Study optimizes auction pricing for strategic bidders in repeated auctions.
problem Optimizing revenue in auctions with multiple strategic bidders.
method Proposes a novel algorithm with strategic regret bound of O(log log T).
result Algorithm learns strategic buyer's valuation with theoretical guarantees.
New algorithm learns optimal policies in strategic MDPs with private types.
problem Optimal policy learning in strategic MDPs with private types and information asymmetry.
method PLAN algorithm using instrumental variable regression and pessimism principle.
result PLAN achieves near-optimal policy with 1 / K 1 / \sqrt{K} 1/ K optimality. Study optimal pricing algorithms for strategic buyers in repeated auctions.
problem Optimizing revenue in auctions with strategic buyers over multiple rounds.
method Proposed a novel algorithm that never decreases prices and has a strategic regret bound of Θ(log log T).
result Closed the open research question on no-regret horizon-independent weakly consistent pricing.
A study on incentivizing strategic arms to share rewards in a multi-armed bandit problem.
problem Designing an algorithm to encourage strategic arms to share their rewards with a principal.
method An algorithm that induces a game among the arms where each arm has a dominant strategy, ensuring the principal sees expected reward μ ′ T − o ( T ) μ'T - o(T) μ ′ T − o ( T ) . result An algorithm that ensures the principal sees expected reward μ ′ T − o ( T ) μ'T - o(T) μ ′ T − o ( T ) , even when arms are strategic or a mix of strategic and non-strategic. Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
Strategic feature selection in high-stakes domains like healthcare.
problem Strategic manipulation of input features in algorithmic predictors.
method Formal study of strategic classification through feature selection and ridge regularization.
result Excluding individual features based on manipulability is generally suboptimal.
Algorithm learns optimal coordination for strategic agents in uncertain settings.
problem Optimizing rewards for strategic agents with private types and actions.
method Combines delaying mechanism, reward angle estimation, and LinUCB algorithm.
result Near optimal regret bound of O ~ ( T ) \tilde{O}(\sqrt{T}) O ~ ( T ) for learning optimal policy. User strategization undermines algorithmic trustworthiness.
problem User strategic behavior corrupts algorithmic data and trust.
method Modeling user-platform interactions as a game, analyzing strategic behavior's short-term benefits and long-term harms.
result User strategization can initially benefit platforms but ultimately harms their ability to make accurate decisions.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
problem Ensuring truthful reporting from strategic agents in online platforms.
method Proposes COBRA, an algorithm for contextual bandits involving strategic agents that disincentivizes strategic behavior.
result COBRA achieves sub-linear regret guarantee and incentive compatibility without monetary incentives.
New algorithm prevents strategic replication in multi-armed bandit problems.
problem Strategic replication by agents can exploit bandit algorithms' balance.
method Designs Hierarchical UCB (H-UCB) and Robust Hierarchical UCB (RH-UCB) algorithms.
result Achieves O ( ln T ) O(\ln T) O ( ln T ) -regret and sublinear regret in realistic scenarios. Classic bandit algorithms are robust to strategic manipulation as long as the total budget is small compared to the time horizon.
problem Behavior of stochastic bandit algorithms under strategic manipulation by self-interested arms.
method Analysis of three popular bandit algorithms: UCB, ε-Greedy, and Thompson Sampling.
result Regret upper bound of O(max{B, KlnT}) for all three algorithms under arbitrary adaptive manipulation.
Models show strategic agents can influence classifier outcomes by investing effort.
problem How strategic agents can influence classifier outcomes.
method Developed a model to characterize strategic effort investment.
result Simple linear mechanisms can incentivize strategic effort effectively.
No-regret learning with strategic experts, incentivized.
problem Online learning with strategic experts who misreport beliefs.
method Building on wagering mechanisms, we provide algorithms for no-regret and incentive compatibility in both full and partial information settings.
result Our algorithms achieve no regret and incentive compatibility for myopic experts, with comparable regret to classic no-regret algorithms and diminishing regret for forward-looking agents.
The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.
problem Strategic agents adjust their features in response to a released policy, causing endogenous distribution shifts.
method Relaxing parametric assumptions, the paper uncovers a low-dimensional structure in distribution shifts and proposes a gradient-based policy optimization algorithm.
result The proposed algorithm achieves high sample efficiency and provides theoretical guarantees for convergence.
Optimizes decisions under strategic individual behavior.
problem Optimal decision-making in strategic environments.
method Characterizes strategic effort, identifies optimal policies under monotonic cost assumptions, develops iterative search algorithm.
result Demonstrates higher utility of decision policies accounting for strategic behavior.
