A New Methodology for the Study of Dynamic Decision-Making with a Continuous Unknown Parameter

70 Pages Posted: 1 Sep 2020

See all articles by Hao Zhang

Hao Zhang

UBC Sauder School of Business

Date Written: August 21, 2020

Abstract

A new methodology is presented to solve an important model of dynamic decision-making with a continuous unknown parameter (or state). The methodology centers on the concepts of “continuation-value function” (which gives the expected value-to-go from every possible state under a feasible policy) and “efficient frontier” of such functions in each period. When the model primitives can be described through a family of basis functions, e.g. polynomials, a continuation-value function retains that property and can be fully represented by a basis weight vector. The efficient frontiers of the weight vectors can be constructed through backward induction, which leads to an essential reduction of problem complexity and enables an exact solution for small-sized problems. A set of approximation methods based on the new methodology are developed to tackle larger problems. The methodology is also extended to the multi-dimensional (multi-parameter) setting, which features the important problem of contextual multi-armed bandits with linear expected rewards. We demonstrate that our approximation algorithm for that problem has a clear edge over three benchmark algorithms in the challenging learning environment with many actions and relatively short horizons.

Keywords: dynamic programming, information learning, dynamic pricing with learning, multi-armed bandits, partially observable Markov decision processes

JEL Classification: C11, C44, C61, D83

Suggested Citation

Zhang, Hao, A New Methodology for the Study of Dynamic Decision-Making with a Continuous Unknown Parameter (August 21, 2020). Available at SSRN: https://ssrn.com/abstract=3679048 or http://dx.doi.org/10.2139/ssrn.3679048

Hao Zhang (Contact Author)

UBC Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada

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