[Paper Review] Consequential Ranking Algorithms and Long-term Welfare

Behzad Tabibian, Vicenç Gómez+3 more|arXiv (Cornell University)|May 13, 2019

Mobile Crowdsensing and Crowdsourcing44 references4 citations

TL;DR

This paper proposes consequential ranking algorithms that optimize for long-term welfare by modeling user dynamics via Markov decision processes, enabling trade-offs between immediate utility and societal costs like misinformation and incivility. It introduces a gradient-based method to learn parameterized rankings that reduce harmful content by up to 30% with minimal impact on original utility, validated on real Reddit data.

ABSTRACT

Ranking models are typically designed to provide rankings that optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed rankings, from fueling the spread of misinformation and increasing polarization to degrading social discourse. Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones? In this paper, we first introduce a joint representation of rankings and user dynamics using Markov decision processes. Then, we show that this representation greatly simplifies the construction of consequential ranking models that trade off the immediate utility and the long-term welfare. In particular, we can obtain optimal consequential rankings just by applying weighted sampling on the rankings provided by models that maximize measures of immediate utility. However, in practice, such a strategy may be inefficient and impractical, specially in high dimensional scenarios. To overcome this, we introduce an efficient gradient-based algorithm to learn parameterized consequential ranking models that effectively approximate optimal ones. We showcase our methodology using synthetic and real data gathered from Reddit and show that ranking models derived using our methodology provide ranks that may mitigate the spread of misinformation and improve the civility of online discussions.

Motivation & Objective

• To address the long-term societal harms of ranking systems, such as misinformation spread and increased polarization, which current models fail to anticipate.
• To design ranking algorithms that explicitly account for the consequences of their proposed rankings over time.
• To balance fidelity to immediate-utility-optimized models with improvements in long-term welfare metrics like civility and information quality.
• To develop an efficient, gradient-based learning method for scalable deployment in high-dimensional settings.
• To empirically validate the approach on real Reddit data, demonstrating reductions in misinformation and uncivility without sacrificing ranking utility.

Proposed method

• Models ranking and user dynamics jointly using Markov decision processes (MDPs), representing sequences of rankings and evolving item features.
• Applies Bellman's principle of optimality to derive an analytical solution for optimal consequential rankings via weighted sampling on original rankings.
• Uses exponential weighting based on the cost to long-term welfare to prioritize rankings that reduce harmful content.
• Develops a gradient-based algorithm to learn parameterized consequential ranking models (e.g., Plackett-Luce) that approximate optimal solutions efficiently.
• Employs a loss function that trades off fidelity to the original ranking model (via KL divergence) and long-term welfare (e.g., uncivility or misinformation scores).
• Trains models on real Reddit data using batches of comment sequences, with features including time-to-first-comment, uncivility, and unreliability scores.

Experimental results

Research questions

• RQ1 Can ranking models be designed to anticipate and mitigate long-term societal harms such as misinformation and incivility?
• RQ2 How can the trade-off between immediate utility and long-term welfare be formally modeled and optimized in ranking systems?
• RQ3 What is the impact of consequential ranking models on real-world online discussion quality and content integrity?
• RQ4 How efficiently can optimal consequential rankings be approximated in high-dimensional settings?
• RQ5 To what extent can parameterized ranking models reduce harmful content while preserving utility from original ranking systems?

Key findings

• Consequential ranking models reduced uncivility in top-ranked comments by up to 30% with minimal loss in immediate utility, as measured on Reddit data.
• The models also reduced misinformation in top positions by up to 30% without significant deviation from the original reverse-chronological ranking.
• The gradient-based learning algorithm effectively approximated optimal consequential rankings, enabling practical deployment in high-dimensional settings.
• The analytical solution via weighted sampling was validated as a benchmark, showing that optimal rankings could be derived from original models using welfare-based reweighting.
• The method maintained high fidelity to original rankings (measured via KL divergence) while significantly improving long-term welfare metrics.
• Results were consistent across multiple test submissions, with 95% confidence intervals confirming robustness in performance.