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Decision science for effective management of populations subject to stochasticity and imperfect knowledge

POPULATION ECOLOGY 2014年第1期56卷 41-53页

作者： Yokomizo, Hiroyuki Coutts, Shaun R. Possingham, Hugh P. Natl Inst Environm Studies Ctr Environm Risk Res Tsukuba Ibaraki 3058506 Japan Univ Queensland Ctr Biodivers & Conservat Sci NERP Environm Decis Hub Brisbane Qld 4072 Australia Univ Queensland Sch Biol Sci Brisbane Qld 4072 Australia Imperial Coll London Dept Life Sci Ascot SL5 7PY Berks England

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science's rational approach to dealing with uncertainty.

关键词： Adaptive management Information-gap decision theory Monitoring stochastic dynamic programming Uncertainty Value of information analysis

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Setting Realistic Recovery Targets for Two Interacting Endangered Species, Sea Otter and Northern Abalone

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CONSERVATION BIOLOGY 2012年第6期26卷 1016-1025页

作者： Chades, Iadine Curtis, Janelle M. R. Martin, Tara G. Ecosci Precinct CSIRO Ecosyst Sci Dutton Pk Qld 4001 Australia Fisheries & Oceans Canada Pacific Biol Stn Nanaimo BC V9S 3E1 Canada

Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species-centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator-prey interactions. Using simulation-based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional-response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short-term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives.

关键词： Enhydra lutris Haliotis kamtschatkana predator-prey relations reinforcement learning species interaction stochastic dynamic programming

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Optimal fire management for maintaining community diversity

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ECOLOGICAL APPLICATIONS 1999年第3期9卷 880-892页

作者： Richards, SA Possingham, HP Tizard, J Univ Adelaide Dept Appl & Mol Ecol Glen Osmond SA 5064 Australia

Disturbance events strongly influence the dynamics of plant and animal populations within nature reserves. Although many models predict the patterns of succession following a disturbance event, it is often unclear how these models can be used to help make management decisions about disturbances. In this paper we consider the problem of managing fire in Ngarkat Conservation Park (CP), South Australia, Australia. We present a mathematical model of community succession following a fire disturbance event. Ngarkat CP is, a key habitat for several nationally rare and threatened species of birds, and because these species prefer different successional communities, we assume that the primary management objective is to maintain community diversity within the park. More specifically, the aim of management is to keep at least a certain fraction of the park, (e.g., 20%), in each of three successional stages. We assume that each year a manager may do one of the following: let wildfires burn unhindered, fight wildfires, or perform controlled burns. We apply stochastic dynamic programming to identify which of these three strategies is optimal, i.e., the one most likely to promote community diversity. Model results indicate that the optimal management strategy depends on the current state of the park, the cost associated with each strategy, and the time frame over which the manager has set his/her goal.

关键词： Australia biodiversity conservation community succession decision theory disturbance events, stochastic fire management model managing wildfire to promote biodiversity Markov model modeling disturbance events stochastic dynamic programming succession

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State-dependent behavioural theory for assessing the fitness consequences of anthropogenic disturbance on capital and income breeders

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METHODS IN ECOLOGY AND EVOLUTION 2017年第5期8卷 552-560页

作者： McHuron, Elizabeth A. Costa, Daniel P. Schwarz, Lisa Mangel, Marc Univ Calif Santa Cruz Dept Ecol & Evolutionary Biol Santa Cruz CA 95064 USA Univ Calif Santa Cruz Inst Marine Sci Santa Cruz CA 95064 USA Univ Calif Santa Cruz Dept Appl Math & Stat Santa Cruz CA 95064 USA Univ Bergen Dept Biol N-9020 Bergen Norway

Anthropogenic disturbance is of increasing concern for wildlife populations, necessitating the development of models that link behavioural changes at the individual level with biologically meaningful changes at the population level. We developed a general framework for estimating the fitness consequences of disturbance that affects foraging behaviour using state-dependent behavioural theory implemented by stochastic dynamic programming (SDP). We illustrate this framework using generalized examples of pinnipeds, a group of marine carnivores that include both capital- and income-breeding species. We examined how disturbance affected pup recruitment separately for each reproductive strategy, and the impact of foraging decisions and parameter values on model results. The effect of disturbance on pup recruitment was greater for income than capital breeders for all disturbance scenarios. Disturbance had negligible effects on pup recruitment when it occurred within less frequented foraging patches, but moderate to large effects when it occurred within an important foraging patch. Model results were sensitive to values of patch productivity (the energy gained from successful foraging), the probability of disturbance and individual behavioural choices in the face of disturbance. State-dependent behavioural theory implemented by SDP is a powerful tool for investigating when behavioural changes in response to disturbance may be meaningful at the population level. This approach allows us to incorporate many factors that are known to influence the behavioural and physiological responses of animals to anthropogenic disturbance, and places disturbance within the context of a temporally and spatially variable environment. The general framework we have developed can be used to estimate the consequences of anthropogenic disturbance across a broad range of species.

