Crowd wisdom enhanced by costly signaling in a virtual rating system

Significance Costly signaling theory from ecology posits that signals will be more honest and thus information will be accurately communicated when signaling carries a nontrivial cost. Our study combines this concept from behavioral ecology with methods of computational social science to show how costly signaling can improve crowd wisdom in human, online rating systems. Specifically, we endowed a rating widget with virtual friction to increase the time cost for reporting extreme scores. Even without any conflicts of interests or incentives to cheat, costly signaling helped obtain reliable crowd estimates of quality. Our results have implications for the ubiquitous solicitation of evaluations in e-commerce, and the approach can be generalized and tested in a variety of large-scale online communication systems.
Costly signaling theory was developed in both economics and biology and has been used to explain a wide range of phenomena. However, the theory’s prediction that signal cost can enforce information quality in the design of new communication systems has never been put to an empirical test. Here we show that imposing time costs on reporting extreme scores can improve crowd wisdom in a previously cost-free rating system. We developed an online game where individuals interacted repeatedly with simulated services and rated them for satisfaction. We associated ratings with differential time costs by endowing the graphical user interface that solicited ratings from the users with “physics,” including an initial (default) slider position and friction. When ratings were not associated with differential cost (all scores from 0 to 100 could be given by an equally low-cost click on the screen), scores correlated only weakly with objective service quality. However, introducing differential time costs, proportional to the deviation from the mean score, improved correlations between subjective rating scores and objective service performance and lowered the sample size required for obtaining reliable, averaged crowd estimates. Boosting time costs for reporting extreme scores further facilitated the detection of top performances. Thus, human collective online behavior, which is typically cost-free, can be made more informative by applying costly signaling via the virtual physics of rating devices.