Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations


Page 28 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

ABSTRACT 

 
This paper is a response to a paper entitled “Is the Gold/

Pray Simulation Model Valid and Is It Really Robust?" 

which was presented at the 2010 ABSEL conference by 

Kenneth Goosen.  The Goosen paper called into question 

the validity and robustness of the Gold/Pray demand 

model.  This paper is written as a response to the argu-

ments advanced by Goosen.  Goosen (2010) has raised five 

problems with the Gold/Pray (1983) model.  The paper will 

proceed by listing and then replying to each of these al-

leged problems.  One of the purposes of the paper is to 

encourage simulation developers, especially total enter-

prise simulation developers, to explain and discuss their 

underlying demand equations and how these equations 

have worked over the life of their simulation usage.   

 

INTRODUCTION AND PURPOSE 
 

In 1974, at the first annual ABSEL conference held in 

Oklahoma City, Churchill (1974) presented a small scale 

deterministic business game called DØG.  In the paper he 

provided some of the details of the formulae and underly-

ing demand functions that had been incorporated into the 

game.  Since Churchill presented this information at the 

first annual ABSEL conference, it would not have been 

surprising to see a number of articles addressing demand 

functions over the course of ABSEL's thirty-seven year 

history.  That has not been the case.  Less than one percent 

of all the articles that have been published in the ABSEL's 

proceedings in the period of time 1974 to 2010 have fo-

cused on the demand functions that are the heart of busi-

ness simulations.  They are several plausible explanations 

for the paucity of articles on this subject.   In 1981, Goosen 

alluded to the challenges in designing appropriate algo-

rithms faced by potential simulation developers, and Pray 

and Gold (1982) state the challenges more explicitly and 

specifically.  In his chapter devoted to designing business 

simulations, Teach (1990) indicates in spite of the fact that 

a great deal of information is provided about several of the 

simulations, the details of  the demand algorithms Mark-

strat or Industrat have not been published.   A perusal of 

the ABSEL literature draws one to the conclusion that 

simulation designers of business simulations have not been 

inclined to reveal the details of their demand functions in 

the literature; perhaps for proprietary reasons.  Pray and 

Gold's use of the term black box is a result of the "secrecies 

of the internal workings" of simulations.    

To focus the discussion of demand functions in simu-

lations, it might be useful to state which, of the full range 

of simulations that fall in the purview of ABSEL, must 

have an integrated demand function in their underlying 

software.  Biggs (1990) utilizes the definition that Cohen 

and Rhenmann (1961) presented when he describes a total 

enterprise game as one "designed to give people experience 

in making decisions at a top executive level and in which 

decisions from one functional area interact with those made 

in other areas of the firm."  Keys and Biggs (1990) describe 

a total enterprise game as "one which includes decisions in 

most of the main functions of business: marketing, produc-

tion, finance and personnel."  Obviously simulations that 

are not total enterprise simulations might not have a built-in 

demand function, but equally obviously, all true total enter-

prise simulations have to address demand and have to have 

a demand function as an integral component.  The follow-

ing discussion of demand functions is limited to demand 

functions incorporated into total enterprise simulations. 

In 1983, Gold and Pray presented their work on simu-

lating market-level and firm-level demand.  In their paper 

DEMAND EQUATION REDUX:  

THE DESIGN AND FUNCTIONALITY OF THE GOLD/PRAY MODEL IN 

COMPUTERIZED BUSINESS SIMULATIONS 

Steven C. Gold 

Rochester Institute of Technology 

sgold@saunders.rit.edu 

  

Peter M. Markulis 

SUNY Geneseo 

markulis@geneseo.Edu 

  

Daniel R. Strang 

SUNY Geneseo 

strang@geneseo.edu 

mailto:sgold@saunders.rit.edu
mailto:markulis@geneseo.Edu
mailto:strang@geneseo.edu


Page 29 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

they indicate several of the alternative functional forms for 

demand models including a linear form, a non-linear form 

and a multiplicative form.  In their article they discuss the 

advantages and disadvantages of each form and ultimately 

suggest the multiplicative form for several reasons, not the 

least of which is its robustness.  Elements of robustness 

include the ability to eliminate conventional wisdom on the 

part of players which would severely limit the effectiveness 

of a simulation if it were discovered. In 1990, Gold and 

Pray discussed the potential problems of a simulation 

"blowing up!"   Clearly, a robust demand model will pre-

vent this potential unfortunate result for simulation play. 

