Origins of Algorithmic Instabilities in Crowdsourced Ranking

TL;DR

This paper investigates how human judgment heuristics and option quality interact in crowdsourcing, revealing potential instabilities in popularity rankings and proposing an inference-based algorithm to improve ranking accuracy.

Contribution

It introduces a model of judgment heuristics affecting rankings and develops an algorithm that outperforms traditional popularity-based methods under certain conditions.

Findings

Popularity rankings can be unstable without sufficient quality difference.

The proposed algorithm guarantees optimal ranking if data matches the model.

Simulations show the algorithm performs well even when data deviates from the model.

Abstract

Crowdsourcing systems aggregate decisions of many people to help users quickly identify high-quality options, such as the best answers to questions or interesting news stories. A long-standing issue in crowdsourcing is how option quality and human judgement heuristics interact to affect collective outcomes, such as the perceived popularity of options. We address this limitation by conducting a controlled experiment where subjects choose between two ranked options whose quality can be independently varied. We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options. The model reveals popularity-ranking can be unstable: unless the quality difference between the two options is sufficiently high, the higher quality option is not guaranteed to be eventually ranked on top. To rectify this instability, we create an algorithm that accounts for judgement heuristics to infer the best option and rank it first. This algorithm is guaranteed to be optimal if data matches the model. When the data does not match the model, however, simulations show that in practice this algorithm performs better or at least as well as popularity-based and recency-based ranking for any two-choice question. Our work suggests that algorithms relying on inference of mathematical models of user behavior can substantially improve outcomes in crowdsourcing systems.