arXiv research

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

Study on learning strategies in matching markets with uncertain preferences.

The paper analyzes investment and consumption strategies under uncertain market conditions.

Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.

This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…

New algorithms learn stable matchings from uncertain user preferences.

In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …

Improved DPO framework penalizes preference uncertainty to avoid overoptimization.

This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.

Algorithm identifies optimal stable matching in uncertain two-sided markets.

Investor optimizes portfolio under dynamic risk preferences.

We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is value in considering different risk measures during learning. In this framework, the preference for risk can be tuned by variation of the p…

Paper addresses online alignment of large language models under uncertain preference feedback.

The paper tackles robust control for insurance contracts under uncertain transition rates.

Investor optimizes worst-case portfolio in uncertain markets.

A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.

Study optimal stopping for group with diverse discount rates using an attitude function.

This study measures price risk aversion using indirect utility functions in a lab experiment.

In this paper, we propose an equilibrium pricing model in a dynamic multi-period stochastic framework with uncertain income streams. In an incomplete market, there exist two traded risky assets (e.g. stock/commodity and weather derivative) and a non-traded underlying (e.g. temperature). The risk preferences are of expo…

Behavior of systems that are functions of anticipated behavior of other systems, whose own behavior is also anticipatory but homeostatic and determined by hierarchical ordering, which changes over time, of sets of possible environments that are not co-possible, is proven to be highly non-linear and sensitively dependen…

Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.

Diffusion approximations optimize sequential experimentation for uncertain parameters.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

The paper optimizes reinsurance under uncertain dependence among insurers.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

New algorithm for uncertain time series classification.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

Unified deep framework for personalized recommendations with uncertainty.

Investor optimizes investment and consumption under uncertain market conditions with constraints.

Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock markets and option markets with uncertain prior distribution are established by Peng's G-s…

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time se…

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

Model quantifies uncertainty's impact on European option prices.

The paper tackles robust control with uncertain dependence using data-driven methods.

Paper proposes online optimization for uncertain systems using machine learning and DRO.

This paper tackles JSSP with uncertain task durations using DRL.

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

This paper improves learning uncertain Bayesian networks from incomplete data.

New method for identifying best designs in vector optimization with uncertain feedback.