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.
FedCoin uses blockchain to fairly distribute incentives in federated learning.
problem Fairly incentivizing data owners in federated learning with privacy concerns.
method FedCoin uses a blockchain-based peer-to-peer payment system with a proof of Shapley (PoSap) protocol to calculate and distribute Shapley Values.
result FedCoin accurately computes Shapley Values and promotes high-quality data contributions.
Study incentive efficiency in monopoly insurance markets with hidden information.
problem Maximizing social welfare in a monopoly insurance market with hidden agent types.
method Maximizes social welfare function subject to incentive compatibility and individual rationality constraints.
result Optimal menus of contracts depend on the level of social welfare weight and agent risk attitudes.
COAD maximizes online auction revenue by quantifying uncertainty without known distributions.
problem Designing incentive-compatible mechanisms for online auctions with unknown bidder values and uncertain future participants.
method COAD uses distribution-free uncertainty quantification techniques and integrates machine learning methods to predict bidder values while ensuring revenue guarantees.
result COAD maximizes revenue in online auctions through bidder-specific reserve prices based on lower confidence bounds of valuations.
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.
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.
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.
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.
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.
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.
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.
Neural networks improve VaR estimation accuracy and robustness.
problem Estimating Value at Risk (VaR) in financial markets.
method Generative regime switching framework with Monte-Carlo simulations, neural networks initialized via best model, balanced incentive function, reduced training data.
result Neural networks outperform traditional methods in VaR estimation, especially with less data.
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.
DICE estimates data influence cascade in decentralized learning networks.
problem Lack of fair incentives discourages participation in decentralized learning.
method Designs DICE to estimate influence cascade in decentralized networks.
result Influence cascade is influenced by data, topology, and loss landscape curvature.
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.
We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed …
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.
How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment? We show that current approaches to penalizing side effects can introduce bad incentives, e.g. to prevent any irreversible changes in the environment, including the actions of other agents. To isolate the source…
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.
Model for open, decentralized network with task load balancing.
problem Complex computational tasks in open, decentralized networks.
method Incentive-based load balancing using economic mechanisms.
result Optimized resource allocation and enhanced system resilience.
Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.
problem Designing optimal auction mechanisms that balance revenue and bidders' regret.
method Introduced permutation-equivariant neural networks to auction mechanisms.
result Permutation-equivariant neural networks decrease expected ex-post regret and improve model generalizability.
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.
Framework trains safe agents avoiding deceptive behavior.
problem Training safe agents from unsafe incentives.
method Formal settings, causal influence analysis, maximizing non-mediated effects.
result Agents avoid manipulating delicate state for rewards.
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.
Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.
problem Rapid growth in electric vehicles requires complementary strategies to decarbonize transportation.
method Analysis of ethanol blending, regulatory incentives, and economic assessments.
result Ethanol blending, especially E15 and E85, can substantially reduce carbon emissions and provide economic benefits.
Paper proposes FMore to incentivize edge nodes in federated learning with MEC.
problem Incentivizing edge nodes in federated learning with MEC resources.
method Multi-dimensional procurement auction with K winners.
result FMore improves model accuracy and reduces training rounds for AI tasks.
Study shows how business cycle affects dividend payout based on managerial stock incentives.
problem Impact of managerial stock incentives on dividend payout policy during business cycles.
method Using S&P 1500 companies data from 2000-2018, analyzing full sample and recession periods.
result Negative relationship between managerial stock options and dividend payouts, significant for medium-sized companies.
When the planning horizon is long, and the safe asset grows indefinitely, isoelastic portfolios are nearly optimal for investors who are close to isoelastic for high wealth, and not too risk averse for low wealth. We prove this result in a general arbitrage-free, frictionless, semimartingale model. As a consequence, op…
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
problem Mitigating energy consumption in commercial buildings through occupant plugload control.
method Field experiments with visual feedback and monetary incentives in government and university buildings.
result Mean energy reduction of ~9.52% in office environments and ~21.61% in university environments with visual feedback.
dYdX updates liquidity provider incentives to enhance trading efficiency.
problem Incentivizing liquidity providers to maintain efficient market structures.
method Analyzed various metrics (makerVolume, depths, spreads) and used historical trades to update the LP Incentives Programme.
result Updated the LP Incentives Programme to encourage more active and efficient liquidity.
The study compares M6 competitors' performance to industry benchmarks and discusses incentives for investment managers.
problem Investors seek to understand the performance and skill of M6 competitors beyond the competition's metrics.
method Comparative analysis using financial metrics, factor models, and new strategies.
result Most competitors do not generate significant out-performance compared to industry benchmarks, but some show skill in recent performance.
We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, …
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.
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. Forward hedging reshapes incentive provision in firms.
problem How does forward hedging affect incentive provision in firms?
method We consider a CARA framework to jointly characterize optimal production, compensation, and static hedging in equilibrium.
result Delegation and external hedging are partial substitutes, and delegation can increase firm value even when the agent is more risk averse.
Paper tackles online learning for DR management with incentives.
problem Estimating baseline consumption in DR programs with consumer incentives.
method Online learning scheme using least-squares with perturbed reward prices.
result Achieves low regret of $\mathcal{O}\left((\log{T})^2
ight)$ compared to optimal.
We study how information perturbations can destabilize two-sided matching markets. In our model, agents arrive on the market over two periods, while agents in the first period do not know the types of those arriving later. Agents already present in the market may match early or wait for the small group of new entrants.…
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.
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.
Agents learn to give rewards to others in a shared learning environment.
problem How to encourage cooperation among RL agents in a shared environment.
method Each agent learns a reward function to influence others, optimizing for its own and others' extrinsic objectives.
result Agents significantly outperform standard RL in Markov games, often finding near-optimal division of labor.
Game theory models incentivizes honesty in collaborative learning among competitors.
problem Incentivizing honest updates among competitors in collaborative learning schemes.
method Formulated a game to model interactions, studied two learning tasks, proposed mechanisms to incentivize honest communication.
result Rational clients are incentivized to manipulate their updates, preventing learning; proposed mechanisms ensure comparable learning quality to full cooperation.
A new mechanism optimizes data marketplace pricing efficiently.
problem Designing fair and efficient pricing mechanisms for data marketplaces.
method Two-stage approach: auctions to estimate value distributions, then optimal posted prices.
result MAPP achieves optimal revenue with minimal price discrimination.
We consider models of financial markets in which all parties involved find incentives to participate. Strategies are evaluated directly by their virtual wealths. By tuning the price sensitivity and market impact, a phase diagram with several attractor behaviors resembling those of real markets emerge, reflecting the ro…