arXiv subjects

Ayumi Hashiguchi

Publications and source records attributed to Ayumi Hashiguchi.

4 recordsLinked to original sources

arXiv2026

Distributionally robust decision-making under ambiguity: case study of water environmental management

Decision-making under uncertainty is ubiquitous in environmental project planning. Environmental processes such as a streamflow discharge often present a subexponential memory, where the autocorrelation persists for a long time. In addition, optimization problems driven by environmental processes encounter the issue of model ambiguity because of a lack of sufficient data for model identification. To facilitate decision-making for the management of aquatic environments (e.g., flood mitigation, water abstraction for hydropower generation), we formulate a unified distributionally robust stochastic optimization problem based on a mixed moving average (MMA) process. The MMA process is a superposition of infinite-dimensional affine stochastic processes that is seemingly complex, but the affine property helps with the formulation and computation of the optimization. Our problem is based on a convex objective with a nonsmooth conditional value-at-risk measure. We present a convergent regularization to obtain its smooth and strictly convex counterpart. The model ambiguity is represented as a distortion of the probability density of the target dynamics, and it is penalized by a divergence with which the optimization problem remains convex and becomes computable. As a case study, we apply the optimization problem to two cases with identified parameter values. The performance of the optimized dynamics is evaluated through a statistical simulation. This paper serves as a multidisciplinary work covering both the theory and application of distributionally robust optimization.

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arXiv2026

Mathematical model for sustainable fisheries resource management accounting for size spectrum

This paper proposes a novel modelling and control framework for growth models that incorporate a size spectrum in conjunction with numerical computation and extensive field surveys. In fisheries management, the size spectrum, characterized by individual differences in body weight and length, is a critical factor, as it influences the physiology and ecology of fish, as well as the preferences of anglers. However, a comprehensive theoretical framework for fisheries modelling and management that accounts for the size spectrum has yet to be established. We apply a growth model that considers the size spectrum to Plecoglossus altivelis altivelis (Ayu), an important inland fisheries resource in Japan. Additionally, we introduce a novel stochastic control theory for the resource management of Ayu, taking its size spectrum into account. The growth model is calibrated using data collected annually from a river system in Japan. Our control problem addresses the size spectrum of fishing benefits and terminal utility (nonlinear expectation) for sustainability, resulting in a nonstandard problem to which the dynamic programming principle does not apply. We address this difficulty using a time-inconsistent formalism, where solving the control problem is reduced to finding an appropriate solution to a system of nonlinear partial differential equations. We numerically compute the system using the finite difference method and explore the fisheries management of Ayu at the study site.

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arXiv2022

Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments

We investigated a cost-constrained static ergodic control problem of the variance of measure-valued affine processes and its application in streamflow management. The controlled system is a jump-driven mixed moving average process that generates realistic subexponential autocorrelation functions, and the static nature of the control originates from a realistic observability assumption in the system. The Markovian lift was effectively used to discretize the system into a finite-dimensional process, which is easier to analyze. The resolution of the problem is based on backward Kolmogorov equations and a quadratic solution ansatz. The control problem has a closed-form solution, and the variance has both strict upper and lower bounds, indicating that the variance cannot take an arbitrary value even when it is subject to a high control cost. The correspondence between the discretized system based on the Markovian lift and the original infinite-dimensional one is discussed. Then, a convergent Markovian lift is presented to approximate the infinite-dimensional system. Finally, the control problem was applied to real cases using available data for a river reach. An extended problem subject to an additional constraint on maintaining the flow variability was also analyzed without significantly degrading the tractability of the proposed framework.

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arXiv2022

Modeling and computation of an integral operator Riccati equation for an infinite-dimensional stochastic differential equation governing streamflow discharge

We propose a linear-quadratic (LQ) control problem of streamflow discharge by optimizing an infinite-dimensional jump-driven stochastic differential equation (SDE). Our SDE is a superposition of Ornstein-Uhlenbeck processes (supOU process), generating a sub-exponential autocorrelation function observed in actual data. The integral operator Riccati equation is heuristically derived to determine the optimal control of the infinite-dimensional system. In addition, its finite-dimensional version is derived with a discretized distribution of the reversion speed and computed by a finite difference scheme. The optimality of the Riccati equation is analyzed by a verification argument. The supOU process is parameterized based on the actual data of a perennial river. The convergence of the numerical scheme is analyzed through computational experiments. Finally, we demonstrate the application of the proposed model to realistic problems along with the Kolmogorov backward equation for the performance evaluation of controls.

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