MFIN
Transcendental Problem Solving
Summer Elective
Professors:
Michael Rolleigh and Mohamed Mostafa
Hult International Business School
Outline for Fourth Meeting
•Biases
• Text (Chapter 4)
• Outside Readings
•Heuristics (Chapter 5)
Biases
Lecture 4
Part 1
Biases
•Humans are ‘bad’ at some types of thinking
• Thousands (10’s of Thousands?) of studies show this
• Psychology
• Behavioral Economics (Finance)
•Reason and Logical Thinking require effort
•Biases are the tendencies to reach inco
ect answers
•Work to overcome these in life and work
Behavioral Economics
• Daniel Kahneman and Amos Tversky created the field
• Both were psychologists (mathematical/quantitative psychologists)
• Study how people reach decisions under uncertainty (problem solving)
• Kahneman awarded 2002 Nobel Prize in Economics for work
• Thinking Fast and Slow is classic text by Kahneman (summarizes most results)
• Directly challenged the rational model of economics and finance
• Highlighted repeated and systematic e
ors in human judgment
• Some results have not always been replicated
Origin Story
• Tversky came to give a talk at Kahneman’s School
• Tversky claimed people understood statistics
• Kahneman disagreed
• They argued and argued and began a collaboration that lasted until
Tversky died
• To test understanding of statistics, they surveyed participants at the
Mathematical Psychologists annual meeting
• Results were BAD; participants confused about p-values and sample size!
• Published a paper ‘Belief in the Law of Small Numbers’ (joke
c not Large)
Bias
Example:
Which Line is
Longer?
Bias
Example:
Which Line is
Longer?
It was a trick question; all lines are the same length!
Combat Bias by Highlighting Reality
Looking back at Behavioral Economics
• Many results have been replicated 100’s to 1000’s of times
• We cover many of these in the following slides
• Some results were not replicated
• These have faded from the list of biases (mostly)
• I hope these continue to fade
• Going back, these were the result of Kahneman and Tversky relying on studies
with small sample sizes; They were LITERALLY falling victim to the topic of
their first paper!
Common Biases
• We now go through some of the most common and pernicious
(bad/stu
orn) biases
• First list and explain
• Then go through strategies to mitigate the effects of bias
Confirmation Bias
• Tendency for people to ignore or discount information that does not agree
with their cu
ent worldview
• Politics: So many examples it hurts my head
• Established businesses think they know the way best or only solutions
• Charles gave a talk to a US Newspaper Association meeting in 1997
• He told them they should wo
y about internet competition for classified ads
• No one believed him (Ebay, Craigslist, FB Marketplace, etc proved him right)
• Everyone exhibits confirmation bias; we do NOT want to be wrong!
• Be prepared to be wrong; I’m often wrong! It’s OK to be wrong.
• It’s fine to be wrong. It is not fine to ignore being wrong.
• Realizing you are often wrong puts you on the path towards being right!
Confirmation
Bias
Anchoring Bias
• Tendency for people to place undue weight on early numbers or data
• The early data ‘anchors’ your thought process too close to the first numbers
• Standard tactic in negotiation
• Buyer offers a very low numbe
• Seller offers a very high price (sticker price in stores)
• Closely related to Confirmation Bias, as you try to Confirm the first
ideas you formed while Anchored to them
Anchoring
Bias as Meme
Loss Aversion Bias
• Tendency for people to fear losing X more than they value gaining X
• People are risk averse; Organizations should be less risk averse
(Charles talk Monday)
• Decisionmakers often lose status
onus/job for making creative
decisions that are not profitable even if they were a good idea!
• Need to ensure decisionmakers are willing to take co
ect decisions
• To fix, use standard tools to make the case: Expected Value, Net
Present Value, Marginal Analysis
Sunk Cost Bias (Related to Loss Aversion)
• Resources spent and not recoverable (sunk costs) should NOT be
considered in future decisions
• Ignore Sunk Costs (the bias is NOT ignoring them)
• Humans are te
ible at this
• Marginal Analysis explicitly ignores sunk costs
• Classic Examples:
• “We cannot let their sacrifice be in vain” to justify keeping US ground forces in
a country (this is not good logic, it is an appeal to Sunk Cost Bias)
• “Don’t throw good money after bad” is good advice for avoiding bias
• Fixed Costs (FC) are Sunk Costs in the short run (you are stuck paying them)
• Operate a firm if Total Revenue > Variable Costs in short run (ignore FC)
Sunk Cost Bias Example: Teamwork I
• Assume you operate a shop
• Costs:
• Rent = $3,000/month (2 year contract)
• Utilities = $500/month (no contract)
• Labor = $1000/month
• COGS = $8000/month
• Revenues:
• Sales = $10,000/month
• What should you advise your
other to do next month?
Sunk Cost Bias Example: Answers
• Monthly basis:
• Total Costs = $12,500
• Total Revenues = $10,000
• So you lose $2,500 per month
• So shut down? NO! You should ignore sunk costs.
• If you shut down, you lose rent = $3000/month
• If you operate at a loss, you lose $2500/month
• Losing $2500 is better than losing $3000!
