To Rule Them All
A Heuristic Theory of Innovation
Unlike compound English words having straightforward meanings, such as straightforward, the word heuristic sounds a bit alien. A brief Google search reveals that the word is based on the Greek term heuriskein, meaning “to find.” It shares the same root as Eureka!, a famous expletive for “I’ve found it!” — properly used when one is problem solving while in the bath. Combined with the meanings of Latin heuristicus and German heuristisch, heuristic gains the flavor of “the art and science of discovery;” a pin-compatible word for the term innovation. Despite its noble ancestry, heuristic is often curtly defined as “a general rule of thumb” and — worse — as the theorist’s pejorative for “experimental common sense lacking a firm theoretical foundation.” In the measurement sciences, a heuristic is used to develop computational models for the purpose of calibration. In the case of a glass electrode used for the determination of solution pH, this transducer’s response to a change in hydrogen ion concentration in solution is modeled by the semiempirical Nicolsky-Eisenman equation. The qualification “semiempirical” being the politically correct way of saying “the operation of a few hundred thousand pH meters is based on a heuristic.”
The process of instrument calibration is a heuristic of wide importance. Currently lacking a complete theoretical proof connecting first principles to solution pH, the calibration model relies on the transducer’s reaction to standard solutions having pH = 4.01 and pH = 10.01, as well as a measurement of temperature which, in turn, has been calibrated to the triple point and boiling point of water. In order to “find” the solution pH, other knowns must be modeled to justify the accuracy of our measurements. This pattern of “heuristics supporting heuristics” is a recurring theme across the spectrum of disciplines ranging from physics to existentialism. Grand patterns often indicate the existence of an underlying theory, so we are presented with a conundrum; the development of a theoretical heuristic framework.
If we could develop this mythical framework, it could serve as a general approach to the process of innovation, the core competency of capitalistic endeavors in science, technology, engineering, sociology, economics, and business. This was the charge of the development team for the Integrated Science, Business, and Technology Program at La Salle University. La Salle desired to create a new Bachelor of Science degree that yielded graduates in the nascent discipline of “innovation.” During the initial scramble for theoretical consistency, we came across the topic of “general systems theory” (GST), a term coined by Austrian-born biologist Karl Ludwig von Bertalanffy, and later refined by colleague Kenneth E. Boulding, an English-born economist. GST is a heuristic that describes the flow of first principles throughout every level of our society. It has had a rocky acceptance since its introduction in the middle of the last century, but rapid discoveries in the physical, computational, and life sciences are bolstering its usefulness.
The GST describes the universe as a recursive heuristic of simpler heuristics — and it is mumbo-jumbo like this statement that has earned its many skeptics. Instead of defining GST backwards from the product, it is much easier to describe it from its beginning. GST starts with the proposition that the most fundamental pattern in the universe is energy. Ask a proponent of string theory, and heads will nod in agreement. GST then suggests these energy strings can be combined to form the material of subatomic particles. The venerable relationship of E = mc² is used to casually deconstruct material into energy and the GST simply asserts the reverse process as a corollary. The next GST level of complexity requires the combination of energy and material to yield a new item, information. At first glance, this connection may not be obvious; however, it is the fundamental heuristic behind the glass electrode mentioned earlier. The pH electrode is an active transducer requiring energy for its operation. The electrode mixes its electrical energy with the material hydrogen ions present in solution to yield a millivolt reading that contains information describing the hydrogen ion concentration. These three levels of energy, material, and information serve as the foundational levels of GST and are the topics of physics, chemistry, and information science.
GST mandates the existence of disciplines that study the interfaces between these levels from both directions and predicts active research in chemical physics and physical chemistry (energy-material interface), and analytical chemistry and chaos theory (material-information interface). Studies primarily within a GST level include those such as kinematics and quantum mechanics, organic and inorganic chemistry, number theory and calculus. But, of course, if you have followed the pattern so far, you already realize these foundational levels can be combined to yield systems of even greater complexity. In nature, these systems coalesce into living cells and then onto multi-cellular organisms studied by biology, while in human-made systems they form the mechanical and electronic components studied in mechanical and electrical engineering. As you increase the amount of information flowing into biological systems you generate awareness and eventually self-consciousness, the discipline of psychology. As we kick off 2008, rampant research into intelligent systems is generating speculation of the emergence of intelligent machines. Whether these self-conscious systems are human or machine, GST describes the formation of self-governing systems to be studied by sociology, political science, and business administration. Entire cultures then produce emergent views of Existentialism having their own systems of philosophy and theology.
So, of what consequence is a heuristic theory of innovation to a magazine covering the topic of scientific computing? You need but read the various article titles of this magazine to see the importance of Information as one of the primary sectors in our society. Along with the energy and materials sectors, general systems theory can serve as a road map for innovation in computational systems. Such a map can help to describe the features expected of the “next big innovation” and increase our use of Eureka!
This material originally appeared as a Contributed Editorial in Scientific Computing 25:2 January 2008, pg. 28.
William L. Weaver is an Associate Professor in the Department of Integrated Science, Business, and Technology at La Salle University in Philadelphia, PA USA. He holds a B.S. Degree with Double Majors in Chemistry and Physics and earned his Ph.D. in Analytical Chemistry with expertise in Ultrafast LASER Spectroscopy. He teaches, writes, and speaks on the application of Systems Thinking to the development of New Products and Innovation.
