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TSEC : A Framework for Online Experimentation under Experimental Constraints

Thompson sampling is a popular algorithm for tackling multi-armed bandit problems, and has been applied in a wide range of applications, from website design to portfolio optimization. In such applications, however, the number of choices (or arms) N can be large, and the data needed to make adaptive decisions require expensive experimentation. One is then faced with the constraint of experimenting on only a small subset of K≪N arms within each time period, which poses a problem for traditional Thompson sampling. We propose a new Thompson Sampling under Experimental Constraints (TSEC) method, which addresses this so-called “arm budget constraint.” TSEC makes use of a Bayesian interaction model with effect hierarchy priors, to model correlations between rewards on different arms. This fitted model is then integrated within Thompson sampling, to jointly identify a good subset of arms for experimentation and to allocate resources over these arms. We demonstrate the effectiveness of TSEC in two applications with arm budget constraints. The first is a simulated website optimization study, where TSEC shows considerable improvements over industry benchmarks. The second is a portfolio optimization application on industry-based exchange-traded funds, where TSEC provides more consistent and greater wealth accumulation over standard investment strategies.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.64 No.4 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 513-523

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EXPERIMENTAL DESIGN
BAYESIAN MODELING
PORTFOLIO OPTIMIZATION
THOMPSON SAMPLING
ONLINE EXPERIMENTATION
WEBSITE OPTIMIZATION

Info Detail Spesifik

https://doi.org/10.1080/00401706.2022.2125443

Pernyataan Tanggungjawab

Simon Mak, Yuanshuo Zhao, Lavonne Hoang, C. F. Jeff Wu

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