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.

This model is a replication model which is constructed based on the existing model used by the following article:
Brown, D.G. and Robinson, D.T., 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 11(1).
The original model is called SLUCE’s Original Model for Experimentation (SOME). In Brown and Robinson (2006)’s article, the SOME model was used to explore the impacts of heterogeneity in residential location selections on the research of urban sprawl. The original model was constructed using Objective-C language based on SWARM platform. This replication model is built by NetLogo language on NetLogo platform. We successfully replicate that model and demonstrated the reliability and replicability of it.

The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.

This simulation is of the 2003 Station Nightclub Fire and is part of the Interdependencies in Community Resilience (ICoR) project (http://www-personal.umich.edu/~eltawil/icor.html). The git contains the simulation as well as csvs of data about the fire, smoke, building, and people involved.

This model allows simulating the impacts of floods on a population. Floods are described by their intensity (flood height) and date of occurrence. Households are more or less severely hit by floods according to their geographical situation. Impacts are measured in terms of reductions in household wealth. Households may take up protection measures against floods, depending on their individual characteristics, a social network and information campaigns. If such measures are taken, flood impacts (wealth reduction) are less severe. Information campaigns increase the probability that households adopt protection measures. Two types of information campaigns are modeled: top-down policies which are the same for all households, people-centered policies, which adapt to the individual characteristics of each household.

The model measures drivers of effectiveness of risk assessments in risk workshops regarding the correctness and required time. Specifically, we model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use Bayesian networks to assess a given risk individually and as a group.

This computational model is an agent-based model (ABM) developed to investigate how repeated failures of emerging niches accumulate and influence the trajectory of socio-technical transitions. Built in AnyLogic 8.7.11, the model simulates the dynamic interactions between a dominant regime and sequential niche entrants within a two-dimensional practice space. It models alignment, movement, and competition based on technological maturity and market penetration. The model utilizes a reinforcing feedback structure linking consumer support, output, resource accumulation, and capacity development (Physical and Institutional Capacity). A complete model specification following the ODD+D (Overview, Design concepts, Details, and Decision) protocol is included in the documentation.

This is the electronic companion to the paper “Modelling Electricity Consumption in Office Buildings: An Agent Based Approach”

Several taxonomies for empirical validation have been published. Our model integrates different methods to calibrate an innovation diffusion model, ranging from simple randomized input validation to complex calibration with the use of microdata.

This model is based on Joshua Epstein’s (2001) model on development of thoughtless conformity in an artificial society of agents.