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...
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...
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...
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...
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-...
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
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...
Speaker: Stefano V. Albrecht (University of Edinburgh)
Title: Deep Reinforcement Learning for Multi-Agent Interaction
Abstract: Our group specialises in developing machine learning algorithms for autonomous systems control, with a particular focus on deep reinforcement learning and multi-agent reinforcement learning. We have a focus on problems of optimal decision making, prediction, and coordination in multi-agent systems. In this talk, I will give an overview of our research agenda along with some recent published papers in these areas, including... Read more about Deep Reinforcement Learning for Multi-Agent Interaction
Talk Title: Loss function design for improved decision-making
Time & Location: 12/1, Friday, 1:30 - 2:30pm, SEC 1.413.
Abstract: Algorithmic predictions are pervasive in our society, and these predictions are used to make decisions in settings ranging from banking to public health. This talk examines the relationship between the structure of downstream decision tasks and the design of algorithms: in particular, on the design of loss functions in...