Use of decision analysis in the evaluation of scientific information
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Use of decision analysis in the evaluation of scientific information. Sakari Kuikka University of Helsinki Maretarium, Kotka Content: Decision making in general and in fisheries Value-of-information Value-of-control Commitment: role of understandability. Main results of the talk.

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Sakari kuikka university of helsinki maretarium kotka content

Use of decision analysis in the evaluation of scientific information

  • Sakari Kuikka

  • University of Helsinki

  • Maretarium, Kotka

  • Content:

  • Decision making in general and in fisheries

  • Value-of-information

  • Value-of-control

  • Commitment: role of understandability


Main results of the talk

Main results of the talk

World 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

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

Bias of ICES stock assessments

Sparholt & Bertelsen, 2002


Part i decision making and decision analysis

Part I : Decision making and decision analysis

”Predicting the outcome is far more difficult than the

ranking of decision options”


Actions and decisions

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

Management of environment and fisheries

  • What are your aims?

  • What are your management tools

  • What do you have to know to use those tools

  • How do you know whether your management is

  • worthwhile


Types of decision analysis

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


Chain of knowledge and actions

Chain of knowledge and actions

Production potential

of the stock (real state

of nature)

How well we can measure/assess ?

= quality of the science

Knowledge

State of nature

Available

knowledge

New state of

nature

Action

= aim

How strong will be the impact

of decision on nature (e.g. implementation

uncertainty)

Utility: dependent on action and on the

real state of nature


Fisheries management chain of humans and nature

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

” Decision to act”

1

Step 2: Change in fishermens behaviour

”How humans act?” Uncert: which vessels?

2

Step 3: Impact on nature

” How the SSB or recruitment will change”

3

Step 4: Degree of success

”How do we valuate changes?”

4

Fisheries management:Chain of humans and nature


Evaluation of decision options

Evaluation of decision options

  • Uncertainties in:

  • Implementation (juridical and socio-economic part)

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

  • Current and future objectives (political/sociological part)


Lack of objectives

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

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


Sakari kuikka university of helsinki maretarium kotka content

Value of information and value of control

  • 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?

  • 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

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

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

+ probabilities


Voi and voc

VOI and VOC

M = .2

M = .4


Example value of information

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


Degree of implementation succes controllability

Degree of implementation succes = controllability

Aim: catch of 100


Inserting implementation uncertainty

Inserting implementation uncertainty


Fisheries system several optional control tools

Fisheries system: several optional control tools


Value of perfect information perfect control

Value of perfect information: Perfect control

Bigger mesh size: system becomes

more information robust

Doing has an effect on the need of knowing


Sakari kuikka university of helsinki maretarium kotka content

Kuikka, 1994


Sakari kuikka university of helsinki maretarium kotka content

Planning of management and monitoring by

a meta-model

Model 2

Water

quality

Catchability

Cod fisheries

Cod

Effort

management

biomass

Fishing

Natural

mortality

Mortality

Model 3

Herring

Yield

recruitment

Model 1

Which variables must be monitored, if I use

variable A as a control variable ?


Some general conclusions

Some general conclusions

  • Usually:

  • The closer the control (decision variable) is to the objective

  • function, 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

Part III: human aspects


Uncertainty1

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

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/impress

colleagues: do we pay a high price on commitment side of actors?

What is good applied science ?


Management of humans

Aims of society

Control:rules, money, info

Uncertainty

of nature

Knowledge

of individuals

Values and aims

of individuals

Reaching of the

aims

Behaviour of individuals

Management of humans

Kausaliteettien voimakkuus, tarvittava informaatio ?


Number of recruits per one spawning fish in one year

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

Number of recruits per one spawning fish in one year

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

Peltomäki 2004


Spr and recruitment size argumentation for fishermen

% SPR and recruitment size: argumentation for fishermen

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

”Biological safetymargin ”

Recruitment size

Increase of freq. of other managementactions

Decrease of freq. of other managementactions

Maturity length


Some final points logic of insurance systems and the message from economic studies

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


Logic of insurance pay to reduce uncertainty

Logic of insurance: pay to reduce uncertainty


Economic view

Economic view

Income (kg or kg * euro)

Profit

Costs

Spawning stock

Fishing effort


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