Chapter 13 - Reasoning and Decision Making Flashcards
1- Loss aversion, neuroeconomics and decision making and reasoning
Loss aversion
* People avoid gambles (choices) when they are equally likely to either lose a smaller amount $10 or win a larger amount $15
- Tested patients with bilateral lesions of the amygdala
- Impairments in processing fear despite normal cognition
- Lacked loss aversion on gambling task
Neuroeconomics
* Studying how we make decisions, formalizing theories and linking it to the development of the brain
Decision making and reasoning
* Reasoning guides decision making
- People make 35,000 decisions a day!
* Reasoning is a thought process that brings an individual to a conclusion
* A broad concept that is an umbrella to other cognitive domains
* Understanding if we are logical
- More like … when are not logical
2- Inductive vs. Deductive forms of reasoning
Inductive reasoning (specific to general)
* Making general conclusions from specific observations
- A detective enters a crime scene. They notice glass from a broken window; strewn books; spilled milk. They use these observations to make a conclusion about what happened
* The conclusions can be false
* A “probably but not definitely true”–type of reasoning
* Making general conclusions from specific observations
* Claire bought ice cream from the same Dairy Queen five times
* Claire enjoyed it each time.
* Claire concludes that the next time she is at Dairy Queen, she will have quality ice cream that she will enjoy
* When we are unaware of inductive reasoning, it can become a heuristic
* But is also the basis of much of human learning
- Applying learned rules to new situations
* Language learning
* Learning the meaning of balloon when you see “the purple balloon dog” and already know ‘purple’ and ‘dog’
Specific observations…. generalizations… theories
Deductive reasoning (general to specific)
* Using general theories to reason
about specific observations
* My general belief is that “The Cog
Dog loves Cognition”
- The Cog Dog is a dog
- I assume all dogs love cognition
Theories… predictions… experiments
3- Tasks to study reasoning: Syllogisms
- Deductive reasoning: formal systems for generating statements that will be true if rules of the system are followed
- Syllogisms
- Premises are presumed to be true
- Determine if the premise statements support the conclusion based on the logical structure, not the content!
- Major premise (general). Ex: all dogs are animals.
- Minor premise (specific). Ex: all animals have four legs (valid but not true)
- Conclusion (test). Ex: all dogs have four legs.
Validity of syllogisms
* Validity: Is the conclusion true given the premises’ logical form?
* Valid = logical rules DOES NOT EQUAL Truth = world knowledge / content
* A valid structure (All A are B : All B are C: Therefore, all A are C)
All birds are animals
All animals eat food
Therefore, all birds eat food
(valid and true)
All birds are animals
All animals have four legs
Therefore, all birds have four legs
(valid, but not true)
Types of syllogisms
* All statements
- All A are B
Ex:
All men are mortal
Socrates is a man
Therefore, Socrates is
a mortal
¤ Negative statements
- No A is a B
- Also means no B is A
Ex:
All psychology professors
have PhDs
No PhD holders are human
Therefore, psychology
professors are not human
¤ Some statements
- Some A are B.
- “At least one, possibly all”
Ex:
No provinces with coastlines are
provinces that are landlocked
Some provinces are landlocked
Therefore, some provinces are
not states with coastlines
Problems arise! Atmosphere effect
* People rate a conclusion as valid when the qualifying word (e.g., ‘all,’ ‘some’) in the premise match those in the conclusion
Example of one that is not valid:
P1: Some Greeks are men.
P2: Some Greeks are clever.
C: Some men are clever. (the two premises are separate!! P2 could be only about women for example)
Problems arise! Negative statements
* Mental model theory
- People construct mental simulations of the world based on statements (e.g., syllogisms) to judge logic and validity
* “Reasoning is more a simulation of the world fleshed out with all our
relevant knowledge than a formal manipulation of the logical skeletons
of sentences”
* Can’t imagine negative statements
Omission bias:
Thus, people tend to have more trouble reasoning with negative information
* Biased thought that ”withholding is not as bad as doing”
- Inaction is harder to classify as wrong than action
Ex:
Which is more immoral?
