making better decisions · egic brushland-dependent wildlife (+) mn’s hunting heritage (+) public...

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)

4

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)

5

“...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

6

7

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

9

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)

10

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

17

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.

18

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

19

• 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