Forum for the Future of Higher Education
CyberCampus Project
Technical Document 2.6
The Simulation Engine: Component Descriptions
This paper describes work in progress on the Higher Education simulation project funded by the Alfred P. Sloan Foundation. Contents may not be used or cited without permission. Limited distribution is provided to obtain comments and criticisms, and to assist potential development partners. Copyright © 1997 by JHHEG.
Higher Education is a computer-based simulation game under development that targets both the institutional professional and the interested layperson to participate in leadership challenges in a college or university setting. Players set, monitor, and modify a variety of institutional parameters and policies, allocate resources as they see fit, and watch as results continually unfold. The game provides an opportunity to experiment and succeed or fail in a safe and entertaining fantasy environment. While Higher Education is necessarily a caricature of real academic life, it is grounded in authentic data and will provide serious lessons in higher education. The game will be driven by a sophisticated simulation engine that models five broad areas:
1. enrollment management
2.
3.
4. .
5
This document summarizes the underlying mathematical models used in Higher Education. The elements discussed herein pertain to the simulation engine, not to the player interface. Many will be transparent to the player. The purpose of this paper is to provide an overview of the model to help potential development partners estimate programming and memory requirements. Functional descriptions of the engine elements can be found in Technical Documents 2.1 through 2.5. A macro flow chart is attached as an appendix.
Components (or "objects" in some programming terminologies) represent the building blocks of the simulation engine. They perform the simulation’s major operations and store its basic data. Associated with each component are one or more:
• procedures: algorithms that cause the component to act—to do something that furthers the progress of the simulation. Some procedures use linear or quadratic programming (they are noted in the writeup); the others use only standard algebraic functions.
• variables: data elements that store information used by or produced by the procedures.
Some components replicate themselves over subcomponents. The "Student applicants" component encompasses 7 market segments, for example. Each subcomponent has its own variable set (all the sets are identical), and the component’s procedures apply to all subcomponents. The number of subcomponents is multiplicative: e.g., 7 market segments and 4 gender-ethnic groups produces 28 subcomponents. The number of replications equals one where no subcomponents are shown.
The component descriptions are substantially complete with respect to procedures and major variable sets, but they do not represent a comprehensive variable dictionary. The approximate numbers of variables and subcomponents are shown in parentheses. Storage requirements equal the number of variables times the number of subcomponents.
1. Enrollment Management
The enrollment management model is the first model that the simulation engine runs after it sets initial defaults and after the player sets initial policies and parameters that specify the type of institution he or she desires to "lead" for the current game session. The model analyzes data and generates output for three phases of the enrollment process: student applications, institutional admission offers, and student matriculation decisions. The results (i.e., the number of new students for the player-created institution) then inform the next model in the engine sequence, academic operations. The model is run again before each new "academic year" as the game progresses.
Using both empirical data stored in Higher Education’s permanent database and preference functions informed from real life, the enrollment management model first determines the number of prospective student applications for the universe of all existing institutions ("market demand") and breaks down the results by institutional market segment, student market segment and subsegment, and student level. Then, it compares the parameters and policies of the player-created institution to institutional norms across market segments and calculates the number of applications for the player-created institution based on market segment similarities. Next, the model simulates the institutional decision-making process that determines which students will be offered admission. Admission decisions are generated for both the universe of institutions and for the player-created institution (PCI) based on parameters and policies previously established by the player. Finally, the model simulates the behavior of accepted prospective students in deciding where to matriculate and calculates appropriate yields for the universe and for the player’s institution.
C1. Market Demand: Institutional Segments
Purpose: to hold and update information about institutional market segments.
Procedure: updates the institutional segment data, based on exogenous factors.
Variables (~45): number of institutions; number of intake places per institution (by student level); institutional utility weights; institutional characteristics; financial aid policies; cost of attendance; and constraints on the characteristics of the entering class (by student level). Some key definitions follow.
• Institutional characteristics:
– prestige
– educational quality
– student amenities
– athletic prowess
– average size (student FTEs)
• Financial aid policies:
– fraction of need to be covered (traditional undergraduates only)
– academic merit aid policies: minimum academic rating, fixed and variable aid amounts for students over the minimum (by student level)
– athletic aid policies (minimum athletic rating, fixed aid amount (traditional undergraduates only)
• Cost of attendance:
– tuition rate
– room and board rate
– fraction of students on aid and average award (by type of aid and student level).
