THE EFFECT OF INFORMATION ON A STOCHASTIC,SPATIALLY DISTRIBUTED FISHERY

ABSTRACT. Survival rates and carrying capacities in a fishery may be strongly affected by variations in climatic factors. When the stock is under the control of a single manager, information about the stochastic growth parameters leads to improved economic returns. However, when the stock is transboundary, additional information concerning the stochastic parameters can lead to over harvesting and in turn to lower economic returns. When the harvests are taken sequentially by more than one fleet, additional information will benefit the first harvester while harming those who follow.     

A STATE SPACE BIOECONOMIC MODEL OF PACIFIC HALIBUT

Abstract Evaluation of potential economic consequences of alternative management actions requires an understanding of how the biological stock will be affected by the management action and an understanding of the response of economic systems to changes in the timing, magnitude, and size distribution of harvests and changes in the location and catchability of the biological stock. We use a hybrid structural time series model to represent Pacific halibut (Hippoglossus stenolepis) stock and recruitment dynamics and a system of structural equations to represent supply and demand relationships for Pacific halibut from Alaska and British Columbia. Model simulations explore the economic effects of changes in recruitment success, growth rate, and carrying capacity, and changes in international supplies of halibut.     

THE INCOMPLETE‐INFORMATION SPLIT‐STREAM FISH WAR: EXAMINING THE IMPLICATIONS OF COMPETING RISKS

ABSTRACT. . It is now widely recognized that climactic regime shifts, which aperiodically alter a harvested fish stock's biomass and spatial distribution, may lead to distorted fisheries management decisions which negatively impact the fishery, both biologically and economically. This is particularly true for trans‐boundary migratory stocks, where optimal management relies on coordination among independent nation‐states. Unanticipated changes in stock distribution and abundance can upset expectations of national authorities, leading them to sanction inappropriate harvesting levels by their separately managed fleets targeting the same breeding fish stock. Our theoretical studies are based on a spatially‐distributed stochastic model, which we have called the “split‐stream model,‘ where two separately managed fleets harvest simultaneously at two separate sites. Our key assumption is that competing fleet managers, when harvesting noncooperatively, hold incomplete and asymmetric private information of current stock recruitment and spatial distribution. When subsequently negotiating to coordinate their harvests, they agree that they will share their information and then bargain over partition of the gains from their cooperation. This bargaining process takes into account the fleet's relative competitive strengths, particularly due to private information asymmetries. In this present article we introduce a more complex information structure than had been assumed in our earlier work (McKelvey and Golubtsov [2002], McKelvey, Miller and Golubtsov [2003], Mckelvey et al. [2004]). Specifically, both stock‐growth and stock‐split parameters vary stochastically and asynchronously. Thus, when harvesting noncooperatively, each fleet may possess private knowledge which is unavailable to the other. We examine the interplay of the harvesting game's information structure with other fishery characteristics, such as the fleets' economics and operating characteristics and their attitudes toward risk, to determine the implications of such structure for the outcome of the harvesting game. All of these changes are made to capture new conceptual phenomena and expand the range of applicability of the model.     

Optimal fishery with coastal catch

In many spatial resource models, it is assumed that an agent is able to harvest the resource over the complete spatial domain. However, agents frequently only have access to a resource at particular locations at which a moving biomass, such as fish or game, may be caught or hunted. Here, we analyze an infinite time‐horizon optimal control problem with boundary harvesting and (systems of) parabolic partial differential equations as state dynamics. We formally derive the associated canonical system, consisting of a forward–backward diffusion system with boundary controls, and numerically compute the canonical steady states and the optimal time‐dependent paths, and their dependence on parameters. We start with some one‐species fishing models, and then extend the analysis to a predator–prey model of the Lotka–Volterra type. The models are rather generic, and our methods are quite general, and thus should be applicable to large classes of structurally similar bioeconomic problems with boundary controls. Recommedations for Resource Managers

• Just like ordinary differential equation‐constrained (optimal) control problems and distributed partial differential equation (PDE) constrained control problems, boundary control problems with PDE state dynamics may be formally treated by the Pontryagin's maximum principle or canonical system formalism (state and adjoint PDEs).
• These problems may have multiple (locally) optimal solutions; a first overview of suitable choices can be obtained by identifying canonical steady states.
• The computation of canonical paths toward some optimal steady state yields temporal information about the optimal harvesting, possibly including waiting time behavior for the stock to recover from a low‐stock initial state, and nonmonotonic (in time) harvesting efforts.
• Multispecies fishery models may lead to asymmetric effects; for instance, it may be optimal to capture a predator species to protect the prey, even for high costs and low market values of the predators.

