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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Use of decision analysis in the evaluation of scientific information
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
Sparholt & Bertelsen, 2002
”Predicting the outcome is far more difficult than the
ranking of decision options”
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
of the stock (real state
How well we can measure/assess ?
= quality of the science
State of nature
New state of
How strong will be the impact
of decision on nature (e.g. implementation
Utility: dependent on action and on the
real state of nature
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”
Step 4: Degree of success
”How do we valuate changes?”
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
Value of information and value of control
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
M = .2
M = .4
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
Aim: catch of 100
Bigger mesh size: system becomes
more information robust
Doing has an effect on the need of knowing
Planning of management and monitoring by
Which variables must be monitored, if I use
variable A as a control variable ?
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 ?
Aims of society
Control:rules, money, info
Values and aims
Reaching of the
Behaviour of individuals
Kausaliteettien voimakkuus, tarvittava informaatio ?
Mean: 0,6 recruits per one spawning fish and year
Impact of SSB on the number of recruits per one spawning fish and year in the Bothnian Sea herring stock
Recruitment size and maturity size & ”spawn at least once policy”
”Biological safetymargin ”
Increase of freq. of other managementactions
Decrease of freq. of other managementactions
Income (kg or kg * euro)