• Constraints on the characteristics of the entering class (by student level):
– minimum fraction female
– minimum fraction minority
Subcomponents (7): seven institutional segments as defined by the Institute for Research on Higher Education, the project’s data partner
C2. Market Demand: Potential Students
Purpose: to hold and update information about the characteristics of potential students.
Procedure: updates the data on student market segments and subsegments, based on exogenous factors.
Variables (~15 ): number of applicants; applicant characteristics; population attributes; percent preferring small institutions; number of applications submitted per person, applicant utility weights. Tentative student characteristics and utility weight definitions follow:
• applicant characteristics: academic, extracurricular, and athletic ability; average income (income may be included for traditional undergraduates only)
• applicant utility weights: the relative importance of institutional prestige, educational quality, student life amenities, athletic prowess, cost of attendance, and financial aid (student life, athletic prowess, and some financial aid variables apply to traditional undergraduates only)
Subcomponents (~140): student levels; market segments; market subsegments. Tentative definitions follow:
• student levels:
– undergraduate: traditional degree-seeking
– undergraduate: nontraditional degree-seeking
– graduate: masters and professional
– graduate: doctoral
– non-matriculated
• student market segments:
– Blue Chip: students with top academic and extracurricular qualifications
– Scholar: students with top academic qualifications
– Extracurricular: students with top extracurricular qualifications
– Athlete: students with top athletic qualifications
– Balanced: students with respectable academic and extracurricular qualifications
– Average: students with unremarkable academic, extracurricular, and athletic qualifications
– Stretch: students for which college represents a "stretch" goal because of shortfalls in ability or preparation
• student market subsegments, also known as gender-ethnic groups:
– nonminority males
– minority males
– nonminority females
– minority females
C3. Market Demand: Student Applicants
Purpose: to simulate the behavior of prospective students as they decide where to apply.
Procedure: allocates the number of applications submitted by each potential student across institutional segments; projects the per-student results to the market segment as a whole; uses quadratic programming. (It may prove desirable to develop a second, simpler and faster, method for use with some of the student levels.)
Variables (~21): the number of applications a typical student submits to each institutional segment ("applications rate"); minimum probability of being admitted to at least one institution (by student segment); total applications submitted to each institutional segment.
Subcomponents (140): student levels; student market segments; gender-ethnic groups.
C4. Market Demand: Institutional Admissions
Purpose: to simulate the behavior of institutions as they decide which applicants to admit.
Procedure: determines what fraction of the applicants from each student market segment to admit; projects the per-institution results to the institutional market segment as a whole; uses quadratic programming.
Variables (~140): fraction of applications accepted ("acceptance rate") and the number of acceptances (by student level, market segment, gender-ethnic group).
Subcomponents (7): institutional market segments
C5. Market Demand: Student Matriculations
Purpose: to simulate the behavior of prospective students as they determine which institutional offers to accept.
Procedure: determines which of the offers received by a typical prospective student will be accepted (by student level, market segment, gender-ethnic group); projects the per-student results to the market segment, subsegment, and student level.
Variables (~15): utility weights; fractions of the offers received from each institutional segment that will be accepted ("yield rates").
C6. Player-Created Institution (PCI): Applications
Purpose: to determine the number of applications received by the player-created institution.
Procedures:
(i) calculates the relative similarity between the PCI’s characteristics, as determined by initial conditions or prior game play, and the characteristics of the seven institutional market segments;
(ii) calculates a weighted average of the institutional segment application rates (by student level, segment, and subsegment) based on the relative similarities;
(iii) sets the number of PCI applications equal to the weighted average.
Variables (~173): PCI attributes; weights for calculating similarities; relative similarities; number of PCI applications.
C7. Player-Created Institution (PCI): Matriculations
Purpose: to determine the number of matriculations for the player-created institution.
Procedure: identical to C6 except that the averaging is performed over the market segment yield rates and the yield rates are multiplied by the number of applications to get matriculations.
Variables (~ 175): PCI attributes; weights for calculating similarities; relative similarities; number of matriculations. The characteristics are substantially the same as in C6; however, weight differences may produce different similarities.
