A GENERALIZED ALGORITHM FOR DESIGNING AND DEVELOPING BUSINESS SIMULATIONS Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 41 A GENERALIZED ALGORITHM FOR DESIGNING AND DEVELOPING BUSINESS SIMULATIONS Kenneth R., Goosen, University of Arkansas at Little Rock ABSTRACT T-he design of business simulations is a complicated and time-consuming task. The time required and the degree of complexity could be greatly reduced by following certain basic procedures in an organized fashion. This paper attempts to outline some of the basic principles that could be applied in designing and developing a business simulation. INTRODUCTION Business simulations have been used in collegiate education since 1956. During this time several hundred games have been designed and catalogued. However, many of these simulations were not designed for general use; and some that were are now outdated because of advances in computer technology. Among all the users of simulations only a small percentage have written general business simulations. Among ABSEL members less than 30 are authors of simulations, and many of these members are no longer active in business simulation design. Consequently, there is still a need to have more individuals involved in the design and development of business simulations. In order to interest more ABSEL members and others in simulation design, a more definitive presentation of business simulation design principles and fundamentals is necessary. The basic principles of simulation design have not been fully set forth in writing. A number of papers at ABSEL conferences have been presented which touch upon aspects of simulation design [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16); however, taken collectively, these papers do not provide enough information to help the novice designer develop business simulations in en efficient manner. The designing and developing of simulations at this time appears to be primarily an art form, a creative skill based on intuitive feel rather than acquired knowledge. There is a pressing need to construct a science of simulation design and development. This paper is an attempt to set forth some basic principles and concepts of business simulation design. While the fundamentals set forth in this paper were primarily derived from the writer’s work with non- interactive business simulations, these fundamentals, nevertheless, apply equally to interactive business simulations. A previous paper by the writer [31 set forth the theoretical framework for noninteractive business simulations. This paper specifically deals with the more detailed mechanics of actual simulation design and development. The writer has had published a relatively complex simulation, Introduction to Managerial Accounting: A Business Game. The design and development of this game was primarily the result of finding simulation design fundamentals at a trial-and-error basis. Through intensive analysis and reflection on this simulation writing experience, the writer has been able to develop a generalized algorithm for writing noninteractive simulations. Had such a step-by- step procedure been available to the writer at the beginning, the time and effort required to develop and write Introduction to Managerial Accounting: A Business Game could have been greatly reduced. GENERAL STEPS OF SIMULATION DESIGN The major steps of a generalized algorithm for designing noninteractive simulations can be outlined as follows: 1. Develop a general outline or scenario of the simulation. 2. Translate this broad scenario into a set of financial statements and other desired reports. 7. For each element of the financial statements (assets, liabilities, capital, revenue and expenses) create an equation which determines the ending balances or amounts. 4. Construct the mathematical functions which give the simulation dynamics and realism necessary to achieve participants’ acceptance. 5. Construct the functional algorithms necessary to produce the decision values required by the financial statement equations. 6. Assign specific values for all parameters and simulation constraints, mathematical functions, and functional algorithms. 7. Write a computer program for processing decisions and producing simulation results. B. Write a student manual. The above steps (general algorithm) indicate that the development of a general business simulation is a rather complicated process of determining the required financial statement equations, mathematical functions, and functional algorithms. The end result of the above eight steps is a rather large integrated mathematical model. The model is capable of processing an almost infinite number of variations in a set of decisions. DEVELOPMENT OF A BUSINESS SIMULATION SCENARIO The design and development of a business simulation requires two main design structures: verbal and mathematical. The verbal structure ultimately becomes the student manual, and the mathematical structure becomes the computer program. Of course, these two design structures are highly interrelated. The term scenario then refers to the verbal description of the business reality which the simulation attempts to represent. The development of the simulation scenario outline requires decisions on the part of the simulation designer. Some of these decisions include: 1. Determination of Simulation Objectives-- Simulation design objectives can be broadly classified as general and specific. General - General design decisions include considerations of whether the simulation is to be used: a. to develop strategic planning skills, b. to develop quantitative skills, c. with graduate or undergraduate students, d. within business policy or other types of courses. Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 42 Specific - Specific design decisions involve consideration of: a. type of business (manufacturing, retail, wholesale), b. type of simulation (functional, general management, or institutional), c. degree of complexity (simple, moderately complex, complex), d. Nature of desired student competition (interactive vs. noninteractive), e. type and number of decisions (marketing, finance, production). 2. Determination of Internal Structure of the Simulated Business--Given the above general and specific simulation design decisions, the internal structure of the simulated business must be considered next. This, in part, involves visualization of the company’s financial statements format. The internal financial structure is largely determined by the decision as to simulation complexity which, in turn, largely directs the number and types of decisions. 3. Determination of Economic Environment-- To give the simulation an air of reality, an economic environment must be developed. This step will involve consideration of: a. type of industry (pure competition, monopolistic competition, monopoly, oligopoly), b. financial markets (stocks, bonds, mortgages), c. interest rates and banks, d. business cycle indices, e. suppliers of materials, f. labor unions and factory workers. 4. Determination of the Economic Environment-- The simulation must have its limits or otherwise it would be unmanageable. Constraints that provide necessary limits would include: a. limits on the amount of labor and wage rates, b. limits as to bank loans, stock shares, bond certificates, c. pricing and production limits, d. conditions for bankruptcy. 5. Determination of the Amount of Decision Information--Another critical consideration is the amount of explicit information needed for making good decisions. The less information provided, the more difficult it will be for students to use quantitative tools. On the other hand, too much information adds to game complexity and also limits intuitive decision-making and risk-taking. 6. Determination of Accounting Policies--The general design and scope of a business simulation must involve some consideration of accounting policies. Choices must be made among alternative depreciation and inventory costing methods. If the simulation scenario involves a manufacturing firm, then consideration must be given to the type of costing system that will be used. Variations in the costing system can cause different net income values to result. Other accounting problems involve the computation and reporting of income taxes. A frequent weakness of many simulations is oversimplification of accounting and tax problems. The development of the scenario does not have to contain minute details at this point. The scenario outline is intended to be a guide to the remaining steps of the general simulation design algorithm. DESIGN OF OUTPUT DATA The accomplishment of Step 1 creates a large number of interrelationships. These interrelationships must be completely recognized, understood, and documented. The output design is best facilitated by recognizing these relationships mathematically and presenting the dependent values of these mathematical equations as financial statement values. If the simulated business is a manufacturing company, three financial statements must be designed in order to capture all the relationships and simulation features established in Step 1. These statements are: a. balance sheet, b. income statement, c. cost of goods manufactured statement. In addition to the three items of output above, a fourth output item is generally desirable. This item may be called “other data” or “summary of results.” It consists, for the most part, of unit data such as undelivered sales, sales orders, number of salesmen, number of salesmen quitting, units of inventory on hand, etc. An example of this output is shown in Exhibit IV. The mechanics of this step involve the following procedure: a. Identification of all financial statement elements (e.g., cash, accounts receivable, sales, etc.), b. The identification of all desired nonfinancial statement data, c. Creation of variable names for each item of desired output. See Exhibits I - V for examples. DEVELOPMENT OF FINANCIAL STATEMENT EQUATIONS After the financial statements are designed in format form, an algebraic equation for each financial statement must be developed. These equations provide financial statement values essential to the printing of financial statements. The financial statement equations alone do not provide all of the necessary values. Decision values (the terms of these equations) are dependent upon certain mathematical functions and functional algorithms. These mathematical functions and functional algorithms are discussed in the next step. The financial statement equation must recognize all financial statement interrelationships so that when all decision values