Study incentivizes sharing economy users to explore less-reviewed options.
problem Lack of reviews leads to neglect of less-popular options, creating a cycle.
method Introduced Coordinated Online Learning (CoOL) to learn optimal incentives.
result Algorithm increases exploration on Airbnb, improving user experience.
Paper proposes incentive mechanism to encourage participation in federated learning.
problem Users are reluctant to participate in federated learning due to privacy concerns.
method Formulated as a two-stage Stackelberg game, designed an incentive mechanism to select and compensate users.
result Demonstrated effectiveness of the proposed incentive mechanism through simulations.
Proposes a bandit framework for dynamic user incentives.
problem Designing personalized incentives for users with evolving preferences.
method Combines greedy matching, UCB, and Markov chain theory.
result Algorithm provides theoretical regret bounds and practical examples.
Two-stage mechanism designs reduce regret in recommender systems with stochastic covariates.
problem Designing effective recommender systems with user covariates sampled online.
method Two-stage algorithm integrating incentivized exploration with offline learning methods.
result Achieves sublinear regret while maintaining incentive compatibility.
Paper proposes incentives for federated learning to ensure truthful contributions.
problem Ensuring truthful contributions from decentralized users in federated learning.
method Introduces a scoring rule based framework to incentivize truthful reporting of local hypotheses at a Bayesian Nash Equilibrium.
result Proposed solution verified using MNIST and CIFAR-10 datasets, showing decreasing scores for low-quality hypotheses.
This research simplifies lending pools in decentralized finance for better understanding and security.
problem Complexity and lack of executable models make lending pools hard to understand and predict.
method Developed a formal model to reflect common features of lending pools and proved general properties.
result Proved correct handling of funds and described vulnerabilities and attacks.
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.
Modeling incentives for content creators on algorithm-curated platforms.
problem Maximizing exposure for content creators on algorithmic platforms.
method Formalized exposure game model, proving effects of algorithmic choices on equilibria, proposing tools for finding equilibria.
result Algorithmic choices significantly affect content exposure and creator behavior.
New model shows most users on Q&A site Stack Overflow ignore badges.
problem Understanding user behavior in Q&A websites with badges.
method Probabilistic model applied to Stack Overflow data.
result Majority of users remain apathetic to badges but still contribute.
New term ADS describes how machine learning can change user behavior.
problem Machine learning systems can unintentionally change user behavior, affecting performance.
method Introduced `unit tests` and mitigation strategy for hidden incentives in auto-induced distributional shift.
result Meta-learning and Q-learning sometimes fail unit tests but pass with mitigation strategy.
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.
Incentive-aware recommender system for online platforms.
problem Myopic agents exploit optimal arms, not exploring alternatives.
method Model as multi-agent bandit problem, incentivizes exploration.
result Asymptotically optimal performance with ex-post fairness.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
RIVCoin stabilizes cryptocurrency portfolios through a DAO and redistributes income.
problem Stabilizing cryptocurrency value and aligning incentives for all users.
method Decentralized DAO, diversified reserves, and income redistribution.
result Aligns incentives for wealthier users to stabilize smaller users' risk.
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. A new framework promotes trustworthy user-generated datasets by ensuring no user benefits from misreporting.
problem Incentivizing data misreporting in user-generated datasets.
method Proposes Licchavi, a global and personalized learning framework with provable strategyproofness guarantees.
result Proves that no user can gain much by replying to Licchavi's queries with deviated answers.
Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.
problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
New algorithms learn stable matchings from uncertain user preferences.
problem Learning stable matchings from uncertain user preferences.
method Stochastic multi-armed bandit problem, incentive-aware learning objective, primal-dual formulation.
result Near-optimal regret bounds for learning stable matchings.
Dark blockchain venues increase miners' profits but raise users' execution risk.
problem Exploitable information leakage in blockchain transactions.
method Economic incentive analysis and empirical study of dark venues.
result Dark venues increase miners' profits but raise users' execution risk.
Unihedge uses HTAX to create unlimited liquidity in prediction markets.
problem Limited liquidity and information incorporation issues in prediction markets.
method Introduces HTAX prediction markets with DPM derivatives and new incentive mechanisms.
result Unlimited liquidity and improved information incorporation in prediction markets.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
Study investigates how AI can create and detect deceptive explanations, finding they can fool humans but ML can detect them.
problem The risk of deceptive AI explanations increasing trust issues and economic risks.
method Investigates creation and detection of deceptive explanations using AI models and machine learning methods.
result Deceptive explanations can fool humans, but ML can detect them with high accuracy.
Paper uses Super-App data to improve income estimation models.
problem Improving accuracy of income estimation models.
method TreeSHAP method for Stochastic Gradient Boosting Interpretation.
result Alternative data from Super-Apps capture more information than traditional financial data.
Optimizes e-commerce traffic sales by incorporating hidden costs into auction mechanisms.
problem Hidden costs from unexpected advertising items in search results.
method Dynamic reserve price design with distributed solving algorithm.
result Ensures a balanced relationship between revenue and user experience.
Motivated by an application of eliciting users' preferences, we investigate the problem of learning hemimetrics, i.e., pairwise distances among a set of n n n items that satisfy triangle inequalities and non-negativity constraints. In our application, the (asymmetric) distances quantify private costs a user incurs when s…
Generative Adversarial Network (GAN) generates user-friendly explanations for loan denials.
problem Lack of explainable AI for financial services, especially in loan denials.
method Developed a GAN to generate explanations for loan denials, using a representative dataset.
result Demonstrated the GAN can generate explanations for various stakeholders, including applicants and decision makers.
