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How We Reinvent Ourselves

I often speak about lazy versus rigorous thinking.  We are all vulnerable into creating surreal realities for ourselves.  My friend Kevin, a very successful guy and very bright is convinced that the moon landing was a hoax. He has a detailed explanation to prove it.


My friend Kevin, a very successful guy and very bright is convinced that the moon landing was a hoax. He has a detailed explanation to prove it.


I have actually told this Orest Bedrij,  a friend and mentor who was director of the Jet Propulsion Laboratory about this.  Kevin believes he has the facts to prove the moon landing never happened.  “All a hoax,” He says. Orest was impressed at how close Kevin’s information is to be correct…but according to Orest it isn’t correct.  Kevin is sure that Orest is a liar and part of a plot.

When I am sharing with you below is from my soon to be published book “Harrison’s Applied Game Theory: How to Solve any Problem Effortlessly”. The book and is an exploration of how we can be very wrong about everything we believe to be so.


 “It was renowned cognitive scientist  Herman Simon who originally proposed that human judgments are limited by available information, time constraints, and cognitive limitations. He called this “bounded rationality”.

  In the early 1970s, psychologists Amos Tversky and Daniel Kahneman  demonstrated three heuristics* that underlie a wide range of intuitive judgments. These findings set in motion the heuristics and biases research program, which had a great influence on game theory research. This research program studies how people make  what seems like common sense  real-world judgments and the conditions under which those judgments are likely to be unreliable. This research challenged the idea that human beings are rational actors, but provided a theory of information processing to explain how people make estimates or choices. This research, which first gained worldwide attention and a large following among game theorists in 1974 with the Science paper “Judgment Under Uncertainty: Heuristics and Biases“, has guided almost all current theories of decision-making. Although the originally proposed approach to heuristics has been challenged by many important thinkers this research program has changed the field by permanently setting the research questions.

   This heuristics-and-biases tradition only becomes problematic when dealing with more complex and extreme problems.   In their initial research, Tversky and Kahneman proposed three heuristics

  • Availability
  • Representativeness
  • Anchoring and adjustment.

There may be many other but let’s focus on these three.


Let’s explore each.

  • Availability: Called “evaluation heuristics” these are used to judge the desirability of possible choices. Let’s explore Tversky and Kahneman’s three heuristics In psychology, availability is the ease with which a particular idea can be brought to mind. When people estimate how likely or how frequent an event is on the basis of its availability, they are using the availability heuristic. When an infrequent event can be brought easily and vividly to mind, people tend to overestimate its likelihood. For example, people overestimate their likelihood of dying in a dramatic event such as a tornado or terrorism. Dramatic, violent deaths are usually more highly publicized and therefore have a higher availability. On the other hand, common but mundane events are hard to bring to mind, so their likelihoods tend to be underestimated. These include deaths from suicidesstrokes, anddiabetes. This heuristic is one of the reasons why people are more easily swayed by a single, vivid story than by a large body of statistical evidence. It may also play a role in the appeal of lotteries: to someone buying a ticket, the well-publicized, jubilant winners are more available than the millions of people who have won nothing.


   Tversky and Kahneman offered the availability heuristic as an explanation for illusory correlations in which people wrongly judge two events to be associated with each other. They explained that people judge correlation on the basis of the ease of imagining or recalling the two events together.

