The model reproduces the spread of environmental awareness among agents and the impact of awareness level of the agents on the consumption of a resource, like energy. An agent is a household with a set of available advanced smart metering functions.

ReMoTe-S is an agent-based model of the residential mobility of Swiss tenants. Its goal is to foster a holistic understanding of the reciprocal influence between households and dwellings and thereby inform a sustainable management of the housing stock. The model is based on assumptions derived from empirical research conducted with three housing providers in Switzerland and can be used mainly for two purposes: (i) the exploration of what if scenarios that target a reduction of the housing footprint while accounting for households’ preferences and needs; (ii) knowledge production in the field of residential mobility and more specifically on the role of housing functions as orchestrators of the relocation process.

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.

The Agent-Based Wildfire Simulation Environment (ABWiSE) translates the concept of a moving fire front as a set of mobile fire agents that respond to, and interact with, vegetation, wind, and terrain. Presently, the purpose of ABWiSE is to explore how ABM, using simple interactions between agents and a simple atmospheric feedback model, can simulate emergent fire spread patterns.

A simulated approach for Personal Carbon Trading, for figuring out what effects it might have if it will be implemented in the real world. We use an artificial population with some empirical data from international literature and basic assumptions about heterogeneous energy demand. The model is not to be used as simulating the actual behavior of real populations, but a toy model to test the effects of differences in various factors such as number of agents, energy price, price of allowances, etc. It is important to adapt the model for specific countries as carbon footprint and energy demand determines the relative success of PCT.

This model simulations social and childcare provision in the UK. Agents within simulated households can decide to provide for informal care, or pay for private care, for their loved ones after they have provided for childcare needs. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. This model extends our previous simulations of social care by simulating the impact of childcare demand on social care availability within households, which is known to be a significant constraint on informal care provision.

Results show that our model replicates realistic patterns of social and child care provision, suggesting that this framework can be a valuable aid to policy-making in this area.

The agent-based model WEEM (Woodlot Establishment and Expansion Model) as described in the journal article, has been designed to make use of household socio-demographics (household status, birth, and death events of households), to better understand the temporal dynamics of woodlot in the buffer zones of Budongo protected forest reserve, Masindi district, Uganda. The results contribute to a mechanistic understanding of what determines the current gap between intention and actual behavior in forest land restoration at farm level.

The purpose of the model is to explore the influence of the design of circular business models (CBMs) on CBM viability. The model represents an Industrial Symbiosis Network (ISN) in which a processor uses the organic waste from suppliers to produce biogas and nutrient rich digestate for local reuse. CBM viability is expressed as value captured (e.g., cash flow/tonne waste/agent) and the survival of the network over time (shown in the interface).

In the model, the value captured is calculated relative to the initial state, using incineration costs as a benchmark. Moderating variables are interactions with the waste incinerator and actor behaviour factors. Actors may leave the network when the waste supply for local production is too low, or when personal economic benefits are too low. When the processor decides to leave, the network fails. Theory of planned behaviour can be used to include agent behaviour in the simulations.

A generalized organizational agent- based model (ABM) containing both formal organizational hierarchy and informal social networks simulates organizational processes that occur over both formal network ties and informal networks.

The purpose of the model is to generate the spatio-temporal distribution of bicycle traffic flows at a regional scale level. Disaggregated results are computed for each network segment with the minute time step. The human decision-making is governed by probabilistic rules derived from the mobility survey.