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RDMTk: A Toolkit for Risky Decision Making

RDMTk: A Toolkit for Risky Decision Making

Vinay Gavirangaswamy, Aakash Gupta, Mark Terwilliger, Ajay Gupta
Copyright: © 2019 |Volume: 13 |Issue: 4 |Pages: 38
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522564591|DOI: 10.4018/IJCINI.2019100101
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MLA

Gavirangaswamy, Vinay, et al. "RDMTk: A Toolkit for Risky Decision Making." IJCINI vol.13, no.4 2019: pp.1-38. http://doi.org/10.4018/IJCINI.2019100101

APA

Gavirangaswamy, V., Gupta, A., Terwilliger, M., & Gupta, A. (2019). RDMTk: A Toolkit for Risky Decision Making. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 13(4), 1-38. http://doi.org/10.4018/IJCINI.2019100101

Chicago

Gavirangaswamy, Vinay, et al. "RDMTk: A Toolkit for Risky Decision Making," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 13, no.4: 1-38. http://doi.org/10.4018/IJCINI.2019100101

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Abstract

Research into risky decision making (RDM) has become a multidisciplinary effort. Conversations cut across fields such as psychology, economics, insurance, and marketing. This broad interest highlights the necessity for collaborative investigation of RDM to understand and manipulate the situations within which it manifests. A holistic understanding of RDM has been impeded by the independent development of diverse RDM research methodologies across different fields. There is no software specific to RDM that combines paradigms and analytical tools based on recent developments in high-performance computing technologies. This paper presents a toolkit called RDMTk, developed specifically for the study of risky decision making. RDMTk provides a free environment that can be used to manage globally-based experiments while fostering collaborative research. The incorporation of machine learning and high-performance computing (HPC) technologies in the toolkit further open additional possibilities such as scalable algorithms and big data problems arising from global scale experiments.

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