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PI Aging Simulation Model


The Current Problem:“Success to the Successful”

More Funds to 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

Higher Success of Older,Experienced PI’s


Basic Structure for Age Group

PIs in the system that have “aged” enough to move to the next age group.

New PIs (i.e., first-time) that enter the NIH pool in this age group.

PIs of this age group that leave the “system.”

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 into this age group.


Connecting Age Groups

Age 1

In Age 1 Out Age 1

PIs Leaving Age 1

New PIs Age 1

Age 2

Out Age 2

PIs Leaving Age 2

New PIs Age 2

Age 3

Out Age 3

PIs Leaving Age 3

New PIs Age 3


Differences Between Models

OB:• Spreadsheet

methodology• Statistical• Focuses on data• Static• No feedback loops

OER:• System Dynamics

(SD) methodology• Operational simulation• Focuses on activities• Dynamic• Feedback loops


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.


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.


Average Length of Service

Age Range 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 199220-24 21.3 0.5 12.0 6.0 11.7 0.0 4.0 0.0 3.0 0.0 17.0 0.0 0.025-29 16.8 17.7 18.1 17.3 14.3 12.8 11.9 15.5 15.8 12.7 13.6 13.5 9.930-34 17.8 16.5 16.3 15.9 14.9 13.4 13.3 12.8 13.2 13.4 11.9 12.0 11.635-39 16.3 15.4 15.1 14.1 13.9 12.9 12.2 12.1 11.9 11.8 11.6 11.3 10.640-44 14.7 13.3 14.0 13.5 13.2 12.9 12.1 11.9 11.1 10.2 10.7 10.2 9.945-49 13.4 12.8 13.2 11.2 11.0 11.2 10.5 10.8 10.4 10.1 10.0 9.1 9.050-54 6.1 12.1 12.1 12.0 9.5 13.3 11.1 7.6 10.1 9.8 9.3 7.6 7.755-59 8.6 7.8 10.6 7.9 8.1 8.5 7.6 7.1 5.6 7.6 7.1 8.1 6.7


Baseline Results


Total Number of PI’s (FY80-FY06)

0

5000

10000

15000

20000

25000

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Year

Qu

an

tity

Actual

Simulation FY86


Baseline: 1991

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Actual

Simulation

Avg Age = 42.7

Avg Age = 45.6


Baseline: 1996

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Actual

Simulation

Avg Age = 44.7

Avg Age = 47.3


Baseline: 2001

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Actual

Simulation

Avg Age = 46.3

Avg Age = 49.0


Baseline: 2006

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Actual

Simulation

Avg Age = 47.5

Avg Age = 50.8


Scenario 1 Results


Total Number of PI’s (FY80-FY16)

0

5000

10000

15000

20000

25000

Year

Qu

an

tity

Actual

Simulation FY86


Scenario 1: 1991

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 42.7


Scenario 1: 1996

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 44.7


Scenario 1: 2001

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 46.3


Scenario 1: 2006

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 47.5


Scenario 1: 2011

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 48.3


Scenario 1: 2016

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 49.8


Scenario 2 Results


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


Scenario 2, No New PI’s: 2006

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 47.5



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 50.5



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 54.3


Scenario 2, All New PI’s <40: 2006

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 47.5



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 44.0



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 41.3


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.


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%


Scenario 2, New PI Distribution 1: 2006

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 47.5



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 47.6



0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

Avg Age = 48.2


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.


Additional Test Scenarios for Final Workforce Group Meeting

November 14, 2007


2011

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

1000: Avg Age = 48.31500: Avg Age = 47.62000: Avg Age = 47.22500: Avg Age = 46.83000: Avg Age = 46.5

Test Scenario: Effect of the Number of New PIs on the Average Age of the Total Pool

Age Distribution24-35: 25%36-40: 20%41-45: 20%46-50: 15%51-55: 10%56-60: 10%61-90: 0%


2016

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

1000: Avg Age = 48.81500: Avg Age = 48.22000: Avg Age = 47.82500: Avg Age = 47.63000: Avg Age = 47.4

Test Scenario: Effect of the Number of New PIs on the Average Age of the Total Pool

Age Distribution24-35: 25%36-40: 20%41-45: 20%46-50: 15%51-55: 10%56-60: 10%61-90: 0%


Distribution #1

Distribution #3

Distribution #2

24-35: 25%36-40: 20%41-45: 20%46-50: 15%51-55: 10%56-60: 10%61-90: 0%

24-35: 25%36-40: 40%41-45: 15%46-50: 10%51-55: 5%56-60: 5%61-90: 0%

24-35: 25%36-40: 60%41-45: 10%46-50: 5%51-55: 0%56-60: 0%61-90: 0%

1500 New PIs

48.2

46.2

45.4

44.2

47.6

46.5

47.5

43.544.044.545.045.546.046.547.047.548.048.549.0

2006 2008 2010 2012 2014 2016 2018

1100 New PIs

48.6

47.246.8

46.3

44.8

48.1

47.5

44.044.545.045.546.046.547.047.548.048.549.0

2006 2008 2010 2012 2014 2016 2018

Test Scenario: Small Changes in the Age Distribution of the New PI pool


2011

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

25 30 35 40 45 50 55 60 65 70 75 80

2016

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

25 30 35 40 45 50 55 60 65 70 75 80

2011

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

25 30 35 40 45 50 55 60 65 70 75 80

2016

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

25 30 35 40 45 50 55 60 65 70 75 80

Distribution #1

Distribution #3

Distribution #2

1100 New PIs 1500 New PIs

Test Scenario: Small Changes in the Age Distribution of the New PI pool


No Competing Grants after 2006,1500 New PIs after 2006, Age Distribution: 25% 24-35, 75% 36-40

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

25 30 35 40 45 50 55 60 65 70 75 80

Age of PI

2006

2011

2016

46.6

38

40.5

Test Scenario: Extreme Case – Replacing the PI Pool


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.


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.

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