Daniel E. Fragiadakis
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
510-589-2832
danielf1@stanford.edu












Curriculum Vitae

Fields:
Experimental Economics, Market Design
Microeconomic Theory
Expected Graduation Date:
June, 2014


Thesis Committee:
Muriel Niederle (Primary):
niederle@stanford.edu

Alvin Roth:
alroth@stanford.edu

Fuhito Kojima:
fkojima@stanford.edu

Job Market Paper
Improving Welfare in Assignment Problems: an Experimental Investigation (pdf) (with Peter Troyan).
Many institutions face the task of allocating objects (such as university dormitories) to individuals (students) without the use of monetary transfers.  A common solution to this problem is the Random Serial Dictatorship (RSD): agents are ordered randomly, and one at a time, each is assigned her favorite good according to her submitted preferences.  While RSD provides each agent with a dominant strategy of ranking objects truthfully, it may produce socially undesirable outcomes whereby it is possible to make some agents substantially better off at only a small cost to others.  In this paper, we study the prospect of raising welfare in assignment problems by incentivizing agents to report goods they value similarly as indifferent.  Specifically, we modify RSD by ordering agents earlier who report more indifference, a method similar to that used by the Stanford Graduate School of Business to assign MBA students to educational trips abroad.  While theory predicts weak welfare gains in equilibrium, this requires agents to calculate nontrivial best response strategies that deviate from simple truth-telling. In practice, it is unknown whether agents will be able to find these equilibria and, if they cannot, what the welfare implications of using such mechanisms will be. Motivated by these observations, we run a lab experiment where we find  that many agents follow natural heuristics that entail reporting indifferences between objects that are similar in value. Average earnings increase significantly compared to RSD, but the way in which indifference is rewarded can alter the variance in earnings.  This suggests that institutions that use RSD can benefit by rewarding indifference, but should choose how to do so carefully.

Other Research Papers
Identifying Predictable Players: Relating Behavioral Types and Predictable Subjects (pdf) (with Muriel Niederle and Daniel Knoepfle).
Behavioral game theory models are useful in organizing data of strategic decision making. However, are subjects classified as behavioral types more predictable than unclassified subjects? Alternatively, how many predictable subjects await new behavioral models to describe them? In our experiments, subjects play two-person guessing games against random opponents and are subsequently asked to replicate or best respond to their past choices. We find that existing behavioral types capture two thirds of strategic subjects, i.e. individuals who can best respond. However, there is additional room for non-strategic rule-of-thumb strategies to describe subjects that can merely replicate their actions.

Market Design under Distributional Constraints: Diversity in School Choice and Other Applications (pdf) (with Peter Troyan).
Distributional constraints are important in many market design settings. Prominent examples include the minimum manning requirements at each branch in military cadet matching and diversity in school choice, whereby school districts impose constraints on the demographic distribution of students at each school. Standard assignment mechanisms implemented in practice are unable to accommodate all of these constraints. This leads policymakers to resort to ad-hoc solutions that eliminate blocks of seats ex-ante (before agents submit their preferences) to ensure that all constraints are satisfied ex-post.

We show that these solutions ignore important information contained in the submitted preferences, resulting in avoidable inefficiency. We introduce a new class of dynamic quotas mechanisms that allow the institutional quotas to dynamically adjust to the submitted preferences of the agents. We show how a wide class of mechanisms commonly used in the field can be adapted to our dynamic quotas framework. Focusing in particular on a new dynamic quotas deferred acceptance (DQDA) mechanism, we show that DQDA Pareto dominates current solutions. While it may seem that allowing the quotas to depend on the submitted preferences would compromise the strategyproofness of deferred acceptance, we show that this is not the case: as long as the order in which the quotas are adjusted is determined exogenously to the preferences, DQDA remains strategyproof. Thus, policymakers can be confident that efficiency will be improved without introducing perverse incentives. Simulations with school choice data are used to quantify the potential efficiency gains.


Strategyproof Matching with Minimum Quotas (pdf) (with Peter Troyan, Atsushi Iwasaki, Suguru Ueda, and Makoto Yokoo). Under Review. (A preliminary version appeared as an extended abstract in the Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012).)
We study matching markets in which institutions may have minimum (in addition to the more standard maximum) quotas. We introduce two new classes of strategyproof mechanisms that allow for minimum quotas as an explicit input, and show that our mechanisms improve welfare relative to current approaches. Because of an incompatibility between standard fairness and nonwastefulness axioms in the presence of minimum quotas, we introduce new second-best axioms and show that they are satisfied by our mechanisms. Last, we use computer simulations to quantify (i) the number of agents who will strictly prefer our mechanisms and (ii) how far they are from the first-best axioms of fairness and nonwastefulness. Combining both the theoretical and simulation results, we argue that our mechanisms should improve the performance of matching markets with minimum quota constraints in practice.

Research in Progress
Identifying Predictable Players Across Games (with Muriel Niederle and Daniel Knoepfle).
Non-equilibrium behavioral game theory models have been useful in organizing experimental data in strategic decision-making studies.  An appropriate next step is whether these models can identify strategic players and make out of sample predictions of their behavior.  Fragiadakis et. al (2013) control subjects' beliefs by having subjects play against random participants, and then against their past selves as in Ivanov et al., who found it quite difficult to use these models to make out of sample predictions in games of incomplete information.  Fragiadakis et. al (2013) adapt their paradigm to simple two-player guessing games of complete information of the form in Costa-Gomes and Crawford (2006).  Most subjects that best respond to their past behavior are those that are "classified" (using the methods in Costa-Gomes and Crawford) in phase I and most classified subjects in phase I are correctly predicted in phase II.  In this paper, we invite the same subjects back to the lab to play additional two-person games of complete information.  We apply the same two-phase paradigm and test for type stability across games.

Teaching Experience
Teaching Evaluations (pdf)
While I enjoy research, teaching is no less important to me.  My solid foundations in mathematics and economics and ability to communicate clearly have led me to be a very effective educator.  I design my lesson plans with exceptional care and go the extra mile by creating supplementary notes and practice problems.  In class, I deliver the material at an appropriate pace, speaking with confidence and clarity that earns immediate respect.  At the same time, I do so in a friendly and engaging way, spurring class participation.  I am always available for my students outside of class as well; I put them first.  All of my experiences as a teacher have been wonderful for me, and I believe the students have felt similarly.  They have rated me very highly in evaluations.  For instance, at Stanford, I received the Outstanding Teaching Assistant Award twice, an award granted to only 6 out of over 50 teaching assistants each term.  Lastly, not only have I taught graduate and undergraduate students, in college I helped run a program at the alternative high school in Berkeley, CA, where I taught computer programming to underrepresented minority students.