Invited Speakers

  • Portrait of keynote speaker: Kaushik Basu
    (Carl Marks Professor of International Studies, Cornell University, Ithaca, NY, USA)

    Kaushik Basu is Professor of Economics and the Carl Marks Professor of International Studies at Cornell University. He was formerly Chief Economist of the World Bank, 2012-16, and Chief Economic Adviser to the Indian Government, 2009-2012. He also served as President of the International Economic Association, 2017-2021. In 2021 he was conferred the Humboldt Research Award, by the Alexander von Humboldt Foundation. In May 2008 he was awarded one of India’s highest civilian awards, the Padma Bhushan, by the President of India.

    Basu received his PhD from the London School of Economics and BA (Hons) from St. Stephen’s College, Delhi. He has received honorary doctorates from several institutes, including IIT Bombay and the University of Bath, U.K. He has published widely in different areas of economics. His books include An Economist in the Real World (MIT Press, 2015) and The Republic of Beliefs (Princeton University Press, 2018). He has also contributed articles to popular news media, including The New York Times, Scientific American, and BBC News Online.

    https://economics.cornell.edu/kaushik-basu
    The talk will begin with a discussion of unexpected ways in which the advance of scientific knowledge can backfire on human welfare in game-theoretic environments. It will then go on to analyze other kinds of interface between knowledge and well-being, in the area of political economy and governance, which sheds interesting light on the rise of authoritarianism and the erosion of democracy. The presentation will end with tentative comments on how individuals in strategic environments can think about creating a priori behavioral rules and laws to avoid some of the pitfalls of knowledge. Inevitably, there will be more open questions than answers.
  • Portrait of keynote speaker: Elizabeth Maggie Penn
    (Political Science and Quantitative Theory and Methods, Emory University, Atlanta, GA, USA)

    Maggie Penn is a Professor of Political Science and Quantitative Theory and Methods at Emory University. She is a formal political theorist whose work focuses on social choice theory, classification, political institutions, and inter-group dynamics. She co-edits the Political Economy of Institutions and Decisions book series at Cambridge University Press. From 2015 through 2022 she was a Managing Editor of Social Choice & Welfare, and currently serves on its advisory board. This coming academic year she will be working on a book about classification algorithms as a visiting fellow at the Russell Sage Foundation in New York City.

    https://www.elizabethmpenn.com/
    Classification algorithms are increasingly used in high-stakes domains such as credit, employment, healthcare, housing, law enforcement, and national security. These systems do more than assign labels--they influence behavior by shaping incentives and expectations. In this lecture, I argue that when classification affects individual behavior, conventional statistical notions of algorithmic fairness can be misleading or incomplete. I introduce a social scientific framework for modeling behavioral responses to classification and analyzing how classification rules shape outcomes across groups. This framework yields new fairness criteria that account for endogenous behavior, individual welfare, and explicability. I'll draw tight connections between statistical, behavioral, welfare, and transparency-based fairness criteria, and identify conditions under which they can align. The results underscore the need to move beyond error-based metrics to evaluate fairness in systems that interact with, and shape, human behavior.
  • Portrait of keynote speaker: Marcus Pivato
    (Centre d'Économie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, Paris, France)

    Marcus Pivato is a Professor at the Centre d'Économie de la Sorbonne at Université Paris 1 Panthéon-Sorbonne. His main research interests are collective decision-making, social welfare, and normative economics. Earlier in his career, he also studied cellular automata and other dynamical systems. He has published over seventy academic research articles in theoretical economics, mathematics, and philosophy, as well as a textbook on linear partial differential equations and Fourier theory. He received his B.Sc. in Mathematics in 1994 from the University of Alberta, and his PhD. in Mathematics in 2001 from the University of Toronto. From 2014 until 2018, he held the Labex MME-DII Chaire d'Excellence at CY Cergy Paris Université. He is a Managing Editor of Social Choice and Welfare, a Co-Editor of Economic Theory and of Economic Theory Bulletin, and an Economic Theory Fellow of the Society for the Advancement of Economic Theory.

    https://sites.google.com/site/marcuspivato/home

    In the “Savage” model of decision-making under uncertainty, there is a space S of possible “states of nature” (representing the source of uncertainty), and a space X of possible “outcomes”. Each course of action (“act”) is represented as a function from S to X. A rational agent has a utility function on X and (probabilistic) “beliefs” about S, and evaluates each act according to its expected utility.