A perusal of the ABSEL literature indicates occa-

sional interest in the modeling of demand functions in the 

period of time from 1974 to 2010.  Each of articles pro-

vided were at best a marginal extension or reflection of the 

basic Gold/Pray demand algorithm.  Decker, LaBarre and 

Adler (1987) presented two distinct approaches to defining 

the underlying functions of simulations, the multiplicative 

and the interpolation model.  In 1998, Lambert and Lam-

bert considered the advertising response in the Gold/Pray 

algorithm and Carvalho addressed the theoretical derivation 

of a basic demand function.  Over the years, Teach (1984, 

1986, 1990) has explored various facets of demand func-

tions.  In 2006, Murff, Teach, Schwartz present an algo-

rithm they developed to establish industry-level demand in 

simulations.  Murff et al. argue that their algorithm resolves 

the monotonicity problems attendant to the Gold/Pray algo-

rithm which they maintain require artificial constraint of 

several of the keys decision variables.  To that point, when 

the Gold/Pray demand algorithm is incorporated in a simu-

lation, additional algorithms utilizing exponential smooth-

ing of key variables can be embedded in the software to 

minimize or eliminate the adverse effects of extreme 

changes in the value of key variables. Although an explora-

tion of these issues might be fascinating, it is not the pur-

pose of this paper.   

So, with the possible exception of the work of Murff, 

Teach and Schwartz, (2006) it is fair to say that ABSEL 

literature has added very little to understanding the model-

ing of the demand functions since Gold and Pray's work in 

1983.  In one respect, the Gold/Pray algorithm has been 

accepted as the standard for modeling demand in simula-

tions. 

In 2010, Goosen presented a paper at the annual AB-

SEL conference which called into question the validity and 

robustness of the Gold/Pray demand model.  This paper is 

written as a response to the arguments advanced by 

Goosen.  Goosen (2010) has raised five problems with the 

Gold/Pray (1983) model.  The paper will proceed by listing 

and then replying to each of these alleged problems.  

 

Impact of Exponential Smoothing on Marketing Expenditures 

Table 1 

Period Marketing (Mn) 

Exponentially Smoothed 

Marketing  (M)    

  b =  0.4 b =  0.6 b =  0.8 

1 $1,000 $1,000.00 $1,000.00 $1,000.00 

2 $0 $700.00 $500.00 $200.00 

3 $0 $490.00 $250.00 $40.00 

4 $0 $343.00 $125.00 $8.00 

5 $0 $240.10 $62.50 $1.60 

6 $0 $168.07 $31.25 $0.32 

7 $0 $117.65 $15.63 $0.06 

8 $0 $82.35 $7.81 $0.01 

9 $0 $57.65 $3.91 $0.003 

10 $0 $40.35 $1.95 $0.001 



Page 30 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

FIRST PROBLEM:  DOES THE GOLD/

PRAY MODEL ALLOW ADVERTISING OR 

R & D TO BE ZERO? 
 

Goosen (2010) states “The first problem concerns the 

effect of marketing (e.g., advertising) on demand. If adver-

tising and R& D in the G/P model are zero, then demand is 

zero. There is no demand when price stands alone without 

advertising and R & D.” 

There are two reasons why this is not true. First the 

Gold/Pray model distinguishes between firm level demand 

and market level demand. With respect to firm level de-

mand, the Gold/Pray model uses a weighting function 

which determines the firm’s market share and its demand. 

In this weighting function there is a constant term added to 

each of the demand variables including price, advertising, 

and R & D. The equation is given as number 6 in Gold and 

Pray (1983) and is shown below as equation 1 of this paper: 

 

(1) Wi = [Pi + k1]-(k2 + k3Pi) [Mi + k4]+(k5 – k6Mi) [Ri + k7]+

(k8 – k9Ri) 

 

where:  

 

Wi = weight of firm i 

Pi   = price of firm i 

Mi = marketing expenditures of firm i 

Ri   = research and development expenditures of firm i 

ki  = parameters or constants i = 1 to 9 

 

Referring to equation 1, the value for the firm’s 

weight (Wi) determines the firm level demand. The con-

stant terms associated with the demand variables of price 

(k1), marketing (k4), and research and development (k7) 

prevent the firm demand from going to zero, even if the 

demand variables are zero. 

