Multiple quiz questions, that need to be answered in 20 minutes.
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 incorrect 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 errors 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 b/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/stubborn) 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 current 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 worry 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 number • 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/bonus/job for making creative decisions that are not profitable even if they were a good idea! • Need to ensure decisionmakers are willing to take correct 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 terrible 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 brother 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 brother 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 (brief) 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 arrived 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 teller • 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 rule • Technically boxes 2, 3, 4 can all falsify the rule; but that was not an answer • 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 brain: 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 Razor • William of Ockham (1287-1347) employed 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 better • Always employ it, usually multiple times • Avoid becoming committed to the first-cut answer Order of Magnitude Cuts • Use Order of Magnitude to determine