RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

 

One of the cool things about professional Monte-Carlo Simulation tools is that they offer the ability to fit data. Fitting enables a modeler to condensate large data sets into representative distributions by estimating the parameters and shape of the data as well as suggest which distributions (using these estimated parameters) replicates the data set best.

Fitting data is a delicate and very math intensive process, especially when you get into larger data sets. As usual, the presence of automation has made us drop our guard on the seriousness of the process and the implications of a poorly executed fitting process/decision. The other consequence of automating distribution fitting is that the importance of sound judgment when validating and selecting fit recommendations (using the Goodness-of-fit statistics) is forsaken for blind trust in the results of a fitting tool.

Now that I have given you the caveat emptor regarding fitting, we are going to see how each tools offers the support for modelers to make the right decisions. For this reason, we have created a series of videos showing comparing how each tool is used to fit historical data to a model / spreadsheet. Our focus will be on :

The goal of this comparison is to see how each tool handles this critical modeling feature.  We have not concerned ourselves with the relative precision of fitting engines because that would lead us down a rabbit hole very quickly – particularly when you want to be empirically fair.

  • 15 May 2011
  • Author: Eric Torkia
  • Number of views: 2380
  • Comments: 0

Excel Simulation Show Down (Part 1) - Defining Inputs and Outputs

Over the last 3 months, we have seen 3 of the 4 major players in the Excel Monte-Carlo Simulation arena introduce new releases. We hear a lot of talk about which tool is best and the truth is there is no perfect answer – it’s a personal thing dictated by user skill, preference and need.

For this reason, we have created a series of videos showing comparing how each tool is used to apply Monte-Carlo simulation to a model / spreadsheet. Our focus will be on :

To keep the playing field level, we have used a simple additive model, which is simply defining a series of distributions (i.e. costs, budget items…), summing them up and analyzing the resulting sensitivity analysis. We have kept things simple, so we are not correlating any of the variables nor using any fancy math.

As you will see, there are definite differences AND similarities regarding how these packages tackle building a model. We are going to focus on those relating to inserting and copying input distributions as well as defining and analyzing model outputs. The objective is to compare the ease, usability and efficiency of each tool and give people the opportunity to choose for themselves which tool reflects their needs and preferences better.

 

Collaboration and TechnologyOrganizations seeking to develop a virtual business model must also be in a position to effectively implement it on a business level and on a technological level. (Venkatraman, 1994; Venkatraman & Henderson, 1993,1998).
 
One of today’s hottest IT topics is how to cheaply and effectively inter-connect processes. Collaboration emerged out of the relative cheapness and ubiquity of Internet technologies. Champy (2002) states ”E-business is a natural reaction to today’s competitive environment[i]. But e-business means a lot of things to a lot of people. In current literature, e-business has taken on several definitions over time i.e.:
 
·         Strategic approach
·         A set of enabling technologies (Porter, 2001),
 
Since technology is a critical success factor to any virtual organizing strategy, the analysis of e-business is interesting due to its business focus and its ability to flexibly and rapidly support changing business needs and requirements. In essence, e-business is a composite of the above-mentioned perspectives and whose definition can be used inter-changeably with virtual organizing because of its open technologies and collaborative strategies.

 

Is there a winner in this battle between Crystal Ball and ModelRisk? To quote that way-too-often-quoted reply: It depends. Some users will value certain technical capabilities over others. Some users will value user-friendliness over accuracy. If there is to be a group deployment of a MCA spreadsheet package, usability may trump technical capabilities overall. Does it matter if one package has more distributions to choose from if there are only three that are of interest for your particular class of stochastic problems? Would it matter what kind of correlation enforcement method is used if, as in many manufactured assemblies, there is practically no correlation between separate components? Probably not. But if they do (as in financial and insurance applications), there will be a clear winner.

Correlation behavior in ModelRisk is enforced with the use of copulas. Copulas offer more flexibility in accurately simulating real data scatter-plot patterns than do single-value correlation coefficients. While this advantage is clear for financial and insurance applications, its implementation in an MCA spreadsheet simulator can make the difference between universal adoption and rejection by a majority of the intended user group. Let us now use ModelRisk (MR) to enforce the correlation behavior between Duke Basketball offense scores and their opponents' scores, based on the '09/'10 historical data.

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