4. Resource Allocation
Resource allocation provides the central policy focus for Higher Education, for it is the creation and distribution of resources that drive most college and university functions. The term "resource allocation" as used here is synonymous with budgeting—the annual process that determines prices, salary guidelines, provisions for cost-rise, and net changes in the expenditure authorizations for academic and nonacademic operations. The resource allocation model converts player-generated policy inputs and changes in exogenous factors to the specific decisions needed to drive the simulation for the ensuing year. The resource allocation model’s main algorithms have been prototyped in Microsoft Excel®. The model will run once per simulated year.
The model begins by defining the institution’s financial structure—its balance sheet and sources and uses of funds. The resulting financial statements will vary in structure and content depending on the player’s choice of institutional characteristics. The statements for public institutions will include state support. Wealthy institutions will have large endowments and cash reserves; struggling colleges might have none. The financial statements provide the initial conditions for the budget model that determines revenue and expense.
The budget model has three components. The first calculates the drivers of aggregate revenues and expenditures: e.g.,, tuition and financial aid, salary increases, and budget adjustments. (Budget adjustments, which will be defined presently, refer to expenditure changes over and above the effects of salary increases and cost-rise.) A second component allocates the budget adjustments among functions and activities: e.g., faculty, other departmental expense, libraries and information technology, athletics, and institutional advancement. The model’s third component, "faculty positions," allocates faculty salary expenditure adjustments among departments. By so doing, it also determines the number of new faculty that each department can hire.
Every spring Higher Education allocates resources for the next academic year. For simplicity, the model assumes accurate estimates for the current year’s financial outcomes. Because resource allocation takes place late in the fiscal year, this is not an unrealistic assumption. The current-year data provide the baseline against which budget adjustments will be considered. The player determines student intake targets as part of budgeting. (Actual admissions may deviate from the targets.) Therefore, the resource allocation model runs before the enrollment management model. The model also runs before the faculty hiring model even though, in real life, faculty hiring is not concentrated in the late spring. The new budget takes effect on July 1 and remains in effect for the next twelve months unless the player decides to rescind expenditure authorizations.
While actual revenues and expenditures tend to conform to the institution’s planned budget, deviations can be expected. The model uses the coming year’s budget as the initial condition for that year’s revenue and expenditure statement. Changes are introduced during the year as the result of endogenous, exogenous, and random events. For example, if the enrollment management model produces a shortfall or overflow of students (an endogenous event), tuition revenue and financial will be adjusted accordingly. A plunge in the stock market (an exogenous event) will produce variances in gift income and investment return. Small random deviations also will be programmed to affect most or all of the other revenue and expense items. The player can view the current revenue/expenditure statement and balance sheet at any time during the year, and it will be offered up for examination at year-end. Finally, the player can rescind amounts from some expenditure lines—for example, from student life or operations and maintenance—if the financial picture worsens during the year.
C8. Balance Sheet
Purpose: to maintain the PCI’s balance sheets for the end of the year being simulated and for all previous years.
(i) Calculate the amount by which plant and residence hall debt is reduced during the year.
(ii) Calculate financial summaries, ratios, and time series as needed for reporting to the player.
(iii) At year-end, substitute the end-of-year information into the beginning-of year slots in preparation for simulating the following year.
Variables (~20): various assets and liabilities, funds balances ("net worth" in business parlance).
C9. Revenue and Expenditure Statement
Purpose: to maintain the PCI’s revenue and expenditure statement balances ("income statement" in business parlance) for the year being simulated and all previous years.
Procedures: calculate income and expense summaries, ratios, and time series as needed for reporting to the player.
Variables (~114): base income and expenditure variables and associated subvariables—e.g., departmental expenditures (base variable): faculty salaries, staff salaries, non-salary expenditures (subvariables)—plus their totals and percentage breakdowns. There are 9 base revenue variables and 11 base expenditure variables.
C10. Aggregate Revenues and Expenditures
Purpose: to simulate the decisions that determine the following year’s base revenue and expenditure variables.
Procedures: working from player-generated or default policy variables and taking account of exogenous factors, determine revenue and expenditure decisions such as tuition and salary increases and budget adjustments; use the results to update the Aggregate Revenue and Expenditure Statement and the Balance Sheet. Uses quadratic programming.
Variables (~62): target values, penalty weights, and upper and lower bounds values for the quadratic program; suggested target values; exogenous factors relating to revenue change and cost-rise.
C11. Faculty Salary Increases
Purpose: to distribute the budgeted sum for faculty salary increases across departments and faculty categories.
