Beliefs

Question 1

what are the three main examples of non-standard beliefs

Answer 1

overconfidence,

law of small numbers,

projection bias

Question 2

what is overconfidence

Answer 2

overestimate one’s own skills (or the precision of estimate) (stock markets think know esact price when in reality impossible) P̃(good statet)>p(good statet)

Question 3

what is the law of small numbers

Answer 3

gambler’s fallacy and overinference in updating P̃(st|st-1)

Question 4

what are the two types of overconfidence

Answer 4

over-optimism: overestimate one’s ability, over-precision: overestimate precision of one’s estimates

Question 5

when is overconfidence greatest

Answer 5

difficult tasks, forecasts with low predictability, undertakings lacking fast, clear feedback

Question 6

motivating facts behind barber and odean (2001)

Answer 6

men more overconfident than women about financial decisions, test hypothesis: men trade more than women, and by trading more they hurt their investment performance more than women, compared investment decisions across men and women

Question 7

difference between overconfidence and risk loving

Answer 7

overconfidence - you think only yourself are going to have a good outcome, risk loving - you would apply this to any other decision as well

Question 8

who tested the link between gender stereotypes and overconfidence

Answer 8

D’Acunto (2015), stereotypes can be (experimentally) manipulated

Question 9

what is priming

Answer 9

evoking associations through stimuli which may change subjects’ behaviour

Question 10

what is the design of the first gender stereotypes experiment

Answer 10

D’Acunto (2015), 1) Elicit subjects’ risk attitudes with lottery choices, 2) randomise assignment to i) control, ii) male prime and iii) female prime, control just read neutral short reading, treated read text priming strong gender identity 3) Elicit subjects’ risk attitudes again

Question 11

results of gender stereotypes first experiment and drawback

Answer 11

D’Acunto (2015), male primer did increase risk tolerance, women became less risk tolerant (insignificantly), preferences or beliefs? (is it changing men’s risk tolerance or beliefs in positive personal outcomes

Question 12

design of second experiment on gender stereotypes and what paper

Answer 12

D’Acunto (2015), same as Exp.1 but different outcomes (all post treatment), guess # of wins in 50/50 lottery for i)yourself ii)a random peer, reward for correct guess

Question 13

results of second experiment for gender stereotypes

Answer 13

D’Acunto (2015), significant effect of priming only on OWN # of wins, only for men, priming changes men’s beliefs of winning game of chance, evidence for beliefs in positive personal outcomes

Question 14

what is a summary of D’Acunto (2015)

Answer 14

experiment 1: gender priming -> ∆RiskTol, experiment 2: gender priming -> beliefs in pos. personal outcomes

Question 15

what is the design of the third experiment for the gender stereotypes experiment

Answer 15

D’Acunto (2015), same as Exp.1 but different treatments/primes, Task: recall instance where one was 1)relaxed (control) OR 2)powerful OR 3)successful, 2)->overconfidence in one’s power (also over random-ness) 3)->overconfidence in one’s ability (doesn’t help w/ random-ness)

Question 16

results of third experiment for gender stereotypes

Answer 16

D’Acunto (2015), overconfidence primer significantly increased risk tolerance, success primer did not cause any significant effects

Question 17

what is the gambler’s fallacy

Answer 17

after a streak of red on the roulette wheel, one “expects” next draw to be black

Question 18

law of small numbers explained

Answer 18

law of large numbers: with very large N, the share of H and T is going to get very close to 0.5, law of small numbers: this doesn’t need to hold for small N individuals expect random draws to be exceedingly representative of the distribution they come from

Question 19

what does law of small numbers mean in terms of the distribution of an event

Answer 19

individuals expect random draws to be exceedingly representative of the distribution they come from

Question 20

what is a conceptual way of understanding law of small numbers from Rabin (2002)

Answer 20

iid signals (H or T) from urn drawn with replacement, many people believe as if drawn from urn size N

Question 21

what are the two cases from Rabin (2002) ways of looking at small numbers

Answer 21

Case 1: probabilities known (≈ Gambler’s fallacy) Case 2: probabilities not known -> overinference, after signals of one type, expect next signal of same type, people exaggerate likelihood that short sequence of iid signals resemble the long-run rate at which those signals are generated

Question 22

what is an example of overinference

Answer 22

firing decisions of football managers

Question 23

what is the setting of case 1 for the experiment on law of small numbers

Answer 23

Rabin (2002), probabilities θ known, returns to mutual fund drawn from urn with 10 balls, 5 Up and 5 Down (with replacement), θ=P(U)=0.5, observe ‘Up, Up’ compute probability of another Up, Bayesian 0.5, law of small numbers 3/8<0.5 (in your head you take out the two that have been taken), expectations driven by how representative outcome would be given underlying distribution

Question 24

what is the setting for case 2 for experiment of law of small numbers

Answer 24

mutual fund with manager of uncertain ability, replacement with 10 balls, Probability 0.5: fund well managed (7 Up 2 Down) Probability 0.5: fund poorly managed (3 Up 7 Down), observe sequence ‘Up, Up, Up’ what is P(Well|UUU) law of small numbers guy overinfers from the distribution they see

Question 25

projection bias d

Answer 25

beliefs systematically biased towards current state

Question 26

what is the other form of projection bias

Answer 26

people under-appreciate adaptation to future circumstances, projection bias about future reference point

Question 27

what is the experiment on the reference point view of projection bias

Answer 27

Gilbert et al. (1998), subjects forecast happiness for an event, compare to responses after event has occurred, assistant professors (lecturers) at US university forecast how getting tenure would improve happiness -> notable difference, compare with assistant professors at same university who already did or did not get tenured -> no notable difference

Question 28

which paper is the simple model of projection bias from

Answer 28

Loewenstein et al. (2003)