We co-host workshops for researchers with the Center for International Research on the Japanese Economy (CIRJE).

Workshop information:

・The workshops are held online using Zoom (pre-registration required). Some workshops are held both in-person and online.
・All times are Japan Standard Time (JST).
・Unless otherwise mentioned, presentations are in ENGLISH.
・For details and how to participate, please see Center for International Research on the Japanese Economy (CIRJE) Microeconomics Workshop 2022.


Future Workshops

[July 26, 2022] Nina Bobkova (Rice University)

Date & Time: 2022/7/26 (Tue) 10:25-12:10

Title: Information Choice in Auctions

Abstract: The choice of an auction mechanism influences which object characteristics bidders learn about and whether the object is allocated efficiently. Some object characteristics are valued equally by all bidders and thus are inconsequential for the efficient allocation. Others matter only to certain bidders, and thus determine the bidder with the highest object value. I show that the efficient auction is the second-price auction: it induces bidders to learn exclusively about object characteristics which matter only to them. An independent private value framework arises endogenously.

[Oct. 4, 2022] Yingni Guo (Northwestern University)

Date & Time: 2022/10/4 (Tue) 10:25-12:10

Title: TBA

Abstract: TBA

Past Workshops

[July 5, 2022] Michael Zierhut (Humboldt University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/7/5 (Tue) 10:25-12:10

Title: Dynamic Inconsistency and Inefficiency of Equilibrium under Knightian Uncertainty

Abstract: This paper extends the theory of general equilibrium with Knightian uncertainty to economies with more than two dates. Agents have incomplete preferences with multiple priors `a la Bewley. These priors are updated in light of new information. Contrary to the two-date model, the market outcomes varies with choice of updating rule. We document two phenomena: First, unless agents apply the full Bayesian rule, consumption decisions may be dynamically inconsistent. Second, unless they apply the maximum-likelihood rule, ambiguous probability mass may dilate, which causes price fluctuations. Either phenomenon results in Pareto inefficient allocations. We ask whether it is possible to design one updating rule that prevent both phenomena. The answer is negative: No such rule exists. Efficiency can be restored by restricting priors: Full Bayesian and maximum-likelihood updates agree when priors are rectangular, and when ambiguity is sufficiently large, all equilibria are Pareto efficient, even if prices and allocations change over time. 

[June 28, 2022] Michihiro Kandori (The University of Tokyo)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/6/28 (Tue) 10:25-12:10

Title: Machine Learning Approach to Uncover How Players Choose Mixed Strategies (joint work with T. Hirasawa and A. Matsushita)

Abstract: How do humans behave in a situation where (i) one needs to make one’s own behavior unpredictable and (ii) one needs to predict the opponent’s behavior? Such a situation can be formulated as a game with a mixed strategy equilibrium. If humans are put in such a situation, it should be obvious that, rather than calculating and following the mixed equilibrium, they use their intuition, hunch, and some heuristics to achieve the above-mentioned goals (i) and (ii). Exactly what kind of mechanisms are employed has not been fully understood. By using a unique big experimental data set we have collected about a game with mixed strategy equilibrium, which has more than 75,000 observations, we compare conventional behavior economics models and some leading machine learning models to uncover how human behavior is determined in such a situation. Our big data enabled us to obtain a reliable comparison of the prediction powers of those models, and we found that machine learning models, most notably a version of the deep learning model LSTM, substantially outperform the leading behavioral model (EWA). Finally, we try to improve the EWA model by incorporating the insights gained by the machine learning models.

[June 21, 2022] YingHua He (Rice University)

Date & Time: 2022/6/21 (Tue) 10:25-12:10

Title: Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice (joint work with Yeon-Koo Che and Dong Woo Hahm)

Abstract: Recent evidence suggests that market participants make mistakes even in a strategically straightforward environment but seldom with significant payoff consequences. We explore the implications of such payoff-insignificant mistakes for inferring students’ preferences from school-choice data. Uncertainties arise from the use of lotteries or other sources in a typical school choice setting; they make certain mistakes more costly than others, thus making some preferences—those whose misrepresentation would be more costly and would thus be avoided by students—more reliably inferable than others. We propose a novel method of exploiting the structure of the uncertainties present in a matching environment to robustly infer student preferences under the Deferred-Acceptance mechanism. We then apply our methods to estimate student preferences through a Monte Carlo analysis capturing canonical school choice environments with single tie-breaking lotteries, and also to New York City’s high school assignment data. We then evaluate the effects of an affirmative action policy on disadvantaged and non-disadvantaged students.

[June 14, 2022] Hongyao Ma (Columbia Business School)

Date & Time: 2022/6/14 (Tue) 10:25-12:10

Title: Randomized FIFO Mechanisms (joint work with Francisco Castro, Hamid Nazerzadeh and Chiwei Yan)

Abstract: We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders’ waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

[June 7, 2022] Jonathan Libgober (University of Southern California)

Date & Time: 2022/6/7 (Tue) 10:25-12:10

Title: Learning Underspecified Models (joint work with In-Koo Cho)

Abstract: This paper considers optimal pricing with a seller who does not possess a complete description of how actions translate into payoffs. We refer to a problem with this property as underspecified. To save computational costs, she delegates the pricing decision to an algorithm, in every period over an infinite horizon. Not knowing the true demand curve, the algorithm is tasked with ensuring that the optimal price emerges with sufficiently high probability, at a rate that is uniform over the set of possible demand curves. The monopolist views the complexity-profit tradeoff lexicographically, seeking an algorithm with a minimum number of parameters subject to achieving the same long run average payoff. For a large class of feasible demand curves, the optimum is achieved by an algorithms that assumes demand is linear even if it is not. Though misspecified, this saves on computational cost, and still achieves an attractive worst-case learning rate.

