Showing posts with label computer science. Show all posts
Showing posts with label computer science. Show all posts

Sunday, November 9, 2025

Economics and CS (AI+ML) in Ithaca in June: call for papers

 Here's the announcement and call for papers from the Econometric Society

2026 ESIF Economics and AI+ML Meeting

June 16 - 17, 2026
Ithaca, United States

2026 ESIF Economics and AI+ML Meeting (ESIF-AIML2026)

June 16-17, 2026
Cornell University Department of Computer Science and Department of Economics

We are pleased to announce the Economics and AI+ML of the Econometric Society Interdisciplinary Frontiers (ESIF) conferences. The 2026 ESIF Economics and AI+ML Meeting (ESIF-AIML2026) hosted by Cornell University Department of Computer Science, Department of Economics, and Center for Data Science for Enterprise and Society, will take place on June 16-17, 2026, in Ithaca, NY.

The Program Committee co-Chairs and host organizers are Francesca Molinari and Éva Tardos, from Cornell University.

Important dates

Submissions open: November 3, 2025
Paper Submission Period: November 3, 2025 – January 17, 2026
Decision Notification Deadline: March 22, 2026
Registration Period (for presenters) March 22, 2026-April 5, 2026
Preliminary Program Announcement: April 26, 2026

Plenary Lectures

David Blei
Columbia University

Mingming Chen
Google

Annie Liang
Northwestern University

Aaron Roth
University of Pennsylvania

Stefan Wager
Stanford University

 

 

Thursday, October 23, 2025

Algorithmic Collusion Without Threats

 Quanta magazine reports on a recent paper on algorithmic collusion (in which a big class of "dumb" strategies can settle on high prices):

The Game Theory of How Algorithms Can Drive Up Prices
Recent findings reveal that even simple pricing algorithms can make things more expensive
  by Ben Brubaker 

" how can regulators ensure that algorithms set fair prices? Their traditional approach won’t work, as it relies on finding explicit collusion. “The algorithms definitely are not having drinks with each other,” said Aaron Roth(opens a new tab), a computer scientist at the University of Pennsylvania.

...

" if you want to guarantee fair prices, why not just require sellers to use algorithms that are inherently incapable of expressing threats?

"In a recent paper(opens a new tab), Roth and four other computer scientists showed why this may not be enough. They proved that even seemingly benign algorithms that optimize for their own profit can sometimes yield bad outcomes for buyers. “You can still get high prices in ways that kind of look reasonable from the outside,” said Natalie Collina(opens a new tab), a graduate student working with Roth who co-authored the new study.

...

"“Without some notion of a threat or an agreement, it’s very hard for a regulator to come in and say, ‘These prices feel wrong,’” said Mallesh Pai(opens a new tab), an economist at Rice University. “That’s one reason why I think this paper is important.”

...

"So, what can regulators do? Roth admits he doesn’t have an answer. It wouldn’t make sense to ban no-swap-regret algorithms: If everyone uses one, prices will fall. But a simple nonresponsive strategy might be a natural choice for a seller on an online marketplace like Amazon, even if it carries the risk of regret.

“One way to have regret is just to be kind of dumb,” Roth said. “Historically, that hasn’t been illegal.”

#######

And here's the paper:

Algorithmic Collusion Without Threats 

There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors who refuse to support high prices, and these strategies can be automatically learned. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to optimize their payoff. Is this intuition correct? Would preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can emerge even when both players are using algorithms which do not encode threats, and which optimize for their own revenue. We study sequential pricing games in which a first mover deploys an algorithm and then a second mover optimizes within the resulting environment. We show that if the first mover deploys any algorithm with a no-regret guarantee, and then the second mover even approximately optimizes within this now static environment, monopoly-like prices arise. The result holds for any no-regret learning algorithm deployed by the first mover and for any pricing policy of the second mover that obtains them profit at least as high as a random pricing would -- and hence the result applies even when the second mover is optimizing only within a space of non-responsive pricing distributions which are incapable of encoding threats. In fact, there exists a set of strategies, neither of which explicitly encode threats that form a Nash equilibrium of the simultaneous pricing game in algorithm space, and lead to near monopoly prices. This suggests that the definition of ``algorithmic collusion'' may need to be expanded, to include strategies without explicitly encoded threats.

 

 



 

  

Saturday, September 13, 2025

Kidney exchange in Operations Research (and elsewhere)

 Kidney exchange is an important medical innovation that has given rise to literatures not only in medicine but in economics, computer science and operations research. (That diversity of literatures is related to the interdisciplinary growth of market design.)

Here's a new survey of the OR literature on kidney exchange.

Mathijs Barkel, Rachael Colley, Maxence Delorme, David Manlove, William Pettersson, Operational research approaches and mathematical models for kidney exchange: A literature survey and empirical evaluation,  European Journal of Operational Research, 2025, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2025.08.059.


