Modeling fishermen behavior in the Santa Barbara Channel Islands geog.Mike & econ.John .
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Transcript of Modeling fishermen behavior in the Santa Barbara Channel Islands geog.Mike & econ.John .
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