Learn some basics about what Cognitive Biases are, and how to deal with them.

1. For Internal Use Only / Not for Distribution to the Public
Overview of Cognitive Biases
Common Decision-Making Traps and How to Mitigate
Them
Sankar G Vyakaranam

2. For Internal Use Only / Not for Distribution to the Public
Agenda
• Bounded Rationality, Satisficing and Heuristics
• General Biases
• Confirmation Bias
• Hindsight Bias
• Framing Bias, Prospect Theory
• Sunk Cost Effect (Escalation of Commitment)
• Heuristics & Associated Biases
• Availability Heuristic
– Biases: Ease of Recall, Recency Effect
• Representativeness Heuristic
– Biases: Base Rate Fallacy, Insensitivity to Sample Size, Regression to
the Mean, Misperception of Chance/Gambler’s Fallacy
• Anchoring and Adjustment Heuristic
– Anchoring Bias
• Some ways to attenuate the effects of the biases

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Bounded Rationality, Satisficing and Heuristics
• “Purely rational judgment is bounded by insufficient information about the definition of the
problem and the relevant criteria, time and cost constraints on the quality and amount of
data available, as well as by mental and perceptual constraints that inhibit decision
makers’ ability to determine the optimal choice.” – Herbert Simon, Nobel Laureate
• Satisfy + Suffice = Satisfice
• Decision makers “forego the best (‘perfectly optimal’) solution in favor of one that is
acceptable or reasonable.”
• Example: how I bought my car –
– I didn’t want Maruti nor Hyundai
– Budget was below 7 L
– hatch back
– Wanted Bluetooth
• Heuristics are simple strategies that affect our judgment. They are “the standard rules
that implicitly direct our judgment” – Amos Tversky and Daneil Kahneman, Nobel
Laureate
• They serve as a mechanism for coping with the complex environment surrounding our
decisions.
• On average, use of heuristics produce far more adequate than inadequate decisions. We
are oblivious to them mostly. The lack of awareness of these in our decision making can
lead to trouble sometimes.

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Confirmation Bias
• Confirmation Bias is the tendency to search for, interpret, or recall
information in a way that confirms one's beliefs or hypotheses.
• Biased search, interpretation and memory have been invoked to explain –
• attitude polarization (when a disagreement becomes more extreme even
though the different parties are exposed to the same evidence)
• belief perseverance (when beliefs persist after the evidence for them is
shown to be false)
• the irrational primacy effect (a greater reliance on information
encountered early in a series)
• illusory correlation (when people falsely perceive an association between
two events or situations)
• Explanations: wishful thinking, limited human capacity to process information,
people weigh up the costs of being wrong, rather than investigating in a
neutral, scientific way.
• Confirmation biases contribute to overconfidence in personal beliefs and can
maintain or strengthen beliefs in the face of contrary evidence.

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Hindsight Bias
• Hindsight bias, also known as the knew-it-all-along effect or creeping
determinism, is the inclination, after an event has occurred, to see the
event as having been predictable, despite there having been little or no
objective basis for predicting it.
• Examples:
• You knew the movie was going to be bad.
• You know the food was going to be bad.
• You know that route will lead to traffic jam.
• You know India was going to lose that game.
• …the list is long…
• One way to mitigate this – record your predictions, hypotheses, assumptions
and then go back and check.

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Framing Bias, Prospect Theory
• The first step in making a decision is to frame the question. It’s also one of the most dangerous
steps.
• Example: Suppose there is a virus outbreak which is expected to kill 600 people. 2 alternative
programs to combat the disease have been proposed. Assume that the exact scientific estimate
of the consequences of the program are known and are as follows:
• Scenario 1:
• Program A: If Program A is adopted, 200 people will be saved.
• Program B: If Program B is adopted, there is a 1/3rd probability that 600 will be saved and
2/3rd probability that no one would be saved.
• Which of these 2 programs do you favor?
• Scenario 2:
• Program C: If program C is adopted, 400 people will die.
• Program D: If Program D is adopted, there is a 1/3rd probability that nobody will die. However,
there is a 2/3rd probability that 600 people will die.
• Which of these 2 programs do you favor?
• Framing as Gains vs. Losses – Prospect Theory
• People are “loss averse” - they experience more pain
from losses than pleasure from gains
(Daniel Kahneman and Amos Tversky)

