A Heuristic Approach to Explore: The Value of Perfect Information

64 Pages Posted: 21 May 2019 Last revised: 8 Feb 2021

See all articles by Shervin Shahrokhi Tehrani

Shervin Shahrokhi Tehrani

University of Texas at Dallas

Andrew T. Ching

Johns Hopkins University - Carey Business School

Date Written: April 30, 2019


How do people choose in a dynamic stochastic environment when they face uncertainty about the return of their choices? There is a growing literature that investigates the validity of boundedly rational models in this type of environment. In this research, we contribute to this literature by proposing a new heuristic decision process called Myopic-VPI, which extends the Value of Perfect Information (VPI) idea first proposed by Howard (1966) and Dearden et al. (1998, 1999) in engineering and computer science. This approach provides an intuitive and computationally tractable way to capture the value of exploring uncertain alternatives. In our approach, a decision-maker investigates the benefits of a subset of information, which can improve her myopic decision outcome. More specifically, the Myopic-VPI approach only involves ranking the alternatives and computing a one-dimensional integration to obtain the expected future value of exploration. In terms of computational costs, we show that Myopic-VPI is very attractive compared with dynamic programming, Index Strategy, and other heuristics, although its performance in accumulated rewards is not the strongest. Using individual-level scanner data, we find evidence that Myopic-VPI is able to capture actual consumers’ choices very well compared with other models under consideration (it provides the best in-sample fit, and very competitive out-of-sample fit). Our simulation and estimation results suggest that although consumers sacrifice some accumulated rewards by adopting Myopic-VPI, it allows them to save in cognitive costs. We argue that practitioners should consider Myopic-VPI as a serious alternative “as if” consumer model because of its relatively superior empirical performance in capturing actual consumer choice, and low implementation cost.

Keywords: Learning, Bounded Rationality, Heuristic Approach, Value of Perfect Information

JEL Classification: D3, D12, D83, D90, M21, M31

Suggested Citation

Shahrokhi Tehrani, Shervin and Ching, Andrew T., A Heuristic Approach to Explore: The Value of Perfect Information (April 30, 2019). Johns Hopkins Carey Business School Research Paper No. 19-05, Available at SSRN: https://ssrn.com/abstract=3386737 or http://dx.doi.org/10.2139/ssrn.3386737

Shervin Shahrokhi Tehrani

University of Texas at Dallas ( email )

800 West Campbell Rd, SM32
SOM 13.219
Dallas, TX TX 75080
United States

Andrew T. Ching (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

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