Mobile payment incentives optimized using merchant transaction networks.
problem Optimizing marketing campaigns with limited budgets.
method Graph representation learning on transaction networks.
result Effective modeling of merchant sensitivity to incentives.
We refine toxicity bounds for dynamic liquidation incentives in CP-AMM systems.
problem Ensuring stability in dynamic liquidation incentives in automated market makers.
method Derived state-dependent toxicity bounds for dynamic liquidation incentives, reconciling them with CP-AMM price dynamics.
result State-dependent bounds and liquidity-depth-only condition for dynamic liquidation incentives.
Model shows government incentives boost green bond investment.
problem Increasing green investments through government incentives.
method Optimal incentives indexed on bond prices and covariation, applied to a portfolio of bonds.
result Method outperforms current tax-incentives systems in green investments.
Study optimal incentives for cleaner energy production.
problem Accelerate transition to cleaner technologies in energy market.
method Stochastic control models for three scenarios: single firm, two firms, and two firms without incentives.
result Optimal strategies for investment and production emerge, highlighting firm interactions and incentive effects.
New framework shows algorithmic recourse can be harmful.
problem Risks of providing algorithmic recourse in binary classification.
method Abstract learning-theoretic framework comparing risks with and without recourse.
result Providing recourse can be harmful, increasing class uncertainty and mistakes.
Exchange uses incentives to optimize limit order book dynamics.
problem Optimizing market liquidity in fragmented electronic markets.
method Modeling limit order book as SPDE and using control theory to design incentives.
result Exchange can design incentives to modify order book shape and increase liquidity.
New findings link causal models to strategic classification, improving robustness and alignment.
problem Strategic adaptation by users in classification tasks.
method Causal models to bound worst-case out-of-distribution risk.
result Causal classification optimizes classification error after adaptation under certain noise conditions.
New MAB model incentivizes user arm-pulling with self-reinforcing preferences.
problem Balancing exploration and exploitation in recommender systems with incentivized user preferences.
method Proposes a new MAB model with random arm selection and two policies: At-Least- n n n Explore-Then-Commit and UCB-List. result Achieves O ( l o g T ) O(log T) O ( l o g T ) expected regret and O ( l o g T ) O(log T) O ( l o g T ) expected payment over a time horizon T T T . Framework scores DeFi users based on liquidity and trading behavior.
problem Distinguishing between liquidity provision and active trading in DeFi.
method Rule-based decomposition, deep residual neural network, pool-level context.
result Deep residual neural network improves user scoring and risk assessment.
AI task delegation faces incentive collapse with unbounded payments as AI accuracy rises.
problem Incentive collapse in AI-assisted task delegation schemes.
method General impossibility result and sentinel-auditing payment mechanism.
result Sentinel-auditing mechanism enforces positive human effort at finite cost, independent of AI accuracy.
Study on liquidity and market efficiency in auction games with imperfect information.
problem Generating liquidity in illiquid auction markets with imperfect information.
method Characterized Nash equilibria in a two-player game with imperfect information, linking market spreads to signal strength.
result Without incentives, the market is inefficient and does not lead to trades. Quadratic fees indexed on half spread can generate liquidity.
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.
New approach to avoid bad incentives in reinforcement learning agents.
problem Designing safe reinforcement learning agents that avoid unnecessary disruptions.
method Break down side effects penalties into baseline state and deviation measure; introduce new stepwise inaction baseline and relative reachability deviation measure.
result Combination of new design choices avoids undesirable incentives, while simpler alternatives fail.
A novel incentive mechanism improves fairness and participation in federated learning.
problem Low-quality clients and lack of fairness in federated learning.
method Client selection process and money transfer mechanism to ensure fairness and participation.
result The proposed incentive mechanism improves the duration and fairness of federated learning.
The paper develops an economic foundation for multi-agent learning in markets.
problem Learning dynamics in markets with strategic externalities.
method A two-phase incentive mechanism that estimates and uses implementable transfers to steer long-run dynamics.
result The mechanism achieves sublinear social-welfare regret and asymptotically optimal welfare under mild rationality and exploration conditions.
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. Method uses ANN to estimate incentive salience from large behavioral data.
problem Estimating incentive salience in naturalistic settings.
method Artificial Neural Networks (ANNs) for latent state approximation.
result ANNs produce better representations for predicting future behaviour.
This paper addresses reward estimation and incentive design for agents with hidden rewards.
problem Estimating and incentivizing agents with unknown rewards in a learning setting.
method Repeated adverse selection game with a self-interested learning agent and a learning principal. Introduces an estimator for consistent reward estimation and a data-driven incentive policy.
result Finite-sample consistency of the estimator and a rigorous regret bound for the principal.
Model shows incentives in shared order book can lead to free-rider problem.
problem Incentives in shared order books can lead to free-rider problem.
method Developed a Principal-Agent model with CARA utility functions.
result Equilibrium analysis shows incentives can lead to reduced competition.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
Token economics improves energy systems with incentives and efficiency.
problem Traditional energy systems have inefficiencies and lack incentives.
method Integrating token economy and blockchain technology.
result Token economic systems enhance energy efficiency and reduce emissions.
A crowdsourcing framework improves communication efficiency in federated learning.
problem Improving communication efficiency in federated learning with uncoordinated clients.
method Formulated a utility maximization problem and proposed a crowdsourcing framework.
result Simulated results show up to 22% gain in offered reward.