A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited budget by providing incentives such as coupons, commissions to merchants. As a result, incentive optimization is the key to maximizing the c…
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection…
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.
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 …
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…
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…
Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security aspects, but they neglect the incentive mechanism. In MEC, edge nodes would not l…
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.
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…
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.
Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incen…
When an insurance note is also a derivative a serious problem arises because a derivative must be fulfilled immediately. This feature of derivatives prevents claims processing procedures that screen out ineligible claims. This, in turn, creates a perverse incentive for insured holders of notes to commit acts that resul…
Proposes a Carbon Equivalence Principle for financial products to align incentives and drive sustainability.
problem Align financial market incentives with carbon emissions to limit global warming.
method Introduces a Carbon Equivalence Principle requiring financial products to describe equivalent carbon flows alongside cash flows.
result Transparency of carbon flows in financial products can align incentives and reduce future costs, necessitating project re-structuring and financial net-zero designs.
Study optimal reinsurance contracts to prevent moral hazard under non-concave premium principles.
problem Preventing moral hazard in reinsurance contracts under non-concave premium principles.
method Develops optimal reinsurance contracts under a diffusion risk model with incentive compatibility constraints and extended distortion premium principles.
result An optimal reinsurance contract exists and is characterized by solving a double obstacle problem.