Decision-Making

Automatic Thinking

immediate thinking

Controlled Thinking

required deliberation (“what if” thinking)

Bounded Rationality

due to the limited amount of time and info we have, our decisions are optimal

Normative

how people should make decisions

Descriptive

how people actually do make decisions

Expected Value

amount x chance (%)

Perceived Probability

people tend to overvalue the likelihood of small probability events and undervalue the likelihood of large probability events

Sunk costs effect

taking post investments in a decision alternative into account, even when they should not affect decisions about the future

Honoring a sunk cost

choosing to continue investing in a less optimal option on the basis of past investments

Overinclusive thinking

considering extraneous information while making a decision

Primacy effect

a decision maker’s thoughts are usually dominated by the initial impression, confirmatory hypothesis testing

Incomplete thinking

make decisions without considering all relevant information

Decision Trees

consider all possible decisions and all possible outcomes

Probability Theory

expected utility is the probability-weighted sum of the utilities possible outcomes, used to compare and choose between risky options under uncertainty

Value

how much gamble will pay

Utility

how much the gamble is worth to decision maker

The Lens Model

we make decisions about underlying criteria based on multiple cues

Decision Making Process

1.) Possible actions

2.) Possible consequences

3.) Uncertainty about those consequences

4.) Goals or values

Judgment

estimate/prediction

Decision

choosing an action based partly on judgments

Heuristics

methods from making decisions or solving problems

Heuristics

“quick & dirty” methods if making a decision

The Availability Heuristic

estimating the likelihood of an event on the basis of how easily memories of such events can be recalled

Biased Samples IN memory

vividly emotionally-charged events can be recalled more easily

Overgeneralization

cognitive distortion in which someone draws broad, inaccurate conclusion based on limited evidence

Based Sampling FROM memory

easier recall increases estimate of frequency,

more likely to recall the vivid events than the mundane events, after learning of both

Availability to the Imagination

Events that are more easily imagined are judged to be more probable

Subadditivity

the actual probability of all events is less than the sum of estimated probabilities of all possible events (greater than 100%)

Superadditivity

the actual probability of all events is greater than the sum of estimated probabilities of all possible events (less than 100%)

Anchoring

people make an initial estimate of a situation, which may be arbitrary

Adjustment

people use new info to adjust their estimate from the previous anchor

Preference reversals

a case in which people choose inconsistently (irrationally) depending on how a problem is presented

Judgment by Similarity

similarity to a known category is often give more weight than it should be relative to probability in decision-making

Subjective

Belief

Objective

calculated

Conditional Probability

P (A|B) (a give b)

The Contrast Model

we tend to judge similarity of an instance to a category by looking for matches or mismatches between attributes of an instance and attributes of a category

Stereotypes

examples of categories

Similarity heuristic

judging the probability of category of membership on the basis of similarity to the category

Baye’s rule

The conditional probability of an event A given B is P(A | B) = (P (B|A)P(A)) / P(B)), allowing you to update prior beliefs using new evidence

Counterfactual

a situation that could’ve happened but didn’t

The Endowment Effect

people are willing to pay less money to buy an object than they would demand in payment to sell the same object

Illusion of Control

the perception that one can control chance events

The Hot Hand Fallacy

the perception that one who is winning is likely to continue winning, even when the events are random (underestimate probability of change)

The Gambler’s Fallacy

the false perception that the outcome of a current random event is dependent on the outcomes of preceding random events, especially in overestimating the likelihood of changes from one event to the next (overestimate probability of change)

The Texas Sharpshooter Fallacy

it occurs when a person focuses on similarities in data while ignoring differences, creating the illusion of a meaningful pattern

The disjunction probability fallacy

incorrectly rating A or B as less probable than A (or B) often because one of the options seems more vivid, specific, or representative

Regression to the mean

the expected value is (correctly) unchanged, though the next sample is more likely closer to the mean

The Envelope Problem

naive expected-value reasoning incorrect suggests you should always switch envelopes, but correct analysis shows no advantage to switching once a probability distribution is specified

The Monty Hall Problem

switching doors gives a 2/3 chance of winning the car, while staying with your original choice gives only a 1/3 chance

Iowa Gambling Task

Individuals w/ damage to the brain areas damaged in Gage’s brain tend to be insensitive to likely punishment and choose decks A and B

The Somatic Marker Hypothesis

Individuals experience a negative or unpleasant physiological response when contemplating certain potential choices

Counterfactual emotions

an emotion that is generated by the possibility of what if

Weber’s Law

the Just Noticeable Difference between intensities of two stimuli is proportional to the intensity of the stimuli

Fechner’s law

basically a corollary of Weber’s law — the psychological intensity is the logarithm of the physical intensity

Value

the quality of a thing by which its worth its estimated. The fair price of something

Personal value

the value to the person making a decision

Framing Effect

people make different decision depending on whether identical information is presented as a gain or a loss

The value graph

describes how people value gains and losses

The probability graph

shows that people overestimate the likelihood of infrequent events and underestimate the likelihood of more frequent events

Pseudocertainty effect

adopting uncertain future events as the status quo from which prospects are evaluated

Dominance

looking for alternatives that are better than any other alternatives for every attribute considered

Additive Linear

the linear model approach. Each alternative gets assigned an overall score

Additive Difference

consider all possible pairs of alternatives and choose the best, like playoffs in sports

Satisficing (conjuctive)

find the first alternative that is “good enough” on all attributes considered

Lexicographic

pick the best alternative(s) on one attribute. Then consider remaining attributes successively to pare down the list to one remaining alternative

Elimination by aspects

like lexicographic, but set a minimum threshold for each attribute in turn and throw out the alternatives that don’t meet the threshold. Then repeat for the remaining attributes

Recognition Heuristic

name or brand recognition

Disjunctive

find the alternatives that are good enough on at least one of the attributes considered

Normative Theory

the utility is the product of the value of an outcome (x) and how likely the outcome is (p)

U = PxX

Descriptive Theory

the value V of each prospect is V(X)^xpi(p), where V and po are nonlinear functions

Perceived value

v(x)

Confirmation Bias

pick likely scenario, and then only look for confirming evidence.

Persistence of discredited evidence

people tend to fail at discounting discredited evidence, treating it as though it were still valid

Satisfaction of search

disengaging a search for additional objects or events after finding one instance of the searched for objects or event

Probability matching

selecting options at frequencies that match payoff probabilities instead of always selecting the option with the highest payoff