syst/0r 699-project final report - george mason universityseor.vse.gmu.edu/~klaskey/capstone/mason...

George Mason University SEOR Department of 41

SYST/0R 699-Project Final Report

Modeling the Mason Research Enterprise

May 8th, 2017

Sponsors: Dr. Stephen Nash Dr. Art Pyster Supervisor: Dr. Kathryn Lasky

Prepared By:

Noran Abraham

James Lee

Christopher Murri

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Table of Contents

1 PROBLEM DEFINITION 4

1.1 SPONSORSHIP 4

1.2 BACKGROUND 4

2 PROBLEM STATEMENT 5

2.1 PROBLEM AND NEEDS STATEMENT 5

2.2 OBJECTIVES AND PURPOSE 6

2.3 SCOPE 6

2.4 STAKEHOLDERS 6

3 TECHNICAL APPROACH 7

3.1 METHODOLOGY 7

4 MODEL 9

4.1 ROUSE MODEL 9

4.1.1 ROUSE MODEL – MASON EDITS 10

4.2 SYSTEM DYNAMICS MODEL 12

5 RESULTS AND SENSITIVITY ANALYSIS 21

5.1 ROUSE MODEL 21

5.1.1 SENSITIVITY ANALYSIS/WHAT-IF SCENARIOS 21

5.1.2 FUNCTIONALITY 22

5.1.3 EXAMPLE 22

5.2 SYSTEM DYNAMICS MODEL 23

6 EVALUATION 24

7 RECOMMENDATIONS FOR FUTURE WORK 25

REFERENCES 27

APPENDEX A – MODEL AND ANALYSIS 28

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SD MODEL: CAUSES TREE AND USES TREE 28

DATA GATHERED 33

SD MODEL: EQUATIONS 35

ROUSE MODEL: EQUATIONS 36

APPENDEX B - PROJECT MANAGEMENT 37

PROJECT WORK BREAKDOWN STRUCTURE (WBS) 37

PROJECT GANTT CHART 39

PROJECT EARNED VALUE MANAGEMENT (EVM) 40

TEAM ROLES AND RESPONSIBILITIES 41

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1 PROBLEM DEFINITION

1.1 SPONSORSHIP

The Office of the Vice President for Research at George Mason University (GMU) is interested in building

a model that would help the understanding of the research enterprise mechanism. Although their

ultimate goal is to understand the Research Enterprise of the entire University, to kick off a project that

was feasible and manageable in scope, they have asked Dr.Stephen Nash, the Senior Associate Dean,

and Dr.Arthur Pyster, the Associate Dean for Research, to supervise a Capstone project team to model

the Volgenau School of Engineering specific research activities. The problem statement outlined below

has been derived from conversations with Dr.Nash and Dr.Pyster, the official clients of this project. The

office of the Vice President for Research is looking for a tool that conducts tailored analysis &

characterization of Key indicators of research activity to assess the overall health of the research

enterprise at GMU.

1.2 BACKGROUND

One of George Mason University’s (GMU) strategic goals over the past decade was to become a top-tier

research university. A strong consensus emerged among GMU leaders and faculty during the inclusive

strategic planning process in 2012-2013(GMU, 2016). The consensus was that GMU needed to continue

to reinforce its investment in research enterprise as a continuation of the growth of the university and

as a fulfillment of the public mission of GMU (GMU, 2016). Comparing GMU to institutions in Top-tier

(R1) category of Carnegie Classification, GMU is “the new kid on the block.” (GMU, 2016). However,

since 2012, the GMU community has made major investments in research to achieve R1 status. Such

investments resulted in an increase in the school’s total research expenditures that grew from $77

million in 2008-2009 to $99 million in 2013-2014 (GMU, 2016). On February 1, 2016, that dream became

a reality, as GMU moved into the Top-tier (R1) category of Carnegie Classification, “based on a review of

its 2013-2014 data that was performed by the Center for Postsecondary Research at Indiana University

Schools of Education” (GMU, 2016). The increase in research expenditures was driven by growth in

research expenditures of the Volgenau School of Engineering, which doubled during that period (GMU,

2016). GMU has also “increased the number of doctoral degrees it conferred by 27 percent in that same

period” (GMU, 2016).

