Modeling heterogeneous Modeling heterogeneous fishermen behaviorfishermen behavior

Michael Robinson

UCSB Geography

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.

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?

Research

• Heterogeneous effort distribution

– Satisficer-maximizer continuum

• Learning & memory

– Bayesian updating

• Communication

– Information exchange

Effort distribution

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

10

20

30

40

50

60

70

# Days Fished

# F

ishe

rmen

2005 Urchin Fishing Effort

0 20 40 60 80 100 120 140 160 180 2000

10

20

30

40

50

60

70

80

# Days Fished

# F

ishe

rmen

2004 Urchin Fishing Effort

Fish block/simulation comparison

2004 DFG urchin block data Urchin fishing simulation(uniform effort distribution)

Memory and learning are missing!

Learning, memory, and communication

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.

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”

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

10

20

30

40

50

60

70

80

Image: Wm. B. Dewey, www.islandpackers.com

Questions?Questions?Suggestions?Suggestions?

THANKS…Dave Siegel, Chris Costello,Kostas Goulias, Kristine Barsky,Chris Miller, Pete Halmay