Tuesday, September 23, 2008


Failed Prediction: Based on the Ted.com Video, I would have to say the 1 laptop per child. I am not saying that it will not happen, but only that the cost of a laptop still prohibits ownership of 1 per child considering that some of our population simply could not afford that.

Successful Prediction: teleconferencing. We do this at work. The cost of sending a professional to a location, provide transportation, meals and lodging is expensive. There is also the cost of minimized output due to travel time. We normally budget 2 days lost due to travel to and from the destination. The other cost of traveling is that the professional is away from his/her family. It could add up to family stress.

Ted.com video


Feedback: What my take on this is that this might be a way to quickly disseminate information across the world. Meaning, if a professor does this methodology, then that professor could cast the information across more minds. What I do not like - no interaction. There are some subjects that are best taught with feedback. I also think that anyone getting information in this manner could potentially lose attention. So what is my recommendation on how to improve it? This information dissemination is not bad. However, I would recommend that no video should broadcast longer than 45 minutes. The reason is that the average human has an attention span of 45 minutes. Another recommendation I have is that it would be best if the video would pause every 5 or 10 minutes then recommend activities to the audience. The reason for this is that I find that some humans have different learning methods. But the best or one of the best ways to pass knowledge is to give instructions then show how it is done. For the podcast method, then get the audience off the seats and get them doing something. I think you more so than other instructors are aware of the need to get the audience interested and involved.

Monday, September 1, 2008

‘Points’ Forecasting and Diverging/Converging Probabilities

Joe Evans Jr. (a colleague at work) once explained to me how a simple probabilistic program that does ‘points’ was useful in predicting the likelihood of certain numbers from coming up in a lottery. He is the only person I know of that actually made money from the lottery. His program takes into account the ‘points’ probability of numbers occurring. It gets very complicated, but here is a site to also explain ‘points’ analysis: http://www.softwaremetrics.com/fpafund.html.

The simple explanation of ‘points analysis’ from the website is, “Function Point Analysis is a structured technique of problem solving. It is a method to break systems into smaller components, so they can be better understood and analyzed.”

This methodology can also be used to model our forecasted predictions to scientifically explain what we believe are the important ‘points’ or forecasting drivers that will skew one way or another a forecasting. It is just a way to explain the complexity of diverging probabilities.

This is not a simple analysis method. To better explain the complexity of diverging probabilities, one needs to picture a simple divergence. Imagine leaving the driveway to go to work and thinking of turning left or right on the road. Repeat this simple divergence whenever going through a road cross section. The probabilities are then quickly explained. In reality, we tend to follow a known divergence choices based on speed, distance, etc. to achieve our objectives. But the diverging probabilities concept is a simple concept to explain how any forecasting can get complicated very fast.

A side note, though, that one needs to consider that in this physical universe, there is also the concept of converging probabilities. In the above example, no matter which road one takes to get to work, our car will get to the same spot at some point. For an example, we all die. That is a converging probability, but what is diverging probability is that we might die tomorrow or ten years from now. But no matter the span of time, we can only dodge death so long (diverging probability) but we still die at some point (converging probability).

For the sake of Futuring and Innovations, understanding how diverging and converging probabilities affect our forecasting gives more precision in that we can make predictions knowing that at some time, general predictions will have to come true.

Entropy Effect on Forecasting

Part of the considerations of scientific forecasting from an educated guess is the understanding of the scientific methods and applications related to forecasting. When attempting to understand the precision of forecasting as time moves forward, Stephen Hawking’s book, “A Brief History of Time,” Chapter 9, “The Arrow of Time,” (time moves forward), Page 148 – 149, that “the second law of thermodynamics,” states that, “in any closed system disorder, or entropy, always increases with time. In other words, it is a form of Murphy’s law: things always tend to go wrong! An intact cup on a table is a state of high order, but a broken cup on the floor is a disordered state. One can go readily from the cup on the table in the past to the broken cup on the floor in the future, but not the other way around.”
The relevance of the second law of thermodynamics to the class of Innovations and Futuring is that it implies that forecasting gets less and less accurate the longer time passes. In other words, forecasting in the short term is generally more accurate than forecasting in the long term. Another important point to be made is that since things tend to go wrong, it probably will. (Murphy’s Law) It is in the understanding of what the probability of certain things that could go wrong will happen, and how it affects any forecasting we will do.

