decision making: uncertainty and risks Flashcards
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
bias
deviations from rationality (errors) that are caused by using heuristics
what are the three categories of biases
- biases that affect how we interpret information
- biases that affect how we judge frequency (how often something happens)
- biases that affect how we make predictions
availability heuristic
estimate the probability of an event based on the ease at which it can be brought to mind
leads us to overestimate the probability of events based on how salient they are in our minds
representativeness heuristic
tend to make inferences on the basis that small samples represent the larger population they were drawn from
- related to stereotypes, schemas, and other pre existing knowledge structures
- basing judgements of group membership based on similarity
base rate neglect
when you fail to use information about the prior probability of an event to judge the likelihood of an event
conjunction fallacy
false belief that the conjunction of two conditions is more likely than either single condition
anchoring and adjust heuristic
judgements are too heavily influenced by initial values
people start off with one value and adjust accordingly from there
regression to 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
illusionary correlations
people tend to see causal relationships when there are none
related to our understanding of the roles of reward and punishment on learning
bounded rationality
idea that there is a limitation to our cognitive capacity caused by both environmental constraints and individual constraints
satisficing
people are satisficers: look for solutions that are “good enough”
ecological rationality
see heuristics not as a “good enough” approach, but as the optimal approach
given the right environment, a heuristic can be better than optimization or other complex strategies - better than more deliberate strategies
when does regression towards the mean happen
where there is not a perfect correlation
why do we use heuristics
because we are boundedly rational
why does the conjunction fallacy arise
because people use the representativeness heuristic
perceptual decision making
objective (externally defined) criterion for making your choice
value based decision making
subjective (internally defined) criterion) for making your choice - depends on motivational state and goal
ie. what do I want for dinner?
risk
taking an action despite the outcome being uncertain
specific to value based decision making
ambiguity
can be defined when you have incomplete information about the consequences
extremes in risk taking
stagnant living or addiction and impulsivity
how can we frame risks
as gains or losses
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
behavioural economics
how do people act
arised from the gap between assumptions made by classical economic theories and how people actually act
the framing effect
inconsistent risk preference depending on the framing (loss vs gains) of the problem
people are risk averse when the options are described as gains
people are risk seeking when the options are described as losses
endowment effect
once ownership is established, people are averse to give it up
people are averse to losses
prospect theory
describes how people behave - what people do instead of what people should do
predicts that we have different risk patterns depending on the probability and losses and gains
two major parts:
- shape of utility function
- shape of probability weighting function
utility
subjective value assigned to an object
context dependent
utility is assigned to a monetary amount as a function of someone’s current state (reference point) and not in absolute value (not inherent)
deviations from reference point will determine risk preference
utility function
first part of prospect theory
mathematical function that describes how people map money to satisfaction/treat gains and losses
losses loom larger than gains - linked to the framing effect
describes extra satisfaction you get from gaining a dollar when you only have $1 compared to when you have $1million
probability weighting function
describes how people understand likelihoods
extremity pf event related to perceived probability
availability of an option changes the perceived frequency of the option
means that people tend to overestimate rare events and underestimate mundane events
dual process theory
it is thought that there are two systems for making decisions
system 1: fast, effortless, automatic, intuitive, emotion
- heuristics and biases
- limbic system (emotions)
system 2: slow, deliberative, effortful, explicit, logical
- rational choice
prediction error
the difference between what you predicted would happen and what actually happened
drive learning (reinforcement learning)
can be positive or negative
mood affect real world gambling
changes in mood predict risky decision making —- when people are happy they are more likely to gamble
changes in mood and assesment of risk level
negative mood increase people’s estimated frequency of negative events
risky choices
related to affective (as opposed to deliberative) decision making
- activity in brain areas implicated in emotional processeing
modus ponens
affirming the antecedent
idea that if we observe the antecendent is true, we can conclude that the consequent is true
modus tollens
denying the consequent
occurs when we observe the consequent is false and conclude that the antecedent must be false as well
expected utility hypothesis
classical economic theory idea that assumes when people are faced with multiple options, they will choose the one that returns the highest likely value
propositions
possible facts about the world - can be true or false and can refer to properties of the external world or about our own experiences
deduction vs induction
deduction - conclusion follows logically from the initial premises
induction - relies on generalizing from a certain set of information and extending it to make an informed guess
syllogism
a kind of reasoning in which a conclusion is derived from two or more propositional statements
most common: categorical syllogism, which consists of three statements: two premises and one conclusion
conditional or hypothetical syllogism
contain a conditional claim, which states a rule that relates two propositions
if P, then Q
P=antecedent
Q= consequent
What we tend to do when evaluating syllogisms (evans)
we have a mistaken tendency to try to confirm a syllogism as valid versus establishing that it is invalide
what is the key to testing a rule (watson)
check cases that have the potential to prove it wrong or falsify it
checking cases that may be consistent with it but that can’t disprove it have no utility in testing whether the rule is true
belief bias
a tendency to rate conclusions that are more believable as more valid
atmosphere effect
a tendency to rate conclusions as more valid when the qualifying words in the premises match those in the conclusion
mental models
mental simulation of the world, based on descriptions in the syllogism - if it involves concrete concepts, people will generate visualizations of the sentences and then mentally explore them to see whether the model breaks down
generalization
refers to cases in which we extrapolate from a limited number of observations to draw a conclusion about the broader population or category
statistical syllogism
going from observations about a group to an inference about an individual
argument from analogy
when we observe that two things share some set of properties and conclude that they must share a different property
one shot learning
when a concept is learned from a single example - requires a lot of inductive reasoning
bayesian inference
mathematical model for incorporating existing beliefs, called the prior, with new data, in order to make an educated inference
status quo bias
a tendency to leave things as they currently are, rather than making a change
shown by samuelson and zeckhauser’s inheritance study
emotional factors in decision making
integral emotions: directly related to the decision
incidental emotions: not directly related to the decision but that happen to be the state of the person at the time they are making the decision
