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Value of Information and Value of Control in fisheries management: North Sea herring as an example Samu Mäntyniemi, Sakari Kuikka, Laurence Kell, Mika.

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Presentation on theme: "Value of Information and Value of Control in fisheries management: North Sea herring as an example Samu Mäntyniemi, Sakari Kuikka, Laurence Kell, Mika."— Presentation transcript:

1 Value of Information and Value of Control in fisheries management: North Sea herring as an example Samu Mäntyniemi, Sakari Kuikka, Laurence Kell, Mika Rahikainen and Veijo Kaitala ICES Annual Science Conference, Halifax, Canada, Sep 26 th, 2008

2 Outline 1. Loose definition 2. Conclusions 3. Application to North Sea herring population

3 Value of information (VoI) is the amount a decision maker would be willing to pay for information prior to making a decision.

4 Value of information Can be calculated for information sources e.g. gathering of monitoring data Expected increase in expected profit when using the information source Or maximum price to pay for the information source Or expected loss if you ignore the information source Leave existing knowledge out from stock assessment -> less expected profit from fishery Emphasizes the importance of prior information Used widely in other areas outside fisheries

5 Properties of Value of Information (VoI) VoI depends on the initial amount of information about the state of nature VoI = 0, when the state of nature is already known exactly VoI is highest when the existing knowledge is poor If new information changes the optimal decision, then VoI > 0

6 Conclusions I Value of information can be used to prioritize research activities for management needs For each information source, calculate: Value of Information – Cost of information Then rank the sources If the cost is higher than the value, do not buy the information

7 Conclusions II Could be utilised at different levels Fishing companies: plan investments to technology Managers: plan investments on new research -EU commission: planning of research agenda Prerequisites Probabilistic formulation of information -Bayesian inference in system assessment Numerically stated management objectives -Mix of economic and social objectives = difficult Clearly defined set of alternative management actions Not possible to define VoI for research that may provide completely new hypotheses!

8 Example: North Sea herring Objective: maximize expected profits over next 20 years Herring price and fishing cost assumptions from Rahikainen et al (Presentation nr:o 7, 30 minutes ago) Alternative decisions Increase or decrease the current fishing mortality and keep it constant over the 20 year period. Uncertainty about Natural mortality, fishing mortality, selection curve, type of stock-recruitment (SR) relationship, SR-parameters, true catches at age and about the true status of the stock How much to pay for perfect information about the type of SR relationship?

9 Bayesian stock assessment model Age-structured, models full life cycle Includes both Beverton-Holt and Ricker stock recruitment relationships as hypotheses Fitted to the North Sea data set used by ICES Herring Assessment Working group Output: posterior probabilities for Beverton-Holt and Ricker models Ricker : 0.57 Beverton-Holt : 0.43

10 Value of information Value of Information = 0.43 x x 243 = 240M NOK B-H=235M NOK Ricker=243M NOK Currently optimal Optimal if SRR = B-H Optimal if SRR = Ricker Current knowledge IF Ricker IF B-H Bayesian stock assessment: P(B-H | data)=0.43 P(Ricker | data)=0.57

11 Decide about investing If perfect knowledge about the type of SRR costs less than 240M NOK, go and buy it! Remarks Perfect knowledge impossible Calculate value of imperfect knowledge instead -Need knowledge about the precision of the research -More complex, but possible to do Value of overconfidence? Act as if B-H or Ricker was true, when actually uncertain Reverse the concept of value of information -> loss because of overconfidence -> if forced to, choose to use the one with less loss

12 Thank you!


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