Modeling heterogeneous fishermen behavior Michael Robinson UCSB Geography.
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Transcript of Modeling heterogeneous fishermen behavior Michael Robinson UCSB Geography.
![Page 1: Modeling heterogeneous fishermen behavior Michael Robinson UCSB Geography.](https://reader035.fdocuments.net/reader035/viewer/2022062421/56649d415503460f94a1ba99/html5/thumbnails/1.jpg)
Modeling heterogeneous Modeling heterogeneous fishermen behaviorfishermen behavior
Michael Robinson
UCSB Geography
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Fisherman behavior
• Fishing is fraught with physical and financial risk and is undertaken in a constantly changing environment.
• Effort distribution within a fleet appears to be far from homogenous.
• Successful fishermen exhibit an ability to change their behavior with varying conditions and information.
• Learning and communication are critical components of fishing activity.
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Fisherman behavior
• Research questions…– How are risk behaviors and decision
paradigms set?– How do fishermen learn about their
environment? How does this affect their efficiency and success?
– How do these factors change over seasons, over years, and as catch is (or is not) accumulated?
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Research
• Heterogeneous effort distribution
– Satisficer-maximizer continuum
• Learning & memory
– Bayesian updating
• Communication
– Information exchange
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Effort distribution
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Effort distribution model
• Random fishing probabilities (~[0,1]) applied to each fisherman in fleet– Uniform– Exponential– Highly skewed gamma
• Utility function to decide most attractive patches
0 20 40 60 80 100 120 140 160 1800
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# Days Fished
# F
ishe
rmen
2005 Urchin Fishing Effort
0 20 40 60 80 100 120 140 160 180 2000
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# Days Fished
# F
ishe
rmen
2004 Urchin Fishing Effort
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Fish block/simulation comparison
2004 DFG urchin block data Urchin fishing simulation(uniform effort distribution)
Memory and learning are missing!
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Learning, memory, and communication
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Learning & memory
• Learning: Information received from an individual’s daily fishing effort.
• Bayesian updating
– DeGroot, 1970
– A fisherman updates beliefs about abundance after acquiring signal Sa from visiting site a (these signals follow a normal distribution).
• Good signals (Sa>α0) increase expected abundance at site a.
• Noisy signals (large σ2s) are given less weight.
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Communication
• Communication: Information received from the fleet
• Information exchange matrix– Allen and McGlade,
1987– Matrix of “how well”
and with whom information is shared
• No sharing• Perfect sharing• Imperfect sharing• Develop sharing weights
– Clubs/code groups– Mean of guy that’s gone
200 times vs. mean of guy that’s only gone once
– Variance of signal has an effect on “confidence in the signal”
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Fish block/simulation comparison
Red = fishing, blue = no fishing
2004 DFG urchin block data Urchin fishing simulation(exponential effort distribution)
DayF
ishe
rman
Learning/Commuication Simulation
50 100 150 200 250 300 350
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Image: Wm. B. Dewey, www.islandpackers.com
Questions?Questions?Suggestions?Suggestions?
THANKS…Dave Siegel, Chris Costello,Kostas Goulias, Kristine Barsky,Chris Miller, Pete Halmay