     

SUSTAINABLE YIELDS IN FISHERIES: UNCERTAINTY,RISK‐AVERSION,AND MEAN‐VARIANCE ANALYSIS

Abstract We consider a model of a fishery in which the dynamics of the unharvested fish population are given by the stochastic logistic growth equation Similar to the classical deterministic analogon, we assume that the fishery harvests the fish population following a constant effort strategy. In the first step, we derive the effort level that leads to maximum expected sustainable yield, which is understood as the expectation of the equilibrium distribution of the stochastic dynamics. This replaces the nonzero fixed point in the classical deterministic setup. In the second step, we assume that the fishery is risk averse and that there is a tradeoff between expected sustainable yield and uncertainty measured in terms of the variance of the equilibrium distribution. We derive the optimal constant effort harvesting strategy for this problem. In the final step, we consider an approach that we call the mean‐variance analysis to sustainable fisheries. Similar as in the now classical mean‐variance analysis in finance, going back to Markowitz [1952] , we study the problem of maximizing expected sustainable yields under variance constraints, and with this, minimizing the variance, e.g., risk, under guaranteed minimum expected sustainable yields. We derive explicit formulas for the optimal fishing effort in all four problems considered and study the effects of uncertainty, risk aversion, and mean reversion speed on fishing efforts.     

IS FISHING COMPATIBLE WITH ENVIRONMENTAL CONSERVATION: A STOCHASTIC MODEL WITH AN ELEMENT OF SELF‐PROTECTION

Abstract The purpose of this paper is to introduce the impact of fishing activity on a marine ecosystem. The fishing activity is considered not only through annual harvest but also through a second component, called the degree of protection of the fishery environment. This characterizes the environmental impact of fishing. A stochastic dynamic programming problem is presented in infinite horizon, where a sole owner seeks to maximize a discounted expected profit. The main hypothesis states that the stock–recruitment relationship is stochastic and that both components of the fishing activity have an impact on the probability law of the state of the fishery environment. The optimal fishing policy is obtained and compared with standard models. This optimal policy has the following properties: is not a constant escapement policy and indicates an element of self‐protection by the fishery manager. The paper ends with a discussion on the existence of degrees of protection of the fishery environment that take into account the environmental conservation and preservation of economic activity.     

具有随机资金流的一类效用优化问题研究

现实的金融市场上,当有重大信息出现时,会对股价产生冲击,使得股价产生跳跃,同时投资过程会有随机资金流的介入,考虑股价出现跳跃与随机资金流介入的投资组合优化问题,通过构造倒向-前向随机微分方程并结合随机最优控制理论研究了一般效用函数下的投资组合选择问题,获得最优投资组合策略,然后针对二次效用函数,给出显式表示的最优投资组合策略.     

OPTIMAL HARVESTING OF A SPATIALLY EXPLICIT FISHERY MODEL

Abstract We consider an optimal fishery harvesting problem using a spatially explicit model with a semilinear elliptic PDE, Dirichlet boundary conditions, and logistic population growth. We consider two objective functionals: maximizing the yield and minimizing the cost or the variation in the fishing effort (control). Existence, necessary conditions, and uniqueness for the optimal harvesting control for both cases are established. Results for maximizing the yield with Neumann (no‐flux) boundary conditions are also given. The optimal control when minimizing the variation is characterized by a variational inequality instead of the usual algebraic characterization, which involves the solutions of an optimality system of nonlinear elliptic partial differential equations. Numerical examples are given to illustrate the results.     

