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THE SUM OF ALL PARTS

Optimizing Human Behavior with a STEM Model

by Moises Goldman PhD

 

The Human Conundrum

For the last 15 years I have given numerous seminars aimed at optimizing executive and managerial performance in technology driven firms. The goal is to optimize departmental performance resulting in the larger optimization of an entire firm. As the theory goes: If the whole is the sum of the parts, and each part is optimized, then the whole is optimized.

These experiences have challenged my ability to communicate with people involved in STEM fields. This group represents a highly gifted segment of the population, and they tend to be very results driven. How does one reason, interpret, and convince scientists to modify their own behavior?

At first, I struggled with the appropriate lingo. I pondered how to describe my ideas using managerial jargon. I realized that I needed another language—a language that both empirical and intuitive thinkers will readily grasp and put to good use.

Then my eureka moment came to me. STEM initiatives are defined by basic human bevavior and not the other way around.

To some, this may seem counterintuitive, so let me elaborate. If we first accept and understand any given issue at hand through basic human reasoning, we can then interpret it in a STEM format. Once we do that, we can use the tools of science to bring about an optimized outcome. Let me add some clarity with the following example:

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Kalman Filtering

My Ph.D. is in Inertial Navigation and my Masters in Control Systems. I spent many years as an executive in the aerospace industry and came to be expert in Kalman Filtering, a complex mathematical algorithm used in the guidance and navigation of aerospace vehicles. It occurred to me to apply this knowledge to the human equation.

Kalman Filtering is also known as Linear Quadratic Estimation (LQE), but it’s not necessary to go into the math here. I will attempt to make this example clear and concise. All we need is a simple diagram. I’ll describe it in layman’s terms and then apply it to the human condition.

The diagram below describes the guidance control of a space vehicle. The vehicle is at position “time-zero” or T(0). We want to get to position T(1,000,000). We calculate the location of our target relative to our present location. We recognize that any internal disturbance, such as bad sensors, electronics, and perhaps bad computations must be eliminated. (We get rid of them.)

  • We predict the trajectory of the vehicle over a short increment of time.
  • We measure the actual flight path against our target and factor in real environmental conditions (noise), such as wind speed, meteorites, etc.
  • We correct our trajectory.

The vehicle is now at T(1)—a very small part of the entire trip. T(1) is the next starting position. The algorithm repeats, bringing the vehicle to the next position T(2), then T(3), and so on. We iterate—continue to perform the same steps—predict, measure, correct—to optimize the overall trajectory to the target—T(1,000,000).

Perhaps you recognize this as a description of the way a child learns to walk. It’s commonly called a feedback loop. It governs behavior in many human pursuits. It’s the way our central nervous system directs us to negotiate a curve while driving down the road. It’s the way a baseball player catches a ball and executes a play. It’s how a circus performer walks a tightrope. It’s the way we all learn optimum behaviors.

Our minds perform this function intuitively through ordinary mental concentration, focus, or attentiveness. Concentration is an iterative process and the higher the number of iterations, the higher the degree of accuracy.

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Optimizing Human Behavior

If we can model our human behavior and reasoning in STEM format then we are able to optimize it. As an example, let’s choose a simple human behavior and describe it using Kalman Filtering:

Behavior—Tomorrow I’m taking a final exam; I need to arrive at 8 am—the target.

Method—My class always meets at that time, so I already know approximately when to wake up. Since there cannot be any internal disturbances, I eat a good dinner, plan my breakfast and what to wear to school. I give myself time to study and get to bed early. I set my alarm for 7 am. I’m at position T(0) on the diagram.

  • Prediction—I estimate the time it takes to get ready and walk to the exam. (About the same as a normal day.)
  • Measurement—I reach the door and glance at my watch. It’s raining and I’m running late.
  • Correction—I grab an umbrella while at the same time speeding up my pace.

I get to the exam location on time, and the algorithm repeats itself for the next activity (assuming my intention is to optimize the next behavior).

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A Simple Model for STEM Communication

It’s amazing how simply human behavior can be optimized using a STEM model—whatever the circumstances may be.

We know our current state. [We are on a diet, T(0).]

  • We predict the meal that we are going to eat. [A nice juicy zero carb steak.]
  • We eliminate any internal errors [If we’re cooking it, we make sure all the ingredients are there; check the labels for carbohydrate count; grill in working order; plates and glasses, etc.]
  • We set out to eat, then get a call that we’re needed immediately somewhere else. We make a correction. [Either we eat extremely fast or put the meal away for later, at T(1).]

