RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

How I Learned to Think of Business as a Scientific Experiment

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data
Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
  • 6 November 2018
  • Author: Robert Brown
  • Number of views: 319
  • Comments: 0

Gender Inference from a Specimen Measurement

Bayesian Reasoning using R
Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process for doing this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.
  • 28 October 2018
  • Author: Robert Brown
  • Number of views: 337
  • Comments: 0

A structured way to make sure you got all the info you need

Are you asking the right questions?
Whether you are a businessman or a practicing professionals such as an attorney, a doctor or a consultant, the ability to ask the right questions is imperative along with the ability to capture the information that is important when an answer is provided. Sometimes knowing where to start is the toughest aspect of solving a problem. Usually a sound approach is breaking out complex problems into smaller more manageable components; as the old adage goes “Do you know how to eat an elephant? One bite at a time!” Check out how to break-down tough problems by following the simple 5W question framework.
  • 28 October 2018
  • Author: Eric Torkia
  • Number of views: 291
  • Comments: 0
Why are analytics so important for the virtual organization? Read these quotes.

Since the mid-1990s academics and business leaders have been striving to focus their businesses on what is profitable and either partnering or outsourcing the rest. I have assembled a long list of quotes that define what a virtual organization is and why it's different than conventional organizations. The point of looking at these quotes is to demonstrate that none of these models or definitions can adequately be achieved without some heavy analytics and integration of both IT (the wire, the boxes and now the cloud's virtual machines) and IS - Information Systems (Applications) with other stakeholder systems and processes. Up till recently it could be argued that these things can and could be done because we had the technology. But the reality is, unless you were an Amazon, e-Bay or Dell, most firms did not necessarily have the money or the know-how to invest in these types of inovations.

With the proliferation of cloud services, we are finding new and cheaper ways to do things that put these strategies in the reach of more managers and smaller organizations. Everything is game... even the phone system can be handled by the cloud. Ok, I digress, Check out the following quotes and imagine being able to pull these off without analytics.

The next posts will treat some of the tools and technologies that are available to make these business strategies viable.

  • 26 June 2013
  • Author: Eric Torkia
  • Number of views: 293
  • Comments: 0
Modeling Time-Series Forecasts with @RISK


Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.

This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm.

We will discuss these topics as well as present practical models and applications using @RISK.

  • 18 October 2011
  • Author: Eric Torkia
  • Number of views: 353
  • Comments: 0
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