Automating agent-based modeling : data-driven generation and application of innovation diffusion models

Simulation modeling is useful to understand the mechanisms of the diffusion of innovations, which can be used for forecasting the future of innovations. This study aims to make the identification of such mechanisms less costly in time and labor. We present an approach that automates the generation of diffusion models by: (1) preprocessing of empirical data on the diffusion of a specific innovation, taken out by the user; (2) testing variations of agent-based models for their capability of explaining the data; (3) assessing interventions for their potential to influence the spreading of the innovation. We present a working software implementation of this procedure and apply it to the diffusion of water-saving showerheads. The presented procedure successfully generated simulation models that explained diffusion data. This progresses agent-based modeling methodologically by enabling detailed modeling at relative simplicity for users. This widens the circle of persons that can use simulation to shape innovation

Editorial: Tipping points in societies

Energie and Industri

Modelling decisions on energy-efficient renovations : a review

The buildings sector accounts for more than 30% of global greenhouse gas emissions. Despite the well-known economic viability of many energy-efficient renovation measures which offer great potential for reducing greenhouse gas emissions and meeting climate protection targets, there is a relatively low level of implementation. We performed a citation network analysis in order to identify papers at the research front and intellectual base on energy-efficient renovation in four areas: technical options, understanding decisions, incentive instruments, and models and simulation. The literature was reviewed in order to understand what is needed to sufficiently increase the number of domestic energy-efficient renovations and to identify potential research gaps. Our findings show that the literature on energy-efficient renovation gained considerable momentum in the last decade, but lacks a deep understanding of the uncertainties surrounding economic aspects and non-economic factors driving renovation decisions of homeowners. The analysis indicates that the (socio-economic) energy saving potential and profitability of energy-efficient renovation measures is lower than generally expected. It is suggested that this can be accounted for by the failure to understand and consider the underpinning influences of energy-consuming behaviour in calculations. Homeowners׳ decisions to renovate are shaped by an alliance of economic and non-economic goals. Therefore, existing incentives, typically targeting the economic viability of measures, have brought little success. A deeper understanding of the decisions of homeowners is needed and we suggest that a simulation model which maps the decision-making processes of homeowners may result in refining existing instruments or developing new innovative mechanisms to tackle the situation

Reducing domestic heating demand : managing the impact of behavior-changing feedback devices via marketing

Feedback devices can be used to inform households about their energy-consumption behavior. This may persuade them to practice energy conservation. The use of feedback devices can also - via word of mouth - spread among households and thereby support the spread of the incentivized behavior, e.g. energy-efficient heating behavior. This study investigates how to manage the impact of these environmental innovations via marketing. Marketing activities can support the diffusion of devices. This study aims to identify the most effective strategies of marketing feedback devices. We did this by adapting an agent-based model to simulate the roll-out of a novel feedback technology and heating behavior within households in a virtual city. The most promising marketing strategies were simulated and their impacts were analyzed. We found it particularly effective to lend out feedback devices to consumers, followed by leveraging the social influence of well-connected individuals, and giving away the first few feedback devices for free. Making households aware of the possibility of purchasing feedback devices was found to be least effective. However, making households aware proved to be most cost-efficient. This study shows that actively managing the roll-out of feedback devices can increase their impacts on energy-conservation both effectively and cost-efficiently

Simulating the influence of socio-spatial structures on energy-efficient renovations

To meet climate protection targets it is suggested to increase the number of energy-efficient renovations. Homeowners are the main decision makers regarding renovations in their houses. It is hypothesized that socio-spatial structures affect the decision to renovate. Since it is crucial to consider all important aspects influencing the decision making of homeowners when designing policies to trigger higher energyefficient renovation activity, we developed an agent-based model to examine the influence of socio-spatial structures on these decisions. The simulation results suggest that socio-spatial structures have a considerable effect on the type of renovation measures carried out. The distribution of socio-technical attributes, population density, social network properties, and residential segregation, affects the homeowners' decision to renovate. Additional research is needed to validate the model and make it applicable to evaluate policy instruments designed to promote the diffusion of energy-efficient renovations

Energy-efficiency impacts of an air-quality feedback device in residential buildings : an agent-based modeling assessment

A key factor to energy-efficiency of heating in buildings is the behavior of households, in particular how they ventilate rooms. Energy demand can be reduced by behavioral change; devices can support this by giving feedback to consumers on their behavior. One such feedback device, called the "CO2 meter", shows indoor air-quality in the colors of a traffic light to motivate so called "shock ventilation", which is energy-efficient ventilation behavior. The following effects of the "CO2 meter" are analyzed: (1) the effect of the device on ventilation behavior within households, (2) the diffusion of "CO2 meter" to other households, and (3) the diffusion of changed behavior to households that do not adopt a "CO2 meter". An agent-based model of these processes for the city of Bottrop (Germany) was developed using a variety of data sources. The model shows that the "CO2 meter" would increase adoption of energy-efficient ventilation by c. 12% and reduce heating demand by c. 1% within 15 years. Technology diffusion was found to explain at least c. 54% of the estimated energy savings; behavior diffusion explains up to 46%. These findings indicate that the "CO2 meter" is an interesting low-cost solution to increase the energy-efficiency in residential heating

Agent-based modeling automated : data-driven generation of innovation diffusion models

Simulation modeling is useful to gain insights into driving mechanisms of diffusion of innovations. This study aims to introduce automation to make identification of such mechanisms with agent-based simulation modeling less costly in time and labor. We present a novel automation procedure in which the generation of diffusion models is automated. It comprises three phases: (1) preprocessing of empirical data on the diffusion of a specific innovation, taken out be the user; (2) automated inverse modeling of decision models from a decision model library for their capability of explaining these data; (3) policy simulation automatically assesses user-chosen policy interventions in their potential of influencing the spreading of the innovation. We present a working software implementation of this procedure. We applied this tool to data-analysis on the diffusion of a sustainable innovation, water-saving showerheads. The proposed procedure successfully generated simulation models that explained available diffusion data. This provided a proof of concept. Further, it progresses agent-based modeling by providing model validation by design and by enabling detailed bottom-down modeling at the lower complexity of top-down modeling. We believe the proposed approach can widen the circle of persons that can use simulation modeling and better understand and shape innovation