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Optimizing Complex Behavior

Now let’s apply this same optimization process to a non-linear human behavior—investing in the stock market. We have some money to invest, T(0), in a given company stock. We eliminate all the internal disturbances by doing our homework. We read quarterly statements, look at the fundamentals, research the competition, analyze price and volume activity on a stock chart, and interpret technical indicators such as MACD and Slow Stochastics.

  • We predict our next move—[buy the stock]—T(0).
  • As we are getting ready to buy the stock we hear news of the latest unemployment report and we realize it will have a direct effect on the stock we are buying. We must correct. [We buy more, less, a different stock, or sit tight. Which correction we use will have a direct effect on the optimization.]
  • We decide to buy more of the stock. Now we are at T(1), and must predict T(2)—[sell, hold, or add to position].

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Achieving Greater Accuracy

The more we are able to reduce the size of T (time), the more we increase the Kalman iterations, and the better the optimization. In human terms, optimization is inversely proportional to the size of T, and directly proportional to Intelligence. Please note that human thinking is continuous in time, so the smaller our intervals, the closer we approximate a continuum.

As you see, I found my language for communicating optimization of human activity in any given organization. It is an amazingly powerful tool.

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MORE FROM MOISES COMING SOON

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Moises Goldman at IMSA

About the Author

Dr. Moises Goldman is uniquely involved with STEM (Science, Technology, Engineering, and Mathematics). He is a member of several advisory boards at MIT and is a founding member of the TALENT program at IMSA.

 

Kalman Diagram—Moises Goldman

Portrait of Moises & Chicago Globe—John Jonelis

Other graphics—MS Office

Chicago Venture Magazine is a publication of Nathaniel Press www.ChicagoVentureMagazine.com Comments and re-posts in full or in part are welcomed and encouraged if accompanied by attribution and a web link. This is not investment advice. We do not guarantee accuracy. Please perform your own due diligence. It’s not our fault if you lose money.
.Copyright © 2017 John Jonelis – All Rights Reserved
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THE SUM OF THE WHOLE

Moises GoldmanHigher Education and the Economy

Moises Goldman PhD – resident scientist

In today’s digital environment, the words entrepreneurship and innovation are the flavor of the day. Universities and even certain high schools believe they are preparing their students to go out into the world armed with the necessary tools to excel. But are they?

Parts - Royalty Free 5

Consider the following points:

  • The “whole” is equal to the sum of its parts.
  • The sum of its parts is equal to the “whole”
  • The sum of its wholes makes a bigger-and-better “whole.”

This article will focus on the third idea.

.Parts - Royalty Free 2

The Part

Graduate engineers can usually code in various languages—Python, Flash, Java, CSharp, Ruby on Rails. Perhaps they are able to create “apps.” They are specialists. With diligence and luck, they go to work in enterprise and fill specific roles.

In those roles, they create what might be call “parts.” A project manager pulls together all the parts into a “whole.” As typically happens, several of the parts do not fit. The process provides for other specialists that fill the gaps.

Parts--Royalty free 3

I am describing a typical mode of work. Specialists in cubicles re-design parts designed by specialists in other cubicles until the organization achieves a satisfactory whole. This is an iterative process, but not a creative one. Industry blunders forward. By any economic measure, it is grossly inefficient. Where, one may ask, is the root of the problem?

Moises Goldman PhD

Moises Goldman PhD

What is Optimal?

We need only ask a few questions:

  • Do the individual engineers on any given project understand what impact, Parts - Royalty Free 4implication or influence their developments have on the overall wellness, intent or strategy of the enterprise they serve?
  • Do they take into account current policy, regulatory, ethical, or socioeconomic factors?
  • Do they are work together—focusing on the whole and not their “part alone?

If the answer to any of these is no, then without a doubt their efforts cannot be optimal.

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The Whole

Parts - Royalty Free 1Universities turn out engineers that are themselves essentially parts. I would argue that they should train-up collaborators adept at comprehending the larger view and better understanding their “part” in the “whole”—in other words, people who are themselves whole.

When each specialist embraces the larger picture, each specialty complements the others. The sum of each whole person makes a bigger-and-better whole project. The sum of the wholes is a bigger-and-better whole.

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GO TO PART 2

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Contacts

This article was adapted from a paper for the Institute for Work and the Economy by Moises Goldman PhD.  www.workandeconomy.org

Moises GoldmanMoises6@comcast.net

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Chicago Venture Magazine is a publication of Nathaniel Press www.ChicagoVentureMagazine.com Comments and re-posts in full or in part are welcomed and encouraged if accompanied by attribution and a web link . This is not investment advice. We do not guarantee accuracy. It’s not our fault if you lose money.

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.Copyright © 2013 Moises Goldman – All Rights Reserved
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