Estimating the Value of Computation in Flexible Information Refinement

TL;DR

This paper presents a method to estimate the value of computational effort in flexible algorithms by building empirical models, enabling effective trade-offs between cost and benefit across various decision-making problems.

Contribution

It introduces an empirical approach to estimate the value of computation, specifically applied to influence diagram policy construction, with insights into anytime algorithm features.

Findings

Empirical models can effectively estimate computation value.

The method applies across different decision problems.

Features of the anytime algorithm provide reliable estimates.

Abstract

We outline a method to estimate the value of computation for a flexible algorithm using empirical data. To determine a reasonable trade-off between cost and value, we build an empirical model of the value obtained through computation, and apply this model to estimate the value of computation for quite different problems. In particular, we investigate this trade-off for the problem of constructing policies for decision problems represented as influence diagrams. We show how two features of our anytime algorithm provide reasonable estimates of the value of computation in this domain.