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The Carnegie Classification is a prestigious classification that shows the intense competition among the

universities in our nation. There is a total of 335 universities in this classification: 115 of them are R1,

107 are R2, and 113 are R3 (GMU, 2016). While reaching such a classification is a remarkable

achievement for GMU, the new goal for GMU is to have a robust, high-impact research program that will

lead Carnegie to maintain its categorization of GMU as a top-tier research university.

2 PROBLEM STATEMENT

2.1 PROBLEM AND NEEDS STATEMENT

The ability to forecast the key indicators that would affect the research development is obviously very

important for GMU. What is not as obvious is how GMU would accomplish this feat. There are so many

correlating factors that affect research as shown below in Figure (1), but there is currently no known

tool or a model that conducts tailored analysis and characterization of such factors in order to assess the

overall health of the research enterprise at GMU.

Figure (1): An economic model of complex academic enterprises that captures the key flows (Rouse, 2016)

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2.2 OBJECTIVES AND PURPOSE

The objective of this project is to develop a model to represent relationships among key drivers of the

Mason research enterprise and their interactions with other major activities at the university, focusing

initially on Volgenau School of Engineering (VSE). VSE is one of the top contributors to growth in

research expenditures of science and engineering, which doubled during the period of 2013-2014 (GMU,

2016), and VSE has the most complete data that will be accessible to the team during the project.

2.3 SCOPE

The model required should be implemented as a tool to support:

1. Assessing the overall health of the research enterprise at VSE. 2. Examining “what if” scenarios for different investment strategies. 3. Understanding the relationships between key indicators and research expenditures.

The team will not be providing any recommendations such as:

1. What is the optimal solution?

2. Which investment is better than others?

2.4 STAKEHOLDERS

.

Primary Stakeholders

1. Sponsors: Dr. Stephen Nash

VSE Senior Associate. Dean Dr.Art Pyster

VSE Associate Dean for Research

2. Vice President of Research at GMU: Dr.Deborah Crawford

3. Other Associate Deans for Research

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3 Technical Approach

3.1 METHODOLOGY

By referencing the knowledge of systems engineering processes that we acquired over the course of the

SEOR graduate program at GMU and by leveraging the work and internship experience we gained in

different fields, we have formulated a technical approach that will give the team the best chance of

achieving the sponsor’s goals.

The first of our approaches is an Excel-based numerical model from Dr. William Rouse at Stevens

Institute of Technology. Following the relationships laid out in his text, Universities As Complex

Enterprises (2016), the Rouse model takes University financial, academic, and research data and outputs

long-term projections for various metrics of research and University health. Per our agreement with Dr.

Rouse, the team cannot share technical details of the model save for a handful of approved faculty (such

as our sponsors). However, further, in the paper, we summarize the basic relationships of the model and

the changes the GMU team made to scope the model to our purposes.

We base our second solution around a System Dynamics Model. System Dynamics (SD) is an approach

that facilitates understanding of the linear and nonlinear behaviors of highly complex systems over a

period of time using stocks, flows, and feedback loops. It is an aspect of systems theory that is used to

understand the dynamic behavior of complex systems. The basis of SD is “the recognition that the structure

of any system — the many circular, interlocking, sometimes time-delayed relationships among its

components — is often just as important in determining its behavior as the individual components

themselves” (Wikipedians, n.d., p. 144).

Many different software packages have been used for system dynamic modeling. The team will use the

academic license for the Vensim Software tool that is provided to them through the SEOR department.

