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...

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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
Answered 1 days AfterMay 28, 2021

Answer To: MFIN Transcendental Problem Solving Summer Elective Professors: Michael Rolleigh and Mohamed Mostafa...

Anurag answered on May 30 2021
67 Votes
Order ID: 85324
Answers
1. 15
2. The simplest answer is usually the best
3. Paying careful atten
tion to sunk costs and not ignoring them
4. Confirmation Bias and Anchoring Bias
5. instil an obligation to dissent in the team
6. a weighted average of outcomes where the weights are...
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