Pros and Cons of the Monte Carlo Simulation

Introduction 

Monte Carlo simulation involves a mathematical and computerized technique that enables its users to elaborate and analyze variability accurately and effectively in various processes to boost quantitative evaluation and subsequent decision-making. Monte Carlo technique is vital for adoption and use by professionals operating in various sectors such as engineering, manufacturing, project management, transportations, and finance among others. Essentially, Monte Carlo Simulation involves a process of generating random processes and objects through a computer program. Such objects are naturally secured in systems of modeling that involve real-life situations. Moreover, such situations may involve stock market evolution, movement of neutrons or complex infrastructures such as road networks among others. Techniques used in Monte Carlo Simulation often involve the application of artificial situations in solving problems that are deterministic in nature. As such, Monte Carlo Simulation places a lot of emphasis on sampling through various distributions of probability. The Idea behind Monte Carlo Simulation is to increase the frequency of the process in either artificial or natural setting as ways of obtaining sufficient quantities used for making inferences. Monte Carlo Simulation should be embraced based on its benefits and applications in the professional world despite some of the limitations and challenges associated with its use. 

Pros of the Monte Carlo Simulation 

Monte Carlo analysis displays several benefits over other techniques when it comes to valuations of assets or evaluations of risks. Sanderson (2006) posited that Monte Carlo simulation remains a useful tool in the process of making decisions since it furnishes critical decision-makers with a range of probabilities and possible results associated with the different course of actions. This technique had the ability to indicate situations linked to extreme possibility together with their consequences. Ideally, this would be difficult to describe and analyze in real-life situations. To that extent, the Monte Carlo simulation plays an extremely useful role in enabling decision-makers as well as professionals in various sectors to discuss the likelihood of a risk or an outcome. According to Dowd (2005), Monte Carlo Simulation is regarded as one the most popularly used tools in techniques of science management since it is elegant and simple when it comes to solving problems having probability distributions. Monte Carlo simulation is fundamentally a technique in mathematical processes that produce hypothetical results. Such results and outcomes play a vital role in facilitating quantitative analyses as well as decision-making processes. In this regard, various professionals operating in various fields such as engineering, Information, and Technology, project management, research and development, insurance, finance as well as transportations among others adopt the Monte Carlo simulation for analytical purposes (Dowd, 2005). 

Monte Carlo simulation techniques are useful and critical in modern day operations due to their efficiency and ease of use. This is because of the flexibility of its algorithms that allows for its suitability even in situations characterized by changes brought courtesy of modernization. The techniques used in Monte Carlo simulation are applicable to a wide variety of physical and non-physical systems where it reduces the complexities of such systems. In turn, the simplification achieved through the adoption of Monte Carlo simulation facilitates the implementation of other general models used in other simulations with relatively fewer complexities. Further, the inherent randomness that is associated with the techniques employed in Monte Carlo simulation serves to strengthen its process in various aspects (Golub and Tilman, 2000). Such aspects include that of real-life systems where simulations play a critical role in the computation of deterministic numerical variables. For instance, the randomness of Monte Carlo simulation enables stochastic algorithms to move ways from local optima through natural ways when it is applied in randomized optimization. In turn, this allows for an enhanced examination and analysis of the space meant for research. Monte Carlo simulations operate based on ingredients that are essential when it comes to a deeper and better understanding of statistics and probability functions. This is because the techniques adopted in the processes of this simulation involve random computer experiments with observable outcomes (Heinrich and Schwardt, 2015). 

Additionally, modern statistics adopted in Monte Carlo simulations have continually relied upon computational techniques and tools, which include sampling and analysis of complex sets of data under varied conditions and circumstances. The application of Monte Carlo simulations in the processes of planning and decision-making is largely backed by theoretical justifications. This is because of a rapidly changing and a vast state of statistical and mathematical concepts that underpin the techniques used in Monte Carlo simulations. As such, the use of this simulation allows for the adoption of precise and efficient algorithms. Much of the modern-day analysis and research that use Monte Carlo techniques are focused on coming up with enhanced encodings and rules that are likely to improve computational efficiencies meant for complex estimations, sampling as well as optimization problems. Monte Carlo simulation techniques provide opportunities and platforms for the development of sequences where market returns can be randomly fitted with predetermined or expected outcomes. As such, it becomes easier and simpler to test and examine the manner in which different financial plans are likely to perform considering the various circumstances presented by good or bad environments in the market (Ahuja, 2016). 

