Computational Model Library

Displaying 10 of 327 results for "Chelsea E Hunter" clear search

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers’ decisions in the context of climate-induced water scarcity under varying utility optimization functions. The proposed framework forecasts farmers’ behavior assuming varying utility functions. The framework allows decision makers to forecast the behavior of farmers through a user-friendly platform with clear output visualization. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline.

Study area GIS data available upon request to [email protected]

Protein 2.0 is a systems model of the Norwegian protein sector designed to explore the potential impacts of carbon taxation and the emergence of cultivated meat and dairy technologies. The model simulates production, pricing, and consumption dynamics across conventional and cultivated protein sources, accounting for emissions intensity, technological learning, economies of scale, and agent behaviour. It assesses how carbon pricing could alter the competitiveness of conventional beef, lamb, pork, chicken, milk, and egg production relative to emerging cultivated alternatives, and evaluates the implications for domestic production, emissions, and food system resilience. The model provides a flexible platform for exploring policy scenarios and transition pathways in protein supply. Further details can be found in the associated publication.

A Model to Unravel the Complexity of Rural Food Security

Stefano Balbi Samantha Dobbie | Published Monday, August 22, 2016 | Last modified Sunday, December 02, 2018

An ABM to simulate the behaviour of households within a village and observe the emerging properties of the system in terms of food security. The model quantifies food availability, access, utilisation and stability.

Simulating the evolution of the human family

Paul Smaldino | Published Wednesday, November 29, 2017

The (cultural) evolution of cooperative breeding in harsh environments.

This version of the accumulated copying error (ACE) model is designed to address the following research question: how does finite population size (N) affect the coefficient of variation (CV) of a continuous cultural trait under the assumptions that the only source of copying error is visual perception error and that the continuous trait can take any positive value (i.e., it has no upper bound)? The model allows one to address this question while assuming the continuous trait is transmitted via vertical transmission, unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission. By varying the parameter, p, one can also investigate the effect of population size under a mix of vertical and non-vertical transmission, whereby on average (1-p)N individuals learn via vertical transmission and pN individuals learn via either unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission.

Frotembo

Christophe Le Page Kadiri Serge Bobo | Published Thursday, October 16, 2014

A stylized scale model to codesign with villagers an agent-based model of bushmeat hunting in the periphery of Korup National Park (Cameroon)

FOUR SEASONS

Lars G Spang | Published Tuesday, March 28, 2017

Butterflies (turtles) goes through metamorphism and moves to corresponding patches each season of the year. The number of years and seasons are monitored.

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.

The model simulates seven agents engaging in collective action and inter-network social learning. The objective of the model is to demonstrate how mental models of agents can co-evolve through a complex relationship among factors influencing decision-making, such as access to knowledge and personal- and group-level constraints.

If you have any questions about the model run, please send me an email and I will respond as soon as possible.
Under complex system perspectives, we build the multi-agent system to back-calculate this unification process of the Warring State period, from 32 states in 475 BC to 1 state (Qin) in 221 BC.

Displaying 10 of 327 results for "Chelsea E Hunter" clear search

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