Vensim is a powerful software tool that provides a graphical modeling interface with stock and

flow, and causal loop diagrams as shown below in Figure (2). In this model, the stock variable is

measured at one specific time, and it represents a quantity of a variable at a point of time, while a

flow variable represents a change during a period of time and is measured over an interval of time.

https://en.wikipedia.org/wiki/Systems_theory

https://en.wikipedia.org/wiki/Stock_and_flow

https://en.wikipedia.org/wiki/Stock_and_flow

https://en.wikipedia.org/wiki/Causal_loop_diagram

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Figure (2): shows an example for Stock and flow diagram of new product adoption model (System Dynamics, 2017)

The steps involved in SD simulation are:

“Defining the problem boundary. Identifying the most important stocks and flows that change these

stock levels. Identifying sources of information that impact the flows. Identifying the main feedback

loops. Drawing a causal loop diagram that links the stocks, flows, and sources of information. Writing

the equations that determine the flows. Estimating the parameters and initial conditions using statistical

methods, expert opinion, market research data or other relevant sources of information. Simulating the

model and analyze results.” (Wikipedians, n.d., p. 144).

Causal Loop Diagram

Diagram

A Flow is the rate of accumulation of the Stock

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4 MODEL Our Project is based on two models that will be discussed below:

1- Rouse Model

2- System Dynamics Model (SD)

4.1 Rouse Model

The Rouse model is an Excel-based model developed by Dr. William Bill Rouse at Stevens Institute of

Technology that captures the state of research at an institution. Figure (3) is taken from Dr.Rouse

accompanying text, Universities As Complex Enterprises (2016), summarizes the variables and basic

relationships found within the model.

Figure (3): An economic model of complex academic enterprises that captures the key flows (Rouse, 2016)

The Rouse model is particularly concerned with a University’s Brand Value, a numerical proxy for

reputation coined in the text, and its change over the next 10 – 20 years of institutional growth. In total,

the Rouse model takes generalized academic and financial data applicable to any university and provides

long-term projections for the state of its research and reputation.

The Brand Value is a unit less metric that is calculated according to Rouse model (Rouse, 2016) as

follows:

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4.1.1 Rouse Model – Mason Edits While the Rouse model fits the general financial and academic format of a university, the GMU team

had to make some edits and amendments to suit our sponsors’ needs. The largest of our changes

involved scoping the model to only fit the Volgenau School of Engineering (VSE) rather than the entire

University. In addition, the team had to change a number of formulas and variables to fit how VSE

manages its finances more closely, collects its data, and organizes its research. Additional variables and

formulas, mostly pertaining of costs and staffing, were created and added to the model as well under

the direction of our sponsors.

The following figures: Figures (4-6) show the assumptions we integrated into Rouse Model to fit our

sponsor’s needs in terms of “Total costs and Total Revenue”.

Figure (4) is showing the assumptions we identified during our conversations with our sponsors

to capture the variables of interest for VSE regarding total costs and total revenue.

Figure (5) is showing the new added variable to calculate the total costs of VSE.

Figure (6) is showing the new added variables to calculate the total revenue of VSE

Figure (4): Assumptions for new variables added Total Costs Equation and Total Revenue Equation for VSE

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Figure (5): Total Costs Equation for VSE

Figure (6): Total Revenue Equation for VSE

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4.2 System Dynamics Model

We developed the system dynamics model using a five-step process. For the first three steps, we used

causal loop diagrams to model relationships between key variables that affect the Volgenau Mason

Research Enterprise. First, we modeled relationships between student enrollment and research

expenditure based on the data that were available to us. Second, we modeled the possible investment

strategies that we found through conversations with our sponsors and stakeholders. Third, we modeled

the effect of brand value on the known variables in the Mason Research Enterprise. We then combined

the three causal loop diagrams to create a total representation of the enterprise model. Finally, we

selected some of the known variables and relationships to build a system dynamics model.

