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2. PI Aging Simulation Model

3. The Current Problem:“Success to the Successful” More Funds to Older, Experienced PI’s Higher Success of Older, Experienced PI’s Allocation to Older, Experienced PI’s Instead of Younger, Inexperienced PI’s Lower Success of Younger, Inexperienced PI’s Less Funds to Younger, Inexperienced PI’s

4. Basic Structure for Age Group New PIs (i.e., first-time) that enter the NIH pool in this age group. Represents the number of PIs in the total pool that are in this age group. PIs in the system that have “aged” enough to move to the next age group. PIs in the system that have “aged” enough to move into this age group. PIs of this age group that leave the “system.”

5. Connecting Age Groups

6. OB: Spreadsheet methodology Statistical Focuses on data Static No feedback loops OER: System Dynamics (SD) methodology Operational simulation Focuses on activities Dynamic Feedback loops Differences Between Models

7. Limitations of Simulation Model • Data begins in FY80, so “momentum” inherent in system prior to FY80 is not captured. • Data available for approximately 65% of the R01 equivalents only: • Age data invalid for roughly one-third of R01 data set. • “Length of Service” averages based on total years of service rather than continuous years of service. • Currently, there are no “feedback mechanisms” incorporated into the model: • All trends are based on data and do not change dynamically or in relation with other variables.

8. Simulations • Baseline (FY80-FY06): • FY80-FY06 entrance rate data. • FY80-FY86 duration averages, FY87-FY06 uses FY86 duration averages. • Scenario 1 (FY80-FY16): • Same as Baseline except FY07-FY16 entrance rates use trends based on FY97-FY06 entrance rates. • Scenario 3 (FY80-FY16): • Same as Scenario 2 except FY07-FY16 entrance rates specified to try to keep the PI age distribution consistent with FY06.

9. Average Length of Service

10. Baseline Results

11. Total Number of PI’s (FY80-FY06)

12. Baseline: 1991 Actual Simulation Avg Age = 45.6 Avg Age = 42.7

13. Baseline: 1996 Actual Simulation Avg Age = 47.3 Avg Age = 44.7

14. Baseline: 2001 Actual Simulation Avg Age = 49.0 Avg Age = 46.3

15. Baseline: 2006 Actual Simulation Avg Age = 50.8 Avg Age = 47.5

16. Scenario 1 Results

17. Total Number of PI’s (FY80-FY16)

18. Scenario 1: 1991 Avg Age = 42.7

19. Scenario 1: 1996 Avg Age = 44.7

20. Scenario 1: 2001 Avg Age = 46.3

21. Scenario 1: 2006 Avg Age = 47.5

22. Scenario 1: 2011 Avg Age = 48.3

23. Scenario 1: 2016 Avg Age = 49.8

24. Scenario 2 Results

25. Scenario 2: Approach • Objective is to keep average age and approximate age distribution consistent with 2006 values: • Average age = 47.5 • Possible policy changes to test: • No new PI’s older than 65 – minimal impact • Forced retirement at 70 – minimal impact • Forced distribution of 1500 new PI’s: • No new PI’s at all • All new PI’s <40, evenly spread for each age • All new PI’s forced to fit a specific age distribution

26. Scenario 2, No New PI’s: 2006 Avg Age = 47.5

27. Scenario 2, No New PI’s: 2011 Avg Age = 50.5

28. Scenario 2, No New PI’s: 2016 Avg Age = 54.3

29. Scenario 2, All New PI’s <40: 2006 Avg Age = 47.5

30. Scenario 2, All New PI’s <40: 2011 Avg Age = 44.0

31. Scenario 2, All New PI’s <40: 2016 Avg Age = 41.3

32. What Does This Tell Us? • We have a model that is capable of forecasting the age distributions of the PI pool given assumptions on influxes and tenures. • Making dramatic changes can have dramatic impacts.

33. Scenario 2: New PI Distribution 1 • Constant rate of 1500 New PI’s • Age 25-35: 25% • Age 36-40: 20% • Age 41-45: 20% • Age 46-50: 15% • Age 51-55: 10% • Age 56-60: 10% • Age 61-80: 0%

34. Scenario 2, New PI Distribution 1: 2006 Avg Age = 47.5

35. Scenario 2, New PI Distribution 1: 2011 Avg Age = 47.6

36. Scenario 2, New PI Distribution 1: 2016 Avg Age = 48.2

37. What Does This Tell Us? • The “ideal” age distribution for the PI pool is still an unknown target. • With changes that occur due to feedback loops in the system, the established age distribution policy for new PI’s for future years will likely change every few years. • In other words, there is no constant age distribution policy for incoming new PI’s that will provide the “ideal” PI pool age distribution over the long run.

38. Additional Test Scenarios for Final Workforce Group Meeting November 14, 2007

39. Test Scenario: Effect of the Number of New PIs on the Average Age of the Total Pool Age Distribution 24-35: 25% 36-40: 20% 41-45: 20% 46-50: 15% 51-55: 10% 56-60: 10% 61-90: 0%

40. Test Scenario: Effect of the Number of New PIs on the Average Age of the Total Pool Age Distribution 24-35: 25% 36-40: 20% 41-45: 20% 46-50: 15% 51-55: 10% 56-60: 10% 61-90: 0%

41. Test Scenario: Small Changes in the Age Distribution of the New PI pool Distribution #1 24-35: 25% 36-40: 20% 41-45: 20% 46-50: 15% 51-55: 10% 56-60: 10% 61-90: 0% Distribution #2 24-35: 25% 36-40: 40% 41-45: 15% 46-50: 10% 51-55: 5% 56-60: 5% 61-90: 0% Distribution #3 24-35: 25% 36-40: 60% 41-45: 10% 46-50: 5% 51-55: 0% 56-60: 0% 61-90: 0%

42. Test Scenario: Small Changes in the Age Distribution of the New PI pool Distribution #1 Distribution #2 Distribution #3 1100 New PIs 1500 New PIs

43. Test Scenario: Extreme Case – Replacing the PI Pool

44. Conclusions • The model in its current state matches historical data “qualitatively”, but could use some improvement with “quantitative” accuracy. • The current “backbone” aging model needs to be enhanced to increase the quantitative weaknesses. • The simulation could be improved with the addition of “recycling” of PI’s as well as feedback loops regarding how individuals and institutions act/react to changes in NIH policies. • With improvements, the simulation model could be very useful in understanding the short-term and long-term consequences of NIH policies. • The ideal “age distribution” for the PI pool is still undetermined.

45. Next Steps • Based on feedback from the final workforce group meeting, develop a list of specific model enhancements to be incorporated in a follow-on effort. • On this next effort, focus on increasing the quantitative accuracy of the model compared to historical data. • Report back to workforce modeling group on results from enhanced model.