The Influence of Financial Benefits and Peer Effects on the Adoption of Residential Rooftop Photovoltaic Systems

The uptake of residential photovoltaic systems is essential for energy system transformation towards carbon neutrality and decentralization. However, despite numerous campaigns to incentivize their uptake, adoption by residential homeowners is lacking behind. While countless drivers and barriers have been identified, the decision process is not fully understood. To address this gap, we developed an agent-based residential rooftop photovoltaic adoption model called PVact. Our model analyzes the interactions of potential household adopters based on their utility functions and social network, with a focus on the role of monetary evaluation and social pressure in adoption behavior. In this paper, we aim to assess the influence of monetary evaluation and social pressure in an abstract case study based on real-world data from the municipality of Leipzig, Germany. We consider stochastic dynamics through scenario analysis to investigate the influence of these factors on adoption behavior. Our results show that monetary evaluation and social pressure have a significant impact on adoption behavior. Specifically, we find shifting adoption patterns with an increased requirement for monetary returns and higher level of normative pressure required for households to act. Higher resistance against these pressure shows more stochastic variations

A modular multi-agent framework for innovation diffusion in changing business environments: conceptualization, formalization and implementation

Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation

PVactVal:A Validation Approach for Agent-based Modeling of Residential Photovoltaic Adoption

Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation. However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration. From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails. Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest

The structural basis of inter-individual differences in human behaviour and cognition

Inter-individual variability in perception, thought and action is frequently treated as a source of 'noise' in scientific investigations of the neural mechanisms that underlie these processes, and discarded by averaging data from a group of participants. However, recent MRI studies in the human brain show that inter-individual variability in a wide range of basic and higher cognitive functions - including perception, motor control, memory, aspects of consciousness and the ability to introspect - can be predicted from the local structure of grey and white matter as assessed by voxel-based morphometry or diffusion tensor imaging. We propose that inter-individual differences can be used as a source of information to link human behaviour and cognition to brain anatom