But what about the market level demand? Can the 

market level demand go to zero if all firms in the market 

charge a price of zero, and spend nothing on marketing and 

R & D?  First, in the market demand equation “average” 

Demand at three different levels of Marketing Expenditures 

Table 2 

 $50,000 $75,000 $100,000 

Price Demand-50 Demand-75 Demand-100 

$180 1 3 5 

$160 2 5 9 

$140 3 9 17 

$130 4 12 23 

$120 5 16 30 

$110 7 21 40 

$100 9 28 54 

$90 12 37 71 

$80 16 48 93 

$70 20 63 122 

$60 26 82 158 

$50 34 105 203 

$40 43 134 259 

$30 55 169 326 

$20 68 209 404 

$10 82 254 490 



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marketing expenditures and “average” R & D for the entire 

market is used.  It is highly unlikely that all firms would 

decide not to advertise or do any R & D.  Second, and more 

importantly, even if average values for marketing and R & 

D were zero in a period of the game, the market demand 

would not go to zero in the Gold/Pray model because the 

market level demand is a function of “exponentially 

smoothed” values for both market and firm price, market-

ing expenditures, and research and development expendi-

tures.   Equations 2, 3 and 4 in the Gold & Pray (1983) pa-

per specify exponential smoothing for all demand variables 

and are shown below:  

 

(2)  P   = aPn + (1-a)P0 ; where 0<a<1 

(3)  M  = bMn + (1-b)M0 ; where 0<b<1 

(4)  R = cRn + (1-c)R0 ; where 0<c<1 

 

 where:  

 

P = exponentially smoothed price 

M = exponentially smoothed marketing expenditures 

R = exponentially smoothed R&D expenditures 

a, b, c = smoothing coefficients  

An "o" subscript indicates the last period smoothed 

value  

An "n" subscript indicates the current period value  

 

An exponentially smoothed value for P, M or R will 

take many periods to approach zero and will never have an 

exact value of zero.  

As an example, let’s look at the behavior of the expo-

nentially smoothed marketing value (M) when marketing 

expenditures drop from $1,000 to zero (see Table 1) given 

three values for the coefficient “b” with respect to the ex-

ponentially smoothed marketing variable (M), i.e. 0.4, 0.6, 

0.8. For example, in the case where the exponentially 

smoothed coefficient is 0.4 then the equation for the expo-

nentially smoothed marketing variable becomes: M = 

0.4Mn + 0.6M0. 

The exponentially smoothed value for marketing (M), 

which is used in the market demand model does not reach 

zero even after ten periods of zero expenditures. The lower 

the value of the coefficient for “b”, the longer it takes for 

the exponentially smoothed value to approach, but never 

reach, zero. 

Furthermore, the Gold/Pray (1983) market demand 

equation (number 3) in the model would allow a specifica-

tion where each market variable for price, advertising, and 

development has a constant term associated with it, similar 

to the Gold/Pray firm demand equation 6.  

 

SECOND PROBLEM:   DOES THE GOLD/

PRAY MODEL ALLOW FOR ONLY ONE 

KIND OF SHIFT IN THE DEMAND 

CURVE? 

 
Goosen (2010) states “The Gold/pray model does not 

allow a change in the Y-intercept. The Y-intercept is the 

price value on the Y axis where demand is zero.” 

Demand Shifts Caused by Increases in Marketing Expenditures 

Figure 1 



Page 32 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

This is not correct. The Gold/Pray demand model 

does allow for a change in the Y-intercept. The market de-

mand shifts upward/downward and to the right/left as the 

value for marketing expenditures (or research and develop-

ment) change. To be perfectly accurate, the Gold/Pray de-

mand function approaches the vertical price axis asymptoti-

cally, but never intersects the vertical axis. In this case, the 

price level where demand is close to zero, say 1 unit of 

demand, would be different depending on the level of the 

other non-price factors. 

To illustrate, let’s use the following Gold/Pray market 

demand function: 

 

(4) Q = g1P
-(0.01 + 0.005P) M+(3.9 – 0.0000015M)   

 

where g1 = 1 x 10-16 

 

Using this equation and parameter values, we will 

solve for the relationship between price and quantity de-

manded at three different levels of marketing expenditures 

(M), i.e. $50,000, $75,000, and $100,000. Table 2 shows 

the demand relationship between price and quantity at these 

three different levels of marketing (M): (See Table 2 be-

low) 

The three different demand schedules (demand-50; 

demand-75; and demand-100) are graphed in Figure 1 to 

illustrate the impact of marketing expenditures. It is clear 

from the graph that the y-intercept increases with increases 

in marketing expenditures. 

