Abstract Model Paper Abstract models are decision making tools that aid in the identification and mitigation of certain risks (Argosy University, 2014). Different types of abstract models exist and can be utilized for various problems and risk mitigation strategies. This paper will present four specific abstract models: deterministic, probability, simulation and discipline specific modeling techniques. Additionally, the author will introduce an example problem for each type of abstract model presented.
Deterministic Modeling Deterministic modeling are “precisely determined through known relationships among states and events, without any room for random variation” (Business Dictionary, 2014, para 4). In deterministic modeling, a certain given input will always generate the same output. An example would be a chemical reaction. When two hydrogen atoms attach to an oxygen atom, the known result produced is water (H2O).
A deterministic model can be representated mathematically with the formula y = f (x). One can see real-life determinitic modeling in a retail scenario where y = amount paid and x = almount of the good. Physical and engineering industries are much more difficult to use detiministic modeling for risk mititagtion. For example, the amount of water (capacity) a dam can hold may seems like it could be solve through detministic modeling. Unfortunately, it is impossible to accurately calculate the physical and structural make up of the concrete used down to the atomic level (Texas Tech University, 2010). Therefore, it is impossible to accurately determine the maximinun stress livel that the dam can bear. Determinisitc modeling is even more difficult to utilize in a social sciences arena. For example, there is no way one could accurately predict the gains or losses of the stock market.
Probability Modeling A probability model is a representation Y ~ p(y) (Koneksys, 2012). This model allows one to make “what if” predictions regarding certain events where comparative known data exists, p(y). Unlike in the deterministic model, probability models will not find a solution to the problem with a 100% certainty. Probability models will merely give the likelihood of an outcome as a result of a known event. For example, one could use probability modeling to determine the likelihood of the next customer to purchase a hot dog, bratwurst or Polish sausage. The possible values are “hot dog”, “bratwurst” or “Polish sausage” y p(y) Hot dog .50 Bratwurst .30 Polish sausage .20 Based on the above figure, we can accurate predict the likelihood that out of 100 customers, 50 bought hot dogs, 30 bought bratwurst and 20 bought Polish sausage.
“This "prediction" does not necessarily occur in the past, future, or even the present. It is simply a hypothetical, "what-if" statement. The probability model allows us to predict aggregate outcomes if we were to observe a large number of y values” (Koneksys, 2012, para 6).
Simulation Modeling Modeling and simulation (M&S) is a method of abstract modeling that involves generating resultant information about how something will behave without actually have to perform real life testing (Koneksys, 2012). For example, Boeing uses computer simulation methods in order to improve aerodynamic productivity on their air craft. They gain useful insight in executing risk mitigation strategies before even initializing construction. “More generally, M&S is using models, including emulators, prototypes, simulators, and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions” (Koneksys, 2012. Para 7).
Discipline Specific Modeling

References
Argosy University. (2014). Module 4 Overview. Retrieved from Argosy University Solutions Oriented Business Models: www.myeclassroom.edu
Business Dictionary. (2014). Deterministic Model. Retrieved from Business Dictionary: http://www.businessdictionary.com/definition/deterministic-model.html
Koneksys. (2012). System Data Integration. Retrieved from Koneksys: http://www.koneksys.com/en-us/integrate.php
Texas Tech University. (2010). What is Deterministic Modeling? Retrieved from Texas Tech University: http://courses.ttu.edu/isqs5349-westfall/images/5349/deterministic_stochastic.htm
Vyas, D., Wombwell, E., Russell, E., & Caligiuri, F. (2010). High-fidelity patient simulationseries to supplement introductory pharmacy practice experience. Am J Pharm Educ, 74(9), 169.