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Review
. 2022 Feb;36(1):e13868.
doi: 10.1111/cobi.13868. Epub 2022 Jan 28.

An introduction to decision science for conservation

Victoria Hemming  1 , Abbey E Camaclang  1 , Megan S Adams  1 , Mark Burgman  2 , Katherine Carbeck  1 , Josie Carwardine  3 , Iadine Chadès  3 , Lia Chalifour  1   4 , Sarah J Converse  5 , Lindsay N K Davidson  6 , Georgia E Garrard  7 , Riley Finn  1 , Jesse R Fleri  1   8 , Jacqueline Huard  1 , Helen J Mayfield  9   10 , Eve McDonald Madden  9 , Ilona Naujokaitis-Lewis  11 , Hugh P Possingham  10 , Libby Rumpff  7 , Michael C Runge  12 , Daniel Stewart  1 , Vivitskaia J D Tulloch  1 , Terry Walshe  7 , Tara G Martin  1
Affiliations
Review

An introduction to decision science for conservation

Victoria Hemming et al. Conserv Biol. 2022 Feb.

Abstract
in English, Spanish

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.

Las decisiones sobre la conservación de la biodiversidad son difíciles de tomar, especialmente cuando involucran diferentes valores, objetivos multidimensionales complejos, recursos limitados, urgencia y una incertidumbre considerable. Las ciencias de la decisión incorporan una teoría sobre cómo tomar decisiones difíciles y una variedad extensa de marcos de trabajo y herramientas que transforman esa teoría en práctica. Buscamos mejorar la claridad conceptual y la aplicación práctica de las ciencias de la decisión para ayudar al órgano decisorio a aplicar estas ciencias a los problemas de conservación. Nos enfocamos en las barreras para la aceptación de las ciencias de la decisión, incluyendo la falta de capacitación y de conciencia por estas ciencias; la confusión por la terminología común y cuáles herramientas y marcos de trabajo aplicar; y la impresión errónea de que la aplicación de estas ciencias consume tiempo y debe ser costosa y compleja. Para asistir en la navegación de la literatura extensa y dispar de las ciencias de la decisión, aclaramos el significado de varios términos comunes: ciencias de la decisión, teoría de la decisión, análisis de decisiones, toma estructurada de decisiones y herramientas de apoyo para las decisiones. La aplicación de las ciencias de la decisión no tiene que ser compleja ni debe llevar mucho tiempo; de hecho, todo comienza con saber cómo pensar detenidamente en los componentes de una decisión mediante el análisis de decisiones (es decir, definir el problema, producir objetivos, desarrollar alternativas, estimar consecuencias y realizar compensaciones). Lo anterior se logra de mejor manera mediante la aplicación de una estrategia prototipos rápidos. En cada paso, las herramientas de apoyo para las decisiones pueden proporcionar visión y claridad adicionales, mientras que los marcos de apoyo para las decisiones (p.ej.: gestión de amenazas prioritarias y planeación sistemática de la conservación) pueden asistir en la navegación de los diferentes pasos de un análisis de decisiones para contextos particulares. Resumimos los marcos de trabajo y las herramientas más importantes de apoyo para las decisiones y describimos el paso, y el contexto, del análisis de decisiones para el que es más útil aplicarlos. Nuestra introducción a las ciencias de la decisión apoyará en la contextualización de las estrategias actuales y los nuevos desarrollos, y ayudarán al órgano decisorio a comenzar a aplicar estas ciencias en los problemas de conservación.

Keywords: análisis de decisiones; ciencias de la decisión; ciencias sociales; conservación; conservation; decision analysis; decision science; decision-making; incertidumbre; prioritization; priorización; social science; structured decision-making; toma de decisiones; toma estructurada de decisiones; uncertainty; valores; values.

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Figures

FIGURE 1
FIGURE 1
A model for how decisions should be made. As suggested by Keeney (2004), out of 10,000 decisions, many (∼9000) can be made intuitively or have small consequences and do not warrant more thought or application of decision science. The remaining 1000 decisions are worthy of more thought (challenges in Table 1). Many decisions (∼750) could be improved by simply thinking through the decision consistent with the steps of decision analysis. The remaining decisions (∼250) may require additional analysis, the level of which will be identified by further rapid prototyping of the decision and application of a few simple tools. Very few, typically the most complex decisions (∼50 [0.5%]), will require a full decision analysis and would benefit from more time and resources
FIGURE 2
FIGURE 2
A conceptual overview of decision science and the relationship between key terms. Prescriptive decision theory guides decision analysis (combines insights from normative and descriptive decision theory) (see “Decision theory”). Pr, problem; O, objectives; A, alternatives; C, consequence; T, trade‐offs; D, deciding and implementing; M, monitoring; Pr, O, A, C, and T precede D and M. Decision‐support tools provide insight at each component; decision‐support frameworks help to step through multiple components (see “Decision‐support frameworks and tools”)
FIGURE 3
FIGURE 3
Decision analysis (commonly referred to as structured decision‐making) follows the PrOACT steps (steps 1–5) to help inform decisions. Once a decision is made (step 6), monitoring is often used (step 7) to evaluate the outcomes of the decision or to continue to learn about the consequences (link between 7 and 4) or the problem (link between 7 and 1) (dashed arrows, process is often iterative and return to a previous step may be needed as new information is obtained; white boxes, decision‐support tools available for a step). Appendix S1 describes these tools and provides useful references for their application. Figure adapted from Garrard et al. (2017)
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