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Frequencies of decision making and monitoring in adaptive resource management

Overview of attention for article published in PLOS ONE, August 2017
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Title
Frequencies of decision making and monitoring in adaptive resource management
Published in
PLOS ONE, August 2017
DOI 10.1371/journal.pone.0182934
Pubmed ID
Authors

Byron K. Williams, Fred A. Johnson

Abstract

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 26%
Student > Ph. D. Student 6 19%
Student > Doctoral Student 2 6%
Professor 1 3%
Lecturer > Senior Lecturer 1 3%
Other 2 6%
Unknown 11 35%
Readers by discipline Count As %
Environmental Science 8 26%
Agricultural and Biological Sciences 7 23%
Decision Sciences 1 3%
Medicine and Dentistry 1 3%
Unknown 14 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2017.
All research outputs
#18,567,744
of 22,997,544 outputs
Outputs from PLOS ONE
#156,236
of 196,050 outputs
Outputs of similar age
#243,930
of 318,510 outputs
Outputs of similar age from PLOS ONE
#3,237
of 4,153 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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