• You should also tell your
other that this is a short-term answer until he
can get out of the rent contract
Availability Bias
• Tendency for people to overweight recent or familiar events rather
than honestly weigh the probabilities
• Can also mean treating all problems the same way because you have
dealt with them before: “If all you have is a hammer, then everything
looks like a nail.”
• To combat:
• Remember: an anecdote is not strong evidence!
• Form teams that have diverse starting points of experience
• Explicitly consider the option ‘wait and collect more information’ as a decision
MFIN
Transcendental Problem Solving
Summer Elective
Professors:
Michael Rolleigh and Mohamed Mostafa
Hult International Business School
Outline for Fifth Meeting: Big Guns
• Finish Biases and Heuristics
• Introduction to the Big Guns
•Regression
• Simulation (
ief)
Biases Continued
Lecture 5
Part 1
Overoptimism Bias
• Tendency for people to be overly optimistic rather than realistic in
assessing potential outcomes
• Almost a required trait in successful entrepreneurs
• Mitigate by:
• Explicitly modeling worst case scenarios (Pre-mortems analysis)
• Put a pessimist on the team
Important
Biases and
Coping
Strategies
(Text)
Teams Can Mitigate Bias
• Diverse teams (background, experience, beliefs)
• Diverse teams outperform others (Tetlock, “Superforecasting”)
• Obligation to Dissent: Everyone on team MUST feel comfortable
expressing disagreement. Otherwise you limit gains from teams.
• Perspective Taking (Devil’s Advocate): Practice supporting the
positions of other team members even if you disagree. Expands your
mental capacity/flexibility. Related to role-playing.
Factfulness by Hans Rosling
• Introduction and Chapter 1 posted
• Anyone summarize?
• Discuss ideas from Factfulness?
Factfulness by Hans Rosling
• Introduction and Chapter 1 posted
• Rosling demonstrates that almost everyone, including experts, are
vulnerable to cognitive biases
• People answer his questions systematically wrong (worse than random)
• People are very, VERY wrong about the state of the world
• He packages these mistakes into 10 biases to avoid
• The Gap Instinct is the first from chapter 1
• Avoid assuming that groups (customers, countries, etc) are separated by large gaps;
often there is overlap between the groups
• In the modern world, most countries have both rich and poor people in them with
the majority of the populations overlapping in daily routines
• I VERY strongly recommend Factfulness to anyone and everyone
Outcome Bias (1/2)
• Tendency for people to judge a decision based on the outcome rather
than the process that a
ived at the decision
• In an uncertain world, all we can control is the process, not the
outcomes!
• Rosling calls this the Blame Instinct
• Examples:
• If I drive home drunk and make it OK, was it a good decision (NO)
• If I buy a lottery ticket and win, was it a good decision (NO)
• If I roll a die for all decisions, is that good (NO)
Outcome Bias (2/2)
• Create good decision-making
processes and you will make
good decisions more often
• Creating a good team is central
to this process!
Linda Problem
• No real name for this bias yet, but think about Linda
• Linda is committed to social justice, majored in gender studies,
participates in BLM protests.
• Is Linda more likely to be:
• A bank telle
• A bank teller and active in the feminist movement
Linda Problem: Answers
• Linda is more likely to be a bank teller than a bank teller + something
else
• It is straightforward logic to say the weaker condition is satisfied more
often
Bank Tellers
Feminists
Bias from Decision
Science: Teamwork 1
Bias from Decision
Science
• Only Box 3 can clearly falsify the
ule
• Technically boxes 2, 3, 4 can all
falsify the rule; but that was not
an answe
• I would accept boxes 2, 3, and 4
• Why not box 1?
• Showing something is true in
one case does not make the rule
true!
Avoiding Biases: Rolleigh
• Be aware of the existence of the biases
• Look for examples of the biases in your life (you, family, friends, peers)
• Try to act against the biases in low-cost situations
• Retrains your
ain: Cognitive Behavioral Therapy
• Read books on the biases: Factfulness, The Righteous Mind, Thinking Fast
and Slow, Nudge, How to Not Be Wrong,
• Listen better; You are not so special, not so right, not so perfect all the
time. It is likely that you have something valuable to learn from others.
• “The more we become aware of biases, the closer we get to reality”
— Peter Baumann (German composer who studies biases)
Can We ‘Fix’ Biases with Training?
• Kahneman says no
• Younger workers in the field say yes!
• Richard Nisbett created a free Coursera class
• Mindware: Critical Thinking for the Information Age
• I have not taken it, but people who do score better on bias tests
• Nisbett writes good papers on cognitive bias
Heuristics
Lecture 5
Part 2
Heuristics
• Heuristics are thinking shortcuts or tricks to get quick answers
• They can help fight biases
• They are tools in your toolbox
• Text is very good about covering heuristics and provides guidelines for
when to use and what to be careful about when using
Text
Heuristics
and
Guides
Occam’s Razo
• William of Ockham XXXXXXXXXXemployed the philosophical
technique so well that later philosophers named it after him
• Problem Solving principle that “Things should not be multiplied
unnecessarily”
• If two hypothesis explain the same thing, the simpler one is probably
ette
• Always employ it, usually multiple times
• Avoid becoming committed to the first-cut answe
Order of Magnitude Cuts
• Use Order of Magnitude to determine