OPTIMAL FISH HARVESTING FOR A POPULATION MODELED BY A NONLINEAR PARABOLIC PARTIAL DIFFERENTIAL EQUATION

As the human population continues to grow, there is a need for better management of our natural resources in order for our planet to be able to produce enough to sustain us. One important resource we must consider is marine fish populations. We use the tool of optimal control to investigate harvesting strategies for maximizing yield of a fish population in a heterogeneous, finite domain. We determine whether these solutions include no‐take marine reserves as part of the optimal solution. The fishery stock is modeled using a nonlinear, parabolic partial differential equation with logistic growth, movement by diffusion and advection, and with Robin boundary conditions. The objective for the problem is to find the harvest rate that maximizes the discounted yield. Optimal harvesting strategies are found numerically.     

REGIONAL ECONOMIC IMPACTS OF CLIMATE CHANGE: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS FOR AN ALASKA FISHERY

We compute the effects on the Alaska economy of reduced pollock harvests from rising sea surface temperature using a regional dynamic computable general equilibrium model coupled with a stochastic stock‐yield projection model for eastern Bering Sea walleye pollock. We show that the effects of decreased pollock harvest are offset to some extent by increased pollock price, and that fuel costs and the world demand for the fish, as well as the reduced supply of the fish from rising sea surface temperature, are also important factors that determine the economic and welfare effects.     

A necessary condition for optimal control of?initial coupled forward-backward stochastic differential equations with?partial information

This paper is concerned with a class of optimal control problems of forward-backward stochastic differential equations. One feature of these problems is that they are in the case of partial information and state equations are coupled at initial time. In terms of a classical convex variational technique, we establish a partial information maximum principle for the foregoing optimization problems. We also work out an example of partial information linear-quadratic optimal control to illustrate the application of the theoretical results; meanwhile, we find a forward-backward stochastic differential filtering equation, which is essentially different from classical forward stochastic filtering equations.     

Mean–variance efficiency with extended CIR interest rates

We study a mean–variance investment problem in a continuous‐time framework where the interest rates follow Cox–Ingersoll–Ross dynamics. We construct a mean–variance efficient portfolio through the solutions of backward stochastic differential equations. We also give sufficient conditions under which an explicit analytic expression is available for the mean–variance optimal wealth of the investor. Copyright © 2009 John Wiley & Sons, Ltd.     

Numerical solution of the finite horizon stochastic linear quadratic control problem

The treatment of the stochastic linear quadratic optimal control problem with finite time horizon requires the solution of stochastic differential Riccati equations. We propose efficient numerical methods, which exploit the particular structure and can be applied for large‐scale systems. They are based on numerical methods for ordinary differential equations such as Rosenbrock methods, backward differentiation formulas, and splitting methods. The performance of our approach is tested in numerical experiments.     

An optimal switching approach toward cost‐effective control of a stand‐alone photovoltaic panel system under stochastic environment

The operation of a stand‐alone photovoltaic (PV) system ultimately aims for the optimization of its energy storage. We present a mathematical model for cost‐effective control of a stand‐alone system based on a PV panel equipped with an angle adjustment device. The model is based on viscosity solutions to partial differential equations, which serve as a new and mathematically rigorous tool for modeling, analyzing, and controlling PV systems. We formulate a stochastic optimal switching problem of the panel angle, which is here a binary variable to be dynamically controlled under stochastic weather condition. The stochasticity comes from cloud cover dynamics, which is modeled with a nonlinear stochastic differential equation. In finding the optimal control policy of the panel angle, switching the angle is subject to impulsive cost and reduces to solving a system of Hamilton‐Jacobi‐Bellman quasi‐variational inequalities (HJBQVIs). We show that the stochastic differential equation is well posed and that the HJBQVIs admit a unique viscosity solution. In addition, a finite‐difference scheme is proposed for the numerical discretization of HJBQVIs. A demonstrative computational example of the HJBQVIs, with emphasis on a stand‐alone experimental system, is finally presented with practical implications for its cost‐effective operation.     

