Computational Model Library

Displaying 10 of 1137 results for "Sjoukje A Osinga" clear search

Manipulate[
Module[{fDot, mDot, poly, roots, stableRoots, rStar, rIso,
endPointStar, endPointIso},(1. Define the System Dynamics)
fDot = phi1(f/m) - phi2(m/f);
mDot = mu1(f/m) - mu2(m/f);
(*2. Find the Equilibrium Ratio r=f/

Land-Livelihood Transitions

Nicholas Magliocca Daniel G Brown Erle C Ellis | Published Monday, September 09, 2013 | Last modified Friday, September 13, 2013

Implemented as a virtual laboratory, this model explores transitions in land-use and livelihood decisions that emerge from changing local and global conditions.

Multi Asset Variable Network Stock Market Model

Matthew Oldham | Published Monday, September 12, 2016 | Last modified Tuesday, October 10, 2017

An artifcal stock market model that allows users to vary the number of risky assets as well as the network topology that investors forms in an attempt to understand the dynamics of the market.

While the world’s total urban population continues to grow, not all cities are witnessing such growth, some are actually shrinking. This shrinkage causes several problems to emerge including population loss, economic depression, vacant properties and the contraction of housing markets. Such problems challenge efforts to make cities sustainable. While there is a growing body of work on study shrinking cities, few explore such a phenomenon from the bottom up using dynamic computational models. To overcome this issue this paper presents an spatially explicit agent-based model stylized on the Detroit Tri-county area, an area witnessing shrinkage. Specifically, the model demonstrates how through the buying and selling of houses can lead to urban shrinkage from the bottom up. The model results indicate that along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also captured (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to achieve urban sustainability.

Will it spread or not? The effects of social influences and network topology on innovation diffusion

Sebastiano Delre | Published Monday, October 24, 2011 | Last modified Saturday, April 27, 2013

This models simulates innovation diffusion curves and it tests the effects of the degree and the direction of social influences. This model replicates, extends and departs from classical percolation models.

MayaSim: An agent-based model of the ancient Maya social-ecological system

Scott Heckbert | Published Wednesday, July 11, 2012 | Last modified Tuesday, July 02, 2013

MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

Peer reviewed Garbage can model Excel reconstruction

Smarzhevskiy Ivan | Published Tuesday, August 19, 2014 | Last modified Tuesday, July 30, 2019

Reconstruction of the original code M. Cohen, J. March, and J. Olsen garbage can model, realized by means of Microsoft Office Excel 2010

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).

This study employs a hierarchical cross-departmental ABM to explore the question: How and to what extent are the land use policies enforced when assessed against the real-world land use pattern? Specifically, two sub-questions are of interest: How can real-world policy interactions be abstracted into the behavior across hierarchical governmental departments in the model? How can the level of enforcement for each land use policy be quantified under these interactions? We build three hierarchical agents—the central level, the local level that incorporates three departments, and the village collective level—with simplified but plausible processes of land use change, with levels of enforcement of different land use policies as key parameters. We calibrate the model using a genetic algorithm to determine those parameters and answer our research question. We further applied the model to simulate potential land use changes and investigate the implications of different policy options. The results are expected to provide insights into the intricate relationships shaping land use processes, contributing to evidence-based decision-making in urban planning and sustainable land use management.

01a ModEco V2.05 – Model Economies – In C++

Garvin Boyle | Published Monday, February 04, 2013 | Last modified Friday, April 14, 2017

Perpetual Motion Machine - A simple economy that operates at both a biophysical and economic level, and is sustainable. The goal: to determine the necessary and sufficient conditions of sustainability, and the attendant necessary trade-offs.

Displaying 10 of 1137 results for "Sjoukje A Osinga" clear search

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