Study strategic dynamic pricing for buyers with unknown manipulation costs.
problem Strategic buyers manipulate their features to get lower prices, hindering profit maximization.
method Proposes a strategic dynamic pricing policy that incorporates strategic behavior and binary response data.
result Achieves sublinear regret bound of O ( T ) O(\sqrt{T}) O ( T ) compared to linear Ω ( T ) Ω(T) Ω ( T ) regret of non-strategic policies. New algorithms optimize decision rules in strategic scenarios, minimizing prediction risk and incentivizing better outcomes.
problem Strategic agents manipulate features to improve outcomes, complicating decision-making models.
method Efficient algorithms for learning decision rules that minimize prediction risk, incentivize better outcomes, and estimate true model coefficients.
result Optimal decision rules can be learned through testing and observing agent responses, circumventing hardness results.
Modified Perceptron handles strategic agents with limited position changes.
problem Learning linear classifiers in the presence of strategic agents that can manipulate their positions.
method Developed a modified Perceptron algorithm with bounded mistakes under various manipulation costs.
result The modified Perceptron achieves bounded mistakes even when manipulation costs are unknown.
Models analyze strategic risk-taking in continuous action games.
problem Strategic risk-taking dynamics in continuous action games.
method Normal form game, multi-player scenarios, regret minimization algorithms, numerical algorithm for calculation.
result Nash equilibrium also serves as a correlated equilibrium in continuous games.
Look-ahead reasoning helps predict strategic user behavior on learning platforms.
problem Optimization criteria on learning platforms do not reflect users' priorities.
method Formalized level-k thinking and contrasted collective and selfish behavior.
result Coordination benefits users but does not offer higher-level reasoning advantages in the long run.
Study one-shot strategic classification under unknown costs, improving worst-case accuracy.
problem Learning robust decision rules in strategic settings with unknown user costs.
method Formal study of one-shot strategic classification, framing as a minimax problem, designing efficient algorithms for full-batch and stochastic settings.
result Proves efficient algorithms converge to minimax solution, revealing dual norm regularization's value.
The paper tackles strategic behavior in decision-making with counterfactual explanations.
problem Finding optimal counterfactual explanations and policies in a strategic setting.
method NP-hard problem, greedy algorithm, submodularity, randomized algorithm, matroid constraint.
result Optimal counterfactual explanations and policies increase utility.
This paper tackles online strategic decision making with asymmetry and knowledge transportability.
problem Strategic decision making with information asymmetry and knowledge transportability challenges.
method Developed a sample-efficient algorithm for online learning under these conditions.
result Proved sample complexity of O ( 1 / ε 2 ) O(1/ε^2) O ( 1/ ε 2 ) for learning an ε ε ε -optimal policy. New algorithm reduces regret in strategic prediction problem.
problem Designing an IC algorithm with sublinear regret for strategic experts.
method Developed a new algorithm WSU-UX and proved a worst-case regret bound.
result WSU-UX suffers a Ω ( T 2 / 3 ) Ω(T^{2/3}) Ω ( T 2/3 ) lower bound on regret. New approach incentivizes strategic agents to explore, making exploration almost free.
problem Incentivized exploration in multi-armed bandits with long-term strategic agents.
method Simple incentive-provision strategy, best arm identification algorithm, and UCB lower bound.
result Exploration can be (almost) free when there are many learning agents.
New framework for robust uncertainty quantification in strategic settings.
problem Machine learning model predictions can be strategically altered by informed agents.
method Strategic Conformal Prediction framework
result Theoretical guarantees and experimental validation show remarkable effectiveness.
Paper generalizes strategic classification framework and introduces SVC for PAC-learning.
problem Strategic manipulation of testing data to fool classifiers.
method Unified framework for strategic classification, strategic VC-dimension (SVC).
result Characterizes the learnability and computational tractability of linear classifiers.
Study assesses how much security restaking protocols need to pay for.
problem Determining the optimal security level for restaking protocols using token incentives.
method Expanding a model by Durvasula and Roughgarden to include strategic attackers and node operators, constructing an approximation algorithm for token-based incentives.
result Restaking protocols can be secure with proper incentive management, even against strategic adversaries.
New approach reduces simulator exploitation by improving strategic robustness.
problem Simulator exploitation leading to reality gap between simulation and real-world performance.
method Formulated as a zero-sum minimax game, providing theoretical guarantees and a convergent active data selection algorithm.
result Proves convergence and reduces prediction error in strategically important regions by 1.5-2.2 times.
New research shows strategic classification harms individuals and society.
problem Strategic behavior in decision-making leads to unfair outcomes.
method Introducing a social burden metric, the study quantifies the negative externalities of strategic classification.
result Any increase in institutional utility leads to a corresponding increase in social burden.
