Chapter 2 Decision-Making: Normative Models and Anomalies Flashcards

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1
Q

Decisions under certainty

A

– decisions where no probabilities are involved regarding the consequences of alternatives

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2
Q

Decision under uncertainty (Risky/ambiguous =mehrdeutig)

A

– decisions where the consequences of alternatives do only occur with some probability

Risky decisions: probabilities of consequences of alternatives are known

ambiguous decisions: probabilities of consequences of alternatives are only vaguely known

– ambiguity effect: decision-makers prefer risky decisions to ambiguous decisions

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3
Q

Ellsbergparadox

A

decision-makers behave inconsistently between two formulations of the decision situation that have mathematically identical possible gains but differ in ambiguity. Demonstrates the ambiguity effect

Beispiele:
An urn contains 30 red balls and a total of 60 other balls that are either black or yellow.

First game:
1(a) If a red ball is drawn from the urn, the participant wins.
1(b) If a black ball is drawn from the urn, the participant wins.

Second game:
2(a) If a red or yellow ball is drawn, the participant wins.
2(b) If a black or yellow ball is drawn, the participant wins.

Most players inconsistently choose 1(a) but 2(b)

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4
Q

St. Petersburgparadox

A

illustrates that decision-makers do not base decisions on expected value

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5
Q

Subjective expected utility theory (SEU)

A

Besonders subjektiv. St. Petersburgparadoxon bezieht sich darauf, dass nicht die mathematisch sinnvollste Alternative gewählt wird, sondern die subjektive.

– subjective expected utility = sum of products of subjective probabilities and subjective utility of consequences
– choose option with highest subjective expected utility

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6
Q

Normative decision models

A

– specify how an (idealised) individual should make optimal decisions

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7
Q

Prescriptive decision models

A

– use decision theory to offer step-by-step suggestions on how to proceed in a decision situation in order to make an optimal decision

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8
Q

Descriptive decision models

A

– describe how individuals actually make decisions

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9
Q

Economic games

A

empirical study of correspondence between theoretical predictions of normative theories and actual behaviour

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10
Q

Ultimatum