Computational Model Library

Displaying 10 of 71 results for 'Mario Paolucci'

PR-M: The Peer Review Model

Francisco Grimaldo Mario Paolucci | Published Sunday, November 10, 2013 | Last modified Wednesday, July 01, 2015

This is an agent-based model of peer review built on the following three entities: papers, scientists and conferences. The model has been implemented on a BDI platform (Jason) that allows to perform both parameter and mechanism exploration.

Population dynamics

Marco Janssen | Published Monday, December 31, 2018

Simple population dynamics model used in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

Diffusion of innovations

Marco Janssen | Published Tuesday, January 14, 2020

3 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

Governing the commons

Marco Janssen | Published Tuesday, January 14, 2020 | Last modified Sunday, July 17, 2022

Model on the use of shared renewable resources including impact of imitation via success-bias and altruistic punishment.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

Peer reviewed Artificial Anasazi

Marco Janssen | Published Tuesday, September 07, 2010 | Last modified Saturday, April 27, 2013

Replication of the well known Artificial Anasazi model that simulates the population dynamics between 800 and 1350 in the Long House Valley in Arizona.

Consumats on a network

Marco Janssen | Published Tuesday, January 14, 2020 | Last modified Tuesday, May 30, 2023

Consumer agents make choices which products to choose using the consumat approach. In this approach agents will make choices using deliberation, repetition, imitation or social comparison dependent on the level of need satisfaction and uncertainty.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

Provided is a landscape of properties where pastoralists make decisions how much livestock they put on their property and how much to suppress fire from occuring. Rangelands can be grass dominated, or unproductive shrubb dominated. Overgrazing and fire suppresion lead to shrub dominated landscapes. What management strategies evolve, and how is this impacted by policies?
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

Spatial rangeland model

Marco Janssen | Published Tuesday, January 22, 2019 | Last modified Friday, March 04, 2022

Spatial explicit model of a rangeland system, based on Australian conditions, where grass, woody shrubs and fire compete fore resources. Overgrazing can cause the system to flip from a healthy state to an unproductive shrub state. With the model one can explore the consequences of different movement rules of the livestock on the resilience of the system.

The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

Sugarscape with spice

Marco Janssen | Published Tuesday, January 14, 2020 | Last modified Friday, September 18, 2020

This is a variation of the Sugarspace model of Axtell and Epstein (1996) with spice and trade of sugar and spice. The model is not an exact replication since we have a somewhat simpler landscape of sugar and spice resources included, as well as a simple reproduction rule where agents with a certain accumulated wealth derive an offspring (if a nearby empty patch is available).
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

The HUMan Impact on LANDscapes (HUMLAND) 2.0.0 is an enhanced version of HUMLAND 1.0.0, developed to track and quantify the intensity of various impacts on landscapes at a continental scale. The model is designed to identify the most influential factors in the transformation of interglacial vegetation, with a particular focus on the burning practices of hunter-gatherers. HUMLAND 2.0.0 incorporates a wide range of spatial datasets as both inputs and targets (expected modelling results) for simulations across Last Interglacial (~130,000–116,000 BP) and Early Holocene (~11,700–8,000 BP).

Displaying 10 of 71 results for 'Mario Paolucci'

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