bias and decision making

Question 1

define rationality

Answer 1

when decision making appears to be rational

Question 2

norms & rationality

Answer 2

• norms are rules of action or thought which define optimality
• rationality is a set of norms
  –> be consistent (‘coherence’)
  –> correspond to reality (‘correspondence’)
• but nobody can agree on the complete set of norms for reasoning

Question 3

the norms of rationality

Answer 3

• coherence
  –> be consistent
• correspondence
  –> correspond to reality

Question 4

two types of bias

Answer 4

1. availability bias
2. framing bias

Question 5

availability bias

Answer 5

• over estimating the frequency of rare of memorable events
  –> e.g. overestimating the frequency of plane crashes

Question 6

framing bias

Answer 6

• switching decision based on the question framing
  –> e.g. would you buy a new ticket if you dropped it on the way to the cinema?
  vs
  –> if you dropped £10 on the way to the cinema would you buy another ticket?

Question 7

violation of norm and conjunction fallacy

Answer 7

• conjunction fallacy = have made an error in reasoning by assuming that specific conditions are more probable than a single, more general condition
  –> e.g. Linda problem

Question 8

the Linda problem and what it shows

Answer 8

• Linda is 31 years old, single, outspoken, and very bright
• She studied philosophy at University
• As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-war demonstrations
• Which is more likely?
  1. Linda is a bank teller (a person working in a bank)
  2. Linda is a bank teller and is active in the feminist movement
• most people pick 2 despite norms (especially probability)
• option 2 has to be less likely than option 1 because it is a subset of option 1
  –> option 2 require two things to be true, option 1 only requires one

Question 9

how should we make rational decisions? (decision calculus)

Answer 9

• be logical
• use probabilities
• systematic consideration of all options

Question 10

value and utility (probability and decision making)

Answer 10

• difficulty in making decisions:
  1. future is uncertain
  2. need to assess potential risks and benefits
  3. choose a course of action to increase the chance of a positive outcome
  4. need to use knowledge to estimate probabilities of future events

Question 11

probability based on value

Answer 11

• expected value = value an investment will have in the future
• good bet = expected value is greater than amount invested
• bad bet = expected value is less than amount invested
• rational choice = invest to maximise expected value
• value is not always the most important factor in our decision making

Question 12

what is risk aversion?

Answer 12

tendency for people to accept a sure outcome over a riskier outcome

Question 13

expected utility theory

Answer 13

• calculating the option with the highest expected utility is a decision-making method
• rational decision making may assume that we choose the option that maximises our utility

Question 14

premise of the expected utility theory

Answer 14

• expected utility theory is a theory of decision under risk
• each option leads to one set of outcomes
• where the probability is known

Question 15

value is not utility

Answer 15

• utility is compressed with respect to value
  –> you don’t enjoy 10 pizzas 10x more than 1 pizza (although 10 pizzas costs 10x more)
• value correlates with cost more than utility
• utility is how much you enjoy / prefer it = the degree to which it contributes to well being and satisfies your desires
• utility can be measured as ‘willingness to pay’
• according to expected utility theory people should behave to maximise expected utility
  –> even if value is lower

Question 16

marginal utility

Answer 16

• diminishing marginal utility of wealth:
  –> basic idea = as the amount of money one has increases, each addition to one’s fortune becomes less important, from a personal, subjective point of view
  –> extra £1000 means very little to Bill Gates but an extra £1000 to a uni student means quite a lot

Question 17

uncertainty affects expectation

Answer 17

• pizza example:
  –> If you would pay £10 for a pizza, how much would you pay for…
  –> a 10% chance of winning a pizza?
  –> a 1/100 chance that the pizza made you sick?
• we assign our expected utility to these
  –> we go with the one with the greater expected utility
  –> pay more for 10% chance than the 1/100
  –> it costs more but has greater utility

Question 18

equation for calculating expected utility

Answer 18

• E = P x U
  –> E = expected utility
  –> P = probability
  –> U = utility

Question 19

example of expected utility

Answer 19

• expected utility of a bet with a 50% chance of a £40 pay off?
• answer = £20
  –> ( E = 0.5 x 40)

