Computational Model Library

Displaying 10 of 554 results for "Niklas Hase" clear search

The purpose of the model is to better understand, how different factors for human residential choices affect the city’s segregation pattern. Therefore, a Schelling (1971) model was extended to include ethnicity, income, and affordability and applied to the city of Salzburg. So far, only a few studies have tried to explore the effect of multiple factors on the residential pattern (Sahasranaman & Jensen, 2016, 2018; Yin, 2009). Thereby, models using multiple factors can produce more realistic results (Benenson et al., 2002). This model and the corresponding thesis aim to fill that gap.

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 explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.

Informal risk-sharing cooperatives : ORP and Learning

Juliette Rouchier Victorien Barbet Renaud Bourlès | Published Monday, February 13, 2017 | Last modified Tuesday, May 16, 2023

The model studies the dynamics of risk-sharing cooperatives among heterogeneous farmers. Based on their knowledge on their risk exposure and the performance of the cooperative farmers choose whether or not to remain in the risk-sharing agreement.

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.

Transitions between homophilic and heterophilic modes of cooperation

Genki Ichinose | Published Sunday, June 14, 2015 | Last modified Sunday, November 14, 2021

In our model, individual agents are distributed over a two-dimensional square lattice. The agents play the prisoner’s dilemma game with their neighbors, imitate the highest strategy, and then migrate to empty sites based on their tag preference.

This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

This model examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.

The integrated and spatially-explicit ABM, called DIReC (Demography, Industry and Residential Choice), has been developed for Aberdeen City and the surrounding Aberdeenshire (Ge, Polhill, Craig, & Liu, 2018). The model includes demographic (individual and household) models, housing infrastructure and occupancy, neighbourhood quality and evolution, employment and labour market, business relocation, industrial structure, income distribution and macroeconomic indicators. DIReC includes a detailed spatial housing model, basing preference models on house attributes and multi-dimensional neighbourhood qualities (education, crime, employment etc.).
The dynamic ABM simulates the interactions between individuals, households, the labour market, businesses and services, neighbourhoods and economic structures. It is empirically grounded using multiple data sources, such as income and gender-age distribution across industries, neighbourhood attributes, business locations, and housing transactions. It has been used to study the impact of economic shocks and structural changes, such as the crash of oil price in 2014 (the Aberdeen economy heavily relies on the gas and oil sector) and the city’s transition from resource-based to a green economy (Ge, Polhill, Craig, & Liu, 2018).

Displaying 10 of 554 results for "Niklas Hase" clear search

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