arXiv subjects

Yuan Tao

Publications and source records attributed to Yuan Tao.

5 recordsLinked to original sources

arXiv2026

Risk-Averse Bayesian Games with an Unknown Type Distribution: Bayesian Learning, Equilibrium Analysis, and Finite-Sample Guarantees

Classical Bayesian games assume that the joint distribution of players' types is common knowledge, an assumption that rarely holds in practical applications. We address this issue by studying a group of myopic players who repeatedly interact under the Bayesian Nash conjecture and learn a parametric joint type distribution from type profiles observed over time within a Bayesian learning framework. Players may hold heterogeneous priors and may be risk averse toward both epistemic uncertainty about the distributional parameter and aleatoric uncertainty in their rivals' types. We propose two models: a Bayesian Nash equilibrium among risk-averse Bayesian learners (BNE-RABL), in which epistemic and aleatoric risks are evaluated separately, and a BNE based on Bayesian predictive distributions (BNE-BPD), in which the two sources of uncertainty are integrated into a Bayesian predictive distribution and evaluated through a single risk measure. Under suitable conditions, we establish the existence and uniqueness of both equilibria, and derive non-asymptotic convergence rates of the equilibrium sequences toward the corresponding oracle BNE under the true type distribution as the game is repeatedly played and more type profiles are observed. These results further show that the discrepancy between the BNE-RABL and BNE-BPD strategies vanishes as the sample size grows, which illustrates that the two models can be regarded as effective approximations to each other. We apply the proposed models to a price competition problem and numerically illustrate the theoretical results.

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arXiv2026

Risk-averse Decision Making with Contextual Information: Model, Sample Average Approximation, and Kernelization

We consider risk-averse contextual optimization problems where the decision maker (DM) faces two types of uncertainties: problem data uncertainty (PDU) and contextual uncertainty (CU) associated with PDU, the DM makes an optimal decision by minimizing the risk arising from PDU based on the present observation of CU and then assesses the risk of the optimal policy against the CU. A natural question arises as to whether the nested risk minimization/assessment process is equivalent to joint risk minimization/assessment against CU and PDU simultaneously. First, we demonstrate that the equivalence can be established by appropriate choices of the risk measures and give counter examples where such equivalence may fail. One of the interesting findings is that the optimal policies are independent of the choice of the risk measure against the CU under certain conditions. Second, by using the equivalence, we propose computational method for solving the risk-averse contextual optimization problem by solving a one-stage risk minimization problem. The latter is particularly helpful in data-driven environments. We consider a number of risk measures/metrics to characterize the DM's risk preference for PDU and discuss the computational tractability for the resulting risk-averse contextual optimization problem. Third, when the risk-averse contextual optimization problem is defined in the reproducing kernel Hilbert space, we show consistency of the optimal values obtained from solving sample average approximation problems. Some numerical tests, in newsvendor problem and portfolio selection problem, are performed to validate the theoretical results.

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arXiv2026

Necessary Optimality Conditions for Integrated Learning and Optimization Problem in Contextual Optimization

Integrated learning and optimization (ILO) is a framework in contextual optimization which aims to train a predictive model for the probability distribution of the underlying problem data uncertainty, with the goal of enhancing the quality of downstream decisions. This framework represents a new class of stochastic bilevel programs, which are extensively utilized in the literature of operations research and management science, yet remain underexplored from the perspective of optimization theory. In this paper, we fill the gap. Specifically, we derive the first-order necessary optimality conditions in terms of Mordukhovich limiting subdifferentials. To this end, we formulate the bilevel program as a two-stage stochastic program with variational inequality constraints when the lower-level decision-making problem is convex, and establish an optimality condition via sensitivity analysis of the second-stage value function. In the case where the lower level optimization problem is nonconvex, we adopt the value function approach in the literature of bilevel programs and derive the first-order necessary conditions under stochastic partial calmness conditions. The derived optimality conditions are applied to several existing ILO problems in the literature. These conditions may be used for the design of gradient-based algorithms for solving ILO problems.

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arXiv2025

Violation of Svetlichny's inequality in a system of spins $j$

Quantum multi-particle correlations are one of the most intriguing properties of quantum entanglement, arising from collective entangled states of multiple particles. Svetlichny's inequality (SI) was the first method proposed to test the existence of such correlations. Previous studies have primarily focused on $1/2$-spin particle systems. In this paper, we present a unified scheme that enables the violation of SI in arbitrary non-zero spin particle systems. Specifically, for all fermion systems, our scheme achieves the maximal quantum violation of SI for any number of particles. For boson systems, when the particle spin $j\geq2$, our scheme consistently realizes the violation of SI for any number of particles. When the particle spin $j=1$, our scheme can yield SI violation for up to $7$ particles. Furthermore, as the particle spin $j$ approaches infinity, our scheme achieves the maximal quantum violation of SI. To obtain these results, we also prove that the upper bound of Svetlichny's operator within the framework of local hidden variable theory is $\sqrt{2^{N+1}}$. These findings not only enhance our understanding of quantum correlations across various particle systems but also provide valuable insights for the development of quantum communication protocols that utilize entanglement and non-locality in multi-particle configurations.

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arXiv2025

Generalized Bayesian Nash Equilibrium with Continuous Type and Action Spaces

Bayesian game is a strategic decision-making model where each player's type parameter characterizing its own objective is private information: each player knows its own type but not its rivals' types, and Bayesian Nash equilibrium (BNE) is an outcome of this game where each player makes a strategic optimal decision according to its own type under the Nash conjecture. In this paper, we advance the literature by considering a generalized Bayesian game where each player's action space depends on its own type parameter and the rivals' actions. This reflects the fact that in practical applications, a firm's feasible action is often related to its own type (e.g. marginal cost) and the rivals' actions (e.g. common resource constraints in a competitive market). Under some moderate conditions, we demonstrate existence of continuous generalized Bayesian Nash equilibria (GBNE) and uniqueness of such an equilibrium when each player's action space is only dependent on its type. In the case that each player's action space is also dependent on rivals' actions, we give a simple example to show that uniqueness of GBNE is not guaranteed under standard monotone conditions. To compute an approximate GBNE, we restrict each player's response function to the space of polynomial functions of its type parameter and consequently convert the GBNE problem to a stochastic generalized Nash equilibrium problem (SGNE). To justify the approximation, we discuss convergence of the approximation scheme. Some preliminary numerical test results show that the approximation scheme works well.

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