In most cases, financial planners make their work easier and more efficient by adopting and applying Monte Carlo simulation as one of their tools in planning and decision-making. This tool also plays a fundamental role in assisting financial planners to deal with challenges that may have been brought about by conditions and situations characterized by uncertainties. Presently, Monte Carlo simulation and analysis remains as one of the most critical tools at the center of financial planning process alongside other programs and software whose primary role is to determine the feasibility levels of financial plans in order to make appropriate financial decisions. Dowd (2005) established that Monte Carlo makes it possible for one to come up with a wide variety of possible situations and scenarios of operation as opposed to the few scenarios that can be provided through historical information and data. Further, the number of paths used in Monte Carlo simulation is often optimized in such a manner that it becomes manageable for financial planners to effectively observe the performance and features of the simulated financial paths and their impacts on market returns. This implies that Monte Carlo analysis provides a financial planner with the opportunity to carry out an observation involving different sequences of financial return hence he or she is able to gain a deeper and broader perspective on the expected outcomes (Chen and Chen, 2017). 

The use of Monte Carlo simulation allows scenario analysts to acquire an exact perspective in relation to various inputs and parameters under which certain results and outcomes occur. This function is usually not easily achieved through various models that use a combination of different inputs and values to obtain specified outcomes. In this regard, Monte Carlo analysis makes it easier for scenario analysis to pursue further analysis in the future whenever there is a need for that. In the event of sensitivity analysis, Monte Carlo simulation comes in handy to address the challenges that would otherwise be faced through deterministic analysis. In this case, it is much easier for scenario analysis to identify and examine different variables and the manner in which they influence the expected outcomes (Frenkel and Hommel, 2005). Additionally, in Monte Carlo simulation, it is easier to model relationships that involve input variables independently thus enhancing the accuracy of the simulation process in relation to the situation in a real-life circumstance. As financial products in finance and economics continue to become more complex, Monte Carlo technique has increasing become a vital tool in the analysis and evaluation of such complex scenario with the aim of making appropriate financial plans and decisions. Monte Carlo technique of simulation also goes to the extent of facilitating an important role of the analysis of risks that are associated with different financial decisions and paths. Due to the popularity of techniques used in Monte Carlo simulation, most professionals have continued to adapt in is investigating the behaviors and outcomes of various variables and their associated risks (Dowd 2005). 

Various real-life operations require the application and utilization of the Monte Carlo Simulation. The areas of application for the techniques of this simulation include engineering, science, and finance among others. Operations research and industrial engineering represent some of the real-life situations whose processes and functions are largely aided by the application of Monte Carlo Simulation. In this case, the Monte Carlo simulation techniques play a critical role in typical processes such as job scheduling, inventory processes, networks for queuing as well as the reliability of systems among others. Furthermore, there are critical components of operations research such as mathematical optimization and mathematical programming that require that widely depend on the application of Monte Carlo simulation techniques (Dowd, 2005). 

To that extent, the techniques used in this simulation have proven to be reliable when it comes to the provision of frameworks for the control and management of industrial systems, scheduling, and optimal design as well as the new methods used in solving problems arising from classical optimization. The techniques of Monte Carlo simulation are also applicable in designing and controlling robots and autonomous machines. The other area of application for this simulation is that of physical structures and processes where the neutron transportation process takes place through direct simulation. In the chemical industry, the techniques of Monte Carlo simulation are often adopted in studying chemical kinetics where methods of the stochastic simulation are usually considered. Additionally, problems associated with classical systems of transportation are usually addressed through the application of Monte Carlo techniques where the transport of photons is simulated via biological tissues in multilayered structures that are inhomogeneous and complicated. Monte Carlo techniques also play a vital role in the area material engineering and material science, where they are utilized in analyzing and developing materials used in various applications such as Lithium-Ion batteries and organic solar cells among others (Sanderson, 2006). 

In particular, the techniques used in Monte Carlo simulations are largely used in the process involving the virtual design of materials where the production of various stochastic models takes place through the input of experimental data. The required parameters of such materials are then simulated through the performance of numerical experiments. Considering that the analysis and physical development of new structures and materials are often, time-consuming and expensive, the use of Monte Carlo simulations facilitates the experimentation process by making it easier to generate the required data and information. Moreover, this simulation facilitates the production of virtual materials and structures thus validating different parameters for actual production. Heinrich and Schwardt (2015) introduce critical perspective of the Monte Carlo simulation technique by arguing that its design and construction entails a relatively time-consuming and expensive process, which may not be readily affordable and manageable by most professionals, and scenario analysts who may want to use it. Furthermore, there are high tendencies of missing this particular simulation in situations where its application is stretched to the extent that it exceeds the credibility and efficiency limits. Other challenges in this simulation may come up new users are expected to perform simulations and make observations. Their lack of familiarity with the simulations tool and the techniques used is highly likely to result in poor observation and subsequently wrong analysis and conclusion. 