New framework reduces strategic manipulation cost for minority groups in fair classification.
problem Strategic manipulation disparities in fair classification.
method Constrained optimization framework that constructs classifiers to reduce strategic manipulation cost for minority groups.
result Empirically, the approach reduces strategic manipulation cost for minority groups over multiple real-world datasets.
Study compares employers with and without anticipating strategic labor force responses.
problem Understanding and optimizing strategic interactions in labor markets.
method Formulation of causal strategic classification, theory, and experiments.
result Performatively optimal hiring policies improve employer and labor outcomes, but can also harm labor force utility.
A new pricing strategy minimizes regret by controlling strategic buyer behavior.
problem Designing a pricing policy for strategic buyers with limited seller information.
method Phased-structure policy with randomized isolation periods.
result Regret of T T T -period O ~ ( T ) \widetilde{\mathcal{O}}(\sqrt{T}) O ( T ) against a benchmark policy. Symmetric game analysis shows Nash equilibria in three strategic states.
problem Analyzing Nash equilibria in a symmetric multi-player zero-sum game with two strategic variables.
method Using the minimax theorem by Sion to show equivalence of Nash equilibria.
result Nash equilibria are equivalent in three strategic states.
New framework shows strategic behavior is actually a form of causal modeling.
problem Designing classifiers that incentivize strategic behavior to improve quality.
method Developed a causal framework to distinguish between gaming and improvement.
result Proved any procedure for designing incentive classifiers must solve a causal inference problem.
The paper examines how social inequality affects algorithmic classification outcomes.
problem Social inequality impacts algorithmic classification outcomes.
method Adapting models of strategic manipulation to account for social inequality and subsidy effects.
result Subsidizing disadvantaged groups can paradoxically harm both groups and the learner.
Defends against strategic data manipulation in machine learning.
problem Adversaries can tamper with datasets to influence learning outcomes.
method Uses multiple learners and strategic activation to counteract attacks.
result Demonstrates effectiveness of a game-theoretic approach to defense.
The paper develops a mathematical model for strategic shifts.
problem Finding optimal moments for strategy changes in market dynamics.
method Explicit strategy formulation using fluctuation theory.
result Analytical results predict optimal strategy shifts.
New voting rules protect against strategic voting by robust statistics.
problem Strategic voting can skew election outcomes.
method Revisit Mallows model, develop robust estimator.
result Efficient estimator achieves nearly optimal robustness.
New findings show strategic interactions can undermine model expressiveness in machine learning.
problem How strategic interactions affect model performance in machine learning.
method Analyzing model expressiveness and strategic interactions in various machine learning settings.
result Optimizing over less expressive model classes can lead to better equilibrium outcomes in strategic environments.
Improved ε ε ε -greedy handles strategic bidding in PPC auctions.
problem Strategic bidding in PPC auctions with personalization and corruptions.
method Extended ε ε ε -greedy to handle strategic arms in contextual multi-arm bandit. result ε ε ε -greedy is robust to adversarial corruptions and degrades linearly with corruption. Collectives can manipulate learning platforms by coordinated data submission, requiring strategic assessments and algorithms.
problem Collectives can influence learning platforms by altering data, posing risks and requiring strategic planning.
method Developed a theoretical and algorithmic framework to understand and mitigate collective manipulation of learning platforms.
result Demonstrated the need for strategic assessments and implementable coordination algorithms to prevent collective manipulation.
Distributed strategic learning has been getting attention in recent years. As systems become distributed finding Nash equilibria in a distributed fashion is becoming more important for various applications. In this paper, we develop a distributed strategic learning framework for seeking Nash equilibria under stochastic…
Study allocates resources to strategic agents while balancing cost and incentives.
problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) social welfare regret, satisfies all cost constraints, and ensures incentive alignment. The paper analyzes trading strategies in a competitive market with incomplete information.
problem Strategic trading under uncertainty when firms lack full knowledge of competitors' strategies.
method Bayesian games framework to incorporate uncertainty and derive optimal trading strategies.
result Uncertainty significantly impacts trading strategies compared to complete information scenarios.
This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…
Randomised classifiers outperform deterministic ones in strategic classification.
problem Strategic modification of features by agents in classification tasks.
method Theoretical analysis of randomised classifiers in strategic classification.
result Randomised classifiers can achieve better accuracy than deterministic ones under certain conditions.
Strategic traders adjust indicative prices near auctions to achieve nearly diffusive outcomes.
problem Achieving diffusive price behavior in Paris Stock Exchange auctions.
method Analyzing the diffusive properties of indicative auction prices and the strategic behavior of traders.
result Strategic traders adjust their order submission times to achieve nearly diffusive price behavior.