1. A person who accidentally sets fire to a building (action)
2. A person who sees a fire in a building but does not bother to report it (inaction)
* People tend to react more to strongly to harmful actions (1) than to harmful
inactions (2)
Ex: the trolley problem
The belief bias and syllogisms
Which is invalid?
1.
All older adults are tired (All A are B)
Some tired people are irritable (Some C are D)
Therefore, some older adults are irritable (Some A are D)
2.
All students live in Montreal (All A are B)
Some people who live in Montreal are millionaires (Some C are D)
Therefore, some students are millionaires (Some A are D)
BEWARE OF THE “ALL” AND “SOME”.
Both are invalid; but people are more likely to judge (1) as valid as compared to
(2) because it is hard to imagine students as millionaires. Biased by belief.
* People have problems reasoning with syllogisms in which logical validity
conflicts with truth.
Ex:
All mammals walk (All A are B)
Spiders can walk (All C are B)
Spiders are mammals (All C are A)
INVALID, and also not true.
Ex:
All flowers need light (All A are B)
Roses need light (all C are B)
Therefore, Roses are flowers (all C are A)
INVALID, but may seem valid because the conclusion is believable!!!!
* The content of a syllogism can lead to errors due to a belief bias
- The tendency to think a syllogism is valid if the conclusions are believable.
Belief bias summarized:
When a conclusion is believable people are
much less likely to question its logic
When a conclusion is unbelievable, it is
much harder for people to accept, even
when the logic is sound
4- Tasks to study reasoning: Conditional reasoning
- “If P then Q” statements where P is the antecedent and Q is the consequence
- How to test if the conditional statement “If it is raining (P), I will get wet (Q)” is valid?
- What happens if Q is true? If I am wet, is it raining?
- What happens if P is false? If it isn’t raining, am I wet?
- What happens if Q is false? If I am not wet, is it raining?
Wason’s task: Conditional reasoning
* If a card has a vowel on one side, then it has an even number on the other side, which cards should you flip?
* Conditional statement: If ‘vowel’ then ‘even’
E, F, 2, 5
* Many do not test this statement correctly
- Very few turn over card ‘E’ and ‘5’ to test if a vowel = even number and if not even then not vowel
* Confirmation bias: tendency to seek confirmatory evidence for a hypothesis
* The falsification principle: You need to look for situations that would falsify a rule
* General logical rule to solve: “ If P then Q ”
* Choose the P card (is there a not-Q on the back?) and the not-Q card (is there a P on the back?). Eliminate false statements
Familiarity effects
* If a person is drinking a beer (P), then the person is over 21 years old (Q)
* Cards have age on one side and beverage on the other side
* Which card(s) do you need to flip to verify this statement?
Drinking a beer, 22 years old, Drinking a coke, 16 years old
Answer: drinking a beer (P) , 16 years old (not-Q)
5- Heuristics and biases
Heuristics and biases
* Heuristics are generalizations that we apply when reasoning
* When heuristics are over-applied, biases occur
Heuristic: Mental shortcut or rule of thumb that can be used to get a quick and mostly accurate response in some situations but may lead to errors in others
* Biases are systematically inaccurate choices that don’t reflect a current situation
Bias: deviations from rationality (errors) that are caused by using heuristics
* Three categories:
- Heuristics that bias how we interpret information
- Heuristics that bias how we judge frequency
-Heuristics that bias how we make predictions
Interpretation: Representativeness bias
* Probability that an item (person, object,
event) is a member of a category because
it resembles that category
* Related to over-use of schemas, and
other preexisting knowledge structures
* Stereotyping, base-rate neglect and the
conjunction fallacy
Ex:
You assume that all these Chris’ behave the same because they look the same
Other example:
¤ Tom W. is of high intelligence, although lacking in true creativity. He has a need
for order and clarity, and for neat and tidy systems in which every detail finds its
appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and by flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to feel little sympathy for other people and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense.
