Review of Dynamic Optimization Methods in Renewable Natural Resource Management

Williams, Byron K.

doi:10.1111/j.1939-7445.1989.tb00074.x

Cited by 48 publications

(21 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From an analytical perspective, a key concern is how conservation decisions can be linked across scales. For example, the harvesting (or stocking or transplanting) of organisms is routinely viewed as a sequential decision problem, in which decisions in the present affect system status and thus decisions in the future (Anderson 1975, Williams 1989. The goal of such a sequential decision process is to balance present and future values so that the conservation strategy performs well over an extended time frame.…”

Section: Problem Framing and The Issue Of Scalementioning

confidence: 99%

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

Johnson¹,

Williams²,

Nichols³

2013

E&S

View full text Add to dashboard Cite

ABSTRACT. There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the "best" management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimum allows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resilience thinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes.Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

show abstract

Section: Problem Framing and The Issue Of Scalementioning

confidence: 99%

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

Johnson¹,

Williams²,

Nichols³

2013

E&S

View full text Add to dashboard Cite

show abstract

“…Finally, we note that an important aspect of the trend toward formal decision analysis in natural resource management has been the increasing application of methods to analyze dynamic decisions [66][67][68][69]. Dynamic decision problems are ubiquitous in conservation, including sustainable harvesting, the management of habitat disturbance, the control of invasive plants and animals, and even the development of terrestrial reserve systems.…”

Section: What Is Decision Science and How Can It Be Used In Conservatmentioning

confidence: 99%

Training Conservation Practitioners to be Better Decision Makers

et al. 2015

View full text Add to dashboard Cite

Abstract:Traditional conservation curricula and training typically emphasizes only one part of systematic decision making (i.e., the science), at the expense of preparing conservation practitioners with critical skills in values-setting, working with decision makers and stakeholders, and effective problem framing. In this article we describe how the application of decision science is relevant to conservation problems and suggest how current and future conservation practitioners can be trained to be better decision makers. Though decision-analytic approaches vary considerably, they all involve: (1) properly formulating the decision problem; (2) specifying feasible alternative actions; and (3) selecting criteria for evaluating potential outcomes. Two approaches are available for providing training in decision science, with each serving different needs. Formal education is useful for providing simple, well-defined problems that allow demonstrations of the structure, axioms and general characteristics of a decision-analytic approach. In contrast, practical training can offer complex, realistic decision problems requiring more careful structuring and analysis than those used for formal training purposes. Ultimately, the kinds and degree of training necessary depend on the role conservation practitioners play in a decision-making process. Those attempting to facilitate decision-making processes will need advanced training in both technical aspects of decision science and in facilitation techniques, as well as opportunities to apprentice under decision analysts/consultants. Our primary goal should be an attempt to ingrain a discipline for applying clarity of thought to all decisions. OPEN ACCESSSustainability 2015, 7 8355

show abstract

“…Dynamic optimization methods are often used for solving sequential decision problems in natural resource management [50][51][52]. Sequential decision problems are ubiquitous in conservation; examples include the harvesting or stocking of animals, the control of invasive plants and animals and habitat management for imperiled species [53,54].…”

Section: Decision Algorithmmentioning

confidence: 99%

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Breininger

Duncan

Eaton

et al. 2014

Land

View full text Add to dashboard Cite

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require tradeoffs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire OPEN ACCESSLand 2014, 3 875 behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.

show abstract

Review of Dynamic Optimization Methods in Renewable Natural Resource Management

Cited by 48 publications

References 141 publications

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

Training Conservation Practitioners to be Better Decision Makers

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Contact Info

Product

Resources

About