A Heuristic Approach to Explore: The Value of Perfect Information
ارائه کننده: دکتر شروین شاهرخی تهرانی
زمان: چهارشنبه 12 خرداد ۱۴۰۰ ساعت 16:30 تا 18:00
آدرس برگزاری وبینار:
https://vclass.ecourse.sharif.edu/ch/business-research
:About the Speaker
My name is Shervin Shahrokhi Tehrani. I received my Ph.D in Mathematics & Marketing from the University of Toronto. I am a theorist and an empirical researcher in marketing. My first paper was about the benefit of selling the product through competitor outlets. My recent research shows that sending the right message to the right consumers can be profitable even in a competitive market where all firms do advertising targeting. The heart of my research is to construct practical and pragmatic models to explain people’s choice behavior under bounded rationality. Also, I have some work in progress related to advertising strategy in political campaigns, herding behavior, and the dialysis industry in the US.
:Abstract
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 the engineering and computer science literature. 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 approach is very attractive compared with the standard dynamic programming approach, Index Strategy, and other heuristic approaches, although its performance in accumulated rewards is not the strongest. Using individual-level scanner data, we find evidence that Myopic-VPI approach is able to capture consumers’ choices better than all the models under consideration. 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 lower computational time and implementation cost.