Heuristics and Biases 1 Perception and processing constraints

Heuristics and Biases 1

Perception and processing constraints • Expectations influence perceptions. • People see what they want to see. • People experience cognitive dissonance when they simultaneously hold two thoughts which are psychologically inconsistent. 2

Perception and the frame • Perception is not just seeing what’s there – but it is influenced by the frame: – How tall is that sports announcer? – Halo effects: Someone who likes one outstanding attribute of an individual likes everything about the individual – Primacy vs. recency effects 3

Memory tricks • Memory is not a simple matter of information retrieval: – It is reconstructive – It is variable in intensity… • With emotion playing a role – It is prone to self-serving distortion (hindsight bias) 4

Heuristics • Heuristics or rules-of-thumb: decision-making shortcuts. • Necessary because the world, being a complicated place, must be simplified in order to allow decisions to be made. • Heuristics often make sense but falter when used outside of their natural domain. 5

Type 1 & 2 heuristics • Type 1: Autonomic and non-cognitive, conserving on effort. – Used when very quick choice called for – Or when it’s “no big deal” • Type 2: Cognitive & requiring effort. – Used when you have more time to ponder • Type 2 can overrule Type 1. 6

Self-preservation heuristics • Hear a noise with an unknown source? – Move away till you know more • Food tasting off? – Stop eating it • These make good sense. • Other heuristics, which are more cognitive, are related to comfort with the familiar… 7

Example: Diversification heuristic • Observe people at a buffet… – Many people are trying a bit of everything – Nobody wants to miss out on something good • Diversification sometimes comes naturally. 8

Example: Status quo bias or endowment effect • What you currently have seems better than what you do not have. • Experimental subjects valued something that they possessed (after it was given to them) more than they would have if they had to consciously go out and buy the item. 9

Example: Information overload • Experiment involving tasting jams and jellies in a supermarket. • Treatment 1: Small selection. • Treatment 2: Large selection. • Which attracted more interest? – Treatment 2. • Which lead to more buying? – Treatment 1. 10

Hot hand phenomenon • Sometimes people feel that distribution/population should look like sample, but sometimes they feel sample should look like distribution/population. – Former is especially true if people aren’t sure about nature of distribution/population. – As in hot hand phenomenon in sport: • In basketball, it is erroneously thought that you should give ball to hot player 11

Gambler’s fallacy • Gambler’s fallacy may apply if people are fairly sure about nature of population. – They think even small samples should always look like population. • So if you flip coin 9 times getting 6 heads and 3 tails, these people would say that a tail is more likely to come next… • “We are due for heads. ” – Winning lottery numbers are avoided based on mistaken view that they are not likely to come up again for a while. 12

Overestimating predictability • Tendency to underestimate regression to mean – amounts to exaggerating predictability. • GPA example: subjects were asked to predict GPA in college from high school GPA of entrants to the college. – High school average GPAs: 3. 44 (sd = 0. 36); GPA achieved at college was 3. 08 (sd = 0. 40). – One student was chosen: high school GPA of 2. 2. – People underestimated mean regression for this lowachiever. 13

Biases related to representativeness • Recency: – Recent evidence is more compelling. • Salience: – Dramatic evidence is more compelling. • Availability: – Freely available, easily processed information is more compelling. 14

Anchoring • People are initially anchored on their prior belief. • Quickly multiply these eight numbers: 1 * 2 * 3 * 4 * 5 * 6 * 7 * 8 – Most people will come up with a low estimate: anchored on product of first 4 or 5. – A bit better (but still too low) with: 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1 15

Anchoring bias: Example of anchoring to irrelevant info • Wheel with numbers 1 -100 was spun. – Subjects were asked: • 1. Is the number of African nations in the UN more or less than wheel number? • 2. How many African nations are there in the UN? – Answers were highly influenced by wheel: • Median answer was 25 for those seeing 10 from wheel. • Median answer was 45 for those seeing 65 from wheel. – Grasping at straws! 16