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Sunk Cost Effect (Escalation of Commitment)
• Ideally, choices should be based on the incremental or marginal benefits of
their actions and ignore 'sunk' costs. But in fact, in the face of sunk cost,
people become overly committed.
• Casino gambling - double or nothing...
• My own examples: Wasting food at restaurants, Enjoying theme parks
• It’s the tendency for people to escalate commitment to a course of action to
which they've already allocated substantial amount of time, money or
resources.

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Availability Heuristic
• If you can think of it…it must be important!
• Managers assess the frequency, probability, or likely causes of an
event by how “available” the event is in their minds
• Biases:
– Ease of Recall
• Performance Appraisals
• Choosing clients
• Advertising
• Investing and Accounting
– Recency Effect
• We give more weightage to recent events since they are fresh in
our memory

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Representativeness Heuristic
• Tendency to evaluate how likely something is with reference to how
closely it resembles something rather than using probabilities.
• Biases:
• Base Rate Fallacy
• Insensitivity to Sample Size
• Regression to the Mean
• Misperceptions of Chance/Gambler’s Fallacy
– We misperceive randomness of events and conclude a pattern
when it doesn’t exist

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Base Rate Fallacy
• If presented with related base rate information (i.e. generic, general information) and
specific information (information only pertaining to a certain case), the mind tends to
ignore the former and focus on the latter.
• Example: John is a man who wears gothic inspired clothing, has long black hair, and
listens to death metal. How likely is it that he is a Christian and how likely is it that he is
a Satanist?
• If people were asked this question, they would likely underestimate the probability of
him being a Christian, and overestimate the probability of him being a Satanist. This
is because they would ignore that the base rate of being a Christian (there are about
2 billion in the world) is vastly higher than that of being a Satanist (estimated to be in
the thousands). Therefore, even if such clothing choices indicated an order of
magnitude jump in probability of being a Satanist, the probability of being a Christian
is still much larger.
• We ignore the numbers and make decisions and judgments based on our expectations
and stereotypes
• Bayes' theorem describes the probability of an event, based on conditions that might
be related to the event.

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Insensitivity to Sample Size
• Despite what is learned in basic statistics courses, sample size is
rarely a part of human intuition
• Basic statistics tells us that when using a small sample size, the
probability of having an outlier is greater than when utilizing a large
sample size
• Examples:
• “Four out of five _____ recommend….”
• Making judgments based on one or a few experiences with a
vendor or client
• Online shopping and product ratings

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Regression to the Mean
• In statistics, regression toward (or to) the mean 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.
• In no sense does the future event "compensate for" or "even out"
the previous event, though this is assumed in the gambler's fallacy.
• To avoid making incorrect inferences, regression toward the mean
must be considered when designing scientific experiments and
interpreting data.
• We mistake “rare” events for “normal” ones and overlook the
possibility that chance can cause extreme outcomes
• In reality, results tend to regress to the mean.

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Anchoring and Adjustment Heuristic
• How would you answer these two questions:
• Is the population of Turkey greater than 35 million?
• What’s your best estimate of Turkey’s population?
• Anchoring: When considering a decision, the mind gives
disproportionate weight to the first information it receives.
• Initial impressions, estimates, or data anchor subsequent thoughts
and judgments (even they are arbitrary!)
• In business, one of the most common types of anchor is a past event
or trend or last year’s data.
• Other common application of anchoring: bargaining, negotiating.
• Ways to avoid:
• Start with a range, instead of a single number
• Repeat process with multiple anchors

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Some ways to attenuate the effects of the biases
1. Forewarned is forearmed
• Awareness is half the battle won.
2. Tapping into unbiased, outside experts - who can look with fresh
perspective
3. Asking the right questions.
• Have someone pay Devil’s Advocate
• Encourage counterfactual thinking (what if)
4. Perform after action reviews or postmortem meetings and assess
Definitive Guide: “Judgment in Managerial Decision Making” by Max H.
Bazerman and Don A. Moore