Comment on “Misunderstanding the Match: Do Students Create Rank Lists Based on True Preference

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Recovering Preferences From Finite Data

We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying “true” preference. Our conditions are weak and, therefore, valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, and menu choice. Our framework unifies the revealed preference tradition with models that allow for errors.

A new data-driven mathematical model dissociates attractiveness from sexual dimorphism of human faces

Human facial attractiveness is evaluated by using multiple cues. Among others, sexual dimorphism (i.e. masculinity for male faces/femininity for female faces) is an influential factor of perceived attractiveness. Since facial attractiveness is judged by incorporating sexually dimorphic traits as well as other cues, it is theoretically possible to dissociate sexual dimorphism from facial attractiveness. This study tested this by using a data-driven mathematical modelling approach. We first analysed the correlation between perceived masculinity/femininity and attractiveness ratings for 400 computer-generated male and female faces (Experiment 1) and found positive correlations between perceived femininity and attractiveness for both male and female faces. Using these results, we manipulated a set of faces along the attractiveness dimension while controlling for sexual dimorphism by orthogonalisation with data-driven mathematical models (Experiment 2). Our results revealed that perceived attractiveness and sexual dimorphism are dissociable, suggesting that there are as yet unidentified facial cues other than sexual dimorphism that contribute to facial attractiveness. Future studies can investigate the true preference of sexual dimorphism or the genuine effects of attractiveness by using well-controlled facial stimuli like those that this study generated. The findings will be of benefit to the further understanding of what makes a face attractive.

Delay Discounting in Suicidal Behavior: Myopic Preference or Inconsistent Valuation?

Prior studies sought to explain the predisposition to suicidal behavior in terms of stable preference for immediate versus delayed rewards and have generated mixed evidence. We sought to understand whether suicidal behavior is characterized by true preference for smaller immediate rewards or by inconsistent reward valuation. 632 adults (suicide attempters with depression, suicide ideators with depression, nonsuicidal participants with depression, and healthy controls) completed a delay discounting task. Recruitment took place across three sites through inpatient psychiatric units, mood disorders clinics, primary care, and advertisements. Multi-level models revealed varying valuation consistencies among groups in all three samples, with high-lethality suicide attempters exhibiting less consistent valuation that all other groups in Samples 1, 3 and less consistent valuation than the healthy controls or participants with depression in Sample 2. In contrast, group differences in preference for immediate versus delayed rewards were observed only in Sample 2 and were due to the participants with depression but without a suicide attempt history displaying a stronger preference for immediate rewards than controls. The findings were robust to confounds such as cognitive functioning and comorbidities. Seemingly impulsive choices in suicidal behavior are explained by inconsistent reward valuation rather than a true preference for immediate rewards. In a suicidal crisis, this inconsistency could result in a misestimation of the value of suicide relative to constructive alternatives and deterrents.

Modelling the expected probability of correct assignment under uncertainty

When making important decisions such as choosing health insurance or a school, people are often uncertain what levels of attributes will suit their true preference. After choice, they might realize that their uncertainty resulted in a mismatch: choosing a sub-optimal alternative, while another available alternative better matches their needs. We study here the overall impact, from a central planner’s perspective, of decisions under such uncertainty. We use the representation of Voronoi tessellations to locate all individuals and alternatives in an attribute space. We provide an expression for the probability of correct match, and calculate, analytically and numerically, the average percentage of matches. We test dependence on the level of uncertainty and location. We find that the overall mismatch is considerable even for low uncertainty—a possible concern for policy makers. We further explore a commonly used practice—allocating service representatives to assist individuals’ decisions. We show that within a given budget and uncertainty level, the effective allocation is for individuals who are close to the boundary between several Voronoi cells, but are not right on the boundary.

Misunderstanding the Match: Do Students Create Rank Lists Based on True Preferences?

Introduction: The “stable marriage” algorithm underlying the National Residency Match Program (NRMP) has been shown to create optimal outcomes when students submit true preference lists. Previous research has shown students may allow external information to affect their rank lists. The objective of this study was to determine whether medical students consistently make rank lists that reflect their true preferences. Methods: A voluntary online survey was sent to third-year students at a single midwestern medical school. Students were given hypothetical scenarios that either should or should not affect their true residency preferences and rated the importance of six factors to their final rank list. The survey was edited by a group of education scholars and revised based on feedback from a pilot with current postgraduate year 1 residents. Results: Of 175 students surveyed, 140 (80%) responded; 63% (88/140) reported that their “perceived competitiveness” would influence their rank list at least a “moderate amount. Of 135 students, 31 (23%) moved a program lower on their list if they learned they were ranked “low” by that program, while 6% (8/135) of respondents moved a program higher if they learned they were ranked “at the top of the list.” Participants responded similarly (κ = 0.71) when presented with scenarios asking what they would do vs what a classmate should do. Conclusion: Students’ hypothetical rank lists did not consistently match their true residency preferences. These results may stem from a misunderstanding of the Match algorithm. Medical schools should consider augmenting explicit education related to the NRMP Match algorithm to ensure optimal outcomes for students.

A Novel Preference Elicitation Technique Based on a Graph Model and Its Application to a Brownfield Redevelopment Conflict in China

Disputes are very common and pervasive in brownfield redevelopment projects, in which multiple stakeholders or decision-makers (DMs) strategically interact with each other with a conflict of interest. The preference information of DMs involved plays a vital role in identifying possible outcomes or resolutions for resolving a tough brownfield conflict. In this research, a novel preference ranking technique is purposefully proposed within the graph model for conflict resolution (GMCR) paradigm to effectively and accurately garner DMs’ actual preferences, in which states are ranked according to their similarities and closeness to the most and least preferred states instead of subjective option statements or weights in traditional preference ranking methods. Finally, a real-world brownfield conflict which occurred in China is utilized to show how the proposed preference ranking method can be applied for conveniently obtaining the true preference information of DMs and strategically determining the equilibria of a given dispute. The case study indicates that the novel preference elicitation approach is more objective and reasonable than the traditional option prioritization method. Moreover, there exists an equilibrium which can provide strategic advice and meaningful insights for addressing the brownfield conflict.

Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.

Awareness-Based Recommendation Toward a New Preference

In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness Based Recommendation

In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This paper presents the interactive learning system to assist positive change in the preference of a human toward the true preference, then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users.