Sakari KuikkaUniversity of HelsinkiMaretarium, Kotka

Content:

1) Decision making in general and in fisheries2) Value-of-information3) Value-of-control4) Commitment: role of understandability

Use of decision analysis in the evaluation of scientific information

Main results of the talkWorld Cup Icehockey, last night

Canada – Finland 3-2 (1-1,1-1,1-0)

00.52 Joe Sakic (Mario Lemieux, Eric Brewer) 1-0 06.34 Riku Hahl (Toni Lydman, Aki Berg) 1-1

23.15 Scott Niedermayer (Kris Draper, Joe Thornton) 2-1 39.00 Tuomo Ruutu (Toni Lydman) 2-2

40.34 Shane Doan (Joe Thornton, Adam Foote) 3-2

Uncertainty Rowe (1994):

• Temporal uncertainty: future and past states• Structural uncertainty (uncertainty due to

complexity, related to control)• Metrical uncertainty (uncertainty in

measurements)• Translational uncertainty (uncertainty in

explaining uncertain results)

Bias of ICES stock assessments

Errors in assessments (1988-1999)

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9

Ratio SSB-predicted/SSB-truth

Fre

qu

en

cy

Sparholt & Bertelsen, 2002

Part I : Decision making and decision analysis

”Predicting the outcome is far more difficult than the ranking of decision options”

Actions and Decisions

Fisheries management:

”Economically effective control of an uncertain biological system by the politically possible juridical control tools”

Only actions will increase utilities (getting closer to objectives), not predictions or scientific estimates as such

Management of environment and fisheries

1) What are your aims?2) What are your management tools3) What do you have to know to use those tools4) How do you know whether your management is worthwhile

Types of decision Analysis 1) Analysis of objectives: Analytic Hierarchy Process: AHP

= systematic weighting of objectives and their linking

to decision alternatives

2) Analysis of knowledge and actions: Decision trees and influence diagrams.

= analysis of probabilistic information in a decision framework

State of nature Knowledge

Action New state of nature

Production potential of the stock (real state of nature)

How well we can measure/assess ?= quality of the science

Availableknowledge

How strong will be the impactof decision on nature (e.g. implementation uncertainty)

= aim

Utility: dependent on action and on the real state of nature

Chain of knowledge and actions

Step 1: Decision to implement new economic subsidies to decrease the effort

” Decision to act”

Step 2: Change in fishermens behaviour

”How humans act?” Uncert: which vessels?

Step 3: Impact on nature

” How the SSB or recruitment will change”

2

3

1

4Step 4: Degree of success

”How do we valuate changes?”

Fisheries management:Chain of humans and nature

Evaluation of decision optionsUncertainties in:

a) Implementation (juridical and socio-economic part)

b) Biological impact (biological part): the gain of saving a fish

c) Current and future objectives (political/sociological part)

Lack of objectives?

Decision analysis can also show, what must the objectives be, if the available information and decisions are known: transparency

You may be able to show, that even though there are different objectives, they all favor the same decisions

=> stakeholders do not necessarily need to agree on objectives

Part II: value of knowing and value of doing: Basic elements of decision

analysis

Value of information and value of control

1) How much I should pay for the better information? = value-of-information - dependent on e.g. how much decision could change, if new

information is obtained, and how well the new decision can be implemented?

2) How much I should pay for the better control (management) of the system?

= value of control

- how much the expected state of the system could be improved, if the precision of the control would be improved

Value of Information and Control

• Expected Value of Perfect Information (EVPI): new information => choosing a different action with better outcome => information had some value

(dependent on the controllability)

• Value of Control: ability to change the value of a previously uncontrollable variable or improving of controllability (better adjustment of the system)

= Numerical estimates of key elements in the planning of control and information system (monitoring + studies)

Simplified example

Value of information: better estimate for M + decreased F => higher yield per recruit

Value of control: adjustment of M through multispecies context => higher yield per recruit

VOI and VOC

M = .2

M = .4

Example:Value-of-information

If fishing mortality of 0.5 produces catch of 2 million during the Next 20 years, and mortality of 0.7 produces 1.5 million, the information that switched the decision to 0.5 had a value of 0.5 million fish

However, expected value of perfect information EVPI (e.g. Clemen, 1996) is often estimated in advance: the likelihood of future information (study results) under various scenarios must be evaluated

The most useful studies have a high value-of-information.

