Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective
Jiale Chen, Yuqing Kong, Yuxuan Lu

TL;DR
This paper introduces a new evaluation method for subjective group feedback that distinguishes between informative and uninformative responses by analyzing respondents' predictions about others' choices, improving feedback quality assessment.
Contribution
It proposes a novel metric called f-variety that effectively differentiates informative from uninformative feedback in group surveys, even with uniform statistics.
Findings
f-variety successfully separates informative and uninformative feedback
The metric decreases as the ratio of uninformative responses increases
Case studies demonstrate the method's practical effectiveness
Abstract
In the setting where a group of agents is asked a single subjective multi-choice question (e.g. which one do you prefer? cat or dog?), we are interested in evaluating the quality of the collected feedback. However, the collected statistics are not sufficient to reflect how informative the feedback is since fully informative feedback (equal affection of the choices) and fully uninformative feedback (random selection) have the same uniform statistics. Here we distinguish the above two scenarios by additionally asking for respondents' predictions about others' choices. We assume that informative respondents' predictions strongly depend on their own choices while uninformative respondents' do not. With this assumption, we propose a new definition for uninformative feedback and correspondingly design a family of evaluation metrics, called f-variety, for group-level feedback which can 1)…
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Taxonomy
TopicsSports Analytics and Performance · Experimental Behavioral Economics Studies · Decision-Making and Behavioral Economics
