15 orgahead

1

ORGAHEAD -Modeling Network Adaptation as

Simulated Annealing Process

2002

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Organizational Adaptation

• This study only concerned with formal structure• Change precipitated by executive decisions• Limited number of change strategies• Not all strategies can be considered at a time• “Greedy” selection criteria, with some probability of

risky personnel changes

• Thus - locally satisficing process

3

Organizational Adaptation

• Organizational change• Hiring, Firing, Restructuring, Training• Employees learn through experience with task

• Adaptation performance measured against real-world results• Stock market performance, profits• Minimizing costs, maximizing production

4

Adaptation and Environment

• If environment remains static, eventually a suitable (or optimal) profile will be found

• As time goes on, organization less likely to make risky moves• Institutionalization• Competency traps• Unwillingness to accept new technologies

5

Optimization

• Problem space:• Organizational structure (network)• Skill set of personnel• Task assignment

• Step function:• Organizational change

• Fitness function:• Performance, profit, cost

• Goal: • Optimize fitness function

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Orgahead Structure

ORGAHEAD

Knowledge

Task Assignment

TASK

Agent/Knowledge 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1

Communication

team1team2

accuracy

team1team2

workload

Other performance and vulnerabilitymeasures

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ORGAHEAD

STRATEGIC

1 0 1 1 0 0 1 0 1

OPERATIONAL

time

task

organizational decision

Forecasting:Current performancePossible changeExpected performanceWho knows whoWho knows what

actual design change

actual performance

experienceinformation from othersinformation from task

feedback

Feedback

Recommendations

Simulated annealer (expectation learning) + adaptive agents (experiential learning)

Radar Task

Decision: Friendly or Hostile?

Speed > Mach 1?Transponder Code Correct?NATO?Weapons Armed?Heading into our airspace?...

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Agent Decisions

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are needed to see this picture.

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Tasks

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are needed to see this picture.

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Adaptive Agents

• Each agent sees a portion of information• Classify pattern of incoming information

• Raw SIGINT or• Processed information from below

• Classify as “Friendly of Hostile”

• Make decisions based on past experience• Receive feedback on accuracy of their

predictions• Adjust internal knowledge (experience)

Analysts

Managers

CEOs

Inputs

“1”

1 0 1 1 0 1 1 1 0

1 0 0 = 0

1 0 1 = 0

1 1 0 = 0

1 1 1 = 0

0 0 = 0

0 1 = 1

1 0 = 1

1 1 = 0

0 0 = 0

0 1 = 1

1 0 = 0

1 1 = 1

0 0 = 0

0 1 = 1

1 0 = 1

1 1 = 0

0 0 0= 0

0 1 0= 1

1 0 0= 1

1 1 0= 0

0 0 1= 1

0 1 1= 1

1 0 1= 1

1 1 1= 0

1 1 0 0 1 0

100• Agent constraints:1. Limited memory2. Maximum of seven resources/inputs

Decision Rule: If # of 1’s > # of 0’s, then “1” Else “0”

• Organizational activities:1. After every n tasks, propose a change: hire, fire, or change ties.2. Test change.3. Accept all good changes and some bad changes.

• Agent activities:1. Update memory table based on correctness of final decision.2. Report truthfully.

Internal Representations/ Operations

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 12

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Orgahead Strategic Level

• Organization is trying to optimize performance• Performance=Percentage of correct tasks

• CEO alters structure to optimize structure in response to performance

Strategic: Simulated Annealingmetropolis criterion:pj (k, , Temp0) = e -cost*k/Temp

Tempi = · Tempi-1 where 0.0 < < 1.0costj = current perfj - lookahead perfj

Empirical Probabilities of Accepting a Risky Move

0102030405060708090

100

1 10 19 28 37 46 55 64 73

Tasks (in Thousands)

Probability * 100%

1-dimensional solution landscape

heuristic

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 14

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Methods of change

• Add Personnel• Fire/eliminte personnel• Change organizational network

• Redesign reporting structure• Enable interaction (I.e. create edge)

• Change knowledge network• Retask personnel• Training• Change in workload (stress)

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Model Algorithm

• Get initial organization• Train agents in initial organization• Generate org. performance• Choose a way to alter structure• Forecast expected change in performance• Decide whether to accept proposed change• Drop temperature• Repeat

