1. J 568:Inquiry Systems Professor Ian I. Mitroff 308 G Bridge Hall X 00154 ianmitroff@earthlink.net

2. Data Information Knowledge Wisdom What are the key differences between them?

3. Data Raw Numbers Information Compacted, Extracted, Summarized Data Knowledge Implications? How to? Actions? Wisdom E3? Trust the Data? The Right Ethical Actions?

4. Data Raw Numbers Information Compacted, Extracted, Summarized Data Knowledge Implications? How to? Actions? Wisdom E3? Trust the Data? Ethical Actions? Exchange Rate: An ounce of Information is worth a pound of Data An ounce of Knowledge, a pound of Information An ounce of Wisdom, a pound of Knowledge

5. Operator? Transform? Information Data 100 200 300

6. Operator? Transform? Information Data 100 200 300 200 Therefore, the Operator Is?

7. Operator? Transform? Information Data 100 200 300 200 Therefore, the Operator Is? The Average

8. Operator? Transform? Information Data 100 200 300 200 Trust the Output? Guarantee? Validity? The Average

9. Operator? Transform? Information Data 200 100 200 300 Trust the Output? Guarantee? Validity? What do you do when you have a large amount of Data? The general formula is: Average= [Data1+Data2+…+Data1000] / 1000 Average= [∑in Datai ] / n

10. The General Structure of Inquiry Systems Valid Building Blocks/Starting Points of Knowledge Valid Knowledge Operator Outputs Inputs Guarantor

11. MB x ISs = Knowledge Psychological Preferences for Styles of Knowledge AND Mechanisms of Producing Knowledge INTERACT To Produce What Is Regarded as Knowledge or Truth

12. Name Some Different Methods for Producing Truth

13. Model 1: Expert Consensus

14. Coca Cola Belgium Crisis: 1. A number of children reported that Coke tasted and smelled funny. 2. Coke scientists reported that there was nothing wrong technically with the product; therefore, Coke dismissed the claims of the children as merely “psychological,” i.e., mass hysteria.

15. Coca Cola Belgium Crisis: 3. However,the Belgium Health Minister took the children’s claims seriously and ordered all cans and bottles of coke off the shelves throughout Belgium. 4. McDonald’s refused to carry Coke in its restaurants until the case was resolved. 5. From The Company’s, i.e., Coca Cola’s, standpoint, the relevant “experts” were Coke’s Quality Control scientists.They were the standard for “truth,” not the children.

16. The General Structure of Inquiry Systems Valid Building Blocks of Knowledge Valid Knowledge Operator Outputs Inputs Guarantor

17. Model 1: From Coke’s Perspective Valid Knowledge Scientific Experts Product OK Product Data Quality Control Tests Scientific Method Objective Standard Scientists ST

18. Model 1: From the Children’s / Media’s/ Community’s Perspective Valid Knowledge Children As Experts Product NOT OK Children’s Reactions Media/ Health Minister / Children’s Families / Community Human Feelings Children SF / NF

19. The Delphi Method: A More General Example of Model 1 The Asch Effect Polling of Isolated Experts Each Expert Gives a Numerical Estimate, Xi or Data i The Numerical Estimates Are Summed and then Divided by n to Produce the Average, XAV or _ X

20. The Delphi Method: An Example of Model 1 XAV Is Then Fed-Back to the Experts to See If They Want to Change Their Estimates. The Procedure Is Repeated for as Many Rounds as Necessary for XAV to Converge XAV Is Regarded as the Best Estimate of the Truth Essentially, the Delphi Is a Special Survey or Polling Method

21. The Delphi Method: An Example of Model 1 Essentially, the Delphi Is a Special Survey Method, BUT with a Special Wrinkle Experts Whose Estimates Are “Too Far” from XAV Are Eliminated Thus the Delphi Method Is a Way of Forcing Agreement (I.e., the Operator) as Much as It Relies on Agreement as the Guarantor