    However, a single agent might encounter many different sources of uncertainty and many different menus of outcomes, which could be combined together into many different decision problems. Furthermore, these different uncertainty sources (or outcome menus) might be related to one another in various ways:

    1. There may be analogies between different uncertainty sources (or different outcome menus).
    2. At different times, the agent may also have different levels of awareness, or access to different informational resources. Different uncertainty sources (or outcome menus) could represent the agent's subjective perception of the same objective decision problem with different levels of awareness or information.
    3. Some uncertainty sources (or outcome menus) might exhibit internal symmetries.

    Furthermore, in some situations, the state spaces and outcome spaces have additional mathematical structure (e.g. a topology or differentiable structure), and feasible acts must respect this structure (i.e. they must be continuous or differentiable functions). In other situations, the agent might only have access to linguistic descriptions of background conditions, actions, and their consequences (e.g. encoded in a Boolean algebra or some other algebraic structure). Finally, there may be cases where the agent is only cognizant of a set of abstract “acts”, and is unable to specify explicit state spaces and outcome spaces.

    I introduce a modelling framework that addresses all of these issues. I then define and axiomatically characterize a subjective expected utility representation that is “global” in two senses. First: it posits probabilistic beliefs for all uncertainty sources and utility functions over all outcome menus, which simultaneously rationalize the agent’s preferences across all possible decision problems, and which are consistent with the aforementioned analogies, symmetries, and awareness levels. Second: it applies in many different mathematical environments (i.e., different categories), making it unnecessary to develop a separate theory for each one.

    Papers:

  • Portrait of keynote speaker: Tuomas Sandholm
    (Angel Jordan University Professor of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA)
    Dr. Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon and a serial entrepreneur. His research focuses on the convergence of AI, economics, and operations research. He is Founder and Director of the Electronic Marketplaces Laboratory. In parallel with his academic career, he was Founder, Chairman, first CEO, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. Since 2010, his algorithms have been running the national kidney exchange for UNOS, where they autonomously make the kidney exchange transplant plan for 80% of U.S. transplant centers together each week. He also co-invented never-ending altruist-donor-initiated chains and his algorithms created the first such chain. Such chains have led to over 10,000 life-saving transplants. He invented liver lobe and multi-organ exchanges, and the first liver-kidney swap took place in 2019. He has developed the leading algorithms for several game classes. The team that he leads is the multi-time world champion in computer heads-up no-limit Texas hold’em, which was the main benchmark and decades-open challenge problem for testing application-independent algorithms for solving imperfect-information games. Their AI Libratus became the first AI to beat top humans at that game. Then their AI Pluribus became the first AI to beat top humans at the multi-player game. That is the first superhuman milestone in any game beyond two-player zero-sum games. He is Founder and CEO of Strategy Robot, Inc., which builds software products based on game theory for defense applications. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning in applications ranging from recreational games to business strategy, negotiation, strategic pricing, finance, cybersecurity, auctions, political campaigns, and medical treatment. He is Founder and CEO of Optimized Markets, Inc., which is bringing a new optimization-powered paradigm to advertising campaign sales, pricing, and scheduling. Among his honors are the AAAI Award for AI for the Benefit of Humanity, Vannevar Bush Faculty Fellowship, IJCAI Minsky Medal, IJCAI McCarthy Award, AAAI/IAAI Engelmore Award, IJCAI Computers and Thought Award, inaugural ACM Autonomous Agents Research Award, CMU’s Allen Newell Award for Research Excellence, Sloan Fellowship, NSF Career Award, Edelman Laureateship, and Goldman Sachs 100 Most Intriguing Entrepreneurs. He is Fellow of the ACM, AAAI, INFORMS, and AAAS. He holds an honorary doctorate from the University of Zurich.
    https://www.cs.cmu.edu/~sandholm/

    Since the advent of AI, games have served as progress benchmarks, and most real-world settings are imperfect-information games. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, and have been the focus of significant AI research. Beyond calculation needed in regular chess, they require reasoning about information gathering, the opponent’s knowledge, signaling, bluffing, etc. The most popular variant, Fog of War (FoW) chess (aka. dark chess) is a recognized challenge problem in AI after superhuman performance was reached in no-limit Texas hold’em poker. We present Obscuro, the first superhuman AI for FoW chess. It introduces advances to search in imperfect-information games, enabling strong, scalable reasoning. Most prior search techniques - such as those used to achieve superhuman play in no-limit Texas hold’em - require the construction of the “common knowledge set” as a first step, making them unusable for games with this much imperfect information. Experiments against the prior state-of-the-art AI and human players - including the world’s best - show that Obscuro is significantly stronger. FoW chess is now the largest (by amount of imperfect information) turn-based game in which superhuman performance has been achieved and the largest game in which imperfect-information search has been successfully applied.

    This is joint work with my PhD student Brian Hu Zhang.