In recent years, machine learning has been called upon to solve increasingly more complex tasks and to regulate many aspects of our social, economic, and technological world. These applications include learning economic policies from data, prediction in financial markets, learning personalize models across population of users, and ranking qualified candidates for admission, hiring, and lending. These tasks take place in a complex social and economic context where the learners and objects of learning are often people or organizations that are impacted by the learning algorithm and, in return, can take actions that influence the learning process. Learning in this context calls for a new vision for machine learning and economics that aligns the incentives and interests of the learners and other parties and is robust to the evolving social and economic needs. This workshop explores a view of machine learning and economics that considers interactions of learning systems with a wide range of social and strategic behaviors. Examples of these problems include: multi-agent learning systems, welfare-aware machine learning, learning from strategic and economic data, learning as a behavioral model, and causal inference for learning impact of strategic choices.
| Opening remarks (Remarks) | |
| Keynote: Michael I. Jordan (On Dynamics-Informed Blending of Machine Learning and Game Theory) (Keynote) | |
| Keynote: Susan Athey (Machine Learning with Strategic Agents: Lessons from Incentive Theory and Econometrics) (Keynote) | |
| Discussion with Michael Jordan and Susan Athey, moderated by Kevin Leyton-Brown (Discussion Panel) | |
| Spotlight 1: Exploration and Incentives in Reinforcement Learning (Spotlights) | |
| Q&A for Spotlight 1 (Q&A) | |
| Spotlight 2: Models of fairness in federated learning (Spotlights) | |
| Q&A for Spotlight 2 (Q&A) | |
| Spotlight 3: Efficient Competitions and Online Learning with Strategic Forecasters (Spotlights) | |
| Q&A for Spotlight 3 (Q&A) | |
| Spotlight 4: Estimation of Standard Asymmetric Auction Models (Spotlights) | |
| Q&A for Spotlight 4 (Q&A) | |
| Spotlight 5: Strategic clustering (Spotlights) | |
| Q&A for Spotlight 5 (Q&A) | |
| Poster Session | |
| Keynote: Dorsa Sadigh (Theory and Practice of Partner-Aware Algorithms in Multi-Agent Coordination) (Keynote) | |
| Keynote: Vince Conitzer (Automated Mechanism Design for Strategic Classification) (Keynote) | |
| Discussion with Dorsa Sadigh and Vincent Conitzer, moderated by Peter Stone (Discussion Panel) | |
| Concluding Remarks (Remarks) | |
| Alternative Microfoundations for Strategic Classification (Oral) | |
| Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions (Oral) | |
| Normative disagreement as a challenge for Cooperative AI (Oral) | |
| Bounded Rationality for Multi-Agent Motion Planning and Behavior Learning (Oral) | |
| Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps (Oral) | |
| Learning Losses for Strategic Classification (Oral) | |
| Interactive Robust Policy Optimization for Multi-Agent Reinforcement Learning (Oral) | |
| Gaming Helps! Learning from Strategic Interactions in Natural Dynamics (Oral) | |
| The Strategic Perceptron (Oral) | |
| Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality (Oral) | |
| Estimation of Standard Asymmetric Auction Models (Oral) | |
| Scoring Rules for Performative Binary Prediction (Oral) | |
| Information Discrepancy in Strategic Learning (Oral) | |
| Regret, stability, and fairness in matching markets with bandit learners (Oral) | |
| Models of fairness in federated learning (Oral) | |
| Learning through Recourse under Censoring (Oral) | |
| The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity (Oral) | |
| Learning in Matrix Games can be Arbitrarily Complex (Oral) | |
| Approximating Bayes Nash Equilibria in Auction Games via Gradient Dynamics (Oral) | |
| Price Discovery and Efficiency in Waiting Lists: A Connection to Stochastic Gradient Descent (Oral) | |
| Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents (Oral) | |
| Promoting Resilience of Multi-Agent Reinforcement Learning via Confusion-Based Communication (Oral) | |
| Bayesian Persuasion for Algorithmic Recourse (Oral) | |
| Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments (Oral) | |
| Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games (Oral) | |
| Improving Fairness in Credit Lending Models using Subgroup Threshold Optimization (Oral) | |
| Pseudo-Competitive Games and Algorithmic Pricing (Oral) | |
| Pessimistic Offline Reinforcement Learning with Multiple Agents (Oral) | |
| Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations (Oral) | |
| Near-Optimal No-Regret Learning in General Games (Oral) | |
| Efficient Competitions and Online Learning with Strategic Forecasters (Poster) | |
| Negotiating networks in oligopoly markets for price sensitive products (Oral) | |
| Timing is Money: The Impact of Arrival Order in Beta-Bernoulli Prediction Markets (Oral) | |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning (Oral) | |
| On classification of strategic agents who can both game and improve (Oral) | |
| Coopetition Against an Amazon (Oral) | |
| When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits (Oral) | |
| Strategic Classification Made Practical (Oral) | |
| Strategic clustering (Oral) | |
| Reward-Free Attacks in Multi-Agent Reinforcement Learning (Oral) | |
| Game Redesign in No-regret Game Playing (Oral) | |
| Strategic Classification in the Dark (Oral) | |
| Exploration and Incentives in Reinforcement Learning (Oral) | |
| The Platform Design Problem (Oral) | |