The best management schemes have low estimates for the value-of-information = information robustness

0

0.005

0.01

0.015

0.02

0.025

10 30 50 70 90 110 130 150 170

Realized catch

Pro

ba

bil

ity

Degree of implementation succes = controllability

Aim: catch of 100

Inserting implementation uncertainty

State ofnature

Measure-ment / study

Realised effect

ActionSatis-faction

Action

1000 1500 2000

500 0.1Realised 1000 0.8 0.1effect 1500 0.1 0.8 0.1

2000 0.1 0.82500 0.1

Fisheries system: several optional control tools

ProfitPriorstock

CPUE

Fishing mortality

Demand

Natural mortality

Price

Value of the catch

Costs

Income

Catchability Production capacity

Other stressing factors

Incomefrom othersources

Posteriorstock

Environ-mentalfactors

Catches of other stocks

Fishingeffort

Yield

Fishing capacity

Number of fishermen

Predators

Equipment

Taxes

Value of perfect information: Perfect control

Mesh size

Prior variable 120 mm 140 mm

Recruitment process 11.7 5.8Growth 0.04 0.00Biomass criteria 0.00 0.00All variables 12.9 7.6

Bigger mesh size: system becomes more information robust

Doing has an effect on the need of knowing

Kuikka, 1994

Planning of management and monitoring by a meta-model

NaturalMortality

Catchability

Fishing mortality

YieldHerring

recruitment

Cod biomass

Water quality

Effort Cod fisheriesmanagement

Model 1

Model 2

Model 3

Which variables must be monitored, if I use variable A as a control variable ?

Some general conclusionsUsually:

1) The closer the control (decision variable) is to the objectivefunction, the better is the control

2) The closer the information link is to the essential source of uncertainty and the better is the controllability of the system, the higher is the value-of-information

3) The closer the monitored variable is to the objective, the easier it is to evaluate the success of your management

Part III: human aspects

Uncertainty Rowe (1994):

• Temporal uncertainty: future and past states• Structural uncertainty (uncertainty due to

complexity, related to control)• Metrical uncertainty (uncertainty in

measurements)• Translational uncertainty (uncertainty in

explaining uncertain results)

Implementation succes

Succes of management: dependent on fishermen

Identification of effective ”social impact tools”

Identification of sources of commitment

” Social capital” in the fishermen’s organisation

Is the complicated science needed only to convince/impresscolleagues: do we pay a high price on commitment side of actors?

What is good applied science ?

Management of humans

Control:rules, money, info

Knowledgeof individuals

Values and aims of individuals

Behaviour of individuals

Aims of society

Uncertainty of nature

Reaching of the aims

Number of recruits per one spawning fish in one year

Mean: 0,6 recruits per one spawning fish and year

0

0,5

1

1,5

2

0 100000 200000 300000 400000

kutukannan biomassa (t)

rek

ryyt

tejä

/ k

ute

va y

ks

ilö (

kp

l)

Impact of SSB on the number of recruits per one spawning fish and year in the Bothnian Sea herring stock

Peltomäki 2004

Recruitment size and maturity size & ”spawn at least once policy”

Maturity lengthDecrease of freq. of other managementactions

”Biological safetymargin ”

Recruitment size

Increase of freq. of other managementactions

% SPR and recruitment size: argumentation for fishermen

Some final points: logic of insurance systems and the message from

economic studies

Logic of insurance: pay to reduce uncertainty

0

5000

10000

15000

20000

25000

30000

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Fishing effort

Yiel

d/In

com

e

Economic view

0

2000

4000

6000

8000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Costs

Income (kg or kg * euro)

Profit

Spawning stock

Fishing effort