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Performance over Time

45

50

55

60

65

70

75

80

85

90

95

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Sequence Analysisadaptive organizations:HHHTFTTFFFFFHHHTHFFHFTFHFFFHFHTHFHFHHFHHTHHTHHHHTTFTHTHTHTTTTTHTFTFTTTTTTFHTFTTHHFHHFHFFHTHTTTFHHTHHFTFTFHFFFTHFHHTFHHTTHTTTTTTTTHTTTTTTHFTHTHTTTTTTFTTHHHHHTTTTTTTTTTTHTTTTTTTTHTTHFTTTTTTTFTHTTTTHTTHTHFTHTTTHTTHTTTTTTTTTTHTTTHTFTTTTTTTFTTHTHTTTHTTTTHTTTHHFTFFFTTTTFFTFFTTTTTTFTTTFHHTHTTFTTTTFTFTTFHTFFTTTHHTTTTTHTHTTTHTTHTTTTTTHTTHHTTTTTTTHTTTTTTTTTTTTTTTTHFTFFTTTHTTTTHHTHFTHTFHTTHTTFFTTFFTTTTF

maladaptive organizations:TTTTTTTHTTFTTTTTHFFFHFTFTTFTHTFFTTHTHFTTTTTTTHFTFTTTTHTTFTFFTFFFFFFTFHTFTTFFTTFHHHHHHFHHHHFHFHFHHFHTTHFHFFFFFTHHFHHFTFTFTTTFTFFHFHHHHHTHTHFTTHFFTFFHFFHFHFHTFTTTHTFFHHFHHTTFFHTTHFTTFHTFHTHFFHTFHHTTTFTTFTTFTHTTFTFTTFFHTFHFTFHTFFHFTHFHFHFFTFFTTTTFFHFFTFFFFHFFFFFFFHFHHFHFHFHHFFHFTTFHFHTHHTHHHTHTFHTTHFFTHFHTHTFFFFFFHTHTHTTTTTTTTTFFTTTTTFTTFHHHTHHTTFHFFHFFFHFHHHTFFHTTHHFFFFFFFHHFHFFFTFHHFFHHHFHFHF

too many firings

more structural changes than turnover

T = Tie ChangeH = HireF = Fire

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Correlating Performance and Activity

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Firing can Hinder Perfromance

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… But Not Always

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Maladaptive Hiring and Adaptive Firing

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Dynamic Adaptation

Adaptive Organizational Structures

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 24

Adaptive Structures with Tasks

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 25

Maladaptive Structures

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 26

Maladaptive Structures with Tasks

April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 27

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Scaling Up ORGAHEAD

• Currently 2 to 45 DMU's • Could be individuals or groups or divisions• Model is extensible to several hundred – but needs programming

• Currently 1-3 levels• Can be at any meaningful division• Need not be formal authority• Model is extensible to several hundred – but needs programming

• Currently one task at a time• Needs to be converted to multiple co-temporal tasks

• Currently max task complexity is 18• If need more complexity need a different architecture

• To build a larger, more complex organizations• Model at the cell or division level• Build multiple orgahead models – one for each group, cell or division and

combine results• Transactive memory

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levels of analysis

• Multiple levels of analysis possible• JTF (joint task force)• Top management• Group/team• Overall organization

• Multiple levels of input data possible• None• Solely group/cultural parameters• High level indicators/strategies for change• Detailed knowledge networks• Or combination of any of the above

• Level of data influences specificity of predictions generated in analysis

Nodes in ORGAHEAD or Constructare DMU’s

peopleagentsgroupsorganizationsor some combination

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Illustrative high level indicators or data that can be used

• Number of groups• Size of groups• Information on task assignment or job labels• Key resources used or services provided• Number or types of divisions• Average level of education, tenure, gender, age,

race, religion, language• Information on locations• Cohesion within and among groups• Educational areas

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Applications

Study Number of Agents/divisions

Data

Adaptive Organizations

3-45 Hypothetical

Comcargru 5 cells Field Observations

NPS teams 4-6 Questionnaire and Experiment

Crisis Response Units

9-12 Archival Data

Nursing Study 17-150, 35 units Questionnaire

SGI 683, 9 divisions Questionnaire

Schwab Questionnaire