arXiv research

Study allocates resources to strategic agents while balancing cost and incentives.

Framework uses hindsight regret to audit marketing budget allocations.

We study a game-theoretic variant of the maximum circulation problem. In a flow allocation game, we are given a directed flow network. Each node is a rational agent and can strategically allocate any incoming flow to the outgoing edges. Given the strategy choices of all agents, a maximal circulation that adheres to the…

We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…

Study optimizes resource allocation in noisy systems for better control.

We introduce a strategic behavior in reinsurance bilateral transactions, where agents choose the risk preferences they will appear to have in the transaction. Within a wide class of risk measures, we identify agents' strategic choices to a range of risk aversion coefficients. It is shown that at the strictly beneficial…

LLMs can collude in market divisions, maximizing profits.

Although modern portfolio theory has been in existence for over 60 years, fund managers often struggle to get its models to produce reliable portfolio allocations without strongly constraining the decision vector by tight bands of strategic allocation targets. The two main root causes to this problem are inadequate par…

Investors can achieve optimal risk-reward trade-offs with bonds and stocks under mean-reverting stock returns.

Dynamic tracking error framework shows similar performance but varying volatility across different constraints.

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with l…

Optimizes asset allocation with illiquid assets using MPC.

Strategic feature selection in high-stakes domains like healthcare.

Analyzes gaming in federated learning systems and provides design principles.

New algorithm improves insurance company's asset allocation decisions.

Adaptive compute allocation improves model performance by prioritizing harder queries.

Study optimal treatment assignment policies under strategic agent responses.

Optimizes trade execution with reinforcement learning for limit orders.

In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within indiv…

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

AI agents manage portfolios, improving on human oversight.

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward st…

We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumptio…

We propose a design for schedule-based execution trading strategies based on uncertainty bands. This formulation: 1) simplifies strategy specification and implementation; 2) provides for flexible allocation among passive, opportunistic, aggressive, and dark pool crossing execution tactics; 3) allows for rapid enhanceme…

This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.

The study identifies core and satellite segments in the cryptocurrency market.

The study improves Monte Carlo simulations for long-term investments using advanced financial models.

A new machine learning method calculates failure probability efficiently and accurately.

OpenAlpha validates decentralized capital strategies using game theory and market aggregation.

OPAL optimizes labeling strategy for precise inference from uncertain models.

New framework for robust uncertainty quantification in strategic settings.

Quantum computing promises faster insurance contract valuation.

Reference class forecasting is a method to remove optimism bias and strategic misrepresentation in infrastructure projects and programmes. In 2012 the Hong Kong government's Development Bureau commissioned a feasibility study on reference class forecasting in Hong Kong - a first for the Asia-Pacific region. This study …

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

New framework reduces strategic manipulation cost for minority groups in fair classification.

Study compares employers with and without anticipating strategic labor force responses.

This study examines the execution phase of corporate share buy-backs, highlighting inefficiencies and costs.

Paper shows how noisy data can improve robust decision-making.

The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.

New algorithm learns optimal policies in strategic MDPs with private types.

Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adversarial perturbations to benign inputs to fool the DNN model to misclassify. In this paper, we present a cross-layer strategic ensemble fra…

New findings show strategic interactions can undermine model expressiveness in machine learning.

This paper uses DRL for long-short portfolio optimization, improving risk-adjusted returns.

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

Unified algorithm for efficient pure exploration using dual variables.

The paper analyzes trading strategies in a competitive market with incomplete information.

DeltaHedge uses AI to optimize portfolio options trading.