Based on the example equation, the price level re-

quired to restrict quantity demanded to approximately one 

unit is a price of $174 for demand-50, a price of $210 for 

demand-75, and a price of $230 for demand-100.  

THIRD PROBLEM:  DOES THE GOLD/

PRAY MODEL POTENTIALLY CREATE 

UNREALISTICALLY LARGE DEMAND? 

 
Goosen (2010) states “The Gold/Pray multiplicative 

model is driven by linear equations that serve as exponents. 

It should not be surprising then that a multiplicative model 

can have an explosive exponential effect. The multiplicative 

G/P model increases demand exponentially. Eventually, if 

advertising is increased enough the exponential increase 

will be staggering. Whether this problem of an explosive 

demand potential is an inherent flaw in the model or simply 

the result of a poor choice of parameters is at this point not 

clear.” 

The explosive exponential effect of the Gold/Pray 

model illustrated by Goosen (2010) is simply a result of the 

choice of the selected parameters and not an inherent flaw 

in the model. To show explosive growth, Goosen uses the 

parameters derived from Table 3 below, which were taken 

from the example in the original Gold & Pray (1983) paper. 

In this example, the parameters were selected by the de-

signer to have price elasticity that increases very rapidly 

from inelastic (-0.5) at a price of $10 to unit elastic (-1.0) at 

a price of $20. Also, the marketing and R & D elasticities 

were designed to start at very high levels of 3.0 at a price of 

$10.  

To illustrate explosive growth, Goosen (1983) se-

lected a price of $50, which according to the elasticity pa-

rameters in Table 3, would yield an extraordinarily high 

price elasticity; since price elasticity rises with the price 

level. It is unclear why a price of $50 was selected as the 

starting point, rather than a price between $10 and$20. But 

the important point is that with different parameter values, 

Price 

($/unit) 

Price 

Elasticity 

Marketing ($) Marketing 

Elasticity 

 

R & D ($) 

R & D Elastic-

ity 

$10 -0.5 $  50,000 3.0 $  50,000 3.0 

$20 -1.0 $150,000 1.0 $150,000 1.0 

Elasticity Parameters used in Goosen Example 

Table 3 

Marketing R & D Price Demand (units) 

$ 100 $100 $50 1,000,000 

$ 200 $100 $50 1,331,969 

$ 300 $100 $50 1,528,435 

$1000 $100 $50 1,659,586 

Impact of significant increases in Marketing given constant price and R & D 

Table 4 



Page 33 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

the demand function would be stable over a wide range of 

prices, and marketing and R & D expenditures. 

As an alternative example, to illustrate the selection of 

more stable parameters, the following Gold/Pray demand 

function will be illustrated: 

 

(5) Q = g1P
-(0.01 + 0.005P) M+(0.5 – 0.0001M) R+(0.5 – 0.0001R)  ; 

where g1 = 30320.2 

 

Following the same selection of pricing and market-

ing values presented by Goosen (2010) in his Table 2 we 

get the following results. 

As evidenced in Table 4, the result of increasing mar-

keting expenditures from $100 to up to $1,000 does not 

create an explosive result. Even doubling both marketing 

and R & D simultaneously does not create a problem of 

excess growth in demand as illustrated in Table 5. 

Furthermore, the marketing expenditures would be 

exponentially smoothed before being placed in the demand 

equation, this would further dampen the impact increases in 

either marketing expenditures or R & D on firm demand. 

To illustrate assume the following exponential smoothing 

function for marketing expenditures: 

 

(6)  Me
t  = bMt + (1-b)Me

t-1  

 

where:  

 

Me
t = exponentially smoothed value for Mt 

Mt  = marketing expenditures in period t 

b     = smoothing coefficient 

Assuming a value of 0.5 for the smoothing coefficient 

(b), the impact of marketing expenditures on demand is 

shown in Table 5. 

Using the exponentially smoothed value for market-

ing expenditures (Me
t), and comparing Table 6 with Table 

5, the impact on demand is significantly dampened. For 

example, increasing marketing expenditures from $1,000, 

only increases demand 1,749,278 units with exponential 

smoothing (Table 6) as compared to 2,754,228 units with-

out exponential smoothing (Table 5). 

