An agent-based information management model ... - dma.unive.it · Sjoukje OSINGA...

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


Feed SellerAgents

Pig FarmerAgents

Pig BuyerAgents

Multiple levelsLBO Agents(LivestockBureau Officials)

agent level

system level

interactions level

i

i

i

i

i


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


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


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



make decision

+ act

evaluate




visit a nr of farmers

• each tick:


• agent types:



buy pigs

systemlevel variation

buy pigs




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


-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


avg satisfaction extremely low

Demand

Q1 Q2 Q3 max Q1

max Q2

avg Q increases up to max Q1

no class transition


-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


avg satisfaction is low for Q1

Demand

Q1 Q2 Q3 max Q1 avg Q keeps increasing

max Q2some transition to class Q2


Conclusion ‒ varying demand

In our model,

farmers move to another Q-class if there is a demand-incentive if new goal lies within reach



make decision

+ act

evaluate





• each tick:


• agent types:


buy pigsbuy pigs







-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


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:


-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


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



make decision

+ act

evaluate





• each tick:


• agent types:

buy pigsbuy pigs







Initial information of farmers population can varysystemsetting(agent leve

l)


-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


Demand

Q1 Q2 Q3

initialinformationof farmersratio

30 : 70

LBO 25%(partly

informative)

as it was:

max Q1

max Q2


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


-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)


30 : 70

as it was:

max Q1

max Q2


Demand

Q1 Q2 Q3

LBO 100%(fully

informative)


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


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


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

An agent-based information management model ... - dma.unive.it · Sjoukje OSINGA...

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