Tuesday, January 21, 2014

The World As Seen By The Objective Observer

Dispassionate objectivity is itself a passion, for the real and for the truth. - Abraham Maslow

A creation needs not only subjectivity, but also objectivity. - Stephen Chow

I receive thousands of emails from people asking me how I am able to quickly predict success or failure of governments, politicians, businesses, etc.  Many times when “interesting events” are afoot, I am asked to choose which way I think a particular event will finish.

Many people who are on the receiving end of good news from the models I use are grateful that I was able to ferret out details that they had overlooked or had not even thought about.

Many who are on the receiving end of bad news or whose passion is not in congruence with the reality of my models get very angry that an absolute stranger can spend a little time with them and identify their pain and its source or predict their demise (often within a very specific timeframe).

And a few have voiced the opinion that the use of such models makes me more machine than man.  Such assertions by carbon-based life forms seems illogical to me but that’s only after a first pass through my analysis of such statements.

In the spirit of sharing and for the thousands of people who have emailed me (thank you) and accepting that I cannot reply to everyone individually, I offer this explanation of what my Tarot cards, tea leaves or “insert your favorite divination form here” looks like.

Welcome To My World

Everything I do professionally (and sometimes personally) is through the lens of a model.  The models you are about to see, expressed as mind maps, have corresponding math models that I apply to various scenarios, the output of which is used for the specific situation I am analyzing.  The following models are simple on first blush, obfuscating the complexity of the math models underneath.

For example, this is one of the lenses through which I analyze companies and governments:



Which is validated using this methodology (click on the image for a readable version):



This is one of the lenses through which I examine the world that employees live in (click on the image for a readable version):



This is one of the lenses through which I measure someone’s level of authenticity, a means of measuring how congruent their thoughts, words and actions are (click on the image for a readable version):



And this is one of the lenses through which I analyze how governments and companies need to work with the people, not against them or in spite of them (click on the image for a readable version):



These are the simple models.  The scenario analysis, result prediction and risk assessment models are more entertaining (and shareable if one has an interest in exploring them). :-)

I seek the answers to many questions via the execution of these models but two questions are the most important to me and which seem to create the most difficulty for people. The two questions are:


How do you know?

Three things become apparent when I use my models:

1. They are not perfect, although I trust data over emotion and find that it is far more reliable in predicting the future (or analyzing the past).

2. People love or hate my models depending on which side, the good side or the bad side, they find themselves on.

3. I am unable to build a winning NFL fantasy team no matter how hard I try.

All that being said, the use of the models reveals a deep-rooted passion within me that is best explained by Helene Deutsch when she said:

After all, the ultimate goal of all research is not objectivity, but truth.

This simple statement best describes why I do what I do for who I do it for.

And while human beings are fascinating in their complexity and diversity (and maddeningly complex to model), isn’t that what we all seek – truth in business, in government, in our relationships and in ourselves?

In a world filled with complexity, uncertainty and unlimited potential, the ability to discern truth is more important than ever and will determine the future of this planet – good or bad.

All things being equal, I would rather let facts speak for themselves rather than risk myself, my family, my clients, my country and my world to someone’s aggressive, loudly-shouted assertions or opinions (which they mistakenly or intentionally misrepresent as facts).

How about you?

In service and servanthood,


Addendum – January 22, 2014

I sometimes share the results of my models on my blog as I did in March of 2008 where I predicted the financial collapse of September of 2008 (Financial Crisis) or November of 2010 where I noted that Premier Danny Williams’ sudden resignation left a leadership void in the PC Party of Newfoundland and Labrador that could potentially bring the party and his successor down (Premier Williams and His Legacy).  The latter was proven today with the resignation of his successor, Kathy Dunderdale, over leadership concerns as expressed inside and outside the PC Party.

Models and the data that feeds them are rarely if ever perfect (have you listened to an economist or weather forecaster lately?).  However, there are times when data sees obvious things that emotion or ego refuse to acknowledge.

On a separate note, here is a great article in the Economist  (Who’s Good at Forecasts?) for those fascinated by the art of using models for predictions.

Final Thought - Why Data Matters

On a clear sunny morning some years ago, Kermit Tyler received a telephone call from a radar operator who informed him that he was seeing a larger radar return than he was used to seeing on his equipment.

Since it was a quiet Sunday morning and since he knew that a flight of aircraft was expected at a nearby airfield, Mr. Tyler told the radar operator “not to worry about it”.

It was the morning of December 7, 1941.  The radar operator was observing the arrival of the attacking Japanese planes as they prepared to bomb Pearl Harbor.

Mr. Tyler ignored the data and the rest is history.  In his defense, he was cleared of any wrongdoing as noted in this excerpt from Wikipedia:

Tyler had been assigned to the Information Center with little or no training, no supervision, and no staff with which to work. Tyler was subsequently cleared on any wrongdoing by the Board and no disciplinary actions were taken against him.

Ignoring data because one is not trained to process it or to dismiss it because it seems unimportant are common mistakes made in business and in Life.

Do you want to be remembered as the person who was responsible for the result that followed?

I didn’t think so.

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