Abstract Model Paper
Analyze the different types of abstract models and examples of the problems that are applicable to each type of model.
Write the paper in 2–3 pages including the following details: * A brief description of each type of model: * Deterministic * Probability * Simulation * Discipline specific * A brief description of problem for which each model is applicable. * Why each example is applicable to the model for which it was chosen?
Present the paper in Microsoft Office Word document format. Name the file

A deterministic model is the one that contains no random elements. The output of the model determined the parameter values and the initial conditions. Deterministic models samples include accounting, timetables, pricing, a linear programming model and economic quantity models (Nic, 2013). For example, decision-making problems can be deterministic or probabilistic decision models. As deterministic models, decisions to bring good final outcomes. A deterministic model is “you get what you expect” risk-free model, which determines the outcome. It also depends on the influence of the uncontrollable the factors that determine the outcome of a decision and the information the decision-maker input as a predicting factor (Arsham, 1996). According to Schrodt (2004), deterministic models was widely used in the early 18th century to study physical processes to develop differential equations by many mathematicians. These differential equation allow values of a variable as function of its value at any point in time and as a common form of the deterministic concept. The differential equations apply to a variety of astronomical and mechanical phenomena and have produced crucial scientific literature (Schrodt, 2004). The model is important in astronomical and mechanical phenomena because of the finite equations differences is where the value of a variable act as a function of its value within the period lag by finite value.
The probability model is where elements of randomness are incorporated in or some aspect of random variation. It is the mathematical representation of unsystematic phenomenon. It includes sample of space, events within the space of the sample and the probability associated within each event.
This model applies to subjective probability and delay. The suggestion that people behave similar manner in the face of probability and delay was first introduced by Rotter in 1954 and tested 10 years later by Mischel in term of “delay of gratification.” According to Rachlin, et.al (1991) people will choose a smaller immediate reward over a large reward because delay reward promise are rarely given and fulfill or often broken. Thus, delay gratification in some culture act like less-than-unity probabilities; longer delays correspond to lower probabilities. The model applies to the example because it represents a unique reward that is either probabilistic or delayed but not both. The experiment supports the extension of human self-control and delay that are discounting the effect human behavior and decisions making among probabilistic rewards (Rachlin et al., 1991). The simulation model is the process of designing an imagined or real system model and to conduct experiments with the system. The important for simulation model experiments is to understand the behaviors or evaluate the strategy of the operating system (Smith, 1998). What constitutes model is the assumptions that are about the system model and the algorithms, and the relationships is use to outline those premises to reveal how the system works.
The model applies to the business decision-making process. An organization may be interested in a new factory to replace the old, but uncertain if the increase in production will justify investing in a building. In this situation, the management can use a simulation model to evaluate the new building or factory model. The simulation concept model will delineate the floor space needed, numbers of machines require, each machine's production capacity, the number of employees, placement of equipment, and time between machines (Smith, 1998). The simulation model runs will provide production capacity estimates and new factory or building costs and the system evaluation. The information provides the simulation model invaluable in making decisions without building the actual factory to arrive at final decision
According to Smith (1998), simulations are discrete or continuous events due to the ways the state variable changes. The discrete event is believed to change suddenly at striking points in time while continuous simulation variables change steady through the function of time as a variable. Thus, simulation in the example allows for the analysis of a system’s capacities and behaviors without building of the real factory. It may have been expensive with the construction of actual factory to determine its best configuration. In this case, simulation experiment or trial of the building is critical (Smith, 1998). Discipline specific model is a set of understandings than a broad knowledge based field and specific to the discipline or profession that defines a specialist in the area. The model allows businesses and IT professionals to focus on modeling delivery that are compatible with a specific life cycle stage and event (Gimenez, 2008). For example, to pursue employment within a profession that requires a university education, individuals must have a foundation discipline specific that is particular. Discipline-specific model applies to writing in nursing and midwifery genres. Students in this model were asked to rank genres identified from the most difficult to the least difficult to produce. It is concluded that the essay was the most challenging to the students because students were asked to produce their reflective essay at the inception of their program (Gimenez, 2008).
The model shows how students in nursing and midwifery how theory informs practice and how practice shapes theory in many written assignments. In this case, nursing and midwifery students work from practice to theory by demonstrating how experience gained on their clinical placement translate into literature (Gimenez, 2008).

Reference

Arsham, H. (1996). Deterministic Modeling: Linear Optimization with Applications.

Gimenez, J. (2008). Beyond the academic essay: Discipline-specific writing in nursing and midwifery Journal of English for Academic Purposes.

Rachlin, H., Raineri, A., and Cross, D. (1991). Subjective Probability and Delay. Journal of the Experimental Analysis of Behavior. State University of New York, Stony Brook.

Schrodt, P. A. (2004). Deterministic Model.

Smith, R. D. (1998). Simulation