Towards Replication-Robust Analytics Markets

TL;DR

This paper proposes a novel analytics market framework for supervised learning that is inherently resistant to strategic data replication by leveraging causal inference techniques to refine reward calculations.

Contribution

It introduces a causal inference-based approach to design replication-robust rewards in analytics markets, addressing strategic manipulation issues.

Findings

Developed a refined coalitional game model using Pearl's do-calculus.

Derived reward functions that are robust against data replication strategies.

Enhanced market viability by mitigating strategic manipulation.

Abstract

Despite recent advancements in machine learning, in practice, relevant datasets are often distributed among market competitors who are reluctant to share. To incentivize data sharing, recent works propose analytics markets, where multiple agents share features and are rewarded for improving the predictions of others. These rewards can be computed by treating features as players in a coalitional game, with solution concepts that yield desirable market properties. However, this setup incites agents to strategically replicate their data and act under multiple false identities to increase their own revenue and diminish that of others, limiting the viability of such markets in practice. In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl's do-calculus from causal inference to refine the coalitional game by differentiating between observational and interventional conditional probabilities. As a result, we derive rewards that are replication-robust by design.