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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.

168,742 papers · 148 categories

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48 results for Pareto-optimal allocations

Study dynamic Pareto-optimal allocations in multi-period economies with time-consistent risk measures.

problem Optimal allocation in multi-period pure-exchange economies with stochastic endowments and time-consistent risk measures.
method Introduced dynamic Pareto-optimal allocation processes and derived recursive and comonotone improvement theorems.
result Dynamic Pareto-optimal allocation processes can be constructed recursively and are comonotone.

The paper addresses risk sharing and variability measures among agents with general risk preferences.

problem Risk sharing and variability measures among agents with general risk preferences.
method Characterizes Pareto-optimal allocations using Gini deviation, mean-median deviation, and inter-quantile difference as variability measures.
result Optimal allocations are not comonotonic and feature a mixture of pairwise counter-monotonic structures.

The emph{securities market} is the fundamental theoretical framework in economics and finance for resource allocation under uncertainty. Securities serve both to reallocate risk and to disseminate probabilistic information. emph{Complete} securities markets - which contain one security for every possible state of natur…

2013-01-16abs ↗pdf ↗

Study optimal risk sharing in decentralized peer-to-peer markets with robust risk measures.

problem Optimizing risk sharing in decentralized markets with non-convex risk measures.
method Characterization of Pareto-optimal allocations using robust distortion risk measures and probabilistic risk aversion.
result Shape of allocations depends on agents' tail risk assessments.

Bayesian method reduces misclassification errors in ranking Pareto-optimal solutions.

problem Identifying true Pareto-optimal solutions in noisy multiobjective optimization.
method Sequential allocation of extra samples using stochastic kriging to build predictive distributions.
result The proposed method outperforms existing algorithms in reducing misclassification errors.

The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.

problem Characterizing Pareto-optimal risk-sharing allocations for risk-seeking agents.
method Modeling preferences with a common distortion risk measure and analyzing three settings: risk-averse, risk-seeking, and inverse S-shaped distortion.
result Pareto-optimal allocations for risk-seeking agents are counter-monotonic, not comonotonic.

Study risk sharing among agents with varying risk preferences.

problem Risk sharing among agents with heterogeneous risk measures.
method Derive explicit solutions for inf-convolution and counter-monotonic inf-convolution under varying risk seeking.
result Explicit solutions for inf-convolution and counter-monotonic inf-convolution can be represented by a generalization of distortion risk measures.

Comonotonic allocations are restored under certain constraints, improving risk-sharing.

problem Feasibility constraints can distort optimal risk-sharing allocations.
method Identified componentwise convex-order solidity as a sufficient condition to restore comonotonic allocations.
result Componentwise convex-order solidity ensures comonotonic improvements under feasible constraints.

Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.

problem Risk sharing in economies with diverse risk attitudes.
method Modeling preferences with distortion risk measures, using comonotonic and counter-monotonic principles.
result Optimal risk sharing strategies identified based on risk attitudes, reducing the nn-agent problem to a two-agent formulation.

This paper solves aggregation of Pareto optimal models by using Bayesian priors and weighted averaging.

problem How to rationally aggregate Pareto optimal models while preserving Pareto efficiency.
method Four logical steps: 1) Bayesian models, 2) Prior as preference ranking, 3) Consistent aggregation, 4) Weighted average of priors.
result All rational/consistent aggregation rules follow a generalized hierarchical Bayesian model.

The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.

problem Strategic interaction between plan managers and reinsurers in P2P insurance.
method Develops two game-theoretic contract designs: Pareto and Bowley designs, deriving optimal contracts and analyzing their welfare effects.
result The Bowley design yields a unique optimal contract, while the Pareto design allows for multiple Pareto-optimal contracts.

This paper analyzes MORL and proposes efficient algorithms to learn Pareto optimal policies.

problem Understanding and efficiently learning Pareto optimal policies in multi-objective reinforcement learning.
method Systematic analysis of optimization targets, reformulation of Tchebycheff scalarization, online UCB-based algorithm, preference-free framework.
result Identification of Tchebycheff scalarization as a favorable method and efficient algorithms for learning Pareto optimal policies.

This work fills the gap in understanding multi-objective learning generalization.

problem Lack of statistical learning theory insights into multi-objective learning generalization.
method Established generalization bounds and excess bounds for multi-objective learning.
result Showed that all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.

The study shows how probability weighting can lead to betting in a risk-averse economy.

problem Understanding how probability weighting affects economic behavior and risk aversion.
method Examining a von Neumann-Morgenstern economy with an RDU agent to model probability weighting effects.
result Probability weighting can lead to endogenous betting in an economy with common beliefs.

The paper tackles the trade-off between fairness and accuracy in machine learning models.

problem Ensuring fairness in machine learning often reduces model accuracy.
method The paper introduces formal tools for reconciling the fairness-accuracy tension using Pareto optimality from multi-objective optimization.
result The Chebyshev scalarization scheme is superior for finding Pareto optimal solutions compared to the linear scalarization scheme.