¤ Is Tom studying psychology or Library Sciences?
People will think Library Sciences. But that doesn’t even actually exist. He’s studying psychology.
¤ Representative Heuristic: tend to make inferences on the basis that small samples resemble the larger population they were drawn from
Base-rate neglect:
¤ Base Rate Neglect: When you fail to use information about the prior probability of an event to judge the likelihood of an event
¤ Imagine running an HIV test on population of 1000 people, in which only 1% are infected. The false positive rate (falsely diagnosing someone) is 5% with no false negatives. If a test comes back positive what is the likelihood someone has HIV?
- It is actually 17%
- 10 people in the population have HIV
- 50 people without HIV would be falsely identified
¤ Important for doctors diagnosing with low incidence populations
- Need to consider the base rate (prior probability of someone actually having HIV) before diagnosing
- Like in the Tom W problem where people don’t consider the prior probability of being a librarian
Other example related to base-rate neglect:
The Adam example and base-rate neglect
* You randomly select one male from the Canadian population and that male, Adam, wears glasses, speaks quietly and reads a lot. It is more likely Adam is a farmer or a librarian? Most people say librarian
* This choice is a result of representative bias and leads base rate neglect: ignore important rate information when reasoning
- There are more farmers than librarians in Canada
Conjunction fallacy
False assumption that a greater number of specific facts are more likely than a single fact
Conjunction fallacy: False belief that the conjunction of two conditions is more likely than either single condition
* Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice,
and also participated in anti-nuclear demonstrations.
* Which is the most likely alternative:
1. Linda is a bank teller
2. Linda is a bank teller and is active in the feminist movement
People often pick 2, but wrong.
Because the description was more representative of both categories people think the conjunction is the most likely label
The availability bias
* The easier it is to remember something, the more likely you’ll think it is to happen in the future (memory-based bias)
Availability: estimate the probability of an event based on the ease at which it can be brought to mind
Ex: Are there more words in the English language that begin with the letter R or with the letter R in the third letter?
run, rather, rock are easier to recall than arrange, park, word
But wrong.
* We confuse the availability of something in our memory with how frequently it occurs
* Because of news and media coverage …
A person thinks the world is much more violent
- The average American has viewed 8000 murders on TV by age 12
¤ Overestimate the probability of a shark attack after watching JAWS
* Many more people have a fear of flying and consider crashes common
- Excessive coverage of plane crashes in the news
¤ Can explain why people are afraid of flying but not driving
- But also affect heuristics
* We can remember challenges we had to overcome better than other people’s challenges
- Our challenges are more available from memory
- We perceive things as harder for us compared to others
- Some examples (discussion meeting reading for Monday):
- Both Democrats and Republicans think the electoral maps works against their party
- Siblings think parents were harder on them than their sister/brother
Illusory correlations
* Linking two co-occurring events and assuming a relationship
* An illusory correlations if outcomes are overemphasized
- A person wins bingo with a troll doll, so they
always plays with that troll doll
- Growing a play-off beard to bring a win
Regression to the Mean
¤ Regression toward the mean: when a process is somewhat random (i.e. weak
correlation), extreme values will be closer to the mean (i.e. less extreme) when measured a
second time
¤ Related to illusionary correlations
- People tend to see causal relationships even when there are none
Example: Regression Toward the Mean
¤ A pageant mom rewards her daughter when she preforms unexpectedly well and
wins a pageant. But the next pageant she comes in last place, the mom punishes
her and the following competition she does well. The mom concludes punishment
works better than rewards.
¤ Related to our understanding of the roles of reward and punishment on learning
- Can’t always attribute changes in performance to manipulations
- Sometimes it’s just noise
Anchoring & adjustment heuristic
* People’s judgments of the magnitude of something is biased (i.e., adjusted) by some initial value they are exposed (i.e., the anchor)
Anchoring and Adjust judgements are too heavily influenced by initial values
* Which of the following produces a larger number?