I. Step 1

Through our interactions with our sponsors, especially our conversation around Dr. Nash’s “Dashboard

Metrics” as shown below in Figure (7), we were able to better understand the trickle-down effect of

student enrollment on research expenditure. Student growth forced an increase in the number of

faculty, and the university’s decision to hire more tenure-track and tenured faculty would ultimately

drive the research expenditure. Our sponsors also assisted us in identifying variables that were not

explicitly mentioned in the “Dashboard” but were critical in terms of understanding research, such as

the data for proposals, awards, and expenditures as shown below in Figure (8)

Figure (7): VSE Dashboard

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Figure (8): VSE data for proposals, awards, and expenditures

Based on the data we studied from VSE Dashboard and VSE data for proposals, awards, and

expenditures, we created our first model, “Enrollment to Research,” shown below in Figure (9).

Figure (9): First model in System Dynamic Model-Enrollment to Research

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The blue arrows indicate the known relationships, which we had data from 2012 to 2016. The red

circles indicate variables that are directly affected by research investments. The number of

applications drives the number of students we have, which then drives the number of faculty we need.

The school can then decide to either hire tenure-track and tenured faculty or term faculty. Our sponsors

mentioned that term faculty are less costly and less risky as the employment is not permanent. In other

words, it would be “easier” to hire term faculty as a short-term solution. Hiring tenure-track and

tenured (TT&T) faculty would benefit the research enterprise as they would be entitled to dedicate 40%

of their time to research-related activities. Thus, tenure-track and tenured faculty and number of

proposals have a direct relationship, that is, if TT&T increases, the number of proposals (or the quality of

proposals) increases. Ultimately the more and higher-quality proposals are generated, the more will be

awarded, and therefore, research expenditure will increase. Another factor that directly contributes to

the research capability is the number of full-time Ph.D. students. Generally, the higher the school’s

financial support (stipend) to Ph.D. students, the higher the Ph.D. yield (%) as well as the students’

decision to commit full-time.

The following are some of the investment activities that can affect research expenditure:

Applications: The number of applications can be affected by the school’s decision to run

advertising campaigns.

Ph.D. Yield and Full-Time to Part-Time Ph.D. Ratio: Increasing the Ph.D. stipend can increase

the number of Ph.D. students as well as the number of full-time Ph.D. students.

Tenure-Track and Tenured to Total Faculty Ratio: The school may decide to invest in “riskier”

tenure-track faculty rather than term/teaching-only faculty.

Research Capability and Proposal Approval Rate: This is affected by a number of items

highlighted in the next step.

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II. Step 2

In our meetings with sponsors and stakeholders, they were clearly aware of the increase in student

enrollment and that they wanted to “leverage” this growth in terms of research by making the correct

“investments.” In addition to the types of research investments identified in our previous step, we were

able to identify a few more outlined below, resulting in our second model, “Research Investments.” The

blue arrows indicate known relationships, meaning the school is able to track exactly how much is

being spent on the following research investments, which are indicated in red text, as shown below in

Figure (10).

Figure (10): Second model in System Dynamic Model- Research Investment

The following are some of the research investments:

Hiring more research faculty has a direct effect on the number of proposals being written.

Hiring research administrators will increase the proposal approval rate as the quality of

proposals increases.

Cost Sharing: This means investing in equipment and lab space with assistance from a third

party.

Create Research Labs and Purchase Equipment: This will allow faculty to carry out new types of

research.

Seed Grants: A faculty member should be provided funding to begin new research to help

attract larger, more competitive funding once the research is under way.

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III. Step 3

After acquiring a good understanding of brand value and its importance from studying the Rouse model,

we identified places that brand value would directly affect, shown below in Figure (11).

Figure (11): Third model in System Dynamic Model- Brand Value

The text in the green hexagons indicate variables that are directly affected by brand value.

Increase in brand value can cause the following:

More alumni will make significant donations to the school that can be used for more research

investments.

Applications will increase. More students will want to come to VSE.

More Ph.D. students will enroll once admitted.

Increase in brand value will increase research capability, proposal approval rate, and average

award.

o Higher-quality faculty will bring higher-quality research and higher average reward.

o Increased recognition will increase proposal approval rate.

