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ORGAHEAD - Modeling Network Adaptation as Simulated Annealing Process 2002
Organizational Adaptation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Organizational  Adaptation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Adaptation and Environment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Optimization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Orgahead Structure ORGAHEAD Knowledge 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 Other performance  and vulnerability measures Task Assignment TASK Communication team1 team2 accuracy team1 team2 workload
ORGAHEAD Simulated annealer (expectation learning) + adaptive agents (experiential learning) STRATEGIC 1  0  1  1  0  0  1  0  1 OPERATIONAL time task organizational  decision Forecasting: Current performance Possible change Expected performance Who knows who Who knows what actual design change actual   performance experience information from others information from task feedback Feedback Recommendations
Radar Task Decision:  Friendly or Hostile? Speed > Mach 1? Transponder Code Correct? NATO? Weapons Armed? Heading into our airspace? ...
Agent Decisions
Tasks
Adaptive Agents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Internal Representations/ Operations 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 1 0 0 •  Agent constraints: 1.  Limited memory 2.  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. April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Orgahead Strategic Level ,[object Object],[object Object],[object Object]
Strategic:  Simulated Annealing metropolis criterion: p j  ( k,   ,  Temp 0 ) =  e  -cost*k/Temp Temp i  =    ·  Temp i-1   where   0.0 <    < 1.0 cost j  =  current perf j  -  lookahead perf j 1-dimensional solution landscape heuristic April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Methods of change ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Model Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performance over Time
Sequence Analysis adaptive organizations : HHH T F TT FFFFF HHH T H FF H F T F H FFF H F H T H F H F HH F H H T HH T HHHH TT F T H T H T H TTTTT H T F T F TTTTTT F H T F TT HH F HH F H FF H T H TTT F HH T HH F T F T F H FFF T H F HH T F HH T T H TTTTTTTT H TTTTTT H F T H T H TTTTTT F TT HHHHH TT TTTTTTTTT H TTTTTTTT H TT H F TTTTTTT F T H TTTT H T T H T H F T H TTT H TT H TTTTTTTTTT H TTT H T F TTTTTTT F T T H T H TTT H TTTT H TTT HH F T FFF TTTT FF T FF TTTTTT F T TT F HH T H TT F TTTT F T F TT F H T FF TTT HH TTTTT H T H TT T H TT H TTTTTT H TT HH TTTTTTT H TTTTTTTTTTTTTTTT H F T FF TTT H TTTT HH T H F T H T F H TT H TT FF TT FF TTTT F maladaptive organizations : TTTTTTT H TT F TTTTT H FFF H F T F TT F T H T FF TT H T H F TT TTTTT H F T F TTTT H TT F T FF T FFFFFF T F H T F TT FF TT F HHHHHH F HHHH F H F H F HH F H TT H F H FFFFF T HH F HH F T F T F TTT F T FF H F HHHHH T H T H F TT H FF T FF H FF H F H F H T F TTT H T FF HH F HH TT FF H TT H F TT F H T F H T H FF H T F HH TT T F TT F TT F T H TT F T F TT FF H T F H F T F H T FF H F T H F H F H F F T FF TTTT FF H FF T FFFF H FFFFFFF H F HH F H F H F HH FF H F TT F H F H T HH T HHH T H T F H TT H FF T H F H T H T FFFFFF H T H T H TTTTTTTTT FF TTTTT F TT F HHH T HH TT F H FF H FFF H F HHH T FF H TT HH FFFFFFF HH F H FFF T F HH FF HHH F H F H F too many firings more structural changes than turnover T = Tie Change H = Hire F = Fire
Correlating Performance and Activity
Firing can Hinder Perfromance
… But Not Always
Maladaptive Hiring and Adaptive Firing
Dynamic Adaptation
Adaptive Organizational Structures April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Adaptive Structures with Tasks April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Maladaptive Structures April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Maladaptive Structures with Tasks April 2002 Ju-Sung Lee  -  CMU – CASOS – SDS - ICES
Scaling Up ORGAHEAD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
levels of analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nodes in ORGAHEAD or Construct are DMU’s people agents groups organizations or some combination
Illustrative high level indicators or data that can be used ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications Hypothetical 3-45 Adaptive Organizations Questionnaire Schwab Questionnaire 683, 9 divisions SGI Questionnaire 17-150, 35 units Nursing Study Archival Data 9-12 Crisis Response Units Questionnaire and Experiment 4-6  NPS teams Field Observations 5 cells Comcargru Data Number of Agents/divisions Study

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15 Orgahead

  • 1. ORGAHEAD - Modeling Network Adaptation as Simulated Annealing Process 2002
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Orgahead Structure ORGAHEAD Knowledge 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 Other performance and vulnerability measures Task Assignment TASK Communication team1 team2 accuracy team1 team2 workload
  • 7. ORGAHEAD Simulated annealer (expectation learning) + adaptive agents (experiential learning) STRATEGIC 1 0 1 1 0 0 1 0 1 OPERATIONAL time task organizational decision Forecasting: Current performance Possible change Expected performance Who knows who Who knows what actual design change actual performance experience information from others information from task feedback Feedback Recommendations
  • 8. Radar Task Decision: Friendly or Hostile? Speed > Mach 1? Transponder Code Correct? NATO? Weapons Armed? Heading into our airspace? ...