Sunday, August 17, 2008

Forecasting The Future

The future - the future is not yet written. In theory, we can not know what the future will be. We have forecasting methods that can give us an approximation of what the future could be with a degree of scientific confidence. Thisi one of the reasons for taking CS 855 - Futuring and Innovations.

The reason for this blog is for the discussion of whether the future happens because we will it or if the future occurs independently. So the question I have is that if the future is self-fulfilling (we make it happen) or does the future happen independently of our actions. The argument for a self-fulfilling future is that if we forecast that something happens at a future time, do we make all effort to make it happen? The idea of a fully independent future is that we have little or no say in the outcome. So what is it then; or once again is it a mixture of both?

Technology is 'developed' two ways. When there is a need for a specific technology, this is called a "pull", or the users something to happen. As a result, technology is 'developed' to fulfill the users' need. A good example of this is the current problem with the cost of oil. It is getting expensive to fuel vehicles, therefore the vehicles that can deliver the most value to the users are the vehicles that are bought. As a consequence, automakers will make all effort to develop more fuel-efficient vehicles, and retire less fuel efficient vehicles. A "push" technology is when a technology serendipitously happens. Innovations, or new incremental applications of technology, are ten presented to the users. If a user need is identified and fulfilled, then the technology survives and thrives. However, if a need is not identified or fulfilled, then the technology is phased out. Examples of this technology are fads like Chia Pet, bell bottoms, etc...

There are sometimes discussions about how technology follows evolution or revolution. Revolution is the serendipitous discovery of new technology. Examples of this include fire, the wheel, etc. Evolution of technology is discussed above under 'innovations'.

In effect, the above discussions might be seen by some as another tidbit. However, what I wanted to convey is that we need to have an understanding of how technology comes about. The end goal is really to identify opportunities and grab it before someone else does.

Uncertainty Principle

For our class, Futuring and Innovations, I have had to expand my reading to include some temporal aspects. Yes, I am overreaching again! Anyway, I came across this reading about time and uncertainty from Stephen Hawking's book, A Brief History of Time, 10th anniversary edition. Specifically, Chapter Four, contains some of Werner Heisenberg's concepts on the Uncertainty Principle. He states, "In order to predict the future position and velocity of a particle, one has to be able to measure its present position and velocity accurately."

I believe that we can adapt the Uncertainty Principle to our class. I don't remember who told me this, but the difference between forecasting and foretelling the future is that forecasting relies on 'proven' scientific methods to tell the future, while foretelling the future relies on 'other' methods. So forecasting is objective method, while foretelling is subjective methods. The scientific method of forecasting relies on the methods discussed in our textbook, to include statistical methods. My classes in Statistics does not guarantee 100% accuracy, which suggest a 'margin of error' in any measurement.

Something else that caught my eye while reading A Brief History of Time is that there is talk about a deterministic view of the universe. Thankfully, the book do provide a counterpoint in that there are too many unknown variables to consider pointing out that the world is more likely stochastic, or a combination of both. So where does this leave us? I would say that we are almost back to where we started on our textbook, in that there will always be uncertainty in predicting the future. HOWEVER, knowing the errors and the sources of possible errors, such as the uncertainty principle and the point and counterpoint of a deterministic universe gives us a greater understanding of our prediction methodologies. You could say that we are another step closer to truly comprehending forecasting, and hopefully make more accurate predictions.

Thursday, July 17, 2008

CS855 Sample Blog

This is a sample blog for the purpose of CS855. I am still learning about blogging, so please be patient with me.