Making Better Decisions
The argument for
Structured Decision Making
Mike Larson, Forest Wildlife Research Supervisor
• What is SDM & why use it?
• Concepts/tools to remember…
• PrOACT
• Objectives hierarchy
• Consequences table
• Example
• Questions/discussion
Outline
“…we are not so much rational
creatures as we are creatures
capable of rationalizing.”David Orr (2004, Conservation Biolology 18:1459)
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How Decisions Are Usually ResolvedA
ll D
ecis
ion
s
Worthy of thought
Procrastination30%
Too Little Time Spent
20%
No Brainers
Thought About 50%
Gets Systematic Thought 4%
Small Consequences
(SDM steps)Not Clear
30%
Informal16%
Adapted from Keeney (2004)
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“...if you are a resource manager,
all paths lead to a decision.”
Better to “set it up that way from the start.”
– Gregory et al. 2012
Can the problem, issue, challenge, or controversybe reframed as a decision?
Framing Problems as Decisions
Decisions = Choices = Selecting an action
Structured Decision Making
• Established theory, tools & techniques
for complex decisions
• Deconstruct, analyze, synthesize
decision components
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What’s Different
SDM ApproachFamiliar Approaches
• Acknowledges Subjectivity
• Structure Decisions
• Emphasize Objectivity
• Simplify Decisions
8
Common Decision Components
• PrOACT (Hammond et al. 1999. Smart Choices)
• Problem
• Objectives ( Values)
• Alternatives
• Consequences
• Trade-offs
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All
Dec
isio
ns
Worthy of thought
No Brainers
Small Consequences
Get Systematic Thought
“Clear Thinking”75%
Full Decision Analysis
5%Partial Decision
Analysis20%
How Decisions Should Be Resolved
Adapted from Keeney (2004)
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Common Decision Components• PrOACT (Hammond et al. 1999. Smart Choices)
• Problem• Asking the right question, framing, scoping
• Objectives (• What “you” care about (Values) Values)
• Alternatives• Be creative!
• Consequences• Need to predict (Science & Uncertainty)
• Trade-offs• Weighing apples & oranges
Consequence Table
Objectives
Alternatives 1 2 3
A
B
C
Problem:
Consequence Table
Objectives
Alternatives 1 2 3
A
B
C
Problem:
Consequence Table
Objectives
Alternatives 1 2 3
A
B
C
Problem:
Consequence Table
Objectives
Alternatives 1 2 3
A
B
C
Problem:
Consequence Table
Objectives
Alternatives 1 2 3
A
B Consequences
C
Problem:
Decision Making Resources
16Adapted from Rowland et al. (2014)
TriggerImplement
decision
Framework (SDM)
Methods(scenario planning)
Tools(models, Zonation)
Boundaries of SDM Use
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Good Decisions
What makes a decision good
is the process by which it was generated,
not necessarily the ultimate outcome
because we can’t eliminate uncertainty or fully control the system.
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SDM yields decisions that are…
• More likely to achieve objectives
• Deliberative, thorough
• Values-based
• Relies on science, Robust to uncertainty
• More likely to be accepted by others
• Incorporates competing values
• Clarifies roles of science & values
• Transparent, Explicit, Documentable, Replicable
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• Little
• 1 person at their desk, in the field
• 5 min – 1 hour
• Urgent, relatively small scope
• Medium
• Committee, work group
• Days – weeks
• Seasonal, regional scope
• Big
• Committee with leadership, stakeholders involved
• Weeks – years
• State-wide importance
What decisions is SDM good for?
Full SDM Example
Sharp-tailed grouse harvest regulations in EC Minnesota
• Trigger: Concern about population
status
• Scope:
• EC population, maybe
just southern part
• Harvest regulations only,
not habitat management
Problem
Objectives
Additive mortality from
hunting(-)
Me
ans
Hunting opportunities
(+)
Regulatory burden
(-)
Abundance of STGR
(+)
Open brushland systems(+)
Perception that hunting is
limiting the STGR population (-)
Changes to rules, statutes
(-)
Probability of STGR persistence
(+)
Ease of enforcing
regs. (+)
DNR & hunter credibility as conservationists
(+)
Support for habitat
mgt.(+)
Annual admin. of the hunt
(-)
Stra
tegi
c
Brushland-dependent wildlife(+)
MN’s hunting heritage
(+)
Public knowledge of lek locations
(-)
Disturbance to breeding activity
(-)
Public awareness of
STGR & brushlands
(+)
Fun
dam
enta
l
• Strategies based on varying
components of harvest season
structure
• Length of season
• Bag limits
• Who gets to hunt & where
Alternatives
Influence Diagram – STGR Population in EC MN
Blue hexagons = Objectives
Green rectangles = Alternative Actions
Abundance of STGR during spring (+)
Fall-to-spring survival
Nest, chick, & adult survival
during summer
Weather
STGR abundanceduring fall
Regulatory burden (-)
Hunting regulations
Harvest of STGR
Habitat
Hunter-days per year (+)
November 1, 2016
Toxicants, disease,
pheasants
Succession, land use
conversion
Habitat conservation & management
Confidence hunting is justified
(+)
Support for management
Climate change
Consequences
Objectives:STGR
abundance in 20 years Hunting heritage
DNR credibility as conservationists
Regulatory burden
Alterna-tive Metric: (males at leks) (hunter-days) (0-10 index)
(Index of DNR costs)
Status quo 384 (0-1000) 4,882 (1,000-12,400) 3.5 (0-8) 0
Shorter season 399 (0-1000) 4,123 (775-8,600) 5.5 (0-8) 0
Lottery 444 (0-1000) 2,105 (550-5,000) 6.5 (4-10) 20
WMAs as refuges 441 (0-1,100) 4,109 (675-11,400) 5.2 (0-8) 2
Partial closure 1 495 (15-1,475) 3,423 (725-8,500) 6.3 (2-10) 1
Full closure 558 (10-1,650) 0 (0-0) 9.1 (5-10) 1
Consequences
• Status quo, Lottery, & Full closure
ruled out
• Partial closure – also no
• Unspecified objective
• Selected Shorter season
• Would have had highest
weighted sum
Trade-offs
• SDM helps us make better decisions
• Framework for clear thinking
• More likely to achieve objectives
• More likely to be accepted by
• others
• Remember…
• PrOACT
• Objectives hierarchy
• Consequences table
Presentation Summary