tag:blogger.com,1999:blog-9566309661205577772018-04-11T09:39:07.573-07:00m12ECC's M12 SeminarUnknownnoreply@blogger.comBlogger1125tag:blogger.com,1999:blog-956630966120557777.post-68894008125290996132010-06-25T07:58:00.000-07:002011-08-05T17:38:38.833-07:00Deterministic Uncertainties . . .Knowing what to expect when dealing with chance is important, and some of us are better at seeing the potential risks than are others. Even the most mathematically talented among us may make <i>stupid</i> decisions about potential risks, usually supported by intuition but almost always unsupported in hindsight. It's our intuition that needs a boost, and so I believe we need a viable way to accelerate our intuitive understanding of chance. Throughout this seminar, I will try to present a case for using mathematical simulation to clear a path to possible truth, but never absolute truth. So I am hopeful that this seminar along with my guidance will provide a very mild introduction to mathematical simulation, but in no way is meant to replace more rigorous courses on the subject. In the most simplistic of terms, this seminar should introduce you to the probabilistic concept of randomness and how to use a computer to create mathematical models based on randomness. Something as entertaining---yet completely vexing---as the famous Monte Hall problem will be easily solved using the innate ability of your computer to create pseudo-randomness. By developing a simple foundation, we will follow a path that will moderately take us from the simple to the sublime. Hopefully this seminar will inspire you to want to model more complicated random events---maybe even determining the next big Wall Street collapse!<br /><br />In order to understand the content presented in this seminar, you basically need to be familiar with beginning calculus and know how to use a high-level programming language. Certainly you should also have an intuitive concept, but need not be formally versed, in the field of probability. Computer code throughout will be simplistic; essentially the computer is being used to run a vast number of trials that would otherwise take an inordinate amount of time to complete by hand. The reason for simplicity, I believe, is to give the reader a sense of how easy it is to start modeling random events. However, don't let simplistic beginnings prevent you from moving onward to more complicated and realistic probabilistic models. Our applications may seem a lot more complicated than flipping a coin, but are essentially no different.<br /><br />As for the title ``Determinsitic Uncertainties,'' it is an enigmatic way to suggest that our deterministic view of the world may sometimes be ruled by uncertainty. Although we should be humbled by the incessant uncertainties that we face on a daily basis, we should still be willing to and interested in predicting likely outcomes.<br /><br />Now get the <a HREF="http://www.amazon.com/Deterministic-Uncertainties-Basic-Stochastic-Simulation/dp/146117760X/ref=cm_sw_em_r_dp_2Xipob1GHP053_tt">book</a> and start reading!<br /><br />Ron Bannon<br />ron.bannon@mathography.org<br />Department of Mathematics and Physics<br />Essex County College<br />303 University Avenue<br />Newark, New Jersey 07102 USA<br />973.877.1886Unknownnoreply@blogger.com0