THE EFFECT OF DECREASING SYSTEM SIZE ON BIRTH AND DEATH MODELS OF OPEN-ACCESS FISHERIES AND PREDATOR-PREY ECOSYSTEMS

Birth and death simulation, developed by Pielou, is a form of Markov stochastic process for describing the time evolution of populations. Applied to modelling the human element of a fishery, it expresses two features of fishing effort dynamics absent in systems of differential equations: (1) discreteness of events, such as a fishing trip or entry of a vessel into the fleet, and (2) demography stochasticity, expressed as randomness in the time occurrence of successive events. Birth and death simulation is based on randomly selecting the waiting time between events from a negative exponential distribution, derived under the assumption of Markov. Histograms from commercial landings data of waiting times between events of boats returning to port in a Nova Scotia fishery yielded good agreement with the predicted negative exponential. Algorithms are presented for stochastically modelling two processes: (1) catch and (2) the open-access hypothesis for changes in fleet size in response to changing levels of profit. The solutions qualitatively diverge from that predicted by differential equations: As the numbers of vessels and fish schools decline (i.e., as the system size scale shrinks), a birth and death formulation predicts increasing instability of the predator-prey cycle solution about the deterministically stable open-access equilibrium. Open-access models are a form of predator-prey model. In choosing the minimum wilderness preserve area needed to sustain a population of top predators, numbered in the low hundreds, a predator-prey model formulated with differential equations could underestimate instability and thus the risk of extinction, when the discreteness and randomness of predator-prey birth, death, and capture events is significant.     

Time Discretisation and Rate of Convergence for the Optimal Control of Continuous-Time Stochastic Systems with Delay

We study a semi-discretisation scheme for stochastic optimal control problems whose dynamics are given by controlled stochastic delay (or functional) differential equations with bounded memory. Performance is measured in terms of expected costs. By discretising time in two steps, we construct a sequence of approximating finite-dimensional Markovian optimal control problems in discrete time. The corresponding value functions converge to the value function of the original problem, and we derive an upper bound on the discretisation error or, equivalently, a worst-case estimate for the rate of convergence.     

风险的动态定量和一个相关的随机对策问题

本文讨论不完全市场中股票收益率不确定时的动态风险度量问题和一个相关的随机对策问题。该动态风险度量可表示为一个随机最优控制问题的值函数,以倒向随机微分方程为工具我们给出了最优目标具有的形式,并给出随机对策问题上值与下值相等的充分条件和鞍点的存在性。     

Optimal harvesting policy of an inland fishery resource under incomplete information

A new mathematical model for finding the optimal harvesting policy of an inland fishery resource under incomplete information is proposed in this paper. The model is based on a stochastic control formalism in a regime‐switching environment. The incompleteness of information is due to uncertainties involved in the body growth rate of the fishery resource: a key biological parameter. Finding the most cost‐effective harvesting policy of the fishery resource ultimately reduces to solving a terminal and boundary value problem of a Hamilton‐Jacobi‐Bellman equation: a nonlinear and degenerate parabolic partial differential equation. A simple finite difference scheme for solving the equation is then presented, which turns out to be convergent and generates numerical solutions that comply with certain theoretical upper and lower bounds. The model is finally applied to the management of Plecoglossus altivelis, a major inland fishery resource in Japan. The regime switching in this case is due to the temporal dynamics of benthic algae, the main food of the fish. Model parameter values are identified from field measurement results in 2017. Our computational results clearly show the dependence of the optimal harvesting policy on the river environmental and biological conditions. The proposed model would serve as a mathematical tool for fishery resource management under uncertainties.     

条件平均场随机微分方程的最优控制问题

作者研究了一个条件平均场随机微分方程的最优控制问题.这种方程和某些部分信息下的随机最优控制问题有关,并且可以看做是平均场随机微分方程的推广.作者以庞特里雅金最大值原理的形式给出最优控制满足的必要和充分条件.此外,文中给出一个线性二次最优控制问题来说明理论结果的应用.     

IN-SEASON FISHERY MANAGEMENT: A BAYESIAN MODEL

A Bayesian model is presented for optimizing harvest rates on an uncertain resource stock during the course of a fishing season. Pre-season stock status information, in the form of a “prior” probability distribution, is updated using new data obtained through the operation of the fishery, and harvest rates are chosen to achieve a balance between conservation concerns and fishing interests. A series of fishery scenarios are considered, determined by the stock size distribution and the timing distribution; the uncertainty in the fish stock is seen to have a rather complex influence on optimal harvest rates. The model is applied to a specific example, the Skeena River sockeye salmon fishery.