22. The Delphi Method Model 1: The Distribution of Expert Judgments Around the Mean

23. Model 1: Expert Judgments That Are “Too Far” From The Mean Are Excluded The Delphi Method

24. Valid Building Blocks of Knowledge Valid Knowledge Conclusions Based on the Data Average / Operate on the Data Data Tightness of Agreement of the Data

25. The Essence of Model 1 Start with: Data Observations Expert Judgments “Treat” the: Data Observations Expert Judgments Produce Information

26. The Varieties of Model 1: Expert Consensus Polls Consumer Panels Observations Voting Anything That Is Based on Aggregating Data

27. Model 2: Analytic Modeling

28. The Kidney Machine: 1. 13 year-old girl 2. 6 month old baby boy 3. 35 year-old mother of 2 children 4. 85 year old-man 5. 23 year old convicted murderer 6. 8 month old baby girl

29. Model 2: Develop A Single RIGHT Formula Model 1: Get The RIGHT Data and Put It Into theRIGHT Formula

30. Model 2:Develop A Single Formula : ST i =n n = # attributes j = # candidates Wi= weights [Wi x Sji ]=Sj i = 1 max Sj Best Choice

31. Model 1: Put Data Into the Formula : ST 13 girl baby boy mother old man murderer baby girl Life Expectancy 10 7 2 9 10 10 Earning Potential 10 7 1 1 8 9 Contribution Society 10 10 10 1 10 10 Dependents 1 1 10 1 1 1 30 31 34 14 12 29 SCORE = max Sj =34

32. Model 2 Intuitively Obvious Ideas With Regard to Attributes ONE Formula Correct Mathematical Operations Clear Ideas Logic / Mathematics

33. Model 1 & 2 Select THE Correct Person The RightData The Correct Formula Logic AND Data

34. Model 2 Can You Figure Out The Operator In Each Case? Operator 1,2,3,4 10 Operator 1,2,3,5 7,11,13,17

35. Model 2 Can You Figure Out The Operator In Each Case? Operator 1,2,3,5,8 13 Operator 1,2,2,4 8

36. Can You Figure Out How to Represent the Coca Cola Belgium Crisis as an Example of Model 2?

37. The Varieties of Model 2: Fundamental Beliefs Fundamental Assumptions Basic Texts Basic Authorities Anything That Is Based on “True Beliefs”

38. Model 3: Multiple ModelsAn Integration of Models 1 & 2:A Synthesis

39. Executive .…. Formula 1 Formula 2 Formula 3 Formula n .…. Data 1 Data 2 Data 3 Data n Decision Maker’s Formula Decision Maker’s Data

40. More Generally Executive .…. Assumptions1 Assumptions2 Assumptions 3 Assumptions n .…. Data 1 Data 2 Data 3 Data n Decision Maker’s Assumptions Decision Maker’s Data

41. Executive / Decision Maker .…. CEO CFO Legal Security .. CEO Data CFO Data Legal Data Security Data Decision Maker’s Crisis Strategy Crisis Action Plan

42. Model 3 Crisis Action Plan Executive / Decision Maker Mess Multiple Perspectives

43. Model 4: Conflict

44. Executive / Decision Maker .…. CEO CFO Legal Security .. CEO Data CFO Data Legal Data Security Data Decision Maker’s Crisis Strategy Crisis Action Plan

45. Coca Cola Belgium Crisis 1. The Children Are Experts 2. 3. 4. Only Scientists Are Experts 2. 3. 4.

46. Model 4 Decision Maker Shapes Debate Crisis Action Plan Mess INTENSECONFLICT

47. Model 4: The Distribution of Expert Judgements Around the Mean

48. Model 4: Only Those Expert Judgments “Far From” the Mean Are Kept

49. Model 5: Systems Thinking

50. Antisystems Thinking Branches of Knowledge Branches of Knowledge