  • 10. Tasks
  • 11.
  • 12. Internal Representations/ Operations 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 1 0 0 • Agent constraints: 1. Limited memory 2. 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. April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 13.
  • 14. Strategic: Simulated Annealing metropolis criterion: p j ( k,  , Temp 0 ) = e -cost*k/Temp Temp i =  · Temp i-1 where 0.0 <  < 1.0 cost j = current perf j - lookahead perf j 1-dimensional solution landscape heuristic April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 15.
  • 16.
  • 18. Sequence Analysis adaptive organizations : HHH T F TT FFFFF HHH T H FF H F T F H FFF H F H T H F H F HH F H H T HH T HHHH TT F T H T H T H TTTTT H T F T F TTTTTT F H T F TT HH F HH F H FF H T H TTT F HH T HH F T F T F H FFF T H F HH T F HH T T H TTTTTTTT H TTTTTT H F T H T H TTTTTT F TT HHHHH TT TTTTTTTTT H TTTTTTTT H TT H F TTTTTTT F T H TTTT H T T H T H F T H TTT H TT H TTTTTTTTTT H TTT H T F TTTTTTT F T T H T H TTT H TTTT H TTT HH F T FFF TTTT FF T FF TTTTTT F T TT F HH T H TT F TTTT F T F TT F H T FF TTT HH TTTTT H T H TT T H TT H TTTTTT H TT HH TTTTTTT H TTTTTTTTTTTTTTTT H F T FF TTT H TTTT HH T H F T H T F H TT H TT FF TT FF TTTT F maladaptive organizations : TTTTTTT H TT F TTTTT H FFF H F T F TT F T H T FF TT H T H F TT TTTTT H F T F TTTT H TT F T FF T FFFFFF T F H T F TT FF TT F HHHHHH F HHHH F H F H F HH F H TT H F H FFFFF T HH F HH F T F T F TTT F T FF H F HHHHH T H T H F TT H FF T FF H FF H F H F H T F TTT H T FF HH F HH TT FF H TT H F TT F H T F H T H FF H T F HH TT T F TT F TT F T H TT F T F TT FF H T F H F T F H T FF H F T H F H F H F F T FF TTTT FF H FF T FFFF H FFFFFFF H F HH F H F H F HH FF H F TT F H F H T HH T HHH T H T F H TT H FF T H F H T H T FFFFFF H T H T H TTTTTTTTT FF TTTTT F TT F HHH T HH TT F H FF H FFF H F HHH T FF H TT HH FFFFFFF HH F H FFF T F HH FF HHH F H F H F too many firings more structural changes than turnover T = Tie Change H = Hire F = Fire
  • 20. Firing can Hinder Perfromance
  • 21. … But Not Always
  • 22. Maladaptive Hiring and Adaptive Firing
  • 24. Adaptive Organizational Structures April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 25. Adaptive Structures with Tasks April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 26. Maladaptive Structures April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 27. Maladaptive Structures with Tasks April 2002 Ju-Sung Lee - CMU – CASOS – SDS - ICES
  • 28.
  • 29.
  • 30.
  • 31. Applications Hypothetical 3-45 Adaptive Organizations Questionnaire Schwab Questionnaire 683, 9 divisions SGI Questionnaire 17-150, 35 units Nursing Study Archival Data 9-12 Crisis Response Units Questionnaire and Experiment 4-6 NPS teams Field Observations 5 cells Comcargru Data Number of Agents/divisions Study