关键词： mammal pinniped Population Consequences of Disturbance stochastic dynamic programming

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The importance of seasonal variability and tactical responses to risk on estimating the economic benefits of integrated weed management

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AGRICULTURAL ECONOMICS 2006年第3期35卷 245-256页

作者： Jones, Randall Cacho, Oscar Sinden, Jack NSW Dept Primary Ind Orange NSW 2800 Australia Cooperat Res Ctr Australian Weed Management Orange NSW 2800 Australia Univ New England Sch Econ Armidale NSW 2351 Australia

Seasonal variability is an important source of risk faced by farmers and, regardless of an individual's attitude to risk, there are options to tactically adjust production strategies as the outcomes of risk become known. The objective of this article is to measure the economic benefits of alternative approaches to managing weeds, one of the most serious production problems in Australian cropping systems. A bioeconomic model that combines weed biology, crop growth and economics is developed to value the effects of seasonal variability and the role of tactical responses and sequential decision making in determining an optimal integrated weed management strategy. This shows that there are substantial differences in the measured long-term benefits from deterministic and stochastic simulations. It is concluded that, for research evaluation of technologies that involve complex biological and dynamic systems, ignoring the impacts of seasonal variability, responses to risk and sequential decision making can lead to an incorrect estimate of the economic benefits of a technology. In this case study of optimal weed management strategies in Australia, the size of the error is high.

关键词： weeds risk tactical decision making stochastic dynamic programming simulation

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Maintenance/production planning with interactive feedback of product quality

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2011年第3期17卷 281-+页

作者： Njike, Achille N. Pellerin, Robert Kenne, Jean Pierre Ecol Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Ecole Technol Super Dept Mech Engn Montreal PQ Canada Ecole Technol Montreal Dept Math & Ind Engn Montreal PQ Canada

Purpose - This paper seeks to develop an optimal stochastic control model where interactive feedback consists of the quantity of flawless and defective products. The main objective of this study is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs. Design/methodology/approach - The model differs from similar research projects in that, instead of age-dependent machine failure, it considers only defective products as feedback into the optimal model for maintenance and production planning. In this paper a near optimal control policy of the system through numerical techniques is obtained. Findings - In this paper, a new model in which the system's retroaction is the quantity of defective products is presented, considering that defective products are a consequence of global manufacturing system deterioration. Instead of taking into account machine failure and human error separately, it considers a defect in product as being the consequence of a combined failure;this consideration allows one to be more realistic by merging all failure parameters into a single one. A new stochastic control model, which focuses on defective products, inventory, and backlog, has been developed. Research limitations/implications - This approach extended the concept of hedging point policy to the quantity of defective products combined with preventive and corrective maintenance strategies. The control policy obtained has a bang bang structure and is completely known for given parameters. Originality/value - The integration of maintenance and production strategies has been mainly focused on the machine. Many research projects have been focusing on the age when dealing with machine failure. It is considered as the main target of the cost reduction in maintenance engineering departments. The originality of this paper is the taking into account of all operational failures into the same optimization model. It brings a value added to high

关键词： Maintenance Quality Manufacturing systems stochastic dynamic programming Numerical methods

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Reducing the Price of Naivete in return-to-play from sports-related concussion

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PRODUCTION AND OPERATIONS MANAGEMENT 2023年第10期32卷 3081-3099页

作者： Garcia, Gian-Gabriel P. S. Lavieri, Mariel W. McAllister, Thomas A. McCrea, Michael P. Broglio, Steven CARE Consortium Investigators Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor GA USA Indiana Univ Sch Med Dept Psychiat Indianapolis IN USA Med Coll Wisconsin Dept Neurosurg & Neurol Milwaukee WI USA Univ Michigan Sch Kinesiol Ann Arbor MI USA Univ Michigan Michigan Concuss Ctr Ann Arbor MI USA

Patient-reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment decisions. For example, athletes may underreport symptoms to expedite return-to-play (RTP) from sports-related concussion (SRC). Thus, clinicians must implement treatment policies that mitigate the Price of Naivete, that is, the reduction in health outcomes due to naively believing strategically reported symptoms. In this study, we analyze dynamic treatment cessation decisions with strategic patients. Specifically, we formulate the Behavior-Aware Partially Observable Markov Decision Process (BA-POMDP), which optimizes the timing of treatment cessation decisions while accounting for known symptom-reporting behaviors. We then analytically characterize the BA-POMDP's optimal policy, leading to several practical insights. Next, we formulate the Behavior-Learning Partially Observable Markov Decision Process (BL-POMDP), which extends the BA-POMDP by learning a patient's symptom-reporting behavior over time. We show that the BL-POMDP is decomposable into several BA-POMDPs, allowing us to leverage the BA-POMDP's structural properties for solving the BL-POMDP. Then, we apply the BL-POMDP to RTP from SRC using data from 29 institutions across the United States. We estimate the Price of Naivete by comparing the BL-POMDP to naive benchmark policies. Accordingly, the BL-POMDP reduces premature RTP by over 44% and provides up to 3.63 additional health-adjusted athletic exposures per athlete compared to current practice. Overall, changing the interpretation of reported symptoms can better reduce the Price of Naivete over adjusting treatment cessation thresholds. Therefore, to improve patients' health outcomes, clinicians must understand how strategic behavior manifests in PROs.