Procedure: working from the budgeted faculty salary increase figure (from C11) and player-generated or default policy variables, determine the distribution of percentage increases across Higher Education’s ten departments and twelve faculty categories. Uses quadratic programming.
Variables (~13): departmental mission-relevance weights, salary priority weights related to faculty categories and the degree of heterogeneity of increases across categories.
C12. Budget Adjustments
Purpose: to simulate the decisions that how the aggregate budget adjustment amount will be distributed among operating functions.
Procedure: working from player-generated or default policy variables and taking account of the data on faculty attrition, distribute the budget adjustment figure determined in C10 among nine operating functions including faculty salaries, other departmental expense, libraries, information technology resources, athletics, and administration. Uses quadratic programming.
Variables (~37): target values, penalty weights, and upper and lower bounds values for the quadratic program; minimum value for faculty salaries based on attrition from the prior year’s faculty base.
C13. Budgeted Faculty Positions
Purpose: to distribute the budgeted sum for faculty salaries (after salary increases) among departments; to determine the number of new faculty to be authorized for each department.
Procedure: working from the budgeted faculty salaries figure (from C11) and player-generated or default policy variables, determine the number of faculty FTEs for each of Higher Education’s ten departments; calculate new hires as the difference between the newly authorized FTEs and the continuing FTE base. Uses quadratic programming.
Variables (~13): departmental mission-relevance weights, target values and importance weights related to teaching load, sponsored research, and the year-to-year change in FTEs.
C14. Fall Budget Revisions
Purpose: to update the budget based on actual enrollments, actual sponsored research, and randomly introduced fluctuations in other income and expense drivers.
Procedure: working from the aforementioned changes and player-created or default policy variables, adjust the original budget for the year, make the needed adjustments to the revenue and expenditures statement.
Variables (~12): policy variables to determine what will be cut or expanded as a result of the revenue and expense changes.
3. Academic Operations
Working with input from the enrollment management model, the game database, and player-provided parameters, the academic operations model simulates behavior at the department level. The model generates output for student and faculty behavior as reflected in variables like student FTEs, course demand and supply, graduation and drop rates, faculty arrivals and departures, sponsored research funding, and the quality and quantity of publications. Each element of the academic operations model runs once per academic year, after the enrollment management model, in the order presented herein. Its results inform the resource allocation, physical plant, and institution-level performance models—which follow it in the engine sequence—and the enrollment management model when it is applied for the following year.
The academic operations model begins by simulating students’ choice of major and movement toward graduation or exit. This student model produces figures for the number of majors in each department (and in an "undecided" category), and the number of graduates and other departures, for each student level. Then the course demand model determines students’ course choices as a function of major and student level. The faculty model determines the faculty resources that will be available to fulfill course demand and other departmental obligations: it simulates faculty additions, promotions, departures, and retentions. Working from the data on course demand and faculty availability, the course supply and teaching load model generates each department’s course offerings and faculty teaching assignments. Then the faculty discretionary time model simulates how faculty allocate the time available over and above direct student contact hours. The discretionary time results inform various departmental performance models dealing with the quality of teaching, progress on educational development projects, the amount and quality of research, and faculty and student morale. A separate sponsored research model determines each department’s proposal and award volume.
C15. Student FTEs
Purposes: (i) to maintain data on the number of full-time-equivalent students (FTEs) for the year being simulated and all previous years, by student level, student segment, gender-ethnic group, and field of interest; (ii) to simulate new students’ choice of interest field, continuing students’ change-of-field choices, and the number of graduations and dropouts.
Procedures: ("Field of interest" refers to Higher Education’s ~10 academic departments plus an "undecided" category.)
(i) Update the attractiveness coefficients for field of interest, based on departmental performance data and exogenous factors.
(ii) Apply the coefficient to the entering student numbers (obtained from C7) to get entering students by field of interest.
(iii) Update the transition coefficients for existing students based on departmental performance data and exogenous factors.
(iv) Apply the coefficient to the existing student base to get continuing student FTEs.
(v) Use the results of (ii) and (iv) to get each interest field’s enrollment for the coming year, the number of graduations, and the number of dropouts.
(vi) Calculate the probability that an entering student will eventually graduate, the average time to graduation, and the average time to dropout for possible reporting to the player.
(vii) Calculate summaries, ratios, and time series as needed for reporting to the user.