The model also includes the factors that affect brand value, namely, h-index, number of citations,

and number of articles, which are driven by research expenditure.

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IV. Step 4

After developing individual causal loop diagrams for Enrollment to Research, Research Investment, and

Brand Value, we joined these diagrams to create the overall model. The green and red arrows indicate

relationships that we have a general idea of but do not have enough information to accurately

quantify. Specifically, red arrows are the effects of certain research investments on variables that

directly relate to research expenditure. The green arrows are brand value feedback. We know that

brand value affects certain variables, but cannot quantify the exact effect because of lack of data. The

combined causal loop diagrams as shown below in Figure (12) offer an overall view of the enterprise

model. Thus, visualizing relationships, trends, and how each variable fits into the big picture becomes

more straight forward than simply talking about them.

Figure (12): An overall view of the Research Enterprise System Dynamics Model

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V. Step Five.

Since the combined causal loop diagrams of the overall enterprise model showed many relationships

that were not quantifiable (red and green arrows), we focused our efforts in modeling a system

dynamics model of the three components (Enrollment to Research, Research Investment, and Brand

Value Feedback) to the best of our abilities. First, we created a model without brand value feedback, as

shown below in Figure (13). The values at year 0 indicate the current state of VSE in fiscal year (FY) 2016,

and the values at year 5 reflect the aspired values Professor Nash indicated in his “Dashboard.” The

reason we first created a model without feedback loops was to ensure that we correctly captured the

current status and the projected values, and also because feedback loops can complicate the modeling

process.

Figure (13): A run for System Dynamic model without Brand Value feedback

The sliders are a combination of “forces of nature,” meaning things that are out of our control,

results of a research investment, and decisions the school makes. The default values of the sliders

represent the current growth rates, TT&T ratio, and FT Ph.D. ratio for 2016.

“Forces of Nature”

o Student Growth Rate

o Proposal Approval %

o Average Award $

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Results of Research Investment

o Advertising can increase student growth rate

o Stipends can increase Ph.D. growth rate and full time Ph.D. ratio

o The school can decide to hire more tenure track faculty

Decision the school makes

o The student to faculty ratio is decided by the school. It currently sits at about 35

students to a faculty member. According to Dr. Nash, this number will not change for a

while.

The red text indicates sliders that are both “forces of nature” and “results of research investments.”

For example, “UG Growth Rate” is the “force of nature,” while “ADVERTISING” is the “result of

research investments.” The variables highlighted in yellow represent the variables that produce

graphical outputs.

As mentioned before, the details on number of proposals, awards, and expenditure per year were

provided separately, shown above in Figure (8). Using these data, we were able to deduce a relationship

between proposals and number of tenure-track and tenured faculty, how many proposals get awarded,

how much an average award is, and when the award gets logged under the expenditure column.

Considering that a time lag exists between the time a proposal is submitted to when it is awarded, and

when the money is spent, we assumed that the expenditure of FY 2016 was a subset of the award fund

of FY 2015, and the number of awards of FY 2015 was a subset of the number of proposals of FY 2014.

Once we developed a model that accurately represents parts of the Mason Research Enterprise, we

introduced feedback to it, as shown below in Figure (14). Given that the effect of brand value is unclear,

we decided to consider brand value as having a multiplier effect. When research expenditure is less than

20 million, no multiplier is in effect. For research expenditure between 20 million and 30 million, the

multiplier becomes 2; when research expenditure is between 30 million and 40 million, the multiplier

becomes 3; and so on. This multiplier is applied to Undergrad Growth Rate, Master’s Growth Rate, Ph.D.

Growth Rate, Full-Time Ph.D. Ratio, Quality of Tenure-Track and Tenured Faculty, Proposal Quality, and

Average Award $.

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Figure (14): A run for System Dynamic model with Brand Value feedback

The model in Figure (15) shows that with brand value feedback, the research expenditure has

exponential growth as opposed to linear growth from the previous model without brand value feedback.