关键词： behavioral modeling concussion management medical decision making patient-centered care stochastic dynamic programming

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Temporal Difference Learning Waveform Selection

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JOURNAL OF COMPUTERS 2010年第9期5卷 1394-1401页

作者： Wang, Bin Wang, Jinkuan Song, Xin Han, Yinghua Nrotheastern Univ Shenyang Liaoning Peoples R China Nrotheastern Univ Qinhuangdao Qinhuangdao Peoples R China

How to optimally decide or select the radar waveform for next transmission based on the observation of past radar returns is one of the important problems in cognitive radar. In this paper, stochastic dynamic programming model is proposed. Then temporal difference learning method is used to realize the adaptivity of waveform selection. The simulation results show that the uncertainty of state estimation using temporal difference learning is less than that using fixed waveform. Temporal difference learning method approaches the optimal waveform selection scheme but has lower computational cost. Finally, the whole paper is summarized.

关键词： waveform selection stochastic dynamic programming temporal difference learning cognitive radar

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Robust equilibrium strategies in a defined benefit pension plan game

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INSURANCE MATHEMATICS & ECONOMICS 2022年 106卷 193-217页

作者： Guan, Guohui Hu, Jiaqi Liang, Zongxia Renmin Univ China Ctr Appl Stat Beijing 100872 Peoples R China Renmin Univ China Sch Stat Beijing 100872 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

This paper investigates the robust non-zero-sum games in an aggregated overfunded defined benefit pension plan. The sponsoring firm is concerned with the investment performance of the fund surplus, while the participants act like a union to claim a share of the fund surplus. The financial market consists of one risk-free asset and nrisky assets. The firm and the union are ambiguous about the financial market and care about the robust strategies under the worst-case scenario. The union's objective is to maximize the expected discounted utility of the additional benefits. The firm's two objectives are to maximize the expected discounted utility of the fund surplus and the probability of the fund surplus reaching an upper level before hitting a lower level in the worst-case scenario. We present a general robust non-zero-sum game with stopping times, which contains the two objectives as special cases. Hamilton-Jacobi-Bellman-Isaacs equations and verification theorem are presented for the robust optimization problem. We obtain explicit solutions in the related two robust non-zero-sum games for the firm and the union. Numerical results are illustrated to depict the economic behaviors of the robust equilibrium strategies in these two different games. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Overfunded DB pension plan Robust control stochastic differential game Nash equilibrium stochastic dynamic programming

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Simple rules to contain an invasive species with a complex life cycle and high dispersal capacity

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JOURNAL OF APPLIED ECOLOGY 2012年第1期49卷 52-62页

作者： Pichancourt, Jean-Baptiste Chades, Iadine Firn, Jennifer van Klinken, Rieks D. Martin, Tara G. CSIRO Ecosyst Sci EcoSci Precinct Dutton Pk Qld 4102 Australia Queensland Univ Technol Brisbane Qld 4001 Australia

1. Designing practical rules for controlling invasive species is a challenging task for managers, particularly when species are long-lived, have complex life cycles and high dispersal capacities. Previous findings derived from plant matrix population analyses suggest that effective control of long-lived invaders may be achieved by focusing on killing adult plants. However, the cost-effectiveness of managing different life stages has not been evaluated. 2. We illustrate the benefits of integrating matrix population models with decision theory to undertake this evaluation, using empirical data from the largest infestation of mesquite (Leguminosae: Prosopis spp) within Australia. We include in our model the mesquite life cycle, different dispersal rates and control actions that target individuals at different life stages with varying costs, depending on the intensity of control effort. We then use stochastic dynamic programming to derive cost-effective control strategies that minimize the cost of controlling the core infestation locally below a density threshold and the future cost of control arising from infestation of adjacent areas via seed dispersal. 3. Through sensitivity analysis, we show that four robust management rules guide the allocation of resources between mesquite life stages for this infestation: (i) When there is no seed dispersal, no action is required until density of adults exceeds the control threshold and then only control of adults is needed;(ii) when there is seed dispersal, control strategy is dependent on knowledge of the density of adults and large juveniles (LJ) and broad categories of dispersal rates only;(iii) if density of adults is higher than density of LJ, controlling adults is most cost-effective;(iv) alternatively, if density of LJ is equal or higher than density of adults, management efforts should be spread between adults, large and to a lesser extent small juveniles, but never saplings. 4. Synthesis and applications. In this study,

关键词： cost-effective control decision theory detection eradication matrix population model mesquite search source-sink stochastic dynamic programming weed

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