Introduction: Motivation and Charter

Charter

I dedicate this journal to promulgating and testing ideas about mathematics and its role in society and the economy. The popular media seems to pay Mathematics much attention these days.  Imaginations about the potential of the closely related field of artificial intelligence have also been reignited.  IBM's Watson project makes Stanley Kubrick's 2001: A Space Odyssey HAL 9000 character seem almost within reach.

I use this venue to contribute my verse — to borrow from Whitman — to the data science cacophony. Installments will address topics including:

• Consideration potential and limitations of data science to decisively contribute to business and public policy;
• Summary and critical analysis of topics of current interest in the media; 
• Discussion of approaches to applying data science to practical problems;
• Review of noteworthy literature germane to data science; and
• Illustrative results from my own computational tinkering.

Providing a dispassionate view of selected topics related to mathematics will be a successful outcome of this project.  

I intend to attempt weekly installments. One installment a month will be a quantitative case study. Openly available data sources provide rich troves of opportunities for curious analysts.  The first installments will use labor-economics data from the bureau of labor statistics.

The remaining weekly installments will cover "softer" topics. The list of "soft" topics contains fifteen entries. The order is still under consideration.

Motivation

Why write about Mathematics and its role in society and the economy?  A couple of reasons come to front of mind:

1. I love mathematics.  Insight into a nuance satisfies like few other things. Math represents to me The Spiritual; The Mystical; A Glimpse of the Divine.
2. Math has suddenly become cool!  The popular media recently produced shows like Numb3rs.  Harvard Business Review called data science "The sexiest job of the 21st century."   Yes, we still have Big Bang Theory.  But it's a long way from Steve Urkel.

So how can somebody feels this way about Mathematics provide a dispassionate view?  I can because I am the recipient of a scientific education. The scientific calling demands passionate dispassion.

My experience and education also equip me for dispassionate exploration.  For example:

• I follow the pattern of a one-time consultant and mentor.  R.C. Hansen published a widely circulated paper about the fundamental limitations of his field, antennas.  The grip of this arcane, narrow discipline upon him was sufficiently powerful to drive him to edit or write the definitive reference in his field not once, but twice.  The first appeared in 1985 and the second in 1998.  (A copy of each collects dust on my bookshelf.)  This commitment sharpened his objectivity.
• I view the limitations of mathematics as an attribute of its beauty. Applying mathematics to business and public policy is fundamentally grounded in information theory. Cover and Thomas open their Information Theory text with presentations of the Information Inequality and the Data processing Inequality.  These fundamentals constrain data science to the same extent that gravity and drag impose bounds on aerodynamics.

But, the popular media appears to assign limitless potential to mathematical computation perpetually increasing due to the inevitable advance of Moore's Law.  The Gartner Group annually publishes a variety of "Hype Cycles."  Many within the IT industry will be familiar with this methodology.  A hype cycle represents a technology's lifecycle.  Similar methods appear elsewhere in the business literature (e.g., see Moore, HBR, July 2004).

Figure 1 shows Gartner's 2013 installment of its "omnibus" Hype Cycle for Emerging Technologies. (The entire volume is available for purchase at a price accessible to many medium-sized and large businesses.)  "Big Data" appears prominently at the "Peak of Inflated Expectations.  Big Data is frequently applied to the application of data science.

Figure 1 — The Gartner Group's 2013 installation of "Hype Cycle for Emerging Technologies." Source:  http://goo.gl/a4xlEY.

What does this mean for Data Science?  It is presently the boom phase of the boom-bust cycle followed by many industries. Gartner forecasts that "Big Data" will reach the "Plateau of Productivity" stage of industry maturity in five to ten years.  Between now and then, many dreams will be smashed, hearts broken, and fortunes lost.  The illusion that any analytical problem can be solved given enough CPU clock cycles will be shattered.

How does one get from now to then?  Two obvious paths present themselves:

• We can hop on the roller coaster, raise our arms above our heads, and scream at the top of our lungs; or
• We can practice passionate dispassion.

My preference ought to be obvious by now.