Policy Evolution within an Organization

James H. HinesSloan School of Business, Massachusetts Institute of Technology Jody Lee HouseDepartment of Electrical and Computer Engineering, Oregon Graduate Institute

Funded in part by NSF IOC Award#SES-9975942

The Problem

System-wide company improvement is difficult because companies are too complex to “solve.”

How can we improve organizations in the face of ignorance?

A solution?

Biological evolution has produced excellent organizations.

Can we identify analogs of natural evolution that will help human organizations to likewise excel?

Gene:Organism::Policy:Organization

Policy: Implicit or explicit Examples

Pricing Hiring Capacity Expansion Flywheel sales

Synonyms Decision rule Rule of thumb

A policy produces

• A stream of decisions

• Activity in the firm

• Changing the policies, changes the organization

A gene produces

• A stream of proteins

• Activity in the cell

• Changing the genes changes the organism

Where are “evolutionary packets” stored?

• Genes are stored on chromosomes in cells

• Policies are stored – In written manuals?– In committees?– On computers?– In brains of people

Processesvs

Mutation Recombination Natural selection

and the sex drive survival of the

fittest

Innovation Inter-personal

learning Pointing and

pushing mechanisms learning from the

fittest

Genes Policies

Pointing And Pushing Mechanisms

Point to successful people Push others to learn from them Examples

Promotion and hierarchy Pay scales The best and latest computers In house training?

A brief look at sex

Papa Mama

sperm egg

you

recombination

fertilization

cell

chromosome

Grandpa’sstrand

Grandma’sstrand

recombination

• Combine parts of fit organisms to create fitter organism

• Example: 4-digit number, A > B fitter

Recombination is key

8,765 7,999

8,999

Learning is Similar to Biological Recombination

Fred learning

Fred Phyllisbrain

policy

Phyllis teaching

Time 1

Time 2

Why learning is difficult to call to mind

• The donor’s idea is well integrated• The rest of the donor’s idea is difficult to

recognize as an idea

Overview

Step 1: Run systemdynamics simulation

models, using policies ofthe managers

Step 2: Evaluateperformance of thesystem dynamics

models

Step 3: PromoteManagers

Step 4: If usingteams: Mix

managers andreform teams

Step 5: Managerslearn

Step 6: Managersinnovate

Code to write

Correct code

Undiscovered bugs

WritingCode

Writing codecorrectly

Creatingbugs

DiscoveringBugs

ProgrammersProductivity

quality

BugDiscoveryTime

Decides

Step 1: Run SD models

Code to write

Correct code

Undiscovered bugs

WritingCode

Writing codecorrectly

Creatingbugs

DiscoveringBugs

ProgrammersProductivity

quality

BugDiscoveryTime

Decides

Code to write

Correct code

Undiscovered bugs

WritingCode

Writing codecorrectly

Creatingbugs

DiscoveringBugs

ProgrammersProductivity

quality

BugDiscoveryTime

Decides

Step 1 The Project Model Detail

WorkToDo Doing

CorrectlyDone

UndiscoveredBugs

CorrectlyDoing

IncorrectlyDoing

Quality

Productivity

BugDetectingTimeToDetectBugs

DesiredPeople

RemainingTime

DueDate

People

<Time> NormalQuality

HireFireRate

timeToChange

WF

AnticipatedProduction Rate

<People>

AnticipatedTimeTo

CompleteAnticipatedDueDateTimeTo

ChangeSchedule

<Time> <Productivity>

Step 2: Evaluating Performance Fitness function can be based on any

variables in the model Variables can be combined using any

functional form In the following we use two simple

fitness functions Time to ship (LastPossible – Actual) Number of bugs (LinesOfCode – BuggyLines)

Step 3: Promoting managers1. Rank individuals based on

relative performance2. Promote according to rank.

toromotionFacPrPositionPosition oldnew *

The promotion algorithm requires specifying the “promotion base”. A promotion base of 2 means•The highest performing manager’s new position is 2 * theOld•The lowest performing manager’s new position is (1/2) * theOld•Everyone else’s promotion is evenly spread out between 2 and 1/2

Step 3 Promotion Algorithm Detail

11

)1(*2

*

Sizepopulation

rankK

baseValuetoromotionFacPr

toromotionFacPrPositionPositionK

oldnew

toromotionFacPrTeamAPositionPosition oldinewi *,,

Team-based promotion

Step 4: If using teams mix them up Randomly? Spread out the best? Concentrate the best?

Step 5: Learna) Select a teacher by

rouletteb) Learn from the

teacher by recombination

Pos=1 Manager3

Position=2

Pos= 0.5

Manager5

Pos=0.71 Manager2

Position=1.41

Manager1

Step 5 Learn p(learn)

Learner’s Policy10 or 0010 10

Teacher’s Policy32 or 1000 00

Randomly choose a crossover Point, say 2

Randomly choose which part the learner will obtain and which he will retain

OLD

0010__ + ____00 OR____10 + 1000__

0010 00 = 8

1000 10 = 34NEW

Learner’s Policy34 or 100 10

Teacher’s Policy32 or 1000 00

Step 6: Innovate p(innovate)

111111 110111Flip !

Before After

Learning, no pushing/pointing: Learning Drift

Optimal value = 8

Learning, no pushing/pointing Random Consensus

0 5 10 15 20

0

5

10

15

Po

licy

Generation

Learning with Pointing/PushingIndividuals

0 5 10 15 20

0

2

4

6

8

10

12

14

16

Po

lic

y

Generation

Next steps

Measurement through knowledge elicitation with partner companies Who learns from who and why? How are implicit policies a function of

organizational structure? Integrated simulation