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Water @ Michigan A nudge or a heavy lift? Decision support approaches for sustainability Victoria Campbell-Arvai School of Natural Resources & Environment [email protected]

Decision Support Approaches for Sustainability

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Page 1: Decision Support Approaches for Sustainability

Water @ Michigan

A nudge or a heavy lift? Decision support

approaches for sustainability

Victoria Campbell-Arvai School of Natural Resources & Environment

[email protected]

Page 2: Decision Support Approaches for Sustainability

Information deficit model

• For improved decision-

making in environment,

natural resource and

sustainability contexts,

is providing ‘more’ or

‘better’ information

sufficient?

Image: http://www.southernfriedscience.com/?p=18755

Page 3: Decision Support Approaches for Sustainability

Heuristics and Biases

• Heuristics are mental

shortcuts or ‘rules of thumb’

• Helpful, but can lead to

systematic and predictable

biases in our decision-making

• But, we can also leverage

these heuristics to our

advantage

Image: http://www.peterubel.com/wp-content/uploads/2015/04/Anchoring-Heuristic-Dilbert.png

Page 4: Decision Support Approaches for Sustainability

Passive decision support: ‘Nudge’

4 Image: http://www.biashunter.com/blog/category/nudge

0102030405060708090

100

% Choosing a vegetarian meal

Campbell-Arvai, V., Arvai, J. and L. Kalof. 2014. Environment & Behavior 46(4): 453-475.

Page 5: Decision Support Approaches for Sustainability

Active decision support: SDM

• Structured decision-making

(SDM) process:

– Define the Problem

– Identify Objectives & Measures

– Develop Alternatives

– Identify Consequences

– Address Trade-offs

– Implement, monitor and adapt Bessette, D. L., Campbell-Arvai, V., and J.L. Arvai. 2015. Risk Analysis (Early View).

Page 6: Decision Support Approaches for Sustainability

A nudge or a heavy lift? • Nudging:

– Frequent, low-stakes decisions

– Take advantage of our heuristics

and biases

• Structured decision-making:

– Infrequent, high-stakes decisions

– Break decision down into smaller

steps

– Avoid (as much as possible)

heuristics and biases

Image: http://www.lse.ac.uk/publicEvents/events/2011/20111115t1830vOT.aspx