Classical treatments of machine learning rely on the assumption that the data, after deployment, resembles the data the model was trained on. However, as machine learning models are increasingly used to make consequential decisions about people, individuals often react strategically to the deployed model. These strategic behaviors---which effectively invalidate the predictive models---have opened up new avenues of research and added new challenges to the deployment of machine learning algorithms in the real world.
Different aspects of strategic behavior have been studied by several communities both within and outside of machine learning. For example, the growing literature on strategic classification studies algorithms for finding strategy-robust decision rules, as well as the properties of such rules. Behavioral economics aims to understand and model people’s strategic responses. Recent works on learning in games study optimization algorithms for finding meaningful equilibria and solution concepts in competitive environments.
This workshop aims to create a dialogue between these different communities, all studying aspects of decision-making and learning with strategic feedback. The goal is to identify common points of interest and open problems in the different subareas, as well as to encourage cross-disciplinary collaboration.
| Opening remarks (Introduction) | |
| Analysis and interventions in large network games: graphon games and graphon contagion (Invited Talk) | |
| Closing the loop in Machine Learning: Learning to optimize with decision dependent data (Invited Talk) | |
| Strategic Classification and the Quest for the Holy Grail (Invited Talk) | |
| Panel 1 (Discussion Panel) | |
| The Platform Design Problem (Contributed talk) | |
| Learning Losses for Strategic Classification (Contributed talk) | |
| Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents (Contributed talk) | |
| Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps (Contributed talk) | |
| Poster Session (Poster Session, social & break) | |
| Microfoundations of Algorithmic decisions (Invited Talk) | |
| Improving Information from Manipulable Data (Invited Talk) | |
| Revisiting Dynamics in Strategic ML (Invited Talk) | |
| Panel 2 (Discussion Panel) | |
| Algorithmic Monoculture and Social Welfare (Invited Talk) | |
| Leveraging strategic interactions for causal discovery (Invited Talk) | |
| Online intermediation in legacy industries: The effect of reservation platforms on restaurants’ prices and survival (Invited Talk) | |
| Panel 3 (Discussion Panel) | |
| Social | |
| Learning through Recourse under Censoring (Poster) | |
| Pessimistic Offline Reinforcement Learning with Multiple Agents (Poster) | |
| Promoting Resilience of Multi-Agent Reinforcement Learning via Confusion-Based Communication (Poster) | |
| Game Redesign in No-regret Game Playing (Poster) | |
| Pseudo-Competitive Games and Algorithmic Pricing (Poster) | |
| Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps (Poster) | |
| Models of fairness in federated learning (Poster) | |
| Approximating Bayes Nash Equilibria in Auction Games via Gradient Dynamics (Poster) | |
| Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions (Poster) | |
| Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments (Poster) | |
| Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games (Poster) | |
| Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality (Poster) | |
| Exploration and Incentives in Reinforcement Learning (Poster) | |
| Information Discrepancy in Strategic Learning (Poster) | |
| Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations (Poster) | |
| Improving Fairness in Credit Lending Models using Subgroup Threshold Optimization (Poster) | |
| The Platform Design Problem (Poster) | |
| Bayesian Persuasion for Algorithmic Recourse (Poster) | |
| Reward-Free Attacks in Multi-Agent Reinforcement Learning (Poster) | |
| Timing is Money: The Impact of Arrival Order in Beta-Bernoulli Prediction Markets (Poster) | |
| The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity (Poster) | |
| Coopetition Against an Amazon (Poster) | |
| Scoring Rules for Performative Binary Prediction (Poster) | |
| Gaming Helps! Learning from Strategic Interactions in Natural Dynamics (Poster) | |
| Strategic Classification in the Dark (Poster) | |
| Strategic Classification Made Practical (Poster) | |
| Learning in Matrix Games can be Arbitrarily Complex (Poster) | |
| Efficient Competitions and Online Learning with Strategic Forecasters (Poster) | |
| Alternative Microfoundations for Strategic Classification (Poster) | |
| Estimation of Standard Asymmetric Auction Models (Poster) | |
| Near-Optimal No-Regret Learning in General Games (Poster) | |
| Bounded Rationality for Multi-Agent Motion Planning and Behavior Learning (Poster) | |
| When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits (Poster) | |
| Interactive Robust Policy Optimization for Multi-Agent Reinforcement Learning (Poster) | |
| Negotiating networks in oligopoly markets for price sensitive products (Poster) | |
| Strategic clustering (Poster) | |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning (Poster) | |
| Regret, stability, and fairness in matching markets with bandit learners (Poster) | |
| On classification of strategic agents who can both game and improve (Poster) | |
| The Strategic Perceptron (Poster) | |
| Price Discovery and Efficiency in Waiting Lists: A Connection to Stochastic Gradient Descent (Poster) | |
| Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents (Poster) | |
| Learning Losses for Strategic Classification (Poster) | |
| Normative disagreement as a challenge for Cooperative AI (Poster) | |