Seminar Schedule

The EconCS Group holds an Economics & Computer Science research seminar each semester. Spring '24 meetings are at 1:30 - 2:30 PM on Fridays in SEC 1.413. Seminar Coordinators are Shirley Zhang & Tao Lin. SEC 1.413 is on ground level at the NW corner of SEC, which is open to the public. 

map

Google Maps link to SEC

Full schedule for Spring 2024: 

spring 2024 seminar schedule

Full schedule:

  • 9-15-2023: Intro meeting - no talk
  • 9-22-2023: Juan Perdomo, Harvard postdoc
  • 9-29-2023: Inauguration - no talk
  • 10-6-2023: Wei Tang, Columbia postdoc
  • 10-13-2023: Sruthi Gorantla, IISc Bangalore PhD
  • 10-20-2023: Kangning Wang, Stanford postdoc
  • 10-26, 11:00 - 12:00: Hongyao Ma, Columbia AP
  • 10-27-2023: Stephen Bates, MIT professor
  • 11-3-2023: Benjamin Laufer, Cornell Tech PhD
  • 11-10-2023: Stephen McAleer, CMU postdoc
  • 11-17-2023: Hanna Halaburda, NYU professor
  • 11-24-2023: Thanksgiving - no talk
  • 12-1-2023: Jessie Finocchiaro, Harvard postdoc
  • 12-8-2023: Stefano Albrecht, University of Edinburgh

Calendar Spring 2024

2024 Mar 22

Contract Design in Combinatorial Settings

1:30pm to 2:30pm

Location: 

SEC 1.413

Speaker: Tomer Ezra (Harvard)

Title: Contract Design in Combinatorial Settings

Abstract: We study two combinatorial settings of the contract design problem, in which a principal wants to delegate the execution of a costly task. In the first setting, the principal delegates the task to an agent that can take any subset of a given set of unobservable actions, each of which has an associated cost. The principal receives a reward which is a combinatorial function of the actions taken by the agent. In the second setting, we study the single-principal multi-...

Read more about Contract Design in Combinatorial Settings
2024 Mar 07

Learning Bayes-Nash Equilibria in Auctions and Contests

1:30pm to 2:30pm

Location: 

SEC 3.303 (third floor)

Speaker: Martin Bichler

Abstract: Equilibrium problems in Bayesian auction games can be described as systems of differential equations. Depending on the model assumptions, these equations might be such that we do not have an exact mathematical solution theory. The lack of analytical or numerical techniques with guaranteed convergence for the equilibrium problem has plagued the field and limited equilibrium analysis to rather simple auction models such as single-object auctions. Recent progress in equilibrium learning led to algorithms that find approximate...

Read more about Learning Bayes-Nash Equilibria in Auctions and Contests
2024 Mar 04

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

1:30pm to 2:30pm

Location: 

SEC 2.122 + 2.123 (second floor).

Speaker: Brian Zhang (CMU)

Title: Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

Abstract: We introduce a unified framework for understanding optimal equilibria in general games by explicitly augmenting the game with a mediator who can communicate with players. By varying restrictions governing the communication, the framework covers a wide family of solution concepts and problems including---but not limited to---communication...

Read more about Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
2024 Feb 29

Strategic Classification: Learning With Data That 'Behaves'

1:30pm to 2:30pm

Location: 

SEC 3.301+3.302

Speaker: Nir Rosenfeld (Technion)

Title: Strategic Classification: Learning With Data That 'Behaves'

Abstract: The growing success of machine learning across a wide range of domains and applications has made it appealing to be used also as a tool for informing decisions about humans. But humans are not your conventional input: they have goals, beliefs, and aspirations, and take action to promote their own self-interests. Given that standard learning methods are not designed to handle inputs that "behave", it is natural to ask: how should we...

Read more about Strategic Classification: Learning With Data That 'Behaves'
2024 Feb 23

Algorithmic Collusion by Large Language Models & Equilibrium of Data Markets with Externality

1:30pm to 2:30pm

Location: 

SEC 1.413

1st Speaker: Sara Fish (Harvard)

 

Title: Algorithmic Collusion by Large Language Models


Abstract: The rise of algorithmic pricing raises concerns that algorithms might collude to maximize firm profits, to the detriment of consumers. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically on OpenAI's GPT-4. We find that (1) LLM-based pricing...

Read more about Algorithmic Collusion by Large Language Models & Equilibrium of Data Markets with Externality
2024 Feb 09

Eliciting Information without Verification from Humans and Machines

1:30pm to 2:30pm

Location: 

SEC 1.413

Speaker: Yuqing Kong (Peking University)

Title: Eliciting Information without Verification from Humans and Machines


Abstract: Many application domains rely on eliciting high-quality (subjective) information. This presentation will talk about how to elicit and aggregate information from both human and machine participants, especially when the information cannot be directly verified. The first part of the talk presents a mechanism, DMI-...

Read more about Eliciting Information without Verification from Humans and Machines
2024 Feb 02

High-stakes decisions from low-quality data:
AI decision-making for planetary health

1:30pm to 2:30pm

Location: 

SEC 1.413

Speaker: Lily Xu (Harvard University)


Abstract: Planetary health is an emerging field which recognizes the inextricable link between human health and the health of our planet. Our planet’s growing crisis include biodiversity loss, with animal population sizes declining by an average of 70% since 1970, and maternal mortality, with 1 in 49 girls in low-income countries dying from complications in pregnancy or birth. Underlying these global challenges is the urgent need to effectively... Read more about High-stakes decisions from low-quality data:
AI decision-making for planetary health
2024 Jan 26

Eliciting Honest Information From Authors Using Sequential Review

1:30pm to 2:30pm

Location: 

SEC 1.413

Speaker: Yichi Zhang (University of Michigan)

Talk Title: Eliciting Honest Information From Authors Using Sequential Review
Abstract: In the setting of conference peer review, the conference aims to accept high-quality papers and reject low-quality papers based on noisy review scores. A recent work proposes the isotonic mechanism, which can elicit the ranking of paper qualities from an author with multiple submissions to help improve the conference’s decisions. However, the isotonic mechanism relies on the assumption that the author’s...

Read more about Eliciting Honest Information From Authors Using Sequential Review