Figure (15): Comparison between research Expenditure in a model with Brand Value vs. No Brand Value

With Brand Value Feedback

No Brand Value Feedback

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5 RESULTS AND SENSITIVITY ANALYSIS

5.1 Rouse Model

5.1.1 Sensitivity Analysis/What-If Scenarios

Based on the Rouse model changes to fit the VSE needs, an interactive GUI tab was created for the

Rouse model “VSE” as shown below in Figure (16) that allows users to see the long-run outcomes of

various theoretical university states. The interactive GUI tab functions in lieu of particular sensitivity

analyses. Known as the What-If Tool, the interface allows users to set and amend model input variables

while viewing projected outputs. For example, a user might want to understand how a 5% graduate

student population growth rate affects finances compared with a 9% growth rate. The What-If Tool will

run two separate iterations of the model using these inputs and will graph the outputs side by side.

Figure (16): Interactive GUI Tab for Rouse Model

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The current inputs are the top 5 variables that the team and our sponsors believe contribute to brand

value. However, more inputs and outputs can be added if necessary. In addition, the current version of

the GMU Rouse model prevents certain important metrics from being directly set by the user. This is

because the Rouse model treats certain dashboard metrics as dependent variables rather than

independent variables as our sponsors would like. Future model updates should include changing these

variables to allow direct input from the user rather than having to allow the model to calculate them.

5.1.2 Functionality

The What-If Tool is an Excel worksheet that is linked to three other worksheets, each a version of the

GMU Rouse model. Changing a setting in the What-If Tool will alter the same variable in the

corresponding model, and the graphed outputs (also linked from the GMU models) will change to reflect

the new model state.

The user is given three “scenarios” to play with—meaning for each graphed output variable, there will

be three different lines. This allows our sponsors to view projected differences between possible

university states. The first scenario is the actual state of the previous full academic year. Users are

encouraged not to change this setting as it provides a baseline of reality from which to work. The second

and third scenarios are left up to the user to specify. Finally, room for a fourth possible scenario input is

left in case future teams would like to conduct more extensive comparisons.

5.1.3 Example

To demonstrate the functionality of the Rouse model and the

What-If Tool, the brand value outputs of three future scenarios

regarding undergraduate growth are shown below in Figure

(17). This is how the What-If Tool will present model outputs to

the user.

Scenario 1: 13% academic growth (same as AY 2016)

Scenario 2: 9% academic growth

Scenario 3: 6% academic growth

All other variables are set equal to their recorded AY 2016

values.

Inputs Outputs

Undergraduate population growth rate

Projected brand value

Graduate population growth rate

Projected total revenue, costs, and surpluses

Percent faculty that Is tenure/tenure-track

Projected student enrollment and cost per student

Tenure-track teaching load

Term faculty teaching load

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Figure (17): Graph for Rouse What-if Scenario from GUI Tab

5.2 System Dynamics Model

All Scenarios were modified from the base model, meaning the model was re-set after each scenario.

Scenario 1 Increased UG Growth Rate, MS Growth Rate, Ph.D. Growth Rate to 1.3

There was not much of a visible difference in Research Expenditure

Scenario 2 Decreased Student to Faculty ratio from 35 to 25, with same TT&T ratio at 0.7

There was a visible increase in #TT&T from 200 to 240, and Research Expenditure of a couple

million

Scenario 3 Increased TT&T ratio from 0.7 to 0.8

The affects were like Scenario 2, since there is an increase in #TT&T and therefore Research

Expenditure

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Scenario 4 Increased Full Time to Total Ph.D. ratio from 0.6 to 0.7

The affects were similar to Scenario 2 and 3, but the increase in Research Expenditure was not

as significant as increasing #TT&T

Scenario 5 Increased Proposal Approval % from 0.7 to 0.8

There was significant increase in Research Expenditure (close to $10 million)

Scenario 6 Increased Av Award $ from 120000 to 130000

The increase was not as significant as increasing Proposal Approval %

Conclusion

The number of TT&T Faculty was the main driving force of Research Expenditure, which is consistent

with our findings from the Rouse Model. The number of Full-time Ph.D. Students are also a significant

variable, but does not impact expenditure as much as the number of TT&T does. It also was evident that

the variables closer to expenditure, such as # Proposals, # Awarded, and Average Award $ had a higher

impact on expenditure, than the enrollment related variables.