Cons of the Monte Carlo Simulation 

The argument made by Frenkel and Hommel (2005) indicate that there are notable limitations and challenges associated with using Monte Carlo simulation. In this regards, he argues that it is easier to predict the future that to explain the past thus simulations obtained through Monte Carlo simulation brings about a range of significant uncertainties. Considering the application of this simulation in the financial sector, it is worth indicating that the Monte Carlo simulation may largely deviate from accuracy and effectiveness since the use of forecast as part of financial decision-making and plans has evolved and continued to experience changes over time. Estimates relating to future financial returns or performances were previously based on fundamental calculations of the value of time for money. This technique is also referred to as deterministic modeling where future results lack randomness. For instance, a financial plan based on Monte Carlo simulation would forecast a long-term outcome on stocks of about ten percent every year without the variability of time. The adoption of Monte Carlo simulation approach has been on a considerable rise over the last couple of years. This increase is attributable to a reduction in the costs of computing as well as an increase in the level of recognition that returns are acquired randomly thus there is need to present clients and variously stakeholders with robust financial frameworks and plans. 

The observations made by Ahuja (2016) indicate that the tools used in Monte Carlo simulation ensure the testament of inflations and returns as random quantities thus they are largely based on standard deviation, assumed mean and correlations. Such statistical measures involve inputs that are often defined by the user and are likely to have serious influences on the conclusions made at the end of the simulations process. Moreover, the simulation process may not be a reflection of the actual processes that happen in reality. Additionally, the analyses often made by Monte Carlo simulation involve ultimate statistical processes that require assumptions to be made. The circumstances surrounding the making of such assumptions may cause them to be reasonable or sometimes unreasonable. In the event that the assumptions are unreasonable, the outcomes of the simulations end up being inaccurate thus decisions are made based on wrong conclusions. Monte Carlo simulations are designed in relation to specific forms of statistical distribution. In this regard, the use of a wrong or an inappropriate statistical distribution will result in meaningless outcomes. When it comes to input assumptions, Monte Carlo simulations are highly likely to be compromised based on the errors made at the input stage. Sanderson (2006) suggests that the entire process of Monte Carlo simulation may only be as good as the kind of inputs that are considered right from the beginning. 

For instance, a simulation program might be applicable to the evaluation of the value used during the start-up of a company. However, this process may not be successful unless various generalizations are made around various success points of the company and its future prospects. Moreover, Monte Carlo simulations are designed with regard to mathematical formulas and expressions that are likely to generate the end outcomes. In some cases, such formulas and expressions are deniable and not straightforward thus giving invalid interpretations. Chen and Chen (2017) argue that the other limitation associated with the adoption and application of Monte Carlo simulation in various sectors arises from the fact that it contains several built-in formulas and software programs that are primarily expected to assist the user in taking the shortest possible time to do the simulations and obtain results. However, they note that such formulas and programs are often faced with critical limitations in the sense that they are funder on theoretical rather than practical considerations and assumptions. 

Conclusion 

In summing up, Monte Carlo simulation is largely a tool that simplifies many operations in various sectors. However, it is worth indicating that the adoption, application and continued use of this technique calls for care and realistic way of analysis considering its limitations and shortfalls. Thus, it decision-makers in various professional fields are expected to consider both the pros and cons of Monte Carlo simulation in validating its appropriateness in the planning or decision-making processes. As part of the critics associated with Monte Carlo simulation, user error has remained to be a valid concern according to the observations made by most scenario analysis. To that extent, other researchers have argued that the issue of user error does not have much to do with the design of the Monte Carlo simulation technique but it is just a constraint created by the user due to lack of familiarity. Like several other models, Monte Carlo simulation can only give quality and reliable results if the inputs are the right ones and the observer plays his or role effectively. As such, auto-correcting functions may be necessary for incorporation into Monte Carlo simulations in future reviews and improvements. 

References 

Ahuja, N. L. (2016). Corporate finance . Place of publication not identified: Prentice-Hall Of India. 

Chen, D.-G., & Chen, J. D. (2017). Monte-Carlo simulation-based statistical modeling . 

Dowd, K. (2002). An introduction to market risk measurement . Chichester, West Sussex, England: J. Wiley. 

Dowd, K. (2005). Measuring market risk . Chichester, England: John Wiley & Sons. 

Frenkel, M., & Hommel, U. (2005). Risk management: Challenge and opportunity . Berlin: Springer. 

Golub, B. W., & Tilman, L. M. (2000). Risk management: Approaches for fixed income markets . New York: John Wiley & Sons. 

Heinrich, T., & Schwardt, H. (2015). The microeconomics of complex economies: Evolutionary, institutional, neoclassical, and complexity perspectives . Amsterdam: Academic Press. 

Sanderson, C. J. (2006). Analytical models for decision making . Maidenhead: Open University Press.