 

FOURTH PROBLEM:  DOES THE MODEL 

FOR BOTH MARKETING AND ADVER-

TISING CREATE BELL-SHAPED FUNC-

TIONS FOR MARKETING AND R & D? 

 
Goosen (2010) states “The G/P model actually creates 

a marketing function that is bell-shaped… there is still 

some doubt that the results are valid.” 
The quote from Goosen is arguing that the marketing 

literature does not support the possibility that excessive 

marketing expenditures could decrease a firm’s demand. It 

is true that there is some controversy on whether excess 

advertising could cause firm demand to decline.  As 

Goosen (2010) states the most accepted relationship is one 

of diminishing returns but not negative returns.  Yet, there 

is evidence to the contrary.  For example, a seminal paper 

by Dorfman and Steiner (1954) supports the possibility of 

negative returns to advertising. 

Marketing  

(Mt ) 

Exponentially 

Smoothed Me
t 

R & D Price Demand (units) 

$ 100 $100 $ 100 $50 1,000,000 

$ 200 $150 $ 100 $50 1,189,609 

$ 300 $225 $ 100 $50 1,390,487 

$1000 $613 $100 $50 1,749,278 

Impact of exponential smoothing on Marketing and Firm Demand 

Table 6 

Impact of significant increases in both Marketing and R & D given constant price 

Table 5 

Marketing R & D Price Demand (units) 

$ 100 $ 100 $50 1,000,000 

$ 200 $ 200 $50 1,774,143 

$ 300 $ 300 $50 2,336,113 

$1000 $1000 $50 2,754,228 



Page 34 - Developments in Business Simulation and Experiential Learning, Volume 38, 2011 

 

But the significant point with respect to the Gold/Pray 

demand model is that the parameters (coefficients) of the 

model can be set to eliminate negative returns to marketing 

or other variables. Referring to equation 1 above, the pa-

rameters k6 and k9 could be simply set to zero. This is 

demonstrated by re-writing equation 5 above as follows: 

 

(7) Q = g1P
-(0.01 + 0.005P) M+(0.5 – 0.0M) R+(0.5 – 0.0R)   

 

Note that the variable M in the exponent is multiplied 

by a zero parameter value, as well as the R variable. Equa-

tion 6 could then reduces to the following: 

 

(8) Q = g1P
-(0.01 + 0.005P) M+(0.5) R+(0.5)   

 

This form of the Gold/Pray function has diminishing 

returns to marketing and R & D but does not allow for 

negative returns at any level. 

 

FIFTH PROBLEM:  LACK OF PROOF 

THAT THE MULTIPLICATIVE MODEL IS 

DESIRABLE OR SUPERIOR 

 
Goosen (2010) states “The article by Gold and Pray 

in which their model was presented does not present any 

rationale as to why the multiplicative model is better. The 

article simply assumes that it is better.” 
This assertion by Goosen (2010) misrepresents the 

message given in the Gold & Pray (1983) article.  The G/P 

paper never asserted their model was “superior”. The G/P 

paper began by first outlining the advantages and disadvan-

tages of alternative functional forms for modeling demand 

including: linear, non-linear, and multiplicative (page 102), 

and then suggesting a demand system that was argued to 

possess a number of desirable properties, but was never 

argued to be superior to all other demand models. 

The major intent of the G/P paper was clearly stated 

in the conclusion as follows: 

“This paper represents an ongoing attempt to encour-

age open discussion concerning the design and develop-

ment of computerized business simulations”.  (p.106)   

Proving that the Gold/Pray algorithm is desirable or 

even superior may truly not be achievable.  However, in the 

1983 article, Gold and Pray do present a number of com-

pelling arguments for the merits of their algorithm.   

Further, from an experiential or usage view, one can 

point to the many users of the DECIDE simulation by Pray 

and Strang (1980), published by McGraw-Hill, one of the 

premier academic publishing companies.  DECIDE is a 

total enterprise simulation that has been used (and contin-

ues to be used) by several colleges and universities around 

the country (world), as well as in many consulting situa-

tions.  The DECIDE simulation is based on a demand algo-

rithm that utilizes most of the key elements of the Gold/

Pray demand algorithm including a multiplicative demand 

function, demand functions that are generated at the indus-

try level and the firm level, and exponential smoothing of 

the key input variables.  McGraw-Hill required extensive 

alpha and beta testing with successful results as a pre-

requisite condition prior to commercialization of the soft-

ware.  DECIDE has now been used extensively over the 

course of thirty plus years in many business schools and 

according to the authors there has been no reports of the 

DECIDE simulation "blowing up" or yielding unrealistic 

results.  Indeed, if DECIDE has worked without apparent 

flaws, it would follow that its underlying algorithms are 

sound. Although this is anecdotal evidence, its sheer vol-

ume supports the argument that the Gold/Pray demand al-

gorithm is at least very sound, if not desirable and even, 

perhaps, superior. 