Question 20

calculating expected utility with multiple options

Answer 20

• E = (P1 x U1) + (P2 x U2)
• EXAMPLE:
  –> lottery offered for £15
  –> 20% chance of winning £50
  –> 10% chance of winning £100
  –> do you buy?
  –> (expected utility has to exceed the cost)
• ANSWER = YES
  –> P1 x U1 = 10
  –> P2 x U2 = 10
  –> answer = 20 and 20 exceeds 15

Question 21

the effect of uncertainty

Answer 21

• everything is uncertain question is just how much
• can have scenarios where chances of high and low utility is equal
  –> two separate and equal chunks near high and low
• can have scenarios where things are probably good but there is a chance they can go bad
  –> larger chunk of graph near high
• can have scenarios where things are likely to be okay but could go VERY good or VERY bad
  –> one chunk spread from high to low, peak is mid

Question 22

Bias: loss aversion and prospect theory

Answer 22

• often when there is guaranteed loss, people which choose the option where they avoid this over expected utility
  –> loss aversion
  –> people pick options where loss may or may not happen, over option where loss is guaranteed, regardless of expected utility
  –> having £100 and losing £50 is worse than starting with £0 and gaining £50
• we have an innate motive to avoid loss rather than achieving similar gain
• we feel pain of loss more than pleasure of gain
• thus we have a bias in decision making

Question 23

Do we use norms, probabilities and EU in real world decision making?

Answer 23

• Do we always have the knowledge and time to use such methods of decision making in an uncertain world?
  –> links to heuristics

Question 24

rationality is bounded

Answer 24

• world is complex
• decisions need to be made quickly
• our time is limited
• our cognitive capacities are limited

Question 25

ecological rationality

Answer 25

• evolutionary perspective as to why we use heuristics
• rationality is defined by how you should behave in the environment to survive rather than simply by the norms
• correspondence (with the environment - how cognition works in the world) is more important than coherence (logical processing)
  –> behaviours from the past may fail when the environment changes rapidly

Question 26

adaptive value

Answer 26

• the value of an action across evolutionary time
• evolution maximises long term expected value
• errors are permissible if average benefit
• avoiding costly mistakes
• avoid high costs (including decision costs)
  –> example = we still look when crossing the road even if road is clear and quiet

Question 27

what do heuristics make assumptions about?

Answer 27

• the environment
• what things go together (association)
• probabilities (risk)

Question 28

what are heuristics? function?

Answer 28

• general rule of thumb
  –> based on instinct, emotions, environment etc…
• give us strategies in an uncertain world
• they make assumptions about the world
  –> i.e. what keeps us safe
• allows us to make decisions when we are unable to use systematic strategies
• they do not attempt to find the optimal solution but a good enough solution
• but they can have biases

Question 29

recognition heuristic?

Answer 29

• this works when you have some (but not no) knowledge
• works when the likelihood of hearing an option correlates with its value/importance/size
• it doesn’t work when you know too much or too little
• when incidence isn’t related to value it can lead to systematic errors
• successful heuristics need to be ‘fast and frugal’
  –> frugal as they ignore part of the information

Question 30

are all biases flaws?

Answer 30

• no

Question 30

biases / errors defined by norms

Answer 30

• expected utility theory
• laws of logic
• laws of probability (e.g. conjunction problem)
  –> deviation from the above norms/laws can be seen as error
• OR they can be seen as evolutionary adaptiveness (ecological rationality)
  –> strategies have evolved and are adaptive so over evolutionary time they become more successful than not
  –> so there are systematic errors in decision making but the errors are more beneficial than not
  –> hence biases shouldn’t be seen as irrational or as flaws

Question 31

Wason’s 2-4-6 task

Answer 31

• task: find the generating rule I am using to create 3 number sequences. You can propose test examples and be told if they fit or break the rule
• starting example: 2-4-6
• what example will you try next?