6 EVALUATION

The relationships between relevant variables seemed obvious and intuitive at first, but upon combining

all the elements, the model turned out to have more connections and complexities than we originally

thought. The benefits of having the causal loop diagrams and system dynamics model is that they

provide insight to a problem that had no structured or systematic way to understand certain

relationships. The models created provide a visual tool as well as an elementary numerical analysis that

can drive conversation between our sponsors and their target audience. When we showed this to Dr.

Crawford, the VP of Research for George Mason, she commented that she had never seen any model

like it before and that the model would effectively help her in visualizing the relationships and effects of

certain “investments” on research expenditure. The model not only reinforces the conversations our

sponsors can have with their decision makers, but also allowed our sponsors to delve into the workings

of the enterprise, which provided them with an opportunity to transfer some tacit knowledge to active

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knowledge that can be shared with others. These models also laid a solid foundation for future research

to be conducted.

7 RECOMMENDATIONS FOR FUTURE WORK

I. Improve and complete the SD model

All relationships between variables are linear or simplified. Future work should include

performing in-depth data analysis techniques to better define these relationships.

The model only considers the positive scenarios. We are only considering an increase in brand

value since VSE is already on an upward trend with hopes to continue to capitalize on the

student growth. However, negative scenarios can occur, such as change in policies that would

limit research funding, decline in economic climate like the market crash of 2008, or abundance

of high-paying jobs that encourage students to pursue a graduate degree. Modeling these

relationships would be interesting.

This model has no realistic “caps.” The exponential growth that was observed in the model with

brand value feedback could mean that it would eventually go to infinity, but that may not be a

realistic solution. When discussing the results with our sponsors, they believed this type of

growth could be possible for the next five years at least. However, the model would be more

realistic if we considered more realistic upper and lower bounds in terms of number of students

and space.

The current state of the model only considers the known relationships. No data are available to

model some of the critical relationships, especially the relationships stemming from research

investment and brand value. For the effects of research investments, we would have to closely

track the return on investment, and for brand value, we could back in time and input the state

of VSE for different years and see how that affects brand value. We may be able to reverse

engineer the effect of brand value feedback.

II. Collect more data and conduct analysis around actual research

The return on investment should be tracked (as mentioned above).

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There should be better visibility into articles, citations, and h-index, since these variables directly

affect brand value. Currently no method exists in obtaining an accurate count of these data.

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REFERENCES

Wikipedians (Eds.). (n.d.). Complexity and dynamics: Complexity theories, dynamical systems and

applications to biology and sociology. Mainz, Germany: PediaPress.

Mason achieves highest Carnegie research classification. (2016, February 7). Retrieved February

24, 2017, from https://president.gmu.edu/mason-achieves-highest-carnegie-research-classification

Mason achieves top research ranking from Carnegie. (2016, February 3). Retrieved February 24,

2017, from https://www2.gmu.edu/news/182106 Rouse, W. B. (2016). Universities as complex enterprises: how academia works, why it works these

ways, and where the university enterprise is headed. Hoboken, NJ: John Wiley & Sons, Inc. System dynamics. (2017, February 17). Retrieved February 24, 2017, from

https://en.wikipedia.org/wiki/System_dynamics

https://president.gmu.edu/mason-achieves-highest-carnegie-research-classification

https://president.gmu.edu/mason-achieves-highest-carnegie-research-classification

https://www2.gmu.edu/news/182106

https://en.wikipedia.org/wiki/System_dynamics

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APPENDEX A – MODEL AND ANALYSIS

SD MODEL: Causes Tree and Uses Tree

BRAND VALUE CAUSES TREE

BRAND VALUE USES TREE

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RESEARCH INVESTMENTS CAUSES TREE