Anchoring vs. representativeness • Anchoring says new information is discounted. • Representativeness (base rate neglect variety) says people are too influenced by latest information. • Potential conflict between anchoring and representativeness in how people deal with new evidence. • Which is right? – Perhaps both depending on situation… 17

Anchoring vs. representativeness ii. • It is argued that people are “coarsely calibrated. ” • Suppose morning forecast is for sun. Day starts sunny. You go on a picnic. – Some dark clouds start to move in – You are anchored to prior view and discount clouds – More dark clouds: the same thing 18

Anchoring vs. representativeness iii. – Even more dark clouds. – Now you coarsely transition – thinking that “it’s going to rain for sure!” – What is reality? Never 0% or 100%. New information should alter probabilities but a flipflop doesn’t make sense. • Coarse calibration has been used to explain tendency for prices to trend and eventually reverse. 19

financial errors from heuristics and biases • Expectations influence perceptions: – If most people are saying good/bad things about company, you will “find” good/bad things • It has been argued that cognitive dissonance can: – Explain why people don’t exit poorly-performing mutual funds 20

financial errors from heuristics and biases ii. • Diversification heuristic – Stock-bond menu influences risk taking in DC plans • Ambiguity aversion – Under-diversification • Information overload – Lower participation rates for DC plans with more investment choices 21

financial errors from heuristics and biases iii. • Representativeness (and halo effects) – “Good companies are good stocks” thinking may lead to value advantage • Recency – May explain chasing winners • Anchoring and slow adjustment coupled with representativeness – May explain momentum and price reversal 22

Lottery Stock • Stock that is similar to lottery ticket: low price, low chance of winning, but offers high reward when win. • Some investors prefer to invest in lottery stocks. • Gamblers prefer a lottery stock even its odds is unfavourable. They prefer a lottery stock because of its positively skewed payoff: the payoff can either take a large positive value with a small probability or a small positive value with a large probability. 23

An Example: Chinese Warrants Bubbles • Xiong and Yu (2011) 24

• In 2005– 2008, 18 Chinese companies issued put warrants with long maturities ranging from six months to two years. • These warrants give their holders the right to sell the issuing companies’ stocks at predetermined strike prices during a prespecified exercise period. 25

• For each warrant, billions of yuan was traded with an average daily turnover rate over 300 percent, and at substantially inflated prices. • Several features make these warrants particularly appealing for analyzing bubbles: • First, we can reliably measure the warrants’ fundamental values to be close to zero by using the underlying stock prices; second, the publicly observable stock prices also make the warrant fundamentals observable to all market participants; and third, these warrants have predetermined finite maturities. 26

• Explanations: – The restrictive legal ban on short-selling financial securities (including warrants) in China – investors’ heterogeneous beliefs – Investors see warrants as gambles 27

Self Control • Empirical studies on measuring selfcontrol problems among individuals have found a negative relationship between measured self-control and the accumulation of wealth (Ameriks et al. , 2003, Ameriks et al. , 2007). • Lack of self-control can be linked to overspending. 28

Time Preference • Q 1. Choose between A and B in each of the following: – (1) A：$35 today ---- B：$40 in one month – (2) A：$30 today ---- B：$40 in one month – (3) A： $25 today ---- B：$40 in one month – (4) A： $20 today ---- B：$40 in one month – (5) A： $15 today ---- B：$40 in one month – (6) A： $10 today ---- B：$40 in one month 29

Time Preference • Q 2. Choose between A and B in each of the following: – (1) A：$35 in one month --- B：$40元 in two month – (2) A：$30 in one month --- B：$40元 in two month – (3) A： $25 in one month --- B：$40元 in two month – (4) A： $20 in one month --- B：$40元 in two month – (5) A： $15 in one month --- B：$40元 in two month – (6) A： $10 in one month --- B：$40元 in two month 30

Time Preference • An individual is said to have present bias if he/she is less willing to wait when he/she can receive the money today in Q 1. e. g. , always choose B in Q 2, and always choose A in Q 1. • Answers for the above two Qs can be used as an measure of self-control problem. 31