Variables (~170): ): the number of student FTEs associated with each field of interest; attractiveness coefficients for new students; transition probabilities for existing students; numbers of graduates and dropouts; graduation probability and expected time to graduation and dropout (by field of interest).
Subcomponents (140): student level, student segment, and gender-ethnic group. (Current plans call for the procedures are applied separately to each subcomponent. However, timing and memory considerations may require that some subcomponents be combined.))
C16. Course Enrollments
Purpose: to maintain data on each department’s course enrollments for the year being simulated and all previous years, by student level, student field of interest, course type, and academic term; to simulate student course choice behavior.
Procedures: (Enrollment in a department’s courses is not the same thing as having the department as an interest field; for example, an engineering major will take English courses. There are four course types: undergraduate lecture, undergraduate seminar, undergraduate general, graduate. The are three academic terms: fall, winter, summer.)
(i) Update the coefficients that determine demand for each department’s courses, based on departmental performance data and exogenous factors,
(ii) Use the coefficients to calculate the distribution of a typical student’s course enrollments among departments; project to the universe of students based on FTEs. Uses quadratic programming.
(iii) Distribute each department’s enrollments among course types and academic terms, based on course type preference coefficients and trimester preference coefficients. The coefficients may evolve due to exogenous factors.
(iv) Calculate summaries, ratios, and time series as needed for reporting to the user.
Variables (77): the number of enrollments in courses of each type offered during the academic year by each of Higher Education’s ten departments; total number of courses offered during the summer term; departmental performance coefficients; course type preference coefficients; trimester preference coefficients.
Subcomponents (50): student level, field of interest.
C17. Faculty FTEs
Purposes: to maintain data on each department’s faculty FTEs for the year being simulated and all previous years, by faculty category; to simulate faculty persistence, attrition, and promotion.
(i) Update the coefficients that determine how many faculty will continue in rank, be promoted, or depart, based on institutional policies, departmental performance data, and exogenous factors. (The coefficients control transitions among the model’s 12 faculty categories.)
(ii) Apply the coefficients to the previous year’s faculty FTE numbers to get the FTEs and average quality for each faculty category.
(iii) Calculate summaries, ratios, and time series as needed for reporting to the user.
Variables (156): the number of FTEs in each faculty category; the matrix of transition coefficients.
Subcomponents (10): departments.
C18. Faculty New Hires
Purpose: to simulate departments’ faculty hiring decisions.
(i) Working from the open-to-hire data produced by C13, faculty salaries determined by C10, player-generated or default policy variables, and exogenous factors describing the faculty marketplace, determine the faculty category and quality of each new hire. Uses linear programming.
(ii) Add the new-hire numbers to the continuing FTE numbers to get next year’s FTEs and average quality for each faculty category.
Variables (~20): targets and importance weights for the gender-ethnic and rank-age composition of the departmental faculty after the new hire(s); effect of PCI’s salary offer on the attainable faculty quality in each category.
Subcomponents(10): departments.
C19. Course Supply and Faculty Teaching Load
Purpose: to maintain data on each department’s course offerings ("course supply"), average class size by course type, and teaching loads by faculty category; simulate the tradeoff decisions made by faculty as they determine teaching loads, course mix, and class sizes.
(i) Determine how many summer courses (determined in C15) will be taught by faculty as part of their normal duties for the academic year. (Some faculty may be on duty during the summer but not during one of the other trimesters.) The remaining summer courses will be taught on a "piecework" basis, with the cost included in C12’s "other departmental expense" line.
(ii) Update the data for normal class size and normal teaching load, based on the prior year’s actual data and exogenous factors.
(iii) Working from (i), player-generated or default policy variables, the academic-year course demand data produced by C15, and exogenous factors, determine (for each department) the number of sections of each course type to be offered during the academic year and distribute the resultant teaching loads among faculty categories. Uses quadratic programming.
Variables (~66): fraction of summer courses included in regular teaching loads, cost per course for other summer teaching, update parameters for normal class size and teaching load, number of academic-year course sections of each type assigned to each faculty category, targets and importance weights, and upper and lower bounds for class sizes (by course type) and teaching loads (by faculty category).
C20. Faculty Discretionary Time
Purpose: to simulate faculty decisions about the use of their discretionary time. ("Discretionary time" refers to activities other than in-class student contact. It includes class preparation and grading, out-of-class student contact, research and scholarship, institutional and public service, and educational development.)
