100,866 research outputs found

    Agent-Based Models and Human Subject Experiments

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    This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

    Integration of graphical, physics-based, and machine learning methods for assessment of impact and recovery of the built environment from wind hazards

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    2019 Summer.Includes bibliographical references.The interaction between a natural hazard and a community has the potential to result in a natural disaster with substantial socio-economic losses. In order to minimize disaster impacts, researchers have been improving building codes and exploring further concepts of community resilience. Community resilience refers to a community's ability to absorb a hazard (minimize impacts) and "bounce back" afterwards (quick recovery time). Therefore, the two main components in modeling resilience are: the initial impact and subsequent recovery time. With respect to a community's building stock, this entails the building damage state sustained and how long it takes to repair and reoccupy that building. In modeling these concepts, probabilistic and physics-based methods have been the traditional approach. With advancements in artificial intelligence and machine learning, as well as data availability, it may be possible to model impact and recovery differently. Most current methods are highly constrained by their topic area, for example a damage state focuses on structural loading and resistance, while social vulnerability independently focus on certain social demographics. These models currently perform independently and are then aggregated together, but with the complex connectivity available through machine learning, structural and social characteristics may be combined simultaneously in one network model. The popularity of machine learning predictive modeling across multiple different applications has risen due to the benefit of modeling complex networks and perhaps identifying critical variables that were previously unknown, or the mechanism behind how these variables interacted within the predictive problem being modeled. The research presented herein outlines a method of using artificial neural networks to model building damage and recovery times. The incorporation of graph theory to analyze the resulting models also provides insight into the "black box" of artificial intelligence and the interaction of socio-technical parameters within the concept of community resilience. The subsequent neural network models are then verified through hindcasting the 2011 Joplin tornado for individual building damage and the time it took to repair and reoccupy each building. The results of this research show viability for using these methods to model damage, but more research work may be needed to model recovery at the same level of accuracy as damage. It is therefore recommended that artificial neural networks be primarily used for problems where the variables are well known but their interactions are not as easily understood or modeled. The graphical analysis also reveals an importance of social parameters across all points in the resilience process, while the structural components remain mostly important in determining the initial impact. Final importance factors are determined for each of the variables evaluated herein. It is suggested moving forward, that modeling approaches consider integrating how a community interacts with its infrastructure, since the human components are what make a natural hazard a disaster, and tracing artificial neural network connections may provide a starting point for such integration into current traditional modeling approaches

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

    Rational bidding using reinforcement learning: an application in automated resource allocation

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    The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
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