Heuristics

Question 1

what are some of the problems we face when making a judgement or choice

Answer 1

too much or too little information, too many options, attention is limited (you’ve got other shit on), memory is limited (some things you may just forget about), in other words, max U is not possible

Question 2

what are heuristics

Answer 2

intuition and rules (fancy word for “rule of thumb”) for allocating attention and making difficult choices

Question 3

what are the three common heuristics that Tversky and Khaneman (1974) identify

Answer 3

Representativeness Availability Anchoring

Question 4

what is representativeness (Heuristics)

Answer 4

subj. probability of event determined by degree to which… i) … is similar in essential characteristics to its parent population and… ii) … reflects the salient features of the process by which it is generated, similar to law of small numbers where you make assumptions based on limited information, too much attention given to congruence with stereotypes subj. probability mainly determined by resemblance. Too much attention given to congruence with stereotypes when relying on representativeness to make judgements, people are likely to judge wrongly because the fact that something is more representative does not actually make it more likely

Question 5

what is availability (Heuristics)

Answer 5

subjective probability of an event is assessed by the ease with which instances or occurrences can be brought to mind

Question 6

what is an example of availability (Heuristics)

Answer 6

two planes crashed last month, your friend now prefers to take the train, -> objective probability of plane crash hasn’t changed, however, event seems very realistic after crash through video, pictures -> can see yourself in situ, we overestimate probabilities of events which are easier to imagine or remember

Question 7

what is anchoring (Heuristics)

Answer 7

tendency to rely too heavily on the first piece of information offered when making decisions

Question 8

what was the paper giving an example of anchoring (Heuristics)

Answer 8

Tversky and Kahneman (1974), rigged wheel-of-fortune, subjects turn wheel which randomly stops at either 10/65 out of 1-100, then asked percentage of African countries in UN (≈30) -> median estimates of 25/45, used wheel as anchor, we do adjust from that point for the actual answer, problem: adjustment often insufficient

Question 9

what is salience (Heuristics)

Answer 9

some dimensions of a choice problem may receive more/less attention than others

Question 10

what is memory (Heuristics)

Answer 10

information may be forgotten/receive no attention if not sufficiently available

Question 11

In DellaVigna (2009) what does a good’s value comprise of

Answer 11

V = v + o, v: visible component o: opaque component, o can be hidden cost (<0) or hidden feature (>0)

Question 12

what is the difference between the maximisation of a good’s value (V = v + o) for a rational or behavioural consumer (from DellaVigna (2009))

Answer 12

rational consumer maximises actual value V, behavioural consumer maximises perceived value V ̂instead (vhat)

Question 13

what is memory and salience in relation to the maximisation of a good’s value (V = v + o) for a rational or behavioural consumer (from DellaVigna (2009))

Answer 13

Memory: consumer may not remember opaque component o, Salience: o may not get full attention, capture both scenarios with “degree of inattention” θ(s,N): 0>θ>1 with θ=0 as standard case, s: salience of o N: (weighted) number of competing stimuli (message on phone for example, weighted as say partner splitting up will have more weight)

Question 14

what is the equation for the perceived value in the full model for the maximisation of a good’s value from DellaVigna (2009)

Answer 14

V^ = v + (1-θ)o θ: “degree of inattention” - θ(s,N). ∂V^/∂s>0 since ∂V^/∂θ<0 and ∂θ/∂s<0. ∂V^/∂N<0 since ∂V^/∂θ<0 and ∂θ/∂N>0

Question 15

how do you express consumer demand in relation to perceived value V^

Answer 15

consumer demand D[V^], with dD[V^]/dV^>0, hence demand weakly increases (decreases) in hidden quality (cost), Demand weakly D[V^] increases in s Demand weakly D[V^] decreases in θ and N

Question 16

In US retail, where taxes don’t have to be featured on the price tag, how would you put them in to the perceived value equation (V^ = v + (1-θ)o v: visible component o: opaque component, o can be hidden cost (<0) or hidden feature (>0))

Answer 16

Taxes tp therefore like a hidden cost o with ∂V^/∂(tp)<0 Perceived value -> V^=v-(1-θ)tp

Question 17

which paper analysed the effect of taxes in US

Answer 17

Chetty et al. (2009) - inattention to taxes

Question 18

what is the setting of field experiment on effect of taxes (Heuristics)

Answer 18

Chetty et al. (2009) use scanner data from grocery store, Treatment: put price tags explicitly stating also after-tax price, for a few products over 3 weeks. Identification strategy compare sales to… -Avg. pre-treatment sales for same item -sales for other items not -showing after-tax price, sales in control stores

Question 19

results of experiment on effect of taxes (Heuristics)

Answer 19

Chetty et al. (2009)

Treated categories: reduced by 1.3, (mean quantity sold)

Diff in diff: 2.14 (control was increase by 0.84) reduction in items sold (significant)

Triple difference: -2.20

(DDts=-2.14, DDcs=0.06, DDD=-2.20)

(DDcs is diff in diff between control and treatment products in the control store where there was no treatment to any products)

Question 20

motivation behind paper on the inattention to right-digits

Answer 20

Lacetera et al. (2012) general bias: more attention given to left-digits -> £x.99 pricing

Question 21

setting of paper on the inattention to right-digits

Answer 21

Lacetera et al. (2012), look at used-car sales, where mileometer very important determinant of value, perceived value of used car: V^=K-αm^, with m^=floor(m,10000) + (1-θ)mod(m,10000), eg for m=69999 -> m^ = 60000+(1-θ)9999, this predicts jumps in V^ at 10k discontinuities of -αθ10000 while slope is -α(1-θ), buyer pays full attention to complete 10000 but don’t sufficiently factor in between

Question 22

what is the data on paper on inattention to right-digits

Answer 22

Lacetera et al. (2012), 27m wholesale used car auctions, Buyer: used car dealer Seller: car dealer Continuous mileage displayed prominently on auction floor Results: data resembles predicted jumps very well, Sizeable magnitudes: $200

Question 23

how does menu effects fit into heuristics

Answer 23

scenario until now: pick one option from finite set of options, what if set too large? -> evaluate on single criterion, if we have to decide on shares/percentages -> allocation rule, leads to other, more specific heuristics, than ones encountered before

Question 24

what is choice avoidance (Heuristics)

Answer 24

Heuristic: Refusal to choose with choice overload

Question 25

talk about the preference for salience heuristic

Answer 25

heuristic: preference for items that are salient, what happens with large set of options if decision-maker uninformed? possible use of irrelevant, but salient, information to choose

Question 26

what is setting for paper of preference for salience

Answer 26

Ho and Imai (2008), order of candidates on a ballot -Exploit randomisation of ballot order in California -Years 1978-2002, Assumption: for uninformed voters, being first in large list makes candidate salient, Look at “gain” from being listed first across candidates

Question 27

what is confusion (Heuristics)

Answer 27

simply an error in the implementation of the preferences, different from most behavioural phenomena which are directional biases