Sakari Kuikka University of Helsinki Maretarium, Kotka Content:

1. 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

2. 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

3. 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)

4. Bias of ICES stock assessments Sparholt & Bertelsen, 2002

5. Part I : Decision making and decision analysis ”Predicting the outcome is far more difficult than the ranking of decision options”

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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)

12. 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

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

14. 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

15. 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)

16. 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

17. VOI and VOC M = .2 M = .4

18. 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

19. Degree of implementation succes = controllability Aim: catch of 100

20. Inserting implementation uncertainty

21. Fisheries system: several optional control tools

22. Value of perfect information: Perfect control Bigger mesh size: system becomes more information robust Doing has an effect on the need of knowing

23. Kuikka, 1994

24. 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 ?

25. 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

26. Part III: human aspects

27. 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)

28. 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 ?

29. 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 ?

30. 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

31. % 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

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

33. Logic of insurance: pay to reduce uncertainty

34. Economic view Income (kg or kg * euro) Profit Costs Spawning stock Fishing effort