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RDMTk: A Toolkit for Risky Decision Making

Author

Listed:
  • Vinay Gavirangaswamy

    (Western Michigan University, Kalamazoo, USA)

  • Aakash Gupta

    (University of Pennsylvania, Philadelphia, USA)

  • Mark Terwilliger

    (University of North Alabama, Florence, USA)

  • Ajay Gupta

    (Western Michigan University, Kalamazoo, USA)

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.

Suggested Citation

  • Vinay Gavirangaswamy & Aakash Gupta & Mark Terwilliger & Ajay Gupta, 2019. "RDMTk: A Toolkit for Risky Decision Making," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 13(4), pages 1-38, October.
    • Handle: RePEc:igg:jcini0:v:13:y:2019:i:4:p:1-38
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