Abstract: Kidney exchange is a transplant modality that has provided new opportunities for living kidney donation in many countries around the world since 1991. It has been extensively studied from an Operational Research (OR) perspective since 2004. This article provides a comprehensive literature survey on OR approaches to fundamental computational problems associated with kidney exchange over the last two decades. We also summarise the key integer linear programming (ILP) models for kidney exchange, showing how to model optimisation problems involving only cycles and chains separately. This allows new combined ILP models, not previously presented, to be obtained by amalgamating cycle and chain models. We present a comprehensive empirical evaluation involving all combined models from this paper in addition to bespoke software packages from the literature involving advanced techniques. This focuses primarily on computation times for 49 methods applied to 4320 problem instances of varying sizes that reflect the characteristics of real kidney exchange datasets, corresponding to over 200,000 algorithm executions. We have made our implementations of all cycle and chain models described in this paper, together with all instances used for the experiments, and a web application to visualise our experimental results, publicly available.
Keywords: Combinatorial Optimisation; OR in health services; Kidney paired donation; Cycle packing; Computational experiments

 

Thursday, July 31, 2025

COMSOC 2025 workshop on Computational Social Choice, Vienna, Austria, 17-19 September 2025.

 COMSOC 2025 is the 10th workshop in the interdisciplinary workshop series on Computational Social Choice. It will take place at the TU Wien, Vienna, Austria, from 17-19 September 2025.

We welcome not only researchers and professionals but also students, newcomers, and individuals interested in the fields of Economics, Political & Social Sciences, and Computer Science. 

The poster for the conference (including the large program committee) is here.

Tuesday, January 7, 2025

National Medals of Science and Technology (including Cynthia Dwork for differential privacy)

 In one of the final acts of his administration, President Biden celebrates 25 distinguished scientists and engineers. (I'm particularly glad to see Cynthia Dwork recognized for her work on differential privacy.)

 Forbes has the story:

Biden Names 25 Recipients Of National Medals Of Science, Technology, by Michael T. Nietzel

In a statement from the White House, Biden said, “those who earn these awards embody the promise of America by pushing the boundaries of what is possible. These trailblazers have harnessed the power of science and technology to tackle challenging problems and deliver innovative solutions for Americans and for communities around the world.”

...



"The 14 recipients of the National Medal of Science are:

    Richard B. Alley, the Evan Pugh University Professor of Geosciences at Pennsylvania State University. Alley researches the great ice sheets to help predict future changes in climate and sea levels.
    Larry Martin Bartels, University Distinguished Professor of Political Science and Law and the May Werthan Shayne Chair of Public Policy and Social Science at Vanderbilt University. His scholarship focuses on public opinion, public policy, election science, and political economy.
    Bonnie L. Bassler, Squibb Professor in Molecular Biology and chair of the Department of Molecular Biology at Princeton University, for her research on the molecular mechanisms that bacteria use for intercellular communication.
    Angela Marie Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering at MIT and a member of the Koch Institute for Integrative Cancer Research. She was honored for designing materials for applications in solar cells, batteries, and medical imaging.
    Helen M. Blau, Donald E. and Delia B. Baxter Foundation Professor and the Director of the Baxter Laboratory for Stem Cell Biology at Stanford University for her research on muscle diseases, regeneration and aging, including the use of stem cells for tissue repair.
    Emery Neal Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, was recognized for his work revealing how anesthesia affects the brain.
    John O. Dabiri, Centennial Chair Professor at the California Institute of Technology, in the Graduate Aerospace Laboratories and Mechanical Engineering. His research focuses on fluid mechanics and flow physics, with an emphasis on topics relevant to biology, energy, and the environment.
    Ingrid Daubechies, the James B. Duke Distinguished Professor Emerita of Mathematics at Duke University, was honored for her pioneering work on signal processing.
    Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard University, was recognized for research that has transformed the way data privacy is handled in the age of big data and AI.
    R. Lawrence Edwards, Regents and Distinguished McKnight University Professor, Department of Earth and Environmental Sciences at the University of Minnesota. Edwards is known for his refinement of radiocarbon dating techniques to study climate history and ocean chemistry.
    Wendy L. Freedman, the John and Marion Sullivan University Professor in Astronomy and Astrophysics at the University of Chicago, for her observational cosmology research, including pioneering uses of the Hubble Space Telescope.
    Keivan G. Stassun, Stevenson Professor of Physics & Astronomy at Vanderbilt University for his work in astrophysics, including the study of star formation and exoplanets.
    G. David Tilman is Regents Professor and the McKnight Presidential Chair in Ecology, Evolution, and Behavior at the University of Minnesota. He studies biological diversity, the structure and benefits of ecosystems and ways to assure sustainability despite global increases in human consumption and population.
    Teresa Kaye Woodruff is the MSU Research Foundation Professor of Obstetrics, Gynecology and Reproductive Biology and Biomedical Engineering at Michigan State University. She is an internationally recognized expert in ovarian biology and reproductive science.