  • Representativeness: This  heuristic   is  used when making judgments about the probability of an event under uncertainty. It is one of a group of heuristics  (simple rules governing judgment or decision-making). To repeat;  Heuristics are judgmental shortcuts that generally get us where we need to go – and quickly. They are not perfect and occasionally  send us off course. Heuristics are useful because they use effort-reduction and simplification in decision-making. On the other hand when they are used by the police to prevent crime they are often distorted by ethnic biases on the part of law enforcement.       
  • Anchoring and adjustment:: This heuristic is used in many situations where people estimate a number as a starting point.  According to Tversky and Kahneman’s original description, it involves starting from a readily available number—the “anchor”—and shifting either up or down to reach an answer that seems plausible.  In Tversky and Kahneman’s experiments, people did not shift far enough away from the anchor. Hence the anchor contaminates the estimate, even if it is clearly irrelevant.  It is common to say that it is not wise to make assumptions. ‘Anchoring and Adjustment explains this concept in detail. Here you have a cognitive error (anchoring) where an individuals fixates on a target number or value – usually the first one they get, such as an expected economic forecast, a price, or an important statistic concerning a point of view, an alternative reality, or a conspiracy theory that that goes against the mainstream,  or price.  Anchoring occurs when an individual makes new decisions based on the old, anchor number. Giving new information thorough consideration to determine its impact on the original forecast or opinion will help mitigate the effects of anchoring and adjustment.

So to review this: The anchoring and adjustment heuristic describes cases in which a person uses a specific target number or value as a starting point, known as an anchor, and subsequently adjusts that information until an acceptable value is reached over time. Often, those adjustments are inadequate and remain too close to the original anchor. Anchor values can be self-generated, be the output of a pricing model or forecasting tool, or be suggested by an outside individual.

An example of anchoring and adjustment as an influence technique. A politician or ideologue of some type will often offer a seemingly irrational and unsupportable statistic. One that everyone agrees (except the person stating the statistic) is unrealistic. Then the person who made the statement, when questions back off a bit.  Because the original statistic is an anchor, the final statistic will still tend to be inaccurate but a bit more defendable . Used car salesman often do this, as do both Donald Trump, Alex Jones, the fake news promoting radio talk show host, and others in influential positions.  Used car salespeople know that the final price of sale will tend to be higher if the begin with a higher anchor than if they had offered a fair or low price to start. In finance, the output of a pricing model or from an economic forecasting tool may become the anchor for an analyst.

People’s valuation of goods and the quantities they buy, respond to anchoring effects. In one basic example when a stack of soup cans in a supermarket was labeled, “Limit 12 per customer”, the label influenced customers to buy more cans. In another experiment, real estate agents appraised the value of houses on the basis of a tour and extensive documentation. Different agents were shown different listing prices, and these affected their valuations. Anchoring and adjustment have also been shown to affect grades given to students. Numerous studies have implied that anchoring will affect the sentences in criminal trials. These are not just juries that have made these biased judgments but trial judges with, on average, more than fifteen years of experience.

   In economics and decision theory, loss aversion refers to people’s tendency to prefer avoiding losses to acquiring equivalent gains: it’s better to not lose $5 than to find $5. Some studies have suggested that losses are twice as powerful, psychologically, as gains. One of the easiest ways to avoid falling prey to these confirmation biases in the application of statistics  through a concept known as “regression toward  (or to) the mean”. This is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and if it is extreme on its second measurement, it will tend to have been closer to the average on its first.  To avoid making incorrect inferences from confirmation bias regression toward the mean must be considered, especially when designing scientific experiments and interpreting data.

Here is an article on Tvesky and his friendship with Kahneman



Winning The Game Of Life: A Primer On Lewis Harrison’s Applied Game Theory


Lewis Harrison – RealUGuru, is a master lifehacker, writer, mentor, success and wealth coach, content-rich, motivational speaker, and an entrepreneur specializing in problem solving and strategizing  based on game thinking, applied game theory and Game Thinking.

He is the author of over twenty-two books published in five languages.


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This course and all the offerings on www.RealUGuru.com  focus on the application of applied game thinking, gamification, decision science, positive psychology, happiness,  and visionary thinking to solve basic, complex and extreme problems. He is the creator of a free course on business success and human potential.

Here is a short interview with Lewis;


Today’s  blog on Confirmation Bias  is supported by a grant from Events  Chair Massage –www.EventschairMasssage.com –  a company that offers Corporate Chair Massage and Stress Management Services to meeting planners, event planners, party planners and HR for Trade show booths throughout the United States.

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