8 × 7 × 6 × 5 × 4 × 3 × 2 × 1
1 × 2 × 3 × 4 × 5 × 6 × 7 × 8
* Most say the first sequence because it has a higher anchor
*People start off with one value and adjust accordingly from there
-Important when getting ratings from a scale
* Participants given a random number between 0 and 100
- “Is this number higher or lower than the percentage of African nations in the United Nations?”
- Estimate the actual percentage
* Those who were given a HIGH random number gave greater percent estimates than those given a LOW random number
* We even anchor estimates to unrelated information
Ex: Pre-shopping “Puffy Jacket
Budget” $300
Store A: 500$. Too much
Store B: 400$. I’ll buy it!
(Just like my apartment search frl)
Prediction: Gambler’s fallacy
* The false belief that a predicted outcome of an independent event depends on past outcomes
- We assume outcomes are linked when they are random
- A coin flip lands heads three times in a row.
- What are the odds that it will be heads on the next toss
- 50-50, but there is a misperception that a ’tails’ must be coming
* Thinking one is due for a ‘win’ after a run of ‘losses’
Gambler’s fallacy in the real world
* People continue to invest after several losses on the stock market
* U.S. judges in refugee asylum cases are more likely to deny (grant) asylum after granting (denying) asylum to the
previous applicant
* Loan officers are more likely to deny a loan application after approving the previous application
The hot-hand belief
* Thinking that a person who experiences success will keep having success,
- ‘A winning steak’
- Ask basketball fans about player’s shooting abilities
- 91% fans thought that a player is more likely to make a shot after making 2 shots than after missing a shot
* Just because something feels true, doesn’t mean it is true
Predicting risk and optimism
* Rate the likelihood a positive, negative,
neutral events will happen to that person
in the next month
* After a month, provide ratings of
whether the events occurred
* People overestimate the number of
positive predicted events
* This optimism bias was not present in
depression
- Presentation differs with degree of
depression
Heuristics and biases
* Errors in these processes provide insight into underlying mechanisms of reasoning
* Heuristic processing is central for making
intuitive and rapid judgments
- Predictive purpose of cognition
* Over-application can lead to serious errors in our judgments and reasoning
- Stereotyping
- Gambling addictions
Minimize the over-reliance on heuristics
* Post-mortem technique is learning from failures
* Pre-mortem technique is to anticipate and prevent our mistakes before they result in catastrophe
- You are on the verge of making a decision
- Look ahead at challenges that could cause failure
- Create a plan to navigate those challenges
6- Why use heuristics?
Bounded Rationality
¤ Why use Heuristics? People are thought to be Bounded rational (Simon 1957) meaning they are limited by both environmental constraints (e.g. time pressure) and individual constraints (e.g. working memory, attention)
¤ People are Satisficers: look for solutions that are “good enough”
¤ “Making do” with the limitations we have as humans
¤ Although heuristics sometimes provide incorrect answers and lead to biases; they also work
Ecological Rationality
¤ Gigerenzer proposed an alternative view to heuristics called Ecological Rationality which sees heuristics not as a “good enough” approach to solving a problem but as the optimal approach
¤ While previous views on heuristics drew a separation between how we should act and how we do act, Ecological rationality does not distinguish these two
¤ Given the right environment, a heuristic can be better than optimization or other complex
strategies
Example:
¤ Say you have some money you want to invest and a bunch of options to choose form, but limited information about how risky each one is or the past performance…
¤ Equally dividing your assets (money) among the options (1/N heuristic) has been shown to
provide better results than other more complex optimization algorithms
¤ Sometimes heuristics (give the right circumstances) can be better than complex strategies
7- Kinds of decision-making
¤ Perceptual Decision Making: objective (externally defined) criterion for making your choice
- Are the dots moving left or right?
¤ Value-based Decision making: subjective (internally defined) criterion for making your choice
- Do I want cake or ice cream for dessert?
