Cognitive Biases

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Roman Sheremeta, Ph.D.

Professor, Weatherhead School of Management

Case Western Reserve University

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Homo economicus�

• How would you characterize a homo economicus individual?
• Perfectly rational
• Maximizes expected utility
• Cares only about monetary incentives
• Selfish (self-regarding preferences)

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The “standard” model�

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Other-regarding preferences�

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Prospect theory�

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Hyperbolic discounting�

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Level-k thinking�

• Level-k thinking: Decision-making process which is based on the assumption that players have different degrees of rationality
• It is a non-equilibrium concept that describes how people actually behave

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• Solving games using level-k thinking:
• Level 0: Non-strategic (random, reference point, etc.)
• Level 1: Best respond to level 0
• Level 2: Best respond to level 1
• Level 3: Best respond to level 2

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Deviations from standard economics�

• What kinds of deviations and behavioral anomalies (cognitive biases) are important for economists to study?
• Those that are systematic, i.e., they don’t just “balance out” across different types of people
• Those that are persistent, i.e., they don’t disappear with a little training or market experience
• Those that affect market outcomes, and thus, help explain otherwise puzzling empirical phenomena

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Cognitive Biases

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Biases covered in the class�

• A cognitive bias is a systematic (non-random) error in thinking

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• Examples of biases covered in the class:
• Inequity aversion
• Loss aversion
• Reference dependence
• Endowment effect
• Present bias

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Other biases�

• Other cognitive biases:
• Ambiguity (uncertainty) aversion
• Anchoring
• Availability heuristic
• Confirmation bias
• Overconfidence
• Sunk-cost fallacy
• Status quo bias

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Ambiguity aversion�

• Ambiguity aversion: tendency to favor the known over the unknown, including known risks over unknown risks

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• Example (Ellsberg 1961):
• Bag 1 has 50 red and 50 blue balls
• Bag 2 has 100 red and blue balls but the proportion is unknown
• You need to draw a ball from one of two bags and if the ball is red, you get $100
• Most people favor drawing from Bag 1

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• Implications:
• Ambiguity aversion leads people to avoid participating in the stock market, which has unknown risks (Easley and O’Hara 2009)
• Ambiguity aversion leads people to avoid certain medical treatments when the risks are less known (Berger et al. 2013)

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Anchoring�

• Anchoring bias: tendency to rely on the first piece of information as a reference point (“anchor”)
• Discovered by Tversky and Kahneman (1973)

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• Example:
• Participants were asked (1) to write the last three digits of their phone number multiplied by one thousand (e.g., 523 = 523,000), and then (2) to write down an estimate of a house price
• Participants who wrote a higher number had a higher price estimate

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• Implications:
• If a seller initially suggests that the price of a used vehicle is in the range between $20k-$23k, the conversation will likely remain anchored to those numbers

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Availability heuristic�

• Availability heuristic: people make judgments about the likelihood of an event based on how easily an example, instance, or case comes to mind
• Discovered by Tversky and Kahneman (1973)

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• Example:
• We make judgments about the likelihood of events based on information that was recently in the news, ignoring other relevant facts
• After seeing a movie about a nuclear disaster, you are more likely to believe that a nuclear war or accident can happen sometime soon

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• Implications:
• Physicians’ recent experience of a condition increases the likelihood of subsequently diagnosing the condition (Poses and Anthony 1991)

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Confirmation bias�

• Confirmation bias: the tendency to select information that confirms your beliefs
• Related to anchoring and availability heuristics

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• Examples:
• We like to think we're always right, and will go to great lengths to seek out information that supports our preconceptions
• A consumer who likes a particular brand may be motivated to seek out customer reviews that favor that brand

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• Implications:
• Confirmation bias limits our learning (Rabin and Schrag 1999)
• Confirmation bias leads to polarization

