arXiv subjects

M. Tsujimura

Publications and source records attributed to M. Tsujimura.

5 recordsLinked to original sources

arXiv2025

Non-exchangeable evolutionary and mean field games and their applications

A replicator dynamic for non-exchangeable agents in a continuous action space is formulated and its well-posedness is proven in a space of probability measures. The non-exchangeability allows for the analysis of evolutionary games involving agents with distinct (and possibly infinitely many) types. We also explicitly connect this replicator dynamic to a stationary mean field game, which determines the pairwise actions of the heterogeneous agents. Moreover, as a byproduct of our theoretical results, we show that a class of nonlinear voter models, recently the subject of increasing interest, called q-voter models, can be viewed as a replicator dynamic driven by a utility that is a power of the probability density. This implies that non-exchangeable and/or mean-field game formulations of these models can also be constructed. We also present computational examples of evolutionary and mean field game models using a finite difference method, focusing on tragedy of the commons and the q-voter model with non-exchangeable agents, of which are interesting cases from theoretical and computational perspectives.

math.OC↗

arXiv2024

Tractable fish growth models considering individual differences with an application to the fish Plecoglossus altivelis altivelis

Modeling fish growth is an important research topic in ecological and fishery sciences because body weight statistics directly affect the total biomass of fish in a habitat, which in turn affects their population dynamics. Many models of fish growth assume that the fish population in a habitat is homogenous, meaning that there is no physiological spectrum and, therefore, no size spectrum. Moreover, models that account for the size spectrum are not always analytically tractable. We present novel mathematical models of fish growth in which the body weight of each fish is assumed to follow a von Bertalanffy-type model whose proportionality coefficient, representing the maximum body weight, may differ among individual fish. This probabilistic description introduces the size spectrum into the model, owing to which the time-dependent probability density of this model is obtained explicitly. We also consider a misspecified version and a stochastic version of the model as advanced cases. We apply the first model to the real growth data of Plecoglossus altivelis altivelis as a keystone fish species in Japan. The model successfully reproduces the skewed size spectrum of this fish species over multiple years. We further use the stochastic model to investigate how fish growth dynamics are affected by environmental fluctuations.

q-bio.PE↗

arXiv2023

The Robust Orlicz Risk with an Application to the Green Photovoltaic Power Generation

We propose a novel recursive utility for controlling stochastic processes under risk and uncertainty. Our formulation uses a robustified Orlicz risk that can evaluate risk and uncertainty simultaneously. We focus on a control problem of a photovoltaic power generation system that supplies excess electricity to a secondary purpose for generating green hydrogen. The corresponding Hamilton-Jacobi-Bellman equation having a novel nonlinear term is then derived. Computational examples with the available data are finally presented, demonstrating that our methodology can be used for the photovoltaic power generation under different meteorological and operational conditions.

eess.SY↗

arXiv2020

HJB and Fokker-Planck equations for river environmental management based on stochastic impulse control with discrete and random observation

We formulate a new two-variable river environmental restoration problem based on jump stochastic differential equations (SDEs) governing the sediment storage and nuisance benthic algae population dynamics in a dam-downstream river. Controlling the dynamics is carried out through impulsive sediment replenishment with discrete and random observation/intervention to avoid sediment depletion and thick algae growth. We consider a cost-efficient management problem of the SDEs to achieve the objectives whose resolution reduces to solving a Hamilton-Jacobi-Bellman (HJB) equation. We also consider a Fokker-Planck (FP) equation governing the probability density function of the controlled dynamics. The HJB equation has a discontinuous solution, while the FP equation has a Dirac's delta along boundaries. We show that the value function, the optimized objective function, is governed by the HJB equation in the simplified case and further that a threshold-type control is optimal. We demonstrate that simple numerical schemes can handle these equations. Finally, we numerically analyze the optimal controls and the resulting probability density functions.

math.OC↗

arXiv2020

A generalized stochastic control problem of bounded noise process under ambiguity arising in biological management

The objectives and contributions of this paper are mathematical and numerical analyses of a stochastic control problem of bounded population dynamics under ambiguity, an important but not well-studied problem, focusing on the optimality equation as a nonlinear degenerate parabolic partial integro-differential equation (PIDE). The ambiguity comes from lack of knowledge on the continuous and jump noises in the dynamics, and its optimization appears as nonlinear and nonlocal terms in the PIDE. Assuming a strong dynamic programming principle for continuous value functions, we characterize its solutions from both viscosity and distribution viewpoints. Numerical computation focusing on an ergodic case are presented as well to complement the mathematical analysis.

math.OC↗