are computed, assets will equal liabilities plus capital. The complexities of these financial statement interrelationships are illustrated in Exhibit VI. Financial statement equations must recognize all of the relationships indicated by the interconnected boxes. A complete illustration of financial statement equations is presented in Exhibit V. Each equation of the balance sheet consists of the following components: a. Beginning value, b. Decision values (values computed from the mathematical functions and functional algorithms), c. parameters (constraining values built into the simulation. Income statement equations and cost of goods manufactured equations contain only items b and c. The number of these equations varies with simulation complexity. Simulation complexity, as previously mentioned, is determined by the number of decisions and environmental factors built into the simulation. DEVELOPMENT OF MATHEMATICAL FUNCTIONS In the real world of business there exists dynamic relationships between economic variables. Although the general relationships between many of these variables are well known, the impact of a precise change in one variable upon another can seldom be accurately predicted. For example, the general relationship between price and quantity has been abundantly illustrated in economic textbooks. However, it is doubtful that the demand curve for a given business has ever been quantified with any degree of precision. Similarly general relationships between advertising and sales, wage rates and production, salesmen commission rates and Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 43 calls per month have been described but only in a general way. If a business simulation is to be realistic, the same general relationships found in the real world must be found within the simulation. In order for a simulation to have the needed air of realism and dynamism, mathematical functions must be created to simulate these economic forces. Some of the desirable mathematical functions include: a. price and sales quantity, b. advertising and market potential, c. credit terms and sales, d. commission rates and calls per month e. salesmen’s salaries and calls per month, F. wage rate and production rate. To give an example, the relationship between price and quantity could be stated as: P0 - K(P). For a real business, the exact nature of these relationships probably could never be identified and plotted graphically. At best the relationships could only be tested on a trial-and- error basis. However, a simulation designer must specify numerically these relationships; otherwise, the computer would have no quantitative basis whatsoever for computing consequences of different sets of decisions. Thus, in order to simulate reality the simulation designer must conceal or not make precisely known to the simulation participants the quantitative nature of the mathematical functions. This paradox of defining the mathematical functions exactly within the computer program but only vaguely to the participants creates a real challenge for the simulation designer. If the exact nature of these functions is learned too quickly then the uncertainty and risk of decision-making is lost, and the simulation then becomes a mere computational exercise. After the design of financial statements, formulation of financial statement equations, and creation of mathematical functions, the next major step is to develop functional algorithms. Functional algorithms are step- by-step instructions for computing decision values from marketing, production, and financial decisions. The functional algorithms include the mathematical functions, and game parameters and constraints. Functional algorithms are needed for production, sales, credit, arid finance. Production Algorithm The first major computation that must be made in a business simulation is units manufactured. This value is critical to many Other computations. Until units manufactured is computed, the number of units sold and consequently sales proceeds cannot be calculated. The major purpose of the production algorithm is to take into account all of the factors that determine production. These factors include: a. number of production machines (units) in use, b. decisions as to overtime and second shifts, c. number of workers hired, d. materiel available for use, e. wage rates, F. production parameters and constraints. A secondary purpose of the production algorithm is to compute related production values such as total labor hours, material inventories in units, etc. Sales Algorithm A second major computation that must be made is sales orders. Given the sales orders and units available for sale, the actual number of units can be computed. Sales orders depend upon factors such as market potential, number of salesmen, salesmen compensation, credit terms, seasonal indices, number of territories or products. The major purpose of the sales algorithm is to correctly compute the impact of these factors on sales. In addition to computing sales orders, the sales algorithm computes secondary values such as un- delivered sales, finished goods inventory, and cost of goods sold. In regard to finished goods inventory, this algorithm must take into account the inventory costing method; e.g., FIFO or LIFO. Credit Algorithm The primary purpose