1
Q
What is David Hume’s Empiricism?
A
- Causal relations cannot be perceived directly
- Sensory experience is the source of all our knowledge e.g touch does not reflect causality and there is no causality measurement e.g thermometer
- Causal knowledge is constructed using non-causal input
- Relevant input is the presence and absence of events
2
Q
What are the three factors?
A
- Temporal and spatial contiguity between cause and effect: mix of space and time
- Temporal priority of cause before effect
- Constant conjunction of cause and effect
3
Q
What are the Caveats of Empiricism?
A
- Correlation: just because things occur together does not mean they are causation e.g violence on TV and violent behaviour
- Probability of the event occurring with the cause subtracted by the absence of the cause is a good indicator of causal strength = ceiling and floor effects
- e.g when this equation equals 0, you would not conclude that there is no relation, instead experiment is flawed
4
Q
What is tracking the invariants of nature?
A
- Distal stimulus is the thing we want to look at in the real world, proximal stimulus is the 2d image in the brain
- Cannot be done directly, ends up being a combination and reconstruction of something similar to the distal stimulus
5
Q
What is the significance of Funes the Memorious?
A
- Funes had an accident and has infallible perception and memory - but Is also an affliction
- Struggles to track invariance as everything changes all the time - cannot comprehend general things
6
Q
What is the summary of causal judgement and learning?
A
- Causal Powers are distal stimuli
- Observable contingencies e.g stats patterns are proximal stimuli
- We build up representation of the distal stimuli via proximal
- Goal is to track distal (invariant) rather than the proximal stimulus
- Proximal stimulus changes according to circumstance e.g base rate and context
- Causal learning goes beyond associative learning: associations cannot capture causal asymmetries
7
Q
What is the difference between strength and structure of correlation?
A
- Structure: Are variables related?
- Given there is a relationship, how strong is it?
- Different causal structures can produce the same patterns of correlation
8
Q
What are Causal Bayesian Networks?
A
- Use statistical evidence to construct causal models of the world
- Causal dependencies imply various patterns of conditional dependencies
- Intervening on a system to change one variable renders it independent from the other factors = basis of systematic experimentation and hypothesis testing
9
Q
How do heuristics affect bayesian networks?
A
- Probability questions are substituted with something easier
- Which statement makes more causal sense as information flows from cause to effect
10
Q
What are inappropriate Application of Causal Models?
A
- Stereotyping/Prejudice
- Belief preservation
- Confirmation Bias
- Misperception of Randomness
- Not-hand Fallacy: in sport and gambling: if player scores three times, people believe he is on a streak and so others pass to him
11
Q
When does causality interact with time? (Study)
A
- Asked people questions between different historical events and asked how long time differed between them
- There was some correlation between some times
- Asked people how strong they thought there was a causal relationship between the two events: the higher the time, the less the causal strength rating but the lower the time estimate, the causal strength is high
- BECAUSE: attribute sub: temporal question - attribute is substituted by causal info
12
Q
What are perceptual distortions?
A
- Causality warps subjective time and space
- Time perception task: people perceived two lights flash at short time intervals
- Had to reproduce temporal interval by holding down a key = very close to accurate
- Other condition is the ppt presses a key and light shows up: when they reproduce = underestimate a lot
- Reproduced with primary school children and found children anticipate causal outcome earlier
13
Q
What is Newcomb’s Paradox?
A
- Demon can give you 1 mill in one box and 1000 guaranteed in another box, 1 mill disappears if you take both boxes, demon predicts and then you make ur decision
- Expected Utility analysis makes sense: probabilities to get 1 mill
- BUT causal analysis makes sense as shows independence and guaranteed 1000
- Contradicting analyses
- Can sever links to dictate independence so you can change mind after demon makes prediction
14
Q
Why does causal structure matter?
A
- Choices are interventions
- Probabilities derived from observations are often not useful
- Interventional probabilities are useful
- e.g out of 100 men who do chores, 82 are in good health whereas 32 of 100 men who do not help with chores are = doing chores is additional exercise vs men concerned about equality are also concerned about health and help with chores and eat healthier food = should your friend do chores?
- Observed prob/correlations are identical, same evidential relations, different causal = people choice clearly reflect awareness of causal consequences
15
Q
What was an example of why causal structure matters?
A
- Two types of car and both have marketing campaign but can only launch one
- Main competitor selling same cars, and promotes each car 95% of the time the same time as you: he has decided and cannot change his decision
- Regardless of what he does, his decision cannot be changed, so must choose an outcome to maximise YOUR sales
- If he hasn’t decided, you should go off comparisons because there is a direct link between campaigns - base rates come into play
16
Q
When do we choose to ignore Causal Structure? (study)
A
- Stanford UG ppts in a study on how rapid changes affect heart rate after exercise: Cold compressor task until pain threshold reached, one minute vigorous exercise on bike and then redo cold compressor task
- Prior to the task, people were told those with healthy heart have higher tolerance after exercise and higher life expectancy, OR those with healthy heart have lower tolerance after exercise and higher life expectancy
- That changed how long they left their arm in the water: peoples tolerance changed not because they actually were healthier but because they wanted to be but denied any conscious effort to tolerate more/less pain
17
Q
Why do we deceive ourselves?
A
- We want something to be true
- Could be failure to understand causal model
- Could ignore causal model via denying we have control or that we are intervening AND pretending our interventions are observations
- Situation needs to be sufficiently vague to afford self-deception
- Precise feedback from our actions reduces self-deception (when you do it reduces it)
18
Q
Is self-deception irrational?
A
- Deceiving self that people around you like you = increase niceness toward them = they are nicer to you
- Deceiving self that vote counts motivates you to vote, If you stayed home, elections wouldn’t work
- Deceiving self that you have a healthy heart = more inclined to exercise = healthier
- Behaving as you want the world to be can make it more likely that the world actually turns out this way
19
Q
A
PS3212 - Decision Making
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Decks in class (15)
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