Amortized Bayesian Experimental Design for Decision-Making

2024-11-04

arXiv.org

AI Summary

Existing Bayesian experimental design (BED) methods focus primarily on parameter estimation and thus fail to guarantee optimality for downstream decision-making tasks—such as clinical diagnosis or product pricing. This paper proposes a decision-aware amortized BED framework that, for the first time, directly aligns experimental design objectives with decision utility maximization. Our approach features three key contributions: (1) the first end-to-end amortized decision-aware BED model; (2) a novel Transformer-based Neural Decision Process (TNDP) architecture that jointly optimizes experiment selection and decision inference; and (3) an integrated objective combining decision-utility-driven loss with amortized variational inference, enabling end-to-end training of the policy network. Experiments across multiple tasks demonstrate significant improvements in decision accuracy and information efficiency, real-time design speed, and superior performance over conventional BED and state-of-the-art amortized methods.

Technology Category

• Machine Learning: Bayesian Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (BED), but also plays a key part in facilitating downstream decision-making. Most recent BED methods use an amortized policy network to rapidly design experiments. However, the information gathered through these methods is suboptimal for down-the-line decision-making, as the experiments are not inherently designed with downstream objectives in mind. In this paper, we present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design, whilst inferring the downstream decision, thus effectively amortizing both tasks within a unified workflow. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.

Problem

• Optimal Experimental Design
• Decision-Making Optimization
• Treatment Selection and Pricing Strategies

Innovation

• Decision-Aware Experimental Design
• Transformer Neural Decision Process (TNDP)
• Decision Support