of the credit algorithm is to take into account the effect of selling on credit. The major consequence of selling on credit is the creation of accounts receivable. If some cash sales are allowed, then this program determines the proportion of total sales that is credit. Since credit affects cash Flow, this program must compute the amount of receivables in the current period. Furthermore, the selling on credit creates problems pertaining to Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 44 Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 45 bad debts. Bad debt expense and accounts receivable write- offs must be handled by this algorithm. If factoring of accounts receivable is allowed as a decision, then the complexities arising from this option must be properly analyzed and programmed. As in all of the functional algorithms, many of the computed values become decisions values for the financial statement equations. Finance Algorithm The purpose of the finance algorithm is to take into account the impact of all the financial decisions. Basically, finance decisions either increase or decrease cash flow. Therefore, in this algorithm the consequences of issuing stocks, bonds, and bank notes must be computed. Furthermore, the effect retirement of stocks and bonds and payment of bank notes has on cash proceeds must be considered. The major purpose of the functional algorithms (production, sales, credit, and finance) is to compute the decision values needed for the financial statement equations. Although the financial statement equations [discussed as Step 3 of the general algorithm for simulation design] could be dispersed throughout these functional algorithms, a more efficient procedure would be to collectively group all of these equations in a separate subroutine or program. In this separate program physical factors computed in the functional algorithms will be multiplied by cost factors to produce the decision values required by the financial statements equations. When the processing of the financial statements equations is completed the simulation program is then able to provide printouts for financial statements and other planned outputs. Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 46 ASSIGNMENT OF SPECIFIC PARAMETERS AND SIMULATION CONSTRAINTS The design and development of a business simulation to this point has been algebraic and algorithm oriented. In the scenario development stage some or all of the simulation parameters and constraints were roughly considered. Now all simulation parameters arid simulation constraints must be precisely determined and assigned to the proper equations and functional algorithms. Upon assignment of these values, a complete workable model for a business simulation has been constructed. DEVELOPMENT OF COMPUTER PROGRAM The completed mathematical business simulation model is row ready for computer programming. This stage • requires expertise in a computer language. In the writing of the program perhaps the most critical but most often neglected phase is the adequate documentation of the program. The major segments of the computer program will be: MAINLINE--controls the order and direction in which program segments will be processed. PRODUCTION--computes units manufactured and related values SALES--computes total sales orders and related values. CREDIT--computes the effect of credit terms. FINANCE--computes values pertaining to the issue and retirement of stocks, bonds, and bond indebtness. FINANCIAL STATEMENT VALUES--computes final balances or amounts for financial statements equations. FINANCIAL STATEMENTS--prints financial statements and other planned output data. This is the program for the output design. After the program has been written in a computer language, the next phase is to test and debug the program. Many simulation designers will testify that this phase is more time consuming and frustrating than the design and writing stage. DEVELOPMENT OF THE STUDENT MANUAL The basic outline of the student manual was determined in the scenario designing phase of simulation designment. In this step, the student manual is completely written down to all the fine points and details. Accurate and clear description must be made for all simulation data necessary to making decisions. Decision input forms and any other necessary forms to simplify simulation usage must be designed. Also, charts, graphs tables, forms, etc., helpful in presenting and understanding simulation data should be provided. It is important that the student manual be well written. Many good and academically sound simulations have suffered seriously from the failure of the designer to write a good verbal description of the simulation and its objectives. SUMMARY Design and development of a business simulation at best is a challenging and time consuming task. However, if the task of developing the simulation can be seen from a total project point-of-view and the various steps visualized in logical order, the time involved can be greatly reduced. The general design program (algorithm) presented in this paper is intended to help the simulation designer achieve design efficiency. REFERENCES [1] Brooks, Leroy D. , “Flexibility in Simulation Design for Continual Student Motivation,’ Simulations, Games, and Experiential Learning Techniques: On the Road to a New Frontier, James Kenderdine and Bernard Keys, Editors, (Norman, Oklahoma: The Center for Economic and Management Research, The University of Oklahoma, 1974), p. 190. [2] Churchill, Geoffrey, ‘Decision Mathematics Operational Game: An Attempt to Meet Design Criteria,” 13th Annual Symposium National Gaming Council, Vol. II, Jack Belkin and Helen Hazi, Editors, (Pittsburgh, Pennsylvania: School of Urban and Public Affairs, Carnegie-Mellon University, 1974), p. 396. [3] Goosen, Kenneth R., “A Theoretical Framework for the Noninteractive Business Game,” 14th Annual NASAGA Conference Proceedings Richard McGinty and Jolene Elliott, Editors, (Los Angeles, Calif.: University of Southern California Press, University of Southern California, 1975), p. 163. [4] Kidron Aryeh, “Business Canes in the Process of Management Training in Israel and the United States,” Simulations, Games, and Experiential Learning Techniques: On the Road to a New Frontier, James Kenderdine and Bernard Keys, Editors, (Norman, Oklahoma: The Center for Economic and Management Research, The University of Oklahoma, 1974), p. 304. [5] Ferguson, Carl E. “User Oriented Design Considerations for Classroom Simulations,” 13th Annual Symposium National Gaming Council, Vol. II, Jack Delkin and Helen Hazi, Editors, (Pittsburgh, Pennsylvania: School of Urban and Public Affairs, Carnegie-Mellon University, 1974), p. 420. [6] Barton, Richard F., “How to Create Your Own Business Game with Imaginit,” Simulations, Games, and Experiential Learning Techniques: On the Road to a New Frontier, James Kenderdine and Bernard Keys, Editors, (Norman, Oklahoma: The Center for Economic and Management Research, The University of Oklahoma, 1974), p. 15. [7] Keys, Bernard and Jim Thomas, “The Rehabilitation Simulation: An Interdisciplinary Approach to Simulation/Game Design," 14th Annual NASACA Conference Proceedings, Richard McGinty and Jolene Elliott, Editors, (Los Angeles, California: University of Southern California Press, University of Southern California, 1975), p. 216. [8] Leach, Hugh, “Structure Analysis in Simulation Development,” Computer Simulation and Learning Theory, Burnard Sord, Editor, (Austin, Texas: Bureau of Business Research, The University of Texas at Austin, 1976), p. 185. [9] Biggs, William D. and Robana Abderrahman, “A Game of Investment Strategy: Description, Use, Criticism and Modification,” Insights into Developments in Business Simulation & Experiential Exercises, Volume 8, 1981 47 Experiential Pedagogy, Samuel Certo and Daniel Brenenstuhl, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1979), p. 81. [10] Nyerges, Richard and Harry L. Reif, “Computer Aids to Planning: The Budget and Forecasting Module,’ Insights into Experiential Pedagogy, Samuel Certo and Daniel Brenenstuhl, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1979), p. 78. [11] Fuhs, Paul F., “The Design of a Database System to Support Business Simulation and Experiential Learning,” Insights into Experiential Pedagogy, Samuel Certo and Daniel Brenenstuhl, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1979), p. 280. [12] Sandver, Marcus Hart and Harry B. Blame, “The Process of Writing a Collective Bargaining Simulation: A Case Study in Practical Pedagogy,” Insights into Experiential Pedagogy, Samuel Certo and Daniel Brenenstuhl, Editors, ‘Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1979), p. 49. [13] Frazer, Ronald J., “Some Issues in Game Design,’ Experiential Learning Enters the Eighties, Daniel Brenenstuhl and William Biggs, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1980), p. 184. [14] Lambert, David R., “On Compensatory Demand Functions in Marketing Simulations,” Experiential Learning Enters the Eighties, Daniel Brenenstuhl and William Biggs, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1979), p. 80. [15] Rice, William E. and Theodore F. Smith, “Symbol Recognition and Correlation for Evaluating Decision Making in Computer Aided Business Simulations,” Experiential Learning Enters the Eighties, Daniel Brenenstuhl and William Biggs, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1980), p. 1. [16] Barton, Richard V., “Indexing Simulation Model Response for Gaming Flexibility,” Experiential Learning Enters the Eighties, Daniel Brenenstuhl and William Biggs, Editors, (Tempe, Arizona: The Bureau of Business and Economic Research, Arizona State University, 1980), c. 14. Table of Contents Volume 8, 1981 The Promotion: Human Sexuality in Organization The Simulation of Chaos Leadership Development in a Simulated Urban/Suburban (U/S) Environment Tomed: A Computer Game Emphasizing Social Responsibility/or/why the Pop-Top Can? Integrated Brain Activity and the Manager's Job: Utilizing the Troika Model What does R2 Have to do with a Product Management Course? An Analysis of the Effects of Jungian Problem-Solving Style Dimensions on Marketing Decisions Bargaining Behavior in Personal Selling and Buying Exchanges Extending the Simulation Product Life Cycle A Generalized Algorithm for Designing and Developing Business Simulations Operationalizing a Test of a Model of the Use of Simulation Games and Experiential Exercises Pygmalion and Perception: An Experiential Exercise Behavioral Consequences of Reward Regarding Employee Absenteeism in an Industrial Setting: An Operant