Problem Solving With Abstract Models
There are a variety of abstract models that can be used to aid in critical thinking and problem solving, but each is suited only to specific problems and is also limited by the nature and often the amount of information available. Deterministic models are a type of mathematical model were certain rules act upon any input information, such that a given input will always result in the same output (Business Dictionary 2009). Probability models, in contrast, allow for uncertainty and by their very nature do not make predictions with absolute certainty (Yale 1998). Simulation models lie somewhere between these two, using both certain and uncertain input to develop functional explanations of possible outcomes (Frontline Systems 2009). Discipline-specific models can also be developed when information cannot be processed in ways less tailored to the specific situation and available data (Frontline Systems 2009).
Deterministic models are best suited to issues involving known numbers and rates that do not require any conjecture. Calculating the cost of capital based on known rates is one example of a situation where a deterministic model could be utilized; the certainty of the figures involved lends the issue to a straightforward calculation. Probability models can project future events in degrees of uncertainty; forecasting future interest rates based on past changes and projected changes would be an effective use of a probability model as the output's certainty can be quantified despite coming from uncertain input. Simulation models are better suited to situations where information is not so easily quantified. Human behavior, for instance, is much better modeled by simulations; enactments of hypothetical workplace situations can effectively educate and prepare individuals for eventualities. When highly specific individuals and situations are involved, discipline-specific models are best suited.
References
Business Dictionary. (2009). Accessed 30 July 2009. http://www.businessdictionary.com/
Frontline Systems. (2009). "Simulation models." Accessed 30 July 2009. http://www.solver.com/simulation/simulation-model.htm
Yale. (1998). "Statistical modeling." Accessed 30 July 2009. http://www.stat.yale.edu/Courses/1997-98/101/probint.htm
