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Activities

Discrete event/agent based/SD hybrids Supply chains Tangible interfaces Molecules of structure Model analysis Distance program in SD Organizational evolution. Activities. Organizational Evolution. 2. Basic Theory  Simulation environment

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Activities

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

  2. Organizational Evolution 2

  3. Basic Theory  Simulation environment Genetic algorithm + discrete event + SD-type model  Genetic programming + discrete event + SD-type model Status

  4. 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 OrgEv: Applications

  5. Agenda • SD vs. Org Ev • Theory and algorithm • Applications • Innovation • Flavor of the Month Management • Promotion frequency

  6. You use SD to diagnose and treat poor policies (decision rules) You use OrgEv to create an internal environment that breads healthy policies SD vs. OrgEv

  7. Agenda • SD vs. OrgEv • Theory and algorithm • Applications

  8. Imitation and Bacteria Recombination Innovation and Mutation Algorithm Theory

  9. Recombination Learner Teacher 2,146 1,849 2,849

  10. Bacteria Recipient Pilus Donor Imitation

  11. Innovation and Mutation ACGGCTTCG ACTGCTTCG

  12. Initial Algorithm

  13. Project Model

  14. Learning by Recombination Before After Teacher (Donor) 11 1111 111 111 00 0000 Learner (Recipient) 11 0000

  15. Innovation

  16. Learning Drift

  17. Learning Drift and Random Consensus

  18. Corner office Badges Hierarchy Salary Pointing and Pushing Mechanisms

  19. Full Algorithm

  20. The Source of Good Policies

  21. Innovation Prevents Premature Consensus

  22. Agenda • SD vs. OrgEv • Theory and algorithm • Applications • Team promotion • Specialization *

  23. Team Algorithm

  24. The Landscapes

  25. Performance of Teams

  26. Teams:Should we worry about team size or the number of teams?

  27. 1 Team in Population of 50 2 Teams in Population of 50 5 Teams in Population of 50 10 Teams in Population of 50 25 Teams in Population of 50 15 16 16 16 16 14 14 14 14 10 12 12 12 12 Individual Individual Individual Individual Individual 10 10 10 10 5 8 8 8 8 0 6 6 6 6 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 Generation Generation Generation Generation Generation Team Promotion 20 Runs 20 runs each

  28. Final Policy 16 14 12 10 Final Policy 8 6 4 2 0 0 5 10 15 20 25 30 Final Policy # of Teams in Population of 50 16 14 12 10 Final Policy 8 6 4 2 0 0 5 10 15 20 25 Teamsize for 5 Teams in Population

  29. Agenda • Introduction • Motivation • Theory and algorithm • Applications • Team promotion • Specialization

  30. 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

  31. Nix Zip Nil Zilch Nothing Naught Progress after Ten Years …

  32. Required: Community of specialists (Min of 5 people per specialty?) Consequence: Firm is changing dimension of specialization to one that has only three categories Requirement and consequence

  33. Allopatric Sympatric Kinds of Speciation

  34. Frequency dependent selection Sexual selection Sympatric SpeciationProcesses

  35. Passing along benefits in an ecology: The Bucket Brigade

  36. Summary and Reflections

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