The nine individual recipients of the National Medal of Technology and Innovation are:

    Martin Cooper for his work in advancing in personal wireless communications for over 50 years. Cited in the Guinness Book of World Records for making the first cellular telephone call, Cooper, known as the “father of the cell phone,” spent much of his career at Motorola.
    Jennifer A. Doudna, a Nobel Laureate in Chemistry and the Li Ka Shing Chancellor’s Chair in Biomedical and Health Sciences at the University of California, Berkeley. She is a pioneer of CRISPR gene editing.
    Eric R. Fossum is the John H. Krehbiel Sr. Professor for Emerging Technologies at Dartmouth College. He invented the CMOS active pixel image sensor used in cell-phone cameras, webcams, and medical imaging.
    Paula T. Hammond, an MIT Institute Professor, vice provost for faculty, and member of the Koch Institute, was honored for developing methods for assembling thin films that can be used for drug delivery, wound healing, and other applications.
    Kristina M. Johnson, former president of The Ohio State University was recognized for research in photonics, nanotechnology, and optoelectronics. Her discoveries have contributed to sustainable energy solutions and advanced manufacturing technologies.
    Victor B. Lawrence spent much of his career at Bell Laboratories, working on new developments in multiple forms of communications. He is a Research Professor and Director of the Center for Intelligent Networked Systems at Stevens Institute of Technology.
    David R. Walt is a faculty member of the Wyss Institute at Harvard University and is the Hansjörg Wyss Professor of Bioinspired Engineering at Harvard Medical School and Professor of Pathology at Harvard Medical School and Brigham and Women’s Hospital. He was honored for co-inventing the DNA microarray, enabling large-scale genetic analysis and better personalized medicine.
    Paul G. Yock is an emeritus faculty member at Stanford University. A physician, Yock is known for inventing, developing and testing new cardiovascular intervention devices, including the stent.
    Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and a professor of brain and cognitive sciences and biological engineering, was recognized for his work developing molecular tools, including the CRISPR genome-editing system."

#########

Here's my post from ten years ago:

Saturday, February 7, 2015 Differential Privacy: an appreciation of Cynthia Dwork

 

Monday, December 23, 2024

Nicole Immorlica celebrated (and interviewed) in a Microsoft Resarch podcast

 Here's a podcast with Nicole Immorlica, in which she talks about her research origins (including a course and a poem), what computer science brings to economics, and the role of theory in the age of generative AI.

Ideas: Economics and computation with Nicole Immorlica 

December 5, 2024 | Gretchen Huizinga and Nicole Immorlica

When research manager Nicole Immorlica discovered she could use math to make the world a better place for people, she was all in. She discusses working in computer science theory and economics, including studying the impact of algorithms and AI on markets.

 

Line illustration of Nicole Immorlica

Behind every emerging technology is a great idea propelling it forward. In the Microsoft Research Podcast series Ideas, members of the research community at Microsoft discuss the beliefs that animate their research, the experiences and thinkers that inform it, and the positive human impact it targets.

In this episode, host Gretchen Huizinga talks with Senior Principal Research Manager Nicole Immorlica. As Immorlica describes it, when she and others decided to take a computational approach to pushing the boundaries of economic theory, there weren’t many computer scientists doing research in economics. Since then, contributions such as applying approximation algorithms to the classic economic challenge of pricing and work on the stable marriage problem have earned Immorlica numerous honors, including the 2023 Test of Time Award from the ACM Special Interest Group on Economics and Computation and selection as a 2023 Association for Computing Machinery (ACM) Fellow. Immorlica traces the journey back to a graduate market design course and a realization that captivated her: she could use her love of math to help improve the world through systems that empower individuals to make the best decisions possible for themselves.

 

Saturday, November 2, 2024

Prompt Engineering, by Mukund Sundararajan

 Do you want to improve your chats with chatbots based on Large Language Models (LLMs)? (For example, GPT, Gemini, Anthropic/ Claude, Meta...) Here's a chance to learn from Mukund Sundararajan, who knows what those LLMs are good at (and not so good at) and why, and how to think about how to prompt them. His book might have been called How to be a Prompt Engineer.

Thinking Like A Large Language Model: Become an AI manager 
by Mukund Sundararajan
 
"Consider an analogy. You are a manager, you have an employee, and you are trying to get a task done. It helps to understand how the employee thinks, what the employee knows, and how they may behave. What would happen if you made a certain request? How would the employee interpret it? How would they respond? What do our past interactions with the employee tell us about their capabilities? How should we phrase our request so that it is interpreted correctly? Should we break our request into steps? What aspects of their work would we need to verify?

"Now imagine the employee is an AI. It still helps to have answers for the questions above. The goal of this book is to help you arrive at those answers.

"We describe how a Large Language Model (LLM) operates in three different ways: via input-output examples, via an overview of its training process, and via analogies to other familiar concepts.

"You don't have to work with technology to understand this book. It will be accessible to anyone with a high-school education—students, professionals, business-people, executives—anyone who wants to extract value out of LLMs or chatbots.

"Author details: Mukund Sundararajan is a Distinguished Research Scientist at Google DeepMind. He has spent the past decade analyzing, developing, and deploying AI in products—successfully and unsuccessfully. He has also observed others do so. He currently works on the Gemini series of Large Language Models and writes prompts for a living."