Procedures: working from player-generated or default policy variables, teaching loads as determined in C18, and exogenous factors describing the national trends, determine how faculty allocate their discretionary time Uses quadratic programming.
Variables (~25): average weekly hours devoted to each discretionary time category; targets and importance weights to describe the intrinsic and extrinsic incentives that determine discretionary time utilization.
Subcomponents(120): departments, faculty categories.
C21. Sponsored Research
Purpose: to simulate faculty decisions determining the number of sponsored research proposals submitted by the department to outside funding agencies; to simulate the success rate for these proposals.
(i) Working from player-generated or default policy variables, the discretionary time allocation to research as determined in C19, and exogenous factors describing the national research market, determine the number of proposals submitted from members of each faculty category.
(ii) Working from faculty quality, past departmental performance, institutional research infrastructure, and exogenous factors describing the competitive environment for research funding, determine proposal success rate.
Variables (~36): proposal volume and success rates; parameters needed for determining same.
C22. Departmental Performance
Purpose: to determine an array of departmental performance variables, based on the outputs of prior academic operations components, for possible reporting to the player and use by the next year’s enrollment management and academic operations model.
Procedures: single-equation functions for calculating performance variables such as the following (additional functions may be defined later):
(i) Faculty publications: depends on faculty numbers and the associated research quality indices, sponsored research volume, discretionary time allocated to research, , research infrastructure, and the deferred maintenance backlog.
(ii) Departmental prestige: depends on faculty publications, average faculty quality (probably in both teaching and research), and sponsored research volume.
(iii) Technology utilization in teaching: depends on investments in technology and faculty time allocated to educational development. The technology utilization variable applies to both on-campus and distance courses. Heavy utilization implies advanced delivery methods appropriate to the course type.
(iv) Educational quality (the delivered quality of education): depends on the course type mix and class sizes in relation to their norms, any course supply shortfalls, the curricular structure index, technology utilization factors, the faculty teaching and research quality indices, and faculty discretionary time devoted to course preparation, out-of-class student contact, and educational development.
(v) Faculty morale: depends on teaching loads, class sizes, and discretionary time allocations in relation to their norms, technology utilization in teaching, salary levels relative to the competition, turnover rates, and various player-generated policies that affect faculty. Heavy technology utilization may mitigate negative effects from excess class sizes and teaching loads.
(vi) Student morale: depends on the delivered quality of education, any course supply shortfalls, technology utilization, and faculty discretionary time devoted to out-of-class contact.
Variables (~24): outputs and parameters for the above.
4. Physical Plant Activities
The plant activities model deals with the built environment in which Higher Education’s various operations take place. The model consists of four components: space norms and the existing space inventory; plant operations and maintenance (O&M); planning and construction of new facilities; and student residence halls. "Plant" refers to all buildings except those associated with residence halls and associated food service. For simplicity, the residence hall model includes both annual operations and new construction.
The space norms and existing space inventory component translates information about the scale of operations into normal square footage requirements for each academic department and the central administrative and support functions. It also keeps track of the existing space assignments for each unit. The O&M component determines the normal yearly expenditure requirement for the player-generated institution’s existing space inventory and tracks the rise and fall of deferred maintenance. The facilities planning and construction component manages the institution’s capital reserve and plant debt, determines the level of funding for the year’s construction program, and then allocates these funds among alternative construction and conversion projects. (Players may take away a department’s space and convert it for use by another department.) The residence halls component tracks demand for existing residential capacity, floats new residence hall debt and constructs new capacity if warranted by demand and player policy, and sets the room and board rate so that the residence halls operating unit just breaks even.
The physical plant model will run once per simulated year. The residence halls component will run just before the resource allocation model, the other components just after. "Next year" refers to the year for which resource allocation has just been completed. "Base year" refers to the year just being completed.
C23. Space Inventory and Normal Space Requirements
Purpose: to maintain the inventory of square footage for the ten departments plus an administration/support services category; to determine the normal square footage for each operating unit in light of the unit’s activity level.
(i) Update the normal square footage figures based on new activity data. For academic departments, the data are student enrollments, faculty FTEs, and sponsored research. For the administrative support, it is the total extra-departmental expenditures from the prior year’s Revenue and Expense Statement.
(ii) Calculate the amount of space taken out of service during the year due to depreciation.