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Overconfidence�

• Overconfidence: when people’s subjective confidence in their own ability is greater than their objective performance
• Related to optimism: overestimating the probability of positive events in the future

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• Examples:
• Two-thirds of MBA students rank their abilities relative to other students as above average
• 93% of the U.S. drivers say they are above average

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• Implications:
• Too many entrepreneurs enter a market despite the low chances of success (Moore and Healy 2008)
• Among investors, overconfidence has been associated with excessive risk-taking (Hirshleifer and Luo 2001), concentrated portfolios (Odean 1998) and overtrading (Grinblatt and Keloharju 2009)

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Overconfidence�

• Benefits of having overconfidence/optimism bias:
• Overconfident people feel better about themselves and they perform better at interviews and negotiations
• Research shows that people with an optimism bias are more healthy and less depressed

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Overconfidence�

• Daniel Kahneman: The Trouble with Confidence
• https://www.youtube.com/watch?v=tyDQFmA1SpU

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Sunk-cost fallacy�

• Sunk-cost fallacy: people continue a behavior as a result of previously invested resources (time, money or effort)
• Related to loss aversion and status quo bias

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• Examples:
• You order too much food and then overeat to “get your money’s worth”
• You have a $20 ticket to a concert and then drive for hours through a blizzard, just because you have made the initial investment
• Rats, mice and humans are all sensitive to sunk costs after they have made the decision to pursue a reward (Sweis et al. 2018)

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• Implications:
• People who are subject to sunk-cost fallacy overbid in penny auctions (Augenblick 2015)

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Status quo bias�

• Status quo bias: people prefer things to stay the same by doing nothing or by sticking with a decision made previously
• Related to sunk-cost and loss aversion

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• Examples:
• People tend to stick to their current health plan
• When significantly better plan comes out (more favorable premiums and deductibles), new employees are much more likely to sign up for this plan than older employees (who stick to the status quo)

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• Implications:
• Likelihood to be an organ donor is nearly twice as high, when organ donation is the status quo alternative compared to when the status quo alternative is no organ donation (Johnson and Goldstein 2003)
• Similar findings are in relation to vaccination (Chapman et al. 2010)

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References�

• Augenblick, N. (2015). The sunk-cost fallacy in penny auctions. The Review of Economic Studies, 83(1), 58-86.
• Berger, L., Bleichrodt, H., & Eeckhoudt, L. (2013). Treatment decisions under ambiguity. Journal of Health Economics, 32, 559-569.
• Chapman, G. B., Li, M., Colby, H., & Yoon, H. (2010). Opting in vs opting out of influenza vaccination. JAMA, 304(1), 43-44.
• Easley, D., & O’Hara, M. (2009). Ambiguity and nonparticipation: the role of regulation. The Review of Financial Studies, 22(5), 1817-1843.
• Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75(4), 643-669.
• Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. Journal of Finance, 64(2), 549-578.
• Hirshleifer, D., & Luo, G. Y. (2001). On the survival of overconfident traders in a competitive securities market. Journal of Financial Markets, 4(1), 73-84.
• Johnson, E.J., Goldstein, D. (2003). Do Defaults Save Lives? Science, 302, 1338-1339.

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References�

• Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502-517.
• Odean, T. (1998). Volume, volatility, price, and profit when all traders are above average. Journal of Finance, 53(6), 1887-1934.
• Poses, R. M., & Anthony, M. (1991). Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia. Medical Decision Making, 11(3), 159-168.
• Rabin, M., & Schrag, J. L. (1999). First impressions matter: A model of confirmatory bias. Quarterly Journal of Economics, 114(1), 37-82.
• Sweis, B. M., Abram, S. V., Schmidt, B. J., Seeland, K. D., MacDonald, A. W., Thomas, M. J., & Redish, A. D. (2018). Sensitivity to “sunk costs” in mice, rats, and humans. Science, 361(6398), 178-181.
• Tversky, A., & Kahneman, D. (1973). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.

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