Adonis Diaries

My diploma happened to be called “Industrial Engineering”:  Thesaurus for academic fields?

At an advanced age, I decided to engage in a PhD program, out of boredom and lack of expertise in anything, I guess.

It was 26 years ago when I thought that PhD programs provides some kind of expertise in something… I was single and still is.  My former university student colleagues were now my professors, married, and marrying their children.  The program dragged on forever, over 6 years, while a few clever undergraduate students got their PhD at the same year as I did.

The program was a Calvary:  After the first semester paying full tuition as a foreigner, I could no longer afford taking more than a couple of courses:  Quarter-time and then half-time scholarship for correcting tests and exams were leisurely coming my way, while I toiled under several part-time minimum-wage jobs.

In order not to “waste time”, I would enroll in requisite courses as auditor.  A few professors were kind enough to give the full grade without re-attending the next semester as I could afford the tuition.  The administration was quick catching up and put a brake on my “circumventing” method of actions.

I had to work 4 minimum-wage jobs within the university, as constraining laws on foreign students, to make ends meet.  This post is not about the troubles I encountered and the difficulties of my academic program: I already described at length in my category (autobiography).

It happened that the Industrial engineering department lacked professors to open up enough graduate courses to satisfying the schedule.  I was thus dispatched to several academic departments to satisfy the graduate course requirements.  It was my good luck that carried my adventure to a variety of fields such as MBA, finance, economics, marketing, accounting, psychology, sociology, and higher education…

My good luck wanted that most of these graduate courses were tailored-made to applying computer statistical packages for particular designs of experiments.  Thus, I learned varieties of math models, design of experiments, ran experiments (in hard and soft sciences), and analyzed statistical results adapted and used in many academic fields.

I learned that we don’t necessarily design cause and effects experiments, and that there is a wide array for designing experiment and collecting data.  There are not only numerical designs but also qualitative and categorical designs and data.

Every field, depending on levels of math requirement, adopted the easiest set of equations to interpret statistical results, and not necessarily what is the best in providing optimal information on interactions among the factors.

I learned that, actually, experiments designed in hard sciences were mostly antiquated and very inefficient compared to well-developed designs applied in social sciences (involving live subjects and people such as in psychology and sociology…)  

I learned how complex, biased, difficult, and detailed are experiments done on human subjects and how they can hide serious confounding factors that have strong effects but left uncontrolled (due to incredible amount of variability) and how time-consuming they were.

There were times where courses turned out to be like rerun of bad dreams:  I was proficient in the usual model and statistical packages,  but I had to finish the course:  I used these courses as opportunities to learning the terminologies of the field and comparing them to other fields. I discovered that different terminologies in different fields had the exact same meaning.

It was obvious to me that most fields of study were the same from an experimental perspective:  All you had to do is learn the appropriate terminologies since the fundamentals of experimenting were basically the same.  Thus, this idea of issuing a thesaurus of academic terminologies.

My diploma happened to be called “Industrial Engineering”, a very broad field researching the physical and mental capabilities and limitations of people so that workers can be used efficiently and more productively in the management of safer and healthier workplaces.

I learned that the diversity of fields I was engaged in entitled me to work in any field I desired, if it were not for the fictitious constraints and limitations set by syndicates and academic associations intent on giving priority to their own graduates.

I know that I can earn a new degree every two semesters if I am allowed to apply, without undue stress on course load. All that I need is getting initiated and familiar to the few key-words bounced around in the field and reading typical peer-reviewed articles.

Working democracies must structure their academic courses to focusing on how to think right, scientifically, rationally, and practising designing and running experiments.

I wish this openness to other fields of study be instituted and graduates be hired on capabilities of doing the job instead of based on nominal diplomas.

I also wish that mathematicians are encouraged to earning a degree in other fields of application since every statistical analysis is based on a math model. We need statistical models that are closer to how nature and people behave rather than these simple equations used in the packages.  The computers are very powerful to accommodate complex models.

Note:  Statistical analysis packages are driven by math models of a set of constraint equations and an objective equation (mainly operations research models).  I conjecture that it is the moral values and ethical standards of scientists for equitable behaviors toward mankind and sustainable environment that ultimately guide most scientists to conceiving the appropriate constraint and goal equations.

There are an abundance of data to suit or validate any model that scientists can imagine and thus, earning them a Nobel Prize, especially in economics.