Origins of Algorithmic Instabilities in Crowdsourced Ranking
This addresses a long-standing issue in crowdsourcing systems for users seeking high-quality options, though it is incremental as it builds on existing models and focuses on two-choice scenarios.
The study tackled the problem of algorithmic instabilities in crowdsourced ranking by modeling how human judgment heuristics and option quality interact, revealing that higher-quality options may not rank first unless quality differences are large. They developed an algorithm that accounts for these heuristics, which in simulations performed better or as well as popularity-based and recency-based ranking for two-choice questions.
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.