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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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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?”
4Fisheries management:Chain of humans and nature
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: 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
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 ?
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)