- Depends on motivational state and goal
¤ Risk can be defined as taking an action despite the outcome being uncertain
- Specific to Value-based decision making
¤ Ambiguity can be defined when you have incomplete information out the consequences
8- Decision-making under uncertainty and risk: When an given action has several possible outcomes
Risky decision making
¤ It is adaptive to be able to make decisions when there is risk
¤ Most people are risk averse
¤ Extremes in risk taking (high or low) can be very harmful
- Stagnant living (taking very low risk)
- Addiction and impulsivity (taking very high risk)
¤ Risks can be framed as
- Gains
- Losses
Risk attitude profiles
¤ Risk premium: difference between expected gains of a risky option and a certain option
¤ Risk averse: decision maker has positive risk premium
- Need a chance at winning a lot more than a certain option to select the risky option
¤ Risk neutral: decision maker has zero risk premium
- No difference in the options
¤ Risk seeking: decision maker has negative risk premium
- Doesn’t need the chance at winning more than the certain option to gamble
¤ Risk preferences are not themselves irrational
¤ Classic (rational) economic theories (Expected Utility theory) can account for individuals’ risk preferences
¤ However, it has been empirically observed that people are inconsistent in their preferences which has been taken as a bias
¤ These inconsistencies cannot be explained using classical economic theories (How should people act?)
¤ Birth of Behavioural Economics: How do people act?
9- The Framing Effect: The difference between framing an outcome as a Loss vs Gain
¤ Imagine that the country is preparing for the outbreak of an unusual rare disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Which program would you choose?
POSITIVE FRAME
¤ If Program A is adopted, 200 lives being saved.
¤ If Program B is adopted, there is a 1 in 3 probability of saving 600 lives and a 2 in 3 probability of saving no lives.
People usually choose A
VS.
NEGATIVE FRAME
¤ If Program C is adopted, exactly 400 people will die.
¤ If Program D is adopted, there is a 1 in 3 probability that nobody will die and a 2 in 3 probability that all 600 will die.
People usually choose D
BUT BOTH ARE THE SAME! JUST FRAMED DIFFERENTLY
¤ Inconsistent Risk preference depending on the framing (loss vs gains) of the problem
¤ People are risk-averse when the options are described as gains
- They prefer the sure thing and go for safety
¤ The cup is half full – do I need more?
¤ People are risk-seeking when the options are described as losses
- They can tolerate an uncertain thing and risk a loss
- The cup half empty – don’t take any more away!
Framing in Real Life (Gachter et al. 2009)
¤ At an economics conference, PhD students either got:
- An Early bird discount
- Or a late registration penalty fee
¤ 93 % of students signed-up early when they were told they would pay a penalty fee (aka no discount)
¤ But only 67% signed-up early when told they would get a discount (aka no penalty fee)
But they mean the same thing! Just depends on how it’s framed!
Endowment Effect
Some students given mugs, others not. Those with mug were told they could sell it: Willing to sell for on average 4.50$.
Those with no mug were willing to buy them, on average, for 2.25$.
Why?
¤ Once ownership is established, people are averse to give it up
¤ (Kahneman, Knetsch, & Thaler, 1990)
10- Prospect theory: How should we make decisions? Vs How do we make decisions?
Prospect Theory
¤ Birth of Behavioural Economics (Kahneman & Tversky 1979, 1992)
¤ Two major contributions:
- Shape of Utility function (losses vs Gains)
- Shape of Probability Weighting function (Unlikely vs Likely events)
¤ Describes how people do act; not how people should act
Prospect Theory predicts risk preferences when gambling
Ex:
Gains: 1. get 10$ for sure vs. 2. 10% chance of winning 100$/ 90% chance of winning 0$
Losses: 1. If expect to get 100$ but only get 10$, that’s a relative LOSS of 90$ vs. 2. 90% chance of a LOSS of 100$/if expectations is of getting 100$ and get it, then nothing changes=0$ lost.