Paper proposes Adaptive Pareto Exploration for identifying Pareto optimal arms in multi-objective scenarios.

problem Identifying Pareto optimal arms in multi-objective scenarios with relaxed constraints.
method Adaptive Pareto Exploration strategy for different relaxations of Pareto Set Identification.
result Reduction in sample complexity when identifying at most k Pareto optimal arms.

Study on Pareto optimality in multi-objective bandit problems.

problem Pareto optimality in multi-objective multi-armed bandit problems.
method Formulated adversarial multi-objective multi-armed bandit, defined Pareto regrets, presented algorithms, established upper and lower bounds.
result New algorithms are optimal in adversarial settings and nearly optimal in stochastic settings.

New method shows multi-objective bandits are not harder than single-objective ones.

problem Comparing multi-objective bandits to single-objective ones.
method Upper and lower confidence-bound estimators for every arm-objective pair, using top-two races and uncertainty-greedy rule.
result Achieves Pareto regret of \(O( icefrac{\log T}{g^\dagger})\), matching lower bound of \(Ω( icefrac{\log T}{g^\dagger})\).

We prove existence and uniqueness of stochastic equilibria in a class of incomplete continuous-time financial environments where the market participants are exponential utility maximizers with heterogeneous risk-aversion coefficients and general Markovian random endowments. The incompleteness featured in our setting - …

2009-06-01abs ↗pdf ↗

We formulate the notion of minimax estimation under storage or communication constraints, and prove an extension to Pinsker's theorem for nonparametric estimation over Sobolev ellipsoids. Placing limits on the number of bits used to encode any estimator, we give tight lower and upper bounds on the excess risk due to qu…

2015-03-25abs ↗pdf ↗

New algorithms identify Pareto optimal sets in multi-objective bandit problems.

problem Identifying Pareto optimal sets in multi-objective bandit problems.
method Empirical Gap Elimination (EGE) algorithms combining hardness estimation and elimination schemes.
result Two EGE algorithms have exponentially decaying error probabilities with budget.

Study examines risk premium convergence rates in risk sharing contracts.

problem Analyzing risk premium convergence rates in risk sharing contracts.
method Examines the limiting behavior of risk premium associated with Pareto optimal risk sharing contracts under general law-invariant risk measures.
result Risk premium convergence rate is typically n1/2n^{1/2}, not nn.

Study on optimal fees in hedge funds with first-loss compensation.

problem Determining the best fee structure for hedge funds with first-loss compensation.
method Solved the manager's non-concave utility maximization problem, calculated Pareto optimal first-loss schemes, and maximized a decision criterion on this set.
result Traditional fees are not Pareto optimal, and the preferred first-loss coverage guarantee varies with investor and market factors.

We consider the problem of optimal risk sharing in a pool of cooperative agents. We analyze the asymptotic behavior of the certainty equivalents and risk premia associated with the Pareto optimal risk sharing contract as the pool expands. We first study this problem under expected utility preferences with an objectivel…

2016-01-26abs ↗pdf ↗

The paper optimizes reinsurance under uncertain dependence among insurers.

problem Designing Pareto-optimal reinsurance contracts in a market with uncertain dependence.
method Robust optimization approach assuming known marginal distributions and unspecified dependence structure.
result Characterization of optimal indemnity schedules under worst-case scenario and derivation of optimal two-parameter layer contracts for independent risks.

This paper improves fraud prevention rule sets in fintech by generating diverse rules and finding Pareto-optimal subsets.

problem Improving the quality and flexibility of fraud prevention rule sets in fintech.
method Introducing SpectralRules for generating diverse rules, and PORS for finding Pareto-optimal subsets.
result SpectralRules generates diverse rules that improve the quality of final rule subsets.

This paper surveys gradient-based multi-objective deep learning methods.

problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.

New method generates continuous Pareto sets for multi-task learning.

problem Challenges in finding optimal solutions for correlated multi-task learning problems.
method Efficiently generates locally continuous Pareto sets and fronts in multi-objective optimization problems.
result Demonstrates continuous analysis of Pareto optimal solutions in machine learning problems.

FraPPE efficiently identifies Pareto optimal arms in multi-objective bandits.

problem Efficiently identifying Pareto optimal arms in multi-objective bandits with confidence.
method Deriving structural properties and using Frank-Wolfe optimisation to solve the maxmin optimisation problem.
result FraPPE achieves optimal sample complexity and identifies the exact Pareto set.

Study KKT conditions for multi-objective optimization on Hadamard manifolds.

problem Optimizing multi-objective interval-valued functions on Hadamard manifolds.
method Developed KKT conditions for Pareto optimal solutions under different ordering and convexity notions.
result Results are more general than on Euclidean spaces.

A new approach for efficient batch multiobjective optimization using Thompson sampling.

problem Inefficient batch multiobjective optimization due to expensive oracles and hard inner optimization.
method Proposes a Thompson sampling approach (qextttPOTSq exttt{POTS}) that chooses Pareto optimal candidates sequentially.
result Empirically superior performance compared to classical evolutionary approaches and MOBO.