 

CONCLUSION 

 
The Goosen (2010) paper is consistent with the intent 

of the Gold & Pray (1983) article to encourage a healthy 

debate on the design of demand and other functions in 

computerized business simulations; and for this reason ac-

knowledge the value of the contribution provided by 

Goosen. 

We continue to encourage further discussion and 

debate (or critiques) of : 

1. Our own demand equations; 

2. Equations used by other simulation de-

velopers; 

3. Results of long-term use of these equa-

tions in simulations. 

 

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ed.)) 


	Table of Contents
	Volume 38, 2011
	Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise
	From Business Games to Simulations - Simuworlds & Microworlds
	Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations
	Managing Client-Based Learning: Insights from Successful Teaching Project Courses in Marketing
	Tracking Forecast Error Type, Frequency and Magnitude with the Forecast Error Package
	Responding to Facilitate Collaboration
	Simulating Sudden Change and the Value of Timely Information
	Managing Human Resources Simulation
	A Study on Collectivism and Group Decision-Making: An International Comparison of Japan, China, and Russia Using a Gaming Simulation
	The Use of Management Games in the Management Research Agenda
	Gaming On-Line: A Simulation Application
	Positioning and Performance in Simulated Networks
	Supply Chain Management: A Simulation Application
	Simulation as a Teaching Method in Strategic Management Distance Studies
	Entrepreneurship: A Game of Risk and Reward Phase II: The Start-Up
	Return to the Paradise Islands: From Confrontation to Cooperation
	Effect on Market Performance of Displaying Supply and Demand Curves in a Business Simulation
	Appreciating Complexity: The Chief of Staff of the Army Game
	Managing Organizations: Experiential MBA Course Teaches Alternatives to the Machine Model
	A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan
	The Continuing Evoluation of Assessing Project Management as an Academic Learning Outcome (ALO)
	Should College Instructors Change Their Teaching Styles to Meet the Millenial Student?
	The Mouse Game and its Effects on Team Interdependence
	Learning from the Gulf Oil Spill to Prepare for a Brighter Future: A New Game Engaging Stake Holders in Triple Bottom Line Accounting & Strategic Planning
	Video Killed the Biblio Star: The Impact of Digital Media on Student Learning Outcomes
	Exploring Motivation: Using Emoticons to Map Student Motivation in a Business Game Exercise
	An Alternative to PC and Internet Based Simulations: The Internet Integrated Mode
	MiddleState University -- A Crisis in Education
	Complexity Avoidance, Narcissism and Experiential Learning
	Examining the Cognitive, Affective, and Psychomotor Dimensions in Management Skill Development Through Experiential Learning: Developing a Framework
	A Situational Leadership Exercise Based on the Biology of a Starfish
	JOGAI CEFET -- The Industrial Administration Undergraduate Game
	Would You Take a Marketing Man to a Quick Service Restaurant? Modeling Corporate Social Responsibility In A Food Service Menu-Management Simulation
	Use of a Simulation in a Large Class Environment for a Marketing Principles Class: A Qualitative Analysis of Whether Learning Objectives were Met
	A Team Based Information Literacy Exercise
	ABSEL Marketing Communications Plan
	An Interdisciplinary Study of the Impact of Playing a Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles
	Analyzing Construction Planning of Interiro Finish Work of Apartment Building by Simulation
	Doing Murder One Again
	The Simple Business Game and Simulation Transfering the Knowledge of Middle Management to Novices
	Infectious Disease Simulation Model for Estimation of Spreading
	Understanding the Relative Influence of Several Factors in ERP Simulation Performance: An Exploration of Ecological Validity
	Tragedy of the Commons: An Exercise Using Clickers to Illustrate and Teach a Key Concept in Negotiations
	The Meaning of Firm Demand in Business Simulations
	If the Games Work, Why Aren't More Faculty Willing to Play?
	Those Who Do and Those That Don't: A Study of Engaged and Disengaged Business Game Players