How do social biases skew online ratings?
Social Influence Distorts Ratings in Online Interfaces
This paper examines how social influence can distort ratings in online systems. It introduces a mathematical framework demonstrating that when users are influenced by existing ratings, the final average rating can significantly deviate from the true average if individual susceptibility to influence correlates with their independent opinion. This can lead to a system with multiple stable rating outcomes, making the final result unpredictable and susceptible to manipulation by early raters.
For LLM-based multi-agent systems, this research highlights the importance of considering social influence dynamics when designing agent interactions involving evaluations or ratings. It demonstrates that seemingly simple linear influence can produce complex and potentially undesirable system-level outcomes, such as path dependence and susceptibility to manipulation, if agent behaviors (influence propensity) correlate with their internal states (latent ratings). This emphasizes the need for careful design and potential mitigation strategies to ensure robust and accurate aggregate evaluations in multi-agent applications.