Sjoukje OSINGA [email protected] KRAMERGert Jan HOFSTEDE Logistics, Decision and Information SciencesOmid ROOZMAND Wageningen University (Social Sciences Group)Adrie BEULENS The Netherlands
An agent-based information managementmodel in the Chinese pig sector
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 2
Chinese pork sector
Recall, for agents:• autonomity
• heterogenity
• local interactions
• bounded rationality
Government
i
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 3
Feed SellerAgents
Pig FarmerAgents
Pig BuyerAgents
Multiple levelsLBO Agents(LivestockBureau Officials)
agent level
system level
interactions level
i
i
i
i
i
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 4
Assumption (for agent-based model):
To increase pork quality…. … a farmer needs to have acquired information … coming from institutional / social / business agents
SCNpartner
LBOofficial
Friendcolleague
i
Pigfarmer Q
i
i i
institutional
social
business
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 5
Research questions
agent level
system level
interactions level
information management outcome
activities
activities
effectiveness
effectiveness
?
ABM
interactionopportunities
RQ 1
RQ 3
RQ 2
avgQ
avgsatis-faction
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 6
Implemented agent-based model
make decision
+ act
evaluate
farmerproduces certain Q-class
buyerdemands certain Q-class
LBObrings Q-info into system
buy pigs visit a nr of farmers
• each tick:
• each ‘month’:
• agent types:
make decision• find buyer: sell pigs• improve Q (need: i )• exchange i• find friend
evaluate• update satisfaction - nr of unsold pigs - personality• if satisf. too low: pursue other Q-class
buy pigs
according to demand per Q-class (systemlevel setting)
visit farmers• provide iparameters:• # visits per day• support level
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 7
Implemented agent-based model
make decision
+ act
evaluate
farmerproduces certain Q-class
buyerdemands certain Q-class
LBObrings Q-info into system
visit a nr of farmers
• each tick:
• each ‘month’:
• agent types:
make decision• find buyer: sell pigs• improve Q (need: i )• exchange i• find friend
visit farmers• provide iparameters:• # visits per day• support level
buy pigs
systemlevel variation
buy pigs
according to demand per Q-class (systemlevel setting)
evaluate• update satisfaction - nr of unsold pigs - personality• if satisf. too low: pursue other Q-class
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 8
Demand
Q1 Q2 Q3
-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)boundary medium high (%) min Q max Q avg Q
avg satisfaction is ‘OK’
max Q1
max Q2no class transition
avg Q increases up to max Q1
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 9
-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)boundary medium high (%) min Q max Q avg Q
avg satisfaction extremely low
Demand
Q1 Q2 Q3 max Q1
max Q2
avg Q increases up to max Q1
no class transition
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 10
-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)boundary medium high (%) min Q max Q avg Q
avg satisfaction is low for Q1
Demand
Q1 Q2 Q3 max Q1 avg Q keeps increasing
max Q2some transition to class Q2
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 11
Conclusion ‒ varying demand
In our model,
farmers move to another Q-class if there is a demand-incentive if new goal lies within reach
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 12
Implemented agent-based model
make decision
+ act
evaluate
farmerproduces certain Q-class
buyerdemands certain Q-class
LBObrings Q-info into system
visit a nr of farmers
• each tick:
• each ‘month’:
• agent types:
make decision• find buyer: sell pigs• improve Q (need: i )• exchange i• find friend
buy pigsbuy pigs
according to demand per Q-class (systemlevel setting)
evaluate• update satisfaction - nr of unsold pigs - personality• if satisf. too low: pursue other Q-class
visit farmers• provide iparameters:• # visits per day• support level
systemlevel variation
systemlevel variation
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 13
-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)boundary medium high (%) min Q max Q avg Q
avg satisfaction is low for Q1
Demand
Q1 Q2 Q3 max Q1avg Q increases further
max Q2some transition to class Q2
LBO 25%(partly
informative)
as it was:
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 14
-1 00
-80
-60
-40
-20
0
20
40
60
80
1 00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 22 23 2 4 2 5 2 6 2 7 2 8 2 9 3 0
min satisf max satisf avg sa tisf bo unda ry lo w m ediu m (% )boun dary me dium hig h (%) min Q max Q avg Q
Demand
Q1 Q2 Q3
LBO 100%(fully
informative)
avg satisfaction increases w. transition
max Q1
avg Q increases much more
max Q2transition to Q2 transition to Q3
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 15
Conclusion ‒ varying nr of LBO visits
In our model,
full information provision enhances the effect of increasing quality moving to another Q-class
In our model,
full information provision enhances the effect of increasing quality moving to another Q-class
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 16
Implemented agent-based model
make decision
+ act
evaluate
farmerproduces certain Q-class
buyerdemands certain Q-class
LBObrings Q-info into system
visit a nr of farmers
• each tick:
• each ‘month’:
• agent types:
buy pigsbuy pigs
according to demand per Q-class (systemlevel setting)
evaluate• update satisfaction - nr of unsold pigs - personality• if satisf. too low: pursue other Q-class
visit farmers• provide iparameters:• # visits per day• support level
systemlevel variation
systemlevel variation
make decision• find buyer: sell pigs• improve Q (need: i )• exchange i• find friend
Initial information of farmers population can varysystemsetting(agent leve
l)
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 17
-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)boundary medium high (%) min Q max Q avg Q
Demand
Q1 Q2 Q3
initialinformationof farmersratio
30 : 70
LBO 25%(partly
informative)
as it was:
max Q1
max Q2
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 18
Demand
Q1 Q2 Q3
LBO 25%(partly
informative)
initial infoamongfarmersratio
90 : 10-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)
boundary medium high (%) min Q max Q avg Q
avg satisfaction goes up w. transition
max Q1 higher avg Q
max Q2earlier transition to class Q2
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 19
-1 00
-80
-60
-40
-20
0
20
40
60
80
1 00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 22 23 2 4 2 5 2 6 2 7 2 8 2 9 3 0
min satisf max satisf avg sa tisf bo unda ry lo w m ediu m (% )boun dary me dium hig h (%) min Q max Q avg Q
Demand
Q1 Q2 Q3
LBO 100%(fully
informative)
initial infoamongfarmersratio
30 : 70
as it was:
max Q1
max Q2
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 20
Demand
Q1 Q2 Q3
LBO 100%(fully
informative)
initial infoamongfarmersratio
90 : 10-100
-80
-60
-40
-20
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
min satisf max satisf avg satisf boundary low medium (%)
boundary medium high (%) min Q max Q avg Q
higher avg satisfaction (w. transition)
max Q1 higher avg Q
max Q2earlier transitions
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 21
Conclusion ‒ varying initial farmer information
In our model,
initial information of farmers influences the effect of increasing quality moving to another Q-class
In our model,
initial information of farmers influences the effect of increasing quality moving to another Q-class
Sjoukje OSINGA – Artificial Economics 2010 (Sept 9-10) 22
baseABM
experi-mentalABM
simulatewhat-if
analyze
surveydata
parame-terize
concep-tualize
implement
evaluate
evaluate
validate
computermodel
analysesensitivity
calibrate
casestudy
contribute
Theory• Informationmanagement
• GenerativeSocial Science
• AI (cognitivescience)
deduce
results
conceptualmodel
interpret
collect
Research Framework
facevalidity
RQ1: system
RQ2: agent
RQ3: interactions
Thank you
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