[May 31, 2022] Jacob Leshno (The University of Chicago Booth School of Business)

Date & Time: 2022/5/31 (Tue) 10:25-12:10

Title: Price Discovery in Waiting Lists: A Connection to Stochastic Gradient Descent (joint work with Itai Ashlagi, Pengyu Qian, and Amin Saberi)

Abstract: Waiting lists offer agents a choice among types of items and associated non-monetary prices given by required waiting times. These non-monetary prices are endogenously determined by a tâtonnement-like price discovery process: an item’s price increases when an agent queues for it, and decreases when an item arrives and a queuing agent is assigned. By drawing a connection between price adjustments in waiting lists and the stochastic gradient descent optimization algorithm, we show that the waiting list mechanism achieves allocative efficiency minus a loss due to price fluctuations that is bounded by the granularity of price changes. We further consider a price discovery process inspired by the waiting list mechanism and show that this simple price discovery process performs well if the granularity of price changes is chosen to appropriately trade-off the speed of price adaptation and loss from price fluctuations.

[May 24, 2022] Eric Budish (The University of Chicago Booth School of Business)

Date & Time: 2022/5/24 (Tue) 9:00-10:00 

Title: The Economic Limits of Bitcoin and Anonymous, Decentralized Trust on the Blockchain

Abstract: Satoshi Nakamoto invented a new form of trust. This paper presents a three equation argument that Nakamoto’s new form of trust, while undeniably ingenious, is extremely expensive: the recurring, “flow” payments to the anonymous, decentralized compute power that maintains the trust must be large relative to the one-off, “stock” benefits of attacking the trust. This result also implies that the cost of securing the trust grows linearly with the potential value of attack — e.g., securing against a $1bn attack is 1000 times more expensive than securing against a $1m attack. Thus, if Bitcoin is to become significantly more economically useful than it is today, then the cost of maintaining Bitcoin must grow commensurately as well for it to remain trustworthy. A way out of this flow-stock argument is if both (i) the compute power used to maintain the trust is non-repurposable (as has been true for Bitcoin since mid-2013), and (ii) a successful attack would cause the economic value of the trust to collapse. However, vulnerability to economic collapse is itself a serious problem, and the model points to specific collapse scenarios. The analysis thus suggests a “pick your poison” economic critique of Bitcoin and its novel form of trust.

Cancelled [May 10, 2022] Xuan LI (The Hong Kong University of Science and Technology (HKUST))

Date & Time: 2022/5/10 (Tue) 10:25-12:10

Title: TBA

Abstract: TBA

[Apr. 28, 2022] Peng Shi (University of South California)

Date & Time: 2022/4/28 (Thu) 9:00-10:30

Title: Optimal Matchmaking Strategy in Two-Sided Marketplaces

Abstract: Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies: some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder); some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork); others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking, as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of the above matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of the above matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms.

[Apr. 26, 2022] Youjin Hahn (Yonsei University)

Date & Time: 2022/4/26 (Tue) 10:25-12:10

Title: Can STEM Learning Opportunities Reshape Gender Attitudes for Girls?: Field Evidence from Tanzania (joint work with So Yoon Ahn and Semee Yoon)

Abstract: We study how educational opportunities change adolescents’ gender attitudes in Tanzania, using an experiential education program focused on STEM subjects. After the intervention, girls’ gender attitudes became more progressive by 0.29 standard deviations, but boys’ gender attitudes did not change. Perceived improvement in the labor market opportunities appears to be an important channel to explain the result. The intervention also increased girls’ weekly study hours and boosted their interests in STEM-related subjects and occupations. Our results show that providing STEM-related educational opportunities to girls in developing countries can be an effective way of improving their gender attitudes.

[Apr. 12, 2022] Inga Deimen (University of Arizona)

Date & Time: 2022/4/12 (Tue) 10:25-12:10

Title: Communication in the Shadow of Catastrophe (joint work with Dezsö Szalay)

Abstract: We study the role of risk in strategic information transmission. We show that an increased likelihood of extreme states – heavier tails – decreases the amount of information transmission and makes it optimal to alter the mode of decision-making from communication to simple delegation. Moreover, the worst-case losses under communication increase relative to the worst-case losses under delegation when the tails get heavier.

[Apr. 5, 2022] Giovanni Compiani (The University of Chicago Booth School of Business)

Date & Time: 2022/4/5 (Tue) 10:25-12:10

Title: A Method to Estimate Discrete Choice Models that is Robust to Consumer Search

Abstract: We state suffcient conditions under which choice data suffices to identify preferences when consumers are not fully informed about attributes of goods. Standard estimators undervalue hidden attributes, as consumers will be unresponsive to some variation in those attributes. If consumers search goods in order of the component of utility observable to them without search, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds under both full information and a range of search models and is thus robust to what consumers know when they choose. Our approach can be used to recover preferences from choices made by imperfectly informed consumers, to test for full information, and to forecast how consumers will respond to information. We verify in a lab experiment that our approach succeeds in forecasting the response to new information and assessing the value of that information when consumers engage in costly search. In data from Expedia, our method identifies which attribute was not immediately visible to consumers in search results, and we then use the model to compute the value of information about the hidden attribute.