Conditioning Approach The Simlab Program: The Use of Experimental Simulation and Process Analysis for the Development of Management and Organizations Designs for Research on Simulation-Games, Cases, and Other Experiential Exercises The Role of Students in The Case Method Weaknesses in Research Design Critical Variables in Research on the Educational Value of Management Games Research Questions for Cases Research on the Learning Effectiveness of Business Simulation Games - A Review of the State of the Science The Effects of Valuation Techniques on Holding Cost During Inflationary Periods: A Simulation Exercise The Operations Simulation - A Study in Game Development Applying Guided Design to the Production/Operations Management Course: A Progress Report and Evaluation Terminal Data Entry and Retrieval Systems Simulations and Microprocessors Microcomputers and Related Technology for Simulation Gaming Microcomputers - A New Technology for Innovations in Business Simulations Microprocessor Controlled Interactive Video Simulation Business Game Design: From Theory to Practice The Success of a Computerized Simulation in Microeconomic Pedagogy The Test Preview Game: Applying the Game Show Format Providing a Real World View of the Personal Function: A Simulation Finding an Effective Means of Teaching Managerial Behavioral Skills: Two Different Experiential Teaching Methods Compared A Management Development Program Based on the Experiential Learning Model Participant Type Differences in Response to Experiential Methods: An Informal Look Preparing Student Groups to Participate in Experiential Group Projects: An Organizational Development Approach An Instrument for the Assessment of Learning Dimensions: A Progress Report of the Learning Dimension Scale (LDS) An Empirical Analysis Relating the Learning Style Inventory to Memory and Logical Ability Teaching Styles in Simulation Experiential Learning Versus Traditional Teaching Styles Student Perceptions of Effective Teaching Behaviors Problems in Evaluation of Experiential Learning in Management Education Students' Perceptions of Learning by Simulation A Relative Evaluation of Experiential and Simulation Learning in Terms of Perceptions of Effected Changes in Students Overview of Computer Based Business Games in Business Policy Classes Behavioral Decision Theory and Business Policy Giving Accounting Students Writing Experience as Job Preparation Getting to First Base with MBO: An exercise for Writing and Evaluating Objectives Dimensions of Conflict in Experiential Learning Consumer Alienation and Perceived Relevance of the Business Simulation Using the Self-Reference Criterion to Simulate Culture in Internationalized Business Course Experiential Learning in a Cross Cultural Setting- The Practice of Simulation Approach to Business Education in Japanese Universities Student' Perceptions of the Use of a Computerized Simulation in Teaching Management Information Systems Using the Case Study Approach to Develop a Microcomputer Bases, Fully Integrated, Data Base Driven, Management Information System The Case Study as a Tool for Organizational Change: Applying the Steel Ax to the Designers of Management Information Systems Toward a Theory of Teaching Business Policy A Case Study in the Use of Experiential Learning (A Management Game Simulation) to Enhance Student Understanding of Strategy Evaluation and Policy Formulation Suggestions for Integration of the Business Administration Core Publishing Opportunities and Requirements for Business Simulation and Experiential Learning Materials How do we Apply Experiential Learning Intercollegiate Case Competitions for M.B.A. Students: Initiation and Implementation Teaching Business Policy and Strategy Using the Incident Process The Learning Co-Op Approach to the Core Policy Course Meeting the Managerial Skill Shortage - Is Academia Up to It? An Empirical Analysis of Experiential Learning Reinforcement International Experiential Learning: Experience is the Best Teacher Encouraging Student Participation During International Academic Programs European Summer Study Program: Can you, Should you, What Does it Take? Travel Seminars in Europe: How can I Direct One? International Experiential Learning: Student Evaluation ABSEL: Empirical Findings on the State of the Association Sensitivity of Performance Scores in Business Simulations Improving the Learning of a Business Simulation Game by Increasing the Process Content Student Participation in Deciding Performance Criteria for Grading in an OB Course: An Exercise and a Case Study Markup for Profit: A Simulated Self-Administered Experience in Retail Pricing The Investment Decision Game: An Experiential Learning Approach to Stock Market Decisions Through Gaming CHIPS: A Marketing Channels management Game External Validation: An Experimental Approach to Determining the Worth of Simulation Games Teaching Performance Appraisal Skills: An Experiential Approach In Support of Experiential learning: Results of a Follow-Up Survey The Introductory Management Course: Taking Theory Application One Step Further Decision Efficiency and Effectiveness in a Business Simulation The Implications of Cognitive Processing Variables and the Complex Decision Simulating the Simulation for Enhanced Player Rationality Simulation/Experiential Learning Audit