Challenge I - RETAIL ANALYTICS AND AUTOMATION OF CHOICE
Welcome to The Computational Society Challenge I - 2018

Retail Analytics and Automation of Choice (e.g., through IoT)

Computational technologies are automatic choices. For example, building on advanced technologies–Radio Frequency Identification (RFID), wireless sensor networks (WSN), and analytics, amongst others–an emergent Internet of Things (IoT) is automating consumer choices (Gubbi, Buyya, Marusic, & Palaniswami, 2013). Computational technologies are becoming a secondary choice-maker. Traditionally, consumer choice was made only by human mind that comprises neural networks (Setia 2018). In fact, choice in all living beings is driven by their neural networks. Indeed, there are similarities between many aspects of living beings’ cognitions, such as peripheral perpetual and motor processing across human and many mammalian cognitions (Anderson, 2008). For more details on these aspects, participants may read Chapters 2,3, and 4 in Setia (2018) (available for free to the computational society premium members for the first 6 months).
 


The book discusses a model for human choice in the era of computational technologies.
Chapter 2 of the book discusses the foundation concepts of choice, explaining what is choice and the information processing basis for choice. The book identifies two questions: why and how technologies influence choices and argues for the presence of a choice making process that accounts for errors in human choice-making.
Chapter 3 of the book differentiates primary and secondary choice makers. Specifically, the book uses the reductionism as a way to identify the neural networks comprising specific brain regions that engender various decision processes, within the human mind.
Chapter 4 discusses the limitations in information processing (computational) capabilities of brain regions that lead to bounded rationality-a characteristic of all living beings that leads them to make sub-optimal choices. The chapter discusses the role of secondary choice makers-the computational technologies for externalizing computations, underlining their complementing and substitutive relationship with the human brain.

Case Study #1 Topic Choices
Topic 1: Identify consumer choices in retail. What are the new analytics and automation technologies influencing these choices? How are retailers adapting operations, to accommodate the advent of these technologies?
Topic 2: Identify errors in choices that lead to a loss in value (such as due to return costs) for the retailer or the consumer. These errors may arise due to time constraints for human beings. For example, billions worth of gift cards goes unused every year (Tuttle 2012). How are analytics or automation based technologies enabling retailers to overcome these errors?
Topic 3: Complementing human choice makers: Identify scenarios where analytics and automation technologies are complementing human information processing and choices in retail . What are the advantages of using these technologies for retailers or consumers?
Topic 4: Substituting human choice-makers: Identify substitution of human activities through analytics and automation technologies in retail. Projects indicate computational technologies, such as drones or autonomous cars, will substitute human activities. What are the advantages to retailers or consumers, in using computational technologies that substitute their activities? Are there any negative implications of using these computational technologies?
Other topics: Participants are encouraged to find another topic (not listed here) in the domain of retail automation and analytics, for their case study. Please write to us at competitions@thecomsociety.com, to check if the topic may be appropriate for the competition.

  • I. Books and Research Links:

    • Brynjolfsson, E., & McAfee, A. (2014a). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: WW Norton & Company.
    • Edelman, S. (2008). Computing the mind: How the mind really works. Oxford, NY: Oxford University Press.
    • Krugman, H. E. (1971). Brain wave measures of media involvement. Journal of Advertising Research, 11(1), 3—9.
    • O’Shea, M. (2005a). The brain: A very short introduction. Oxford, NY: OUP Oxford.
    • Brynjolfsson, E., & Kahin, B. (2002). Understanding the digital economy: data, tools, and research. Cambridge, MA: MIT press.
    • Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. Journal of Economic Literature, 43(1), 9—64. https://doi.org/10.1257/0022051053737843
    • Kumar, V., & Shah, D. (2004). Building and sustaining profitable customer loyalty for the 21st century. Journal of Retailing, 80(4), 317—329. https://doi.org/https://doi.org/10.1016/j.jretai.2004.10.007
    • Van Essen, D. C., Smith, J., Glasser, M. F., Elam, J., Donahue, C. J., Dierker, D. L., Harwell, J. (2017). The brain analysis library of spatial maps and atlases (BALSA) database. Neuroimage, 144, 270—274. https://doi.org/10.1016/j.neuroimage.2016.04.002
    • Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press.
    • Simon, H. A. (1996). The sciences of the artificial. Cambridge, MA: MIT press.
    • Kurzweil, R. (2013). How to create a mind: The secret of human thought revealed. Penguin.
    • Kaplan, J. (2015). Humans need not apply: A guide to wealth and work in the age of artificial intelligence. New Haven, CT: Yale University Press.
    • March, J. G. (1978). Bounded rationality, ambiguity, and the engineering of choice. The Bell Journal of Economics, 587—608.