A theoretical proof for the mathematical properties of the law of demand is presented. The properties differ significantly from the properties of demand functions commonly used in computerized business simulators. Using a probability distribution that has the mathematical properties required by the law of demand, a market demand model is presented. The new model satisfies economic and marketing theories of consumer behavior and offers considerably more simulation flexibility than possible with business simulators currently available. Suggestions for using the new model to simulate real industries are provided.
Armstrong, W. E.
(1939). The determinateness of the utility function. The Economic Journal, September, 453-467.
2.
Carvalho, G. F
(1991a). Evaluating computerized business simulators for objective learning validity. Simulation & Gaming: An International Journal, 22, 328-348.
3.
Carvalho, G. F.
(1991b). Theoretical derivation of a market demand function for business simulations. In W. Wheatley & J. Gosenpud (Eds.), Developments in business simulation and experiential exercises (Vol. 18, pp. 47-49). Stillwater: Oklahoma State University
4.
Decker, R.
, LaBarre, J., & Adler, T. (1987). The exponential logarithm function as an algorithm for business simulation. In L. Kelley & P. Sanders (Eds.), Developments in business simulation and experiential exercises (Vol. 14, pp. 47-49). Stillwater: Oklahoma State University.
5.
Dittrich, J. E.
(1977). Realism in business games. Simulation & Games: An International Journal, 8, 201-210.
6.
Gold, S. C.
, & Pray, T. (1984). Modeling non-price factors in the demand functions of computerized business simulations. In D. M. Currie & J. W. Gentry (Eds.), Developments in business simulation and experiential exercises (Vol. 11, pp. 240-243). Stillwater, OK: Oklahoma State University.
7.
Goosen, K. R.
(1986). An interpolation approach to developing mathematical functions for business simulations. In A. C. Burns & L. Kelley (Eds.), Developments in business simulation and experiential exercises (Vol. 13, pp. 248-255). Stillwater, OK: Oklahoma State University.
8.
Marshall A.
(1961). Principles of economics (9th variorum ed., annotated by C. W. Guillebaud). New York: Macmillan.
9.
Massy, W. F.
, Montgomery, D. B., & Morrison, D. G. (1970). Stochastic models of buying behavior. Cambridge: MIT Press.
10.
MathCAD. MathSoft, Inc. (1989). Cambridge, MA: MathSoft, Inc. (201 Broadway, Cambridge, MA 02139).
11.
Mood, A. M.
, & Graybill, F. A. (1963). Introduction to the theory of statistics. New York: McGraw-Hill.
12.
Pray, T.
, & Gold, S. (1982). Inside the black box: An analysis of underlying market and demand functions in contemporary business simulations. In D. J. Fritzsche and L. A. Graf (Eds.), Developments in business simulation and experiential learning (Vol. 9, pp. 110-115). Normal: Illinois State University.
13.
Press, W. H.
, Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1985). Numerical recipes: The art of scientific computing. Cambridge: Cambridge University Press.
14.
Stanislaw, H.
(1986). Tests of computer simulation validity. Simulation & Games: An International Journal, 17, 173-191.
15.
Tellis, G. J.
(1988). The price elasticity of selective demand: A meta-analysis of sales response models. Cambridge: Marketing Sciences Institute.
16.
Thavikulwat, P.
(1989). Modeling market demand in a demand-independent business simulation. Simulation & Games: An International Journal, 20, 439-458.
