Computational Model Library

Peer reviewed Neighbor Influenced Energy Retrofit (NIER) agent-based model

Eric Boria | Published Fri Apr 3 02:19:28 2020

The NIER model is intended to add qualitative variables of building owner types and peer group scales to existing energy efficiency retrofit adoption models. The model was developed through a combined methodology with qualitative research, which included interviews with key stakeholders in Cleveland, Ohio and Detroit and Grand Rapids, Michigan. The concepts that the NIER model adds to traditional economic feasibility studies of energy retrofit decision-making are differences in building owner types (reflecting strategies for managing buildings) and peer group scale (neighborhoods of various sizes and large-scale Districts). Insights from the NIER model include: large peer group comparisons can quickly raise the average energy efficiency values of Leader and Conformist building owner types, but leave Stigma-avoider owner types as unmotivated to retrofit; policy interventions such as upgrading buildings to energy-related codes at the point of sale can motivate retrofits among the lowest efficient buildings, which are predominantly represented by the Stigma-avoider type of owner; small neighborhood peer groups can successfully amplify normal retrofit incentives.

The model answers the question how homophily and number of close-links in small-world network influences behavior of consumats. The results show that the more close-links the more probable the consumat follows the major behavior, but homophilly blocks the major behavior and supports survival of the minor behavior.

The study goes back to a model created in the 1990s which successfully tried to replicate the changes of the percentages of female teachers among the teaching staff in high schools (“Gymnasien”) in the German federal state of Rheinland-Pfalz. The current version allows for additional validation and calibration of the model and is accompanied with the empirical data against which the model is tested and with an analysis program especially designed to perform the analyses in the most recent journal article.

Machine Learning simulates Agent-based Model

B Furtado | Published Wed Mar 7 13:10:49 2018

This is an initial exploratory exercise done for the class @ http://thiagomarzagao.com/teaching/ipea/ Text available here: https://arxiv.org/abs/1712.04429v1
The program:
Reads output from an ABM model and its parameters’ configuration
Creates a socioeconomic optimal output based on two ABM results of the modelers choice
Organizes the data as X and Y matrices
Trains some Machine Learning algorithms

Effect of communication in irrigation games

Marco Janssen Jacopo Baggio | Published Wed Jan 14 04:08:32 2015 | Last modified Wed Aug 9 01:28:22 2017

The model includes different formulations how agents make decisions in irrigation games and this is compared with empirical data. The aim is to test different theoretical models, especially explaining effect of communication.

A model of circular migration

Anna Klabunde | Published Wed Aug 7 09:12:48 2013 | Last modified Wed Feb 17 06:09:04 2016

An empirically validated agent-based model of circular migration

Due to the large extent of the Harz National Park, an accurate measurement of visitor numbers and their spatiotemporal distribution is not feasible. This model demonstrates the possibility to simulate the streams of visitors with ABM methodology.

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

Scott Heckbert | Published Wed Jul 11 19:55:24 2012 | Last modified Tue Jul 2 17:14:49 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.

FLOSSSim: An Agent-Based Model of the Free/Libre Open Source Software (FLOSS) Development Process

Nicholas Radtke | Published Sat Dec 31 01:33:55 2011 | Last modified Sat Apr 27 20:18:32 2013

An agent-based model of the Free/Libre Open Source Software (FLOSS) development process designed around agents selecting FLOSS projects to contribute to and/or download.

This code simulates the WiFi user tracking system described in: Thron et al., “Design and Simulation of Sensor Networks for Tracking Wifi Users in Outdoor Urban Environments”. Testbenches used to create the figures in the paper are included.

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