(iii) Calculate summaries and time series as needed for reporting to the player.
Variables (~85): available square footage at the beginning of the year, depreciated square footage, coefficients for the normal square footage calculations; updated normal square footage.
Subcomponents (11): ten academic departments plus the administration and support category.
C24. Operations and Maintenance
Purpose: to determine the normal level of operations and maintenance expenditure given each operating units square footage inventory; to determine changes in the institution’s deferred maintenance backlog.
Procedure: compare the normal O&M requirement with the amount budgeted for the year and adjust the deferred maintenance backlog accordingly.
Variables (~11): normal O&M expenditure per square foot (by department), deferred maintenance backlog.
C25. Facilities Planning and Construction
Purpose: to simulate the facilities planning process; to determine the facilities construction program for the year.
(i) Working from player-generated or default policy variables, determine the amount of expenditures on facilities construction during the coming year and the addition, if any, to plant debt.
(ii) Allocate the year’s facilities construction expenditures among academic departments and the administrative/support unit; where applicable, reallocate space from one department to another. Uses linear programming.
(iii) Prepare a facilities plan summary for possible reporting to the player.
Variables (~55): new construction and renovation projects (by department); parameters for the linear program.
C26. Residence Halls
Purpose: to simulate demand, supply, and occupancy for the student residence halls; to determine whether to construct new residence hall capacity; to calculate the room and board rate.
(i) Calculate student demand based on last year’s room and board rate, traditional undergraduate student numbers, and exogenous factors.
(ii) Reduce room capacity by the amount of depreciation; add in any new capacity constructed during the prior year.
(iii) Set occupancy equal to the lower of demand and capacity; calculate excess demand, if any, for input to the following year’s student morale function.
(iv) Use the capacity and occupancy figures, plus debt service, to calculate residence hall operating cost per-occupant; set the room and board rate equal to operating cost.
(v) Working from player-generated or default policy variables, determine whether to expand capacity during the coming year; increase residence hall debt to pay for the new capacity.
(vi) Prepare a residence hall capacity, occupancy, and cost summary for possible reporting to the player.
Variables (~10): demand, capacity, occupancy, debt, demand, operating cost, and construction parameters.
5. Institution-Level Performance
This is a catchall category for institution-level response functions. The mathematics have not yet been specified but will be straightforward. The functions identified so far are described below.
C27. Institutional Performance
Purpose: to determine an array of nonacademic performance variables, based on the outputs of the academic operations components and exogenous factors, for possible reporting to the player and use in the following year.
Procedures: single-equation functions for calculating performance and morale variables such as the following (additional functions may be defined later):
(i) Libraries: effectiveness depends on current and cumulative past expenditures on library operations.
(ii) Information technology resources: effectiveness depends on current and cumulative past expenditures on information technology, faculty effort devoted to IT innovation.
(iii) Student life: the quality of student life (support services, extracurricular activities, etc.) depends on current expenditures on student life activities and the extracurricular rating of admitted traditional undergraduates.
(iv) Athletics: success in intercollegiate athletics depends on athletics expenditures and the athletics rating of admitted traditional undergraduates. Success translates into wins and losses in the key sports of football, basketball, and track—which in turn affect student, alumni, and trustee attitudes.
(v) Overall student morale: depends on the above plus unmet student residence demand, the deferred maintenance backlog and various departmental performance variables.
(vi) Overall faculty morale: depends on the above, faculty salaries, the deferred maintenance backlog, and various departmental variables.
(vii) Staff morale: depends on the above, the deferred maintenance backlog, and staff salaries.
(viii) Alumni morale: depends on all of the above
(ix) Institutional advancement: success in fund raising depends on institutional advancement expenditures, institutional prestige, departmental performance, student, faculty, and alumni attitudes. Fundraising feeds into the "gifts" line on the Revenue and Expense Statement.
(x) Administration: effectiveness depends on current and past administrative expenditures in relation to institutional scale, staff and faculty morale, the deferred maintenance backlog, and (perhaps) a player-generated decision to invest in administrative restructuring. Failure to maintain administrative effectiveness may result in management foul-ups and audit problems.
(xi) Trustee performance assessments: depends on everything in the simulation. This is the last procedure to be executed each year. The assessments will be used to provide feedback for the president (player).
Variables (~110): outputs and parameters for the above.
