Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple This paper is a significant extension of the following article: Haifeng Zhang, Yevgeniy Vorobeychik, Joshua Letchford, and Kiran Lakkaraju. Data-driven agent-based modeling, with applications to rooftop solar adoption.Auton Agent Multi-Agent Syst heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.
Today's service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow's successful service-oriented computing approaches.This article describes a 15-year roadmap for service-oriented multiagent system research. W e've already seen service-oriented computing (SOC) take hold in cross-enterprise business settings, such as the use of FedEx and UPS shipping services in e-commerce transactions; the aggregation of hotel, car rental, and airline services by Expedia and Orbitz; or bookrating services for libraries, consumers, and bookstores. Given the widespread interest in and deployment of Web services and service-oriented architectures that are occurring in industry, the scope of SOC in business settings will expand substantially. However, the emphasis has been on the execution of individual services and not on the more important problems of how services are selected and how they can collaborate to provide higher levels of functionality. Fortunately, four major trends in computing are addressing this problem:• Online ontologies are enabling meaning and understanding, arguably the last frontier for computing, to be captured and shared in more refined ways -via the Semantic Web initiative, for example, with the development of languages and representations for marking up heterogeneous content. In an alternative approach, shared representations are emerging from the works of (millions of) independent content developers. These ontologies will form models for numerous real-world entities and systems, as well as for the meanings of documents and content. • The widespread availability of many different types of sensors and effectors (including actuators and robotic devices) will enable online entities to not only become aware of the physical world, but also to manipulate, change, and control it.These trends are the new enablers that will drive SOC and multiagent system (MAS) research in the next decade and beyond. They portend an era in which complex systems will be modeled and simulated not just to understand them, but also to form predictions and interpretations that guide the monitoring and managing of them. SOC brings to the fore additional considerations, such as the necessity of modeling autonomous and heterogeneous components in uncertain and dynamic environments. Such components must be autonomously reactive and proactive yet able to interact flexibly with other components and environments. As a result, they're best thought of as agents, which collectively form MASs. Additionally, the key MAS concepts are reflected directly in those of SOC:• Multiagent SystemsThe history of MASs mirrors the history of computing in general. In the 1980s, distributed computing over LANs and advances in expert systems motivated the initial interest in distributed agents. Because the resulting systems functioned in single organizations, cooperation was the main focus. In the...
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