By Desiree L. DePriest, Kaplan University 

The use of technology to determine competitive advantage arguably influences most organizational decision-making in the 21st century. Large and small organizations, for-profit, non-profit and governments, use technology more than physicality to gather data about an interest or entity. Physical wars are being replaced by cyberwars, and virtual techniques are used to measure and quantify the level of risk within a competitor’s firm or investment portfolio over specific time frames. The appropriate software, artificial agent and/or artificial intelligence tool can become the algorithm to explore virtually anything. There are shades of gray space between what is considered legal or illegal, ethical or unethical, upstreamed or intercepted. However, as it is with most aspects of the law, it depends upon the rule and the context. Maybe.

Figure 1:  A Word Cloud of this Article 

First, the guilt or ethical considerations of doing what is necessary to achieve competitive advantage may have reached critical mass in the late 20th century. Nobel laureates like Milton Friedman spoke of the utility in applying the “economic man” (homo economicus) test to the prominent classical economic theories that also framed our political world. Friedman, along with many others, used the economic man as a placeholder to measure economic models that pushed for consumer equilibrium. The utility of the man converted to a mathematical function states that entities (man, corporate, government) know what they want. Choices were driven by rational calculations about how to maximize decision making between choices based on paired vectors with multilayered network comparisons (Krugman, 2007). Very rational decisions look for competitive advantage or some level of added benefit from the alternatives available. In arguing the benefits of comparative advantage, Friedman states that economic theories should further be judged by their ability to predict behavior. 

However, when we combine mathematical formulas and prediction analytics in the 21st century, it is no longer economics or politics in the forefront, it is technology. The comparisons are not two or three dimensional but N dimensions with lattice coordinates. It is seeking the best algorithms to predict the best utility for the entity seeking competitive advantage. Technology is integral to individuals obsessively checking their Facebook pages through to organizations making decisions based on social engineering, intelligent agents tracking your browsing history and planting advertising in your email client, and general trolling. 

Governments rely heavily on technology for national security. Technology is the "phisherman" collecting your information either upstream or through NSAs PRISM in an attempt to predict and persuade your next move. It is no longer concerns of civil liberties and public policy at the bull’s eye of society’s progress, stock exchanges, or even presidential elections. Data discovery, data gathering and collection, data analysis and interpretation are all attempts to make advantageous decisions. 

Figure 2:  A Black Box Image Representing Unknown Internal Processes 

Furthermore, seeking to determine the utility in predicting the future no longer has anything to do with knowing what we want to discover. The technology will tell you what you want. It is more a process of limiting the choices while overwhelming your senses through the utilization of billions of bits of information output from a self-organizing, rabbit-out-of-a-black-box. If you pay attention globally, you will hear more after-the-fact recognitions than predictive preventions of what could have been. In other words, if only the authorities had connected the dots beforehand, they would have prevented the acts of bad guys who were already in the system.

To be fair to mere mortals, self-organizing machines have evolved the economic man of Friedman, to supervised and complex vector pairs and multi-layer feedforward networks. These machines involve myriad repetitions and weight adjustments until the desired output was achieved. Although the supervised machines are more likely to help the entity see the future the way it desires, skewed subjectively, the output is not based on the self-organizing [dimensional] maps with the greatest similarities to the input samples. These best matching units (BMU) assume the reliability of the prediction is more accurate-to-the-input than supervised output managed by human deficits (stupidity). Considering technology more accurate or intelligent than humans becomes especially suspect when humans are trying to ascribe political or competitive advantage to mathematical, technological constructs devoid of emotional intelligence, utilitarian or democratic considerations, human or environmental safety. 

Due to the black box syndrome, we actually do not know how the technology arrives at its conclusions. Yet, the technology is given credit for more sensitivity as individuals look for soulmates on their mobile devices. Transnational corporations and governments are interested in predictive decision-making at near the speed-of-light, to achieve competitive advantage on steroids. Each scenario requires machines operating above the transactional speed of most human beings. For example, NASA and the private space industry want to send people to Mars by the 2030s, but how, or, the NSA is harnessing “big data” to predict crimes, terrorist acts and social upheavals before they happen, but how (Bloomberg, 2016)? The technology provides alternatives of what-to-do when we don’t have a clue what-to-do. It is becoming the majority report – the new intuition - versus the renowned Minority Report film.

As our society tries to maintain structures that defined the thoughts of past centuries such as politics with all its gerrymandered subdivisions and splintered demographics, it becomes futile to identify as superior any political party, religion, race, gender, or economic construct. With the full onset of technology, the millennials simply do not seem to require these limitations.  It is likened to putting an address in your GPS, and then it shows the shortest distance to your destination as being straight over Lake Michigan. Should we determine the GPS results wrong, should we build a bridge across the lake and then drive, buy/rent a boat, or should we fly? It is not advantageous when we expend exorbitant amounts of mental energy trying to figure out logically, understand politically and then decide competitively how to act on what the output is trying to tell us.  It’s not political, its technology, stupid.

Strohm, C. (October 13, 2016). Predicting terrorism from big data challenges U.S. intelligence. Bloomberg, L.P. Retrieved from https://www.bloomberg.com/news/articles/2016-10-13/predicting terrorism-from-big-data-challenges-u-s-intelligence.  

Krugman, P. (February 15, 20017). Who was Milton Friedman. The New York Times Review of Books. Retrieved from http://www.nybooks.com/articles/2007/02/15/who-was-milton-friedman/

Desiree DePriest is the founder of “Quantum Teaming” which seeks to make virtual organization teaming a natural human experience of interactions and relationships. Dr. DePriest sees every team as a unique experience for human evolution through leveraging the natural and honed skills of each member. Through understanding that humans naturally entangle with other humans, we increase the freedom and dimensionality of the teaming experience. The result is superposition where the collective cognition and behavior of a few members produce outcomes previously requiring many members. Uncertainty is moved away from fear and turned into "surprise" moments measurable scientifically. 

DePriest's expertise is in business intelligence (BI) and entrepreneurship including systems analysis and design, decision support systems, and artificial intelligence.  In 2014, Desiree created KapTechnology which is an IT internship offering for students at Kaplan University School of Business and IT. She also created the Graduate Information Technology Association (GITA) and serves as Faculty Advisor.  Desiree loves working extensively with students applying learning to real-world projects. Dr. DePriest is also the Vice-chair of the IRB at Kaplan University. 

Her publications include research in persuasive and predictive analytics, artificial intelligence, augmented reality and pattern recognition. Dr. DePriest’s book on quantum teaming is available at Amazon at: http://www.amazon.com/Quantum-Teaming-Primer-Century-Self-Information/dp/1523492309.  

DePriest holds a Ph.D. in Management & Organization with emphasis in Information Technology, along with two masters degrees (Telecom/Networks and IS/IT respectively). She holds a Bachelor of Science degree in psychology from Howard University. She may be reached at ddepriest@kaplan.edu.