decision making formulations for artificial agents. This code corresponds to equations 1-70 given in the paper “A Mathematical Model of The Beer Game”.

A Model of Social and Cognitive Coherence

Bruce Edmonds | Published Saturday, July 09, 2016 | Last modified Saturday, July 09, 2016

This is a model of coherency based belief within a dynamic network of individuals. Described in an invited talk on workshop on Coherence, Berlin, 9th July 2016.

A spatial model of resource-consumer dynamics

Arend Ligtenberg Guus Ten Broeke George Ak Van Voorn Jaap Molenaar | Published Wednesday, January 11, 2017 | Last modified Thursday, September 17, 2020

The model simulates agents in a spatial environment competing for a common resource that grows on patches. The resource is converted to energy, which is needed for performing actions and for surviving.

Transport simulation in a real road network

Gary Polhill Jiaqi Ge | Published Tuesday, April 17, 2018 | Last modified Tuesday, April 17, 2018

Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.

Displaying 10 of 1121 results for "Clint A Penick" clear search

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