Question 32

Wason’s 2-4-6 task example

Answer 32

• true generating rule: - successive even numbers
• first test: 2 - 4 - 6
  –> answer: “fits the rule”
• second test: 4 - 6 - 8
  –> answer: “fits the rule”
• third test: 4 - 6 - 9.
  –> answer: “doesn’t fit!”
• guess: “It’s successive even numbers”

Question 33

Wason’s 2-4-6 task general results

Answer 33

• result: most people, most of the time, think of a highly specific rule (e.g. ascending even numbers) and generate an example which fits (e.g. 10-12-14)
  –> this a confirmation bias
  –> a ‘positive test strategy’

Question 34

types of test strategies

Answer 34

• positive tests:
  –> seek to verify the hypothesis (but can find it false)
  –> e.g. saying ‘6-8-10’ if you think the rule is ascending even numbers
• negative tests:
  –> seek to falsify hypothesis (but can find it true)
  –> .g. saying ‘3-5-7’ if you think the rule is ascending even numbers

Question 35

positive vs negative strategies, which is best?

Answer 35

• depends on what you believe and how it relates to the truth
• general rules:
  –> positive strategies will always confirm your prior belief, belief never improves
  –> not the best
  –> if you think it is specific, but it’s general, you keep saying specific things and won’t change your belief
• specific rules:
  –> positive strategy rapidly converges on the truth
  –> if you think it’s specific and it is specific, you will prove it

Question 36

Wason task and error in understanding

Answer 36

• 2-4-6 task was the first experiment that showed people to be illogical and irrational

Question 37

everyday example of different test strategies

Answer 37

• knock at door
• hypothesis: someone is at the door
• positive: open the door
• negative: check every other source of knocking
• positive is the best in this case
• fit between hypothesis and reality determines which is best

Question 38

alternative explanations for errors

Answer 38

• can errors in decision making tasks be accounted for in a way that does not suggest thinking is not logical?
• communication interpretation:
  –> participants assume that all of the information is provided for a reason so they use that information to make their decision
  –> like in the Linda task
  –> participants may not be seeing the case as a problem solving situation
  –> thus they want to use all of the information
  –> if it is there, is it really an error?

Question 39

biases are not mere errors

Answer 39

• mistakes are one-off
• systematic errors are consistent
• biases are making the wrong choice but for a reason
• just like visual illusions reveal the normal mechanisms of perception heuristic errors reveal the normal mechanisms of reasoning

Question 40

reasons we might mistake systematic errors

Answer 40

• using a strategy optimised for a different environment
• considering a (different) bundle of choices
• using a different cost/benefit analysis

Question 41

heuristic vs rational thinking

Answer 41

• heuristic / system 1:
  –> quick
  –> automatic
  –> effortless
  –> unconscious
• rational / system 2:
  –> slow
  –> deliberate
  –> effortful
  –> conscious

Question 42

rational vs heuristic thinking - dual process

Answer 42

• system1 and system 2 is a dual process theory
• there are many of them
• predictions:
  –> features should go together (so, for example, no conscious heuristic thinking, no unconscious rational thinking)
  –> people should switch between modes of thinking in the right circumstances (more time = more deliberative thinking)

Question 43

dual process theories

Answer 43

• a psychological theory which posits two processes which work at the same time, or support the same capacity:
  –> usually work together
  –> can be in conflict with each other
  –> one usually has system 1-like properties, one system 2-like properties

Question 44

where you will see biases

Answer 44

• decision biases we discussed today are features of normal cognitive processes
  –> e.g. memory, attention, planning
• can affect experts and laypeople alike
• biases are part of thinking, not an optional extra

Question 45

implications for investigating bias

Answer 45

• If you see a bias try and understand how people understand a decision:
  –> their perception of costs & benefits
  –> their experience of similar decisions
  –> their assumptions about the environment
• do not conclude they are irrational

Question 46

key points:

Answer 46

-Biases are defined against norms (standards of rationality)
- Expected Utility Theory (E=p*U) defines one standard, and makes predictions about how uncertainty should affect decisions
- Heuristics are designed to be ecologically rational - to provide quick, good enough choices
- Biases are not flaws in reasoning, they are a way of understanding how reasoning is done
- Dual process theories suggest we have a fast system 1 and a slow system 2