Embracing Complexity and Uncertainty

Abstract

Geographers can meaningfully and uniquely contribute to problem solving and assist vulnerable populations in making informed decisions. Contemporary environmental and social problems are complex and accompanied by uncertainty. Decisions must be made in the face of this uncertainty. In this address, geographers are encouraged to embrace, rather than minimize, complexity and uncertainty in their research and in their interactions with decision makers. Adaptation to climate change is used to illustrate the ubiquitous uncertainty surrounding problem solving and how the choice of assessment framework can overemphasize some sources of uncertainty and ignore others. A challenge is to communicate the information about complexity and uncertainty that decision makers need for robust and flexible decision making but at the same time prevent uncertainty from being equated with a lack of consensus and used as a reason for inaction. Geographers need to be open to a plurality of approaches to decision making and acknowledge uncertainty in their own research. Reframing the communication of uncertainty and the development of novel educational tools and learning materials for decision makers will facilitate decision making. Sustained engagement with decision makers, including the coproduction of knowledge, can also lead to greater consideration of complexity and uncertainty and to improved decision making. Rather than “keeping it simple,” geographers should “keep it complex.”

地理学者能够对解决问题做出有意义且独特的贡献, 并协助脆弱的人口做出充分告知信息的决策。当代环境和社会问题相当复杂, 并伴随着不确定性。决策必须在此般不确定性中完成。在此一处理问题的要领中, 地理学者被鼓励拥抱研究中及其与决策制订者互动中的复杂性和不确定性— —而非将之最小化。气候变迁的调适, 用来说明围绕着问题解决无所不在的不确定性, 以及评估架构的选择如何可能过度强调若干不确定性的来源, 同时忽略了其他来源。就决策制定者为做出强力且具有弹性的决策所需之复杂性与不确定性的讯息进行沟通, 但同时避免将不确定性等同于缺乏共识, 并被用来当作不行动的理由, 则是一项挑战。地理学者必须对决策方法的多元性保持开放, 并承认自身研究中的不确定性。再架构有关不确定性的沟通, 并为政策制定者发展崭新的教育工具及学习材料, 将能促进决策制订。与决策制定者的持续参与, 包含知识的共同生产, 同时能够更为广泛地考量复杂性与不确定性, 并改进决策制定。与其 “保持简易性”, 地理学者应该“维持复杂性”。

Los geógrafos pueden contribuir de manera significativa y única a la solución de problemas, y ayudar a poblaciones vulnerables a tomar decisiones bien fundamentadas. Sin embargo, los problemas ambientales y sociales contemporáneos son complejos y van acompañados de incertidumbre. Las decisiones deben adoptarse enfrentando esta incertidumbre. En este discurso, los geógrafos son urgidos a que, en vez de minimizarlas, abracen la complejidad y la incertidumbre en sus investigaciones y en sus interacciones con los tomadores de decisiones. Se usa la adaptación al cambio climático para ilustrar la incertidumbre ubicua que rodea la solución de problemas y cómo la escogencia del marco de evaluación puede hacer mayor énfasis sobre algunas fuentes de incertidumbre, e ignorar otras. Reto mayor es comunicar la información sobre la complejidad y la incertidumbre que necesitan los tomadores de decisiones para que la adopción de una determinada decisión sea robusta y flexible, aunque al mismo tiempo se evite que la incertidumbre se equipare con una falta de consenso y que se use como pretexto para la inacción. Los geógrafos necesitan estar abiertos a una pluralidad de enfoques sobre toma de decisiones y reconocer la incertidumbre en sus propias investigaciones. La toma de decisiones se facilitará reformulando la comunicación sobre la incertidumbre y con el desarrollo de herramientas educativas y materiales de aprendizaje novedosos para los tomadores de decisiones. Un compromiso sostenido con los tomadores de decisiones, incluyendo la coproducción de conocimiento, puede también llevar a una consideración más grande de la complejidad y la incertidumbre y a mejorar la toma de decisiones. En vez de “mantener esto simple”, los geógrafos deben “mantenerlo complejo.”

Key Words:

• adaptation
• assessments
• climate change
• complexity
• decision making
• uncertainty

关键词：:

• 评估, 气候变迁, 复杂性, 决策制订, 不确定性。

Palabras clave:

• adaptación
• evaluaciones
• cambio climático
• complejidad
• toma de decisiones
• incertidumbre

Acknowledgments

This address was informed by my involvement in the Pileus Project that developed decision-support tools to help perennial crop producers adapt to climate change, and by the Climate Change and International Markets (CLIMARK) Project that explored frameworks for incorporating spatial linkages into climate change assessments. My thanks to the scientists, stakeholders, and students contributing to these projects for all that you taught me. Also, many thanks to James McCarthy for his thoughtful and helpful comments on an earlier version of this address.

Funding

This address was informed by research and outreach partially supported by the Environmental Protection Agency [# R83081401-0] and the National Science Foundation [SES 0622954, CNH 0909378]. The opinions, findings, and conclusions expressed in this address are my own and do not necessarily reflect the views of the Environmental Protection Agency or the National Science Foundation.

Notes

1. Adaptation and mitigation are overlapping concepts but, to keep the content of this address manageable, I consider them as two distinct entities. This is an acknowledged simplification. Adaptation has been defined many different ways, but I adhere most closely to the definition of Moser and Ekstrom (2010) that “adaptation involves changes in social-ecological systems in response to actual and expected impacts of climate change in the context of interacting nonclimatic changes” (22026). The IPCC definition of “mitigation” is used here, namely that mitigation is “a human intervention to reduce the sources or enhance the sinks of greenhouse gases” (IPCC 2013, 1458).

2. Bias correction adjusts for known biases in simulations from climate models. These biases complicate the use of climate simulations in climate change assessments and introduce error into assessments. Biases are typically identified through the comparison of model simulations for the current climate with observations. Most often, statistical techniques are used to adjust simulations for biases. Downscaling is used to derive the local- to regional-scale climate information needed for many climate change assessments from larger scale model simulations. Downscaling methods are usually described as dynamic (i.e., involving the use of regional climate models) or empirical (i.e., statistical). It is often difficult to separate bias correction from downscaling, as the approach used to infer finer resolution often also adjusts for biases. Winkler and colleagues (Winkler, Guentchev, Liszewska, et al. 2011; Winkler, Guentchev, Perdinan, et al. 2011) provided a summary intended for the nonclimate scientist of bias correction and downscaling procedures.

Additional information

Notes on contributors

Julie A. Winkler

JULIE A. WINKLER is a Professor of Geography in the Department of Geography at Michigan State University, East Lansing, MI 48824. E-mail: [email protected]. Her research interests include applied climatology, synoptic climatology, the use of climate projections in climate change assessments, and the potential impacts of climate variability and change on agriculture and natural resources. She was the 2013–2014 President of the Association of American Geographers.