Modeling fishermen behavior Modeling fishermen behavior in the Santa Barbarain the Santa Barbara

Channel IslandsChannel Islands

geog.Mike & econ.John

www.ucsb.edu

Images: William B. Folsom, NMFS, http://www.photolib.noaa.gov/fish/

Commercially harvested sea urchins ready for offload at the Ventura marina.

Inspecting sea urchin innards.

Fisherman behavior

• Adaptability– Fishing is fraught with physical and financial

risk and is undertaken in a constantly changing environment.

– 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

• Adaptability– How do fishermen learn about their

environment? How does this affect their efficiency and success?

– How are risk behaviors and decision paradigms set?

– Do these factors change significantly over seasons, over years, or as catch is (or is not) accumulated?

Research

• Heterogeneous effort distribution– Satisficer-optimizer continuum

• Learning & memory– Bayesian updating

• Communication– Information exchange matrix

Heterogeneous effort distribution

Effort distribution model

• Random fishing probabilities (~U [0,1]) applied to each fisherman in fleet– Probably closer to an

exponential or highly skewed gamma distribution

• Variable weather• Temporal closures

0 20 40 60 80 100 120 140 160 180 2000

10

20

30

40

50

60

70

80

# days fished

Fis

herm

an

2004 Fishing Effort

Effort distribution model

• Utility Function to decide most attractive patch

• where:F = “attractiveness” weight applied to patch

D = distance of patch from home port

cDbeaFQ

Effort distribution model

• We let the utility function be probabilities and treat as a cdf– Sort probabilities in ascending order– Keep track of corresponding patch numbers

Effort distribution model

• If a fisherman decides to go fishing we draw a (constrained) random number and find the corresponding value (inverse CDF) in the range 1:Npatches (# of patches)

1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

sorted patch number

prob

abili

ty

Effort distribution model

• Determine whether or not each fisherman in the fleet goes fishing– decision paradigm– weather, temporal closures, gear maintenance

• Determine where each fisherman goes fishing– patch attractiveness– information & learning (eventually)

• Determine "attractiveness" of each patch for next day– count number of "hits" to each patch & reduce abundance– attractiveness should increase at first as a patch is exploited and

then decrease as fish are removed

• Loop over the year• Loop over multiple years

Results

1 2 3 4 5 6 7 8 9 100

500

1000

1500

2000

2500Effort at each patch

Patch

Tim

es f

ishe

d

PatchIDInitial Patch

AttractivenessAverage

Hits

0 ----- 5574

1 0.30332 505

2 0.37466 452

3 0.11809 0

4 0.11903 0

5 0.74498 2329

6 0.18652 0

7 0.16972 0

8 0.025214 0

9 0.57544 266

10 0.41354 0

Results

Day

Fis

herm

anFishing patch

50 100 150 200 250 300 350

5

10

15

20

250

1

2

3

4

5

6

7

8

9

Results

FishermanIDDecisionParadigm

AverageFishing Effort

1 0.20791 52

2 0.45583 112

3 0.85126 213

4 0.77714 197

5 0.75392 185

6 0.52262 119

7 0.57483 148

8 0.77253 192

9 0.9271 233

10 0.66157 163

11 0.12144 28

12 0.23549 58

13 0.73946 184

FishermanIDDecisionParadigm

AverageFishing Effort

14 0.90988 222

15 0.78506 199

16 0.47394 117

17 0.37541 97

18 0.50925 114

19 0.043319 10

20 0.64754 162

21 0.81629 198

22 0.48307 121

23 0.0688 18

24 0.99486 248

25 0.69553 167

Gung-Ho!(optimizer)

Results

FishermanIDDecisionParadigm

AverageFishing Effort

1 0.20791 52

2 0.45583 112

3 0.85126 213

4 0.77714 197

5 0.75392 185

6 0.52262 119

7 0.57483 148

8 0.77253 192

9 0.9271 233

10 0.66157 163

11 0.12144 28

12 0.23549 58

13 0.73946 184

FishermanIDDecisionParadigm

AverageFishing Effort

14 0.90988 222

15 0.78506 199

16 0.47394 117

17 0.37541 97

18 0.50925 114

19 0.043319 10

20 0.64754 162

21 0.81629 198

22 0.48307 121

23 0.0688 18

24 0.99486 248

25 0.69553 167

Gun shy!(satisficer)

Fish block/simulation comparison

2004 DFG urchin block data Urchin fishing simulation

Learning, memory, and communication

Learning, memory, and communication

• Learning Information received from an individual’s daily fishing effort

• Communication Information received from the rest of the fleet

• Bayesian updating:– Modify an individual’s belief about the

environment if they go fishing– Influence their decision the following day

based on the beliefs of the entire fleet

Learning & memory

• Bayesian updating (DeGroot, 1970)

22

22

22

20

2

,~|s

s

s

saa

SNS

Good signals (Sa > α0) increase expected abundance at site a Noisy signals (large σ2

s) are given less weight

α = abundance (α0 = mean)Sa = signal, which is normally distributed

A fisherman updates beliefs about abundance after acquiring signal Sa from visiting site a. These updated beliefs follow a normal distribution.

Communication

• Information exchange matrix– Allen and McGlade,

1987

• Matrix of “how well” and with whom information is shared between each member of fleet

• No sharing• Perfect sharing• Imperfect sharing• Ignore information• Developing sharing

“weights”– 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”

Learning, memory, and communication

Information exchange model output(red = fishing, blue = no fishing)

Fish block/simulation comparison

Information exchange model output(red = fishing, blue = no fishing)

2004 DFG urchin block data Urchin fishing simulation

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

Questions?Questions?Suggestions?Suggestions?

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