"Strategic" Behavior in a Strategy-proof Environment

Hassidim, Avinatan; Romm, Assaf; Shorrer, Ran I.

doi:10.1145/2940716.2940751

Cited by 53 publications

(68 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen and Sönmez find that a significant fraction of subjects (on average 36%) were not truthful under DA. This finding was corroborated in numerous follow-up studies, which also connected misrepresentation with having more information about other players' payoffs (Pais and Pintér, 2008), having lower cognitive ability (Basteck and Mantovani, 2016), and having lower priority (Hassidim, Romm and Shorrer, 2016;Li, 2016).…”

Section: Preference Misrepresentationmentioning

confidence: 55%

“…In unique settings, however, alternatives are objectively ranked, thus allowing one to detect evident preference misrepresentations. Hassidim, Romm and Shorrer (2016) study one such setting. They use data from the IPMM, a matching-with-contracts environment (Hatfield and Milgrom, 2005) in which applicants express their preferences over combinations of study track and contractual terms such as funding and degree type (Master's or Ph.D.).…”

Section: Preference Misrepresentationmentioning

confidence: 99%

See 1 more Smart Citation

The Mechanism Is Truthful, Why Aren't You?

Hassidim

Marciano

Romm

et al. 2017

American Economic Review

Self Cite

View full text Add to dashboard Cite

Traditionally, strategy-proofness is considered an extreme form of strategic simplicity. When a strategy-proof mechanism is in place, honesty is the best policy: no matter what actions others are taking, the best course of action is to report one's true preferences. For this reason, such mechanisms are often referred to as truthful, and the preferences that are reported to them are interpreted at face value.Truthfulness is thought to reduce the costs of strategizing, promote equity (by not giving advantage to more sophisticated players), provide robustness, eliminate the costs associated with the collection of information on others, and simplify the interpretation of reported preferences. 1 These desirable features led many centralized markets, especially in education and entry-level labor markets, to adopt truthful mechanisms. More specifically, the mechanisms in use are often based on the applicant-proposing version of the Deferred Acceptance (DA) algorithm, which is strategy-proof for applicants.But do truthful mechanisms actually induce truthful reporting? Recent evidence that emerged from the Israeli Psychology Master's Match (IPMM) strongly indicates that the assumption of truthful reporting is false. Additional evidence from the field and from the lab suggests that this phenomenon of preference misrepresentation is pervasive. A recurring finding is that misinterpretation rates are higher in weaker segments of markets. This motivates us to further investigate who are the individuals that misrepresent their preferences under DA and what drives this behavior.Our hope is that our findings will inform market designers and policy makers about the prevalence of misrepresentation and its systematic nature. A better understanding of when and why individuals misrepresent their preferences can guide these practitioners in designing specific and targeted interventions to promote truthful reporting. For example, if members of particular groups are more likely to (err and) misrepresent their preferences, then this behavior may have * Hassidim: Bar-Ilan

show abstract

Section: Preference Misrepresentationmentioning

confidence: 55%

Section: Preference Misrepresentationmentioning

confidence: 99%

The Mechanism Is Truthful, Why Aren't You?

Hassidim

Marciano

Romm

et al. 2017

American Economic Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Strategic behavior was studied extensively in other markets besides fair division, particularly in matching markets (Castillo and Dianat, 2016). A remarkable finding in such experiments is that people try to manipulate even when the mechanism is truthful and thus manipulation cannot help (Artemov et al, 2017;Hassidim et al, 2016Hassidim et al, , 2017Rees-Jones, 2017;Parco and Rapoport, 2004).…”

Section: Strategic Manipulationmentioning

confidence: 99%

Fair Cake-Cutting in Practice

Kyropoulou

Ortega

Segal-Halevi

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

View full text Add to dashboard Cite

Using a lab experiment, we investigate the real-life performance of envy-free and proportional cake-cutting procedures with respect to fairness and preference manipulation. We find that envy-free procedures, in particular Selfridge-Conway, are fairer and also are perceived as fairer than their proportional counterparts, despite the fact that agents very often manipulate them. Our results support the practical use of the celebrated Selfridge-Conway procedure, and more generally, of envy-free cake-cutting mechanisms.We also find that subjects learn their opponents' preferences after repeated interaction and use this knowledge to improve their allocated share of the cake. Learning reduces truth-telling behavior, but also reduces envy.

show abstract

“…Ordinal algorithms are ubiquitous in mechanism design. They are often used in real world applications, such as the National Residency Matching Program [41] (even when married couples insert their preferences together [3]), school choice applications [1], and university admittance [30,31]. One reason for this is that it is relatively easy for people to state ordinal preferences.…”

Section: Introductionmentioning

confidence: 99%

Fair Allocation with Diminishing Differences

Segal-Halevi¹,

Hassidim

Aziz

2020

jair

Self Cite

View full text Add to dashboard Cite

Ranking alternatives is a natural way for humans to explain their preferences. It is being used in many settings, such as school choice, course allocations and residency matches. In some cases, several "items" are given to each participant. Without having any information on the underlying cardinal utilities, arguing about fairness of allocation requires extending the ordinal item ranking to ordinal bundle ranking. The most commonly used such extension is stochastic dominance (SD), where a bundle X is preferred over a bundle Y if its score is better according to all additive score functions. SD is a very conservative extension, by which few allocations are necessarily fair while many allocations are possibly fair. We propose to make a natural assumption on the underlying cardinal utilities of the players, namely that the difference between two items at the top is larger than the difference between two items at the bottom. This assumption implies a preference extension which we call diminishing differences (DD), where X is preferred over Y if its score is better according to all additive score functions satisfying the DD assumption. We give a full characterization of allocations that are necessarily-proportional or possibly-proportional according to this assumption. Based on this characterization, we present a polynomial-time algorithm for finding a necessarily-DD-proportional allocation if it exists. Using simulations, we show that with high probability, a necessarily-proportional allocation does not exist but a necessarily-DD-proportional allocation exists, and moreover, that allocation is proportional according to the underlying cardinal utilities. We also consider chore allocation under the analogous conditionincreasing-differences. (Avinatan Hassidim), UNSW Sydney and Data61 CSIRO, Australia. haris.aziz@unsw.edu.au (Haris Aziz)A preliminary version of this paper was published in the proceedings of IJCAI 2017 [42]. In the present version, the proofs are substantially revised and simplified, omitted proofs are included, and there are two new sections: one on division of chores (section 9) and one on binary utilities (section 10).

show abstract

"Strategic" Behavior in a Strategy-proof Environment

Cited by 53 publications

References 8 publications

The Mechanism Is Truthful, Why Aren't You?

The Mechanism Is Truthful, Why Aren't You?

Fair Cake-Cutting in Practice

Fair Allocation with Diminishing Differences

Contact Info

Product

Resources

About