¤ Utility: Subjective value assigned to an object (i.e. satisfaction)
- Context dependent
¤ Utility is assigned to a monetary amount as a function of someone’s current state (reference point) and not in absolute value
- Deviations from the reference point will determine risk preference
- Anchor and Adjustment heuristic
Ex: if you’re in the desert, thirsty, and someone offers you a water bottle vs. a diamond, you’ll pick a water bottle. If you’re in the city, you’ll pick the diamond.
¤ Utility function: Describes how people map
money to satisfaction
¤ The extra satisfaction earned from gaining a
dollar is larger when you only have $1 vs when you have $1M
¤ Asymmetrical: Steeper for Losses than Gains $1 lost hurts more than one dollar earned
¤ Losses loom larger than gains (framing
effect)
Example: Prospect Theory
¤ Let’s say you’re at the pizza parlor ready to eat pizza
¤ You can map pizza to the satisfaction you get from it (Utility function)
¤ Being given 1 pizza slice is worth more to you when you had none then when you have 12
¤ Happiness (utility) of receiving a slice of pizza is less than the sadness (disutility) after giving a slice away
you have a full pizza and someone gives you one more piece… ok thanks i guess (sorta happy) vs. you have a full pizza and someone takes a piece away… nooooo :’(((
¤ Probabilities are not treated objectively
- Extreme events tend to be rare
¤ What causes more deaths: dying in a car
crash or dying of cancer?
¤ Extremity of event related to perceived
probability
- Unlikely events are overestimated (car crash)
- Likely events are underestimated (cancer)
¤ Availability of an option changes the perceived frequency of occurrence (we hear more about car crashes on the news than people dying of cancer)
Prospect Theory predicts the Fourfold Pattern
High probability + Losses = risk seeking
(ex: horror movie logic… high probability of dying and loss of life… seek risks like running in the forest)
High probability + Gains = risk averse
(changing salary/jobs… they are a gain with a high probability but risk of changing jobs and switching what salary you have so people don’t like taking that risk)
Low probability + Losses = risk averse
(ex: insurance… it’s a low probability with loss)
Low probability + Gains = risk seeking (ex: lottery… it’s a low probability of a gain, but people are risk seeking)
11 - Emotional factors that affect decision making
Dual Process Theory
¤ It is thought that there are two systems for making decisions
- System 1: Fast, effortless, automatic, intuitive, emotional
¤ Heuristics and biases
¤ Limbic System
- System 2: Slow, deliberative, Effortful, explicit, logical
¤ Rational choice
¤ Frontal cortex
Risks as feelings: Gains, loss & emotion activation
De Martino et al., 2010
¤ Participants made choices between two outcomes framed as gains or losses
- A risky (A gamble) outcome
- A safe (A Sure thing) outcome
¤ Increased amygdala activity for chosen safe outcomes for gains and chosen risky outcomes for losses suggests that an emotional response may underlie the
framing effect
¤ Choices are influenced by your affect (Lowenstein et al., 2001)
Assessing risk and emotion
¤ Participants read newspaper stories
designed to induce positive (happy) or
negative (sad) affect
¤ Then, the participants estimated
frequencies of death for various causes
- High risk
- Non-fatal risk
- Low risk life problems
¤ There were higher estimates of death
frequency when people were in a negative
mood compared to a positive mood
Prediction Error
¤ Prediction error (PE): The difference between what you predicted would happen and what actually happened
¤ Example: You go to buy Perrier
¤ Surprise! It’s on special (cheaper than usual)
¤ Inflation! It’s marked up (more expensive than usual)
¤ Prediction Errors are thought to drive learning (reinforcement learning)
¤ Prediction errors can be
- Positive: Unexpectedly good outcome
- Negative: Unexpectedly Bad outcome
Mood affect real world Gambling
¤ Prediction errors in sports outcomes
and the weather have been found to
affect people’s mood
¤ Positive PE increases positive affect
¤ Negative PE increases negative affect
¤ Changes in mood predict risky
decision-making : when people are
happy they are more likely to gamble