RESEARCH INVESTMENTS USES TREE

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RESEARCH CAPABILLITY & QUALITY # OF PROPOSALS CAUSES TREE

RESEARCH CAPABILLITY & QUALITY # OF PROPOSALS USES TREE

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APPLICATIONS CAUSES TREE

APPLICATIONS USES TREE

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AVERAGE AWARD CAUSES TREE

PROPOSAL APPROVAL RATE CAUSES TREE

Ph.D. Yield % CAUSES TREE

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Data Gathered

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Type Min Max Incr EquationsInitial

ValueUG Growth Rate:

ADVERTISING Constant0 2 0.01 1.08

MS Growth Rate:

ADVERTISING Constant0 2 0.01 1.1

Ph.D. Growth Rate:

ADVERTISING / Constant0 2 0.01 1.04

# Undergrad Level 1000 7000 ("UG Growth Rate: ADVERTISING "+UG x)*250 4894

UG x Auxiliary (Brand Value Multiplier-1)*0.01

# MS Level 1000 2000 ("MS Growth Rate: ADVERTISING" +MS x)*70 1388

MS x Auxiliary (Brand Value Multiplier-1)*0.01

# Ph.D.Level

200 1000("Ph.D Growth Rate: ADVERTISING /

STIPEND" +"Ph.D x")*40323

Ph.D. x Auxiliary (Brand Value Multiplier-1)*0.01

# Students Auxiliary # MS+"# Ph.D"+"# Undergrad"

# FT Ph.D. Level # Ph.D*("FT : Ph.D STIPEND"+" FT Ph.D x")/10 194

FT : Ph.D.

STIPEND Constant0.1 1 0.1 0.6

FT Ph.D x Auxiliary (Brand Value Multiplier-1)*0.01

# Faculty Auxiliary # Students/"Student: Faculty "

Student: Faculty Constant 15 50 5 35

# TT&TLevel

# Faculty*("TT&T : Faculty HIRE MORE

TT&T "+"Quality TT&T")/10121

Quality TT&T Auxiliary (Brand Value Multiplier-1)*0.01

TT&T : Faculty

HIRE MORE TT&T Constant0.1 1 0.1 0.7

# ProposalsAuxiliary

100 400(("# TT&T"*1.2)+"# TT&T"*1.5^(("# FT Ph.D"/"#

TT&T")))-130

# AwardedLevel

# Proposals*("Proposal Approval % "+Proposal

Quality)150

Proposal Approval % Constant 0.1 1 0.1 0.6

Proposal Quality Auxiliary (Brand Value Multiplier-1)*0.01

Research

Expenditure Level# Awarded*Avg Award *Award x/10 1.60E+07

Avg Award $ Constant 70000 300000 10000 120000

Award x Auxiliary 1+((Brand Value Multiplier-1)*0.01)

Brand Value

MultiplierAuxiliary

IF THEN ELSE(Research Expenditure<2e+007, 1 ,

IF THEN ELSE(Research Expenditure<3e+007, 2 ,

IF THEN ELSE(Research Expenditure<4e+007, 3,

IF THEN ELSE(Research Expenditure<5e+007, 4,

5))))

SD Model: Equations

of 41

ROUSE Model: Equations

of 41

APPENDEX B - PROJECT MANAGEMENT

Project Work Breakdown Structure (WBS)

George Mason University SEOR Department of 41

Project GANTT CHART

of 41

Project Earned Value Management (EVM)

of 41

Team Roles and Responsibilities

Team Member

Roles and Responsibilities

Noran Abraham

Team Lead

Rouse Model Analysis – VSE version

Data Gathering Management

James Lee

Technical Lead: System Dynamic Model

Sponsors Point of Contact

Christopher Murri

Website Lead

What -If Tool Modeling for Rouse Model

syst/0r 699-project final report - george mason universityseor.vse.gmu.edu/~klaskey/capstone/mason...

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