Download - 15 Orgahead
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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
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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
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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
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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)
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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
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
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Tasks
QuickTime™ and aTIFF (LZW) decompressor
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)
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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
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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
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Adaptive Organizational Structures
April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 24
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Adaptive Structures with Tasks
April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 25
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Maladaptive Structures
April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES 26
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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