Optimizing for confidence - Costs and opportunities at the frontier between abstraction and reality

TL;DR

This paper investigates how computing costs influence human confidence in system behavior, exploring methods to optimize confidence through system design choices rather than solely correctness, with a focus on cost trade-offs and incremental trust-building.

Contribution

It introduces a comparative analysis of bottom-up and black-box approaches for building confidence in systems, highlighting cost trade-offs and practical methods to enhance confidence efficiently.

Findings

Bottom-up trust relies on I/O device accuracy and incurs costs.

Black-box methods can achieve confidence at lower costs.

Trade-offs exist between trust assumptions and resource expenditure.

Abstract

Is there a relationship between computing costs and the confidence people place in the behavior of computing systems? What are the tuning knobs one can use to optimize systems for human confidence instead of correctness in purely abstract models? This report explores these questions by reviewing the mechanisms by which people build confidence in the match between the physical world behavior of machines and their abstract intuition of this behavior according to models or programming language semantics. We highlight in particular that a bottom-up approach relies on arbitrary trust in the accuracy of I/O devices, and that there exists clear cost trade-offs in the use of I/O devices in computing systems. We also show various methods which alleviate the need to trust I/O devices arbitrarily and instead build confidence incrementally "from the outside" by considering systems as black box entities. We highlight cases where these approaches can reach a given confidence level at a lower cost than bottom-up approaches.