# Decision Making of Maximizers and Satisficers Based on Collaborative Explanations

arXiv: https://arxiv.org/abs/1805.11537 · HTML: https://mlsanity.com/paper/1805.11537/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 3 of 3)

- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation](https://mlsanity.com/paper/2412.14193/) (2024)
- [The Utility of LLMs in Recommender Systems Explanation Evaluation](https://mlsanity.com/paper/2609.01627/) (2026)

## Abstract (arXiv)

Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Largely left unexplored, however, is the issue to what extent the descriptives of rating distributions influence the decision making of online consumers. We conducted a conjoint experiment to explore how different summarizations of rating distributions (i.e., in the form of the number of ratings, mean, variance, skewness or the origin of the ratings) impact users' decision making. Results from over 200 participants indicate that users are primarily guided by the mean and the number of ratings and to a lesser degree by the variance, and the origin of a rating. We also looked into the maximizing behavioral tendencies of our participants, and found that in particular participants scoring high on the Decision Difficulty subscale displayed other sensitivities regarding the way in which rating distributions were summarized than others.