Copyright © 2001,2002 Jim Hines and Jody House Activities Discrete event/agent based/SD hybrids Supply chains Tangible interfaces Molecules of structure

Copyright © 2001,2002 Jim Hines and Jody House

Activities

• Discrete event/agent based/SD hybrids• Supply chains• Tangible interfaces• Molecules of structure• Model analysis• Distance program in SD• Organizational evolution

Copyright © 2001,2002 Jim Hines and Jody House 2

Organizational Evolution


Status

• Basic Theory • Simulation environment

– Genetic algorithm + discrete event + SD-type model

– Genetic programming + discrete event + SD-type model


OrgEv: Applications

• Team management• Flavors of the month management• Frequency of promotions• Mergers and acquisitions• Rank-and-fire policies• New companies (punctuated equilibrium)• Specialization• Evolution of (internal) markets


AgendaSD vs. Org Ev

• Theory and algorithm

• Applications• Innovation

• Flavor of the Month Management

• Promotion frequency


SD vs. OrgEv

• You use SD to diagnose and treat poor policies (decision rules)

• You use OrgEv to create an internal environment that breads healthy policies


Agenda

SD vs. OrgEvTheory and algorithm

• Applications


Theory

• Imitation and Bacteria

• Recombination

• Innovation and Mutation

• Algorithm


Recombination

2,146 1,849

Teacher Learner

2,849


Imitation

Pilus

Recipient

Bacteria

Donor


Innovation and Mutation

ACGGCTTCG ACTGCTTCG


Initial Algorithm

Run divisionmodels using

managers'policies

Managers learn

Managersinnovate


Project Model


Learning by Recombination

Teacher (Donor)

Learner (Recipient) 00 0000

111 111

11 1111

Before After

11 0000


Innovation

11 1 111 11 0 111 Flip !

Before After

11 1 111 11 0 111 Flip !


Learning Drift

0 5 10 15 20 25 300

2

4

6

8

10

12

14

16

Generation

Ave

rage

# o

f P

rogr

amm

ers

0 5 10 15 20 25 300 5 10 15 20 25 300

2

4

6

8

10

12

14

16

0

2

4

6

8

10

12

14

16

Generation

Ave

rage

# o

f P

rogr

amm

ers


Learning Drift and Random Consensus

05

1015

2025

020

40600

5

10

15

GenerationIndividual

Num

ber

of

Prog

ram

mer

s

05

1015

2025

020

40600

5

10

15


Num

ber

of

Prog

ram

mer

s


Pointing and Pushing Mechanisms

• Corner office

• Badges

• Hierarchy

• Salary


Full Algorithm


managers'policies

Managers learn

Managersinnovate

Evaluate divisionperformance

Promotemanagers


The Source of Good Policies

05

1015

2025

020

40600

5

10

15


Num

ber

of

Prog

ram

mer

s

05

1015

2025

020

40600

5

10

15


Num

ber

of

Prog

ram

mer

s


Innovation Prevents Premature Consensus

05

1015

2025

0

20

40

0

5

10

15


Num

ber

of P

rogr

amm

ers

05

1015

2025

0

20

40

0

5

10

15


Num

ber

of P

rogr

amm

ers


AgendaSD vs. OrgEvTheory and algorithm

• ApplicationsTeam promotion

• Specialization *


Team Algorithm


managers'policies

Managers learn

Managersinnovate

Evaluate divisionperformance

Promotemanagers

Mix teams


The Landscapes


Performance of Teams


Teams:

Should we worry about team size or the number of teams?


Team Promotion

20 runs each0 20 40 60 80 100

6

8

10

12

14

16

Generation

Ind

ivid

ua

l

10 Teams in Population of 50

0 20 40 60 80 1006

8

10

12

14

16

Generation

Ind

ivid

ua

l


0 20 40 60 80 1006

8

10

12

14

16

Generation

Ind

ivid

ua

l


0 20 40 60 80 1000

5

10

15

Generation

Indi

vid

ual

1 Team in Population of 50

0 20 40 60 80 1006

8

10

12

14

16

Generation

Ind

ivid

ua

l


20 Runs


Final Policy

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30

# of Teams in Population of 50

Fin

al P

olic

y

Final Policy

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25Teamsize for 5 Teams in Population

Fin

al P

olic

y


AgendaIntroductionMotivationTheory and algorithm

• ApplicationsTeam promotionSpecialization


• Practice group in medium-sized law firm

• Believe the law is becoming so complex that no one know it all

• Solution: specialization

• Initial approach identify areas of the law and have people “sign up”

The Situation


Progress after Ten Years …

• Nix

• Zip

• Nil

• Zilch

• Nothing

• Naught

•

•

•

•

•

•


Requirement and consequence

• Required: Community of specialists (Min of 5 people per specialty?)

• Consequence: Firm is changing dimension of specialization to one that has only three categories


Kinds of Speciation

XAllopatricSympatric


Sympatric SpeciationProcesses

• Frequency dependent selection

• Sexual selection


Passing along benefits in an ecology: The Bucket Brigade

Finderpoints

FinderBidOnWork

fractionBid

Minder/Grinderpoints 0

Drafterpoints 0 0MinderBid

OnWork 0Drafter Bidon Work 0 0

paymentForWork 0 0

Minder/Grinderpoints 1 Drafter

points 1 0MinderBidOnWork 1

Drafter Bidon Work 1 0

paymentForWork 1 0

Drafterpoints 0 1Drafter Bid

on Work 0 1paymentForWork 0 1

Drafterpoints 1 1Drafter Bid

on Work 1 1paymentFor

Work 1 1


Summary and Reflections

Documents

Copyright © 2001,2002 Jim Hines and Jody House Activities Discrete event/agent based/SD hybrids Supply chains Tangible interfaces Molecules of structure