# Presentation: 528531

## 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

## 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

## Baseline Results

## Total Number of PI’s (FY80-FY06)

## Baseline:  1991

**Actual**

**Simulation**

**Avg Age = 42.7**

**Avg Age = 45.6**

## Baseline:  1996

**Actual**

**Simulation**

**Avg Age = 44.7**

**Avg Age = 47.3**

## Baseline:  2001

**Actual**

**Simulation**

**Avg Age = 46.3**

**Avg Age = 49.0**

## Baseline:  2006

**Actual**

**Simulation**

**Avg Age = 47.5**

**Avg Age = 50.8**

## Scenario 1 Results

## Total Number of PI’s (FY80-FY16)

## Scenario 1:  1991

**Avg Age = 42.7**

## Scenario 1:  1996

**Avg Age = 44.7**

## Scenario 1:  2001

**Avg Age = 46.3**

## Scenario 1:  2006

**Avg Age = 47.5**

## Scenario 1:  2011

**Avg Age = 48.3**

## Scenario 1:  2016

**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

**Avg Age = 47.5**

## Scenario 2, No New PI’s:  2011

**Avg Age = 50.5**

## Scenario 2, No New PI’s:  2016

**Avg Age = 54.3**

## Scenario 2, All New PI’s <40:  2006

**Avg Age = 47.5**

## Scenario 2, All New PI’s <40:  2011

**Avg Age = 44.0**

## Scenario 2, All New PI’s <40:  2016

**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

**Avg Age = 47.5**

## Scenario 2, New PI Distribution 1:  2011

**Avg Age = 47.6**

## Scenario 2, New PI Distribution 1:  2016

**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

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

**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%**

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

**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%**

## 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%**

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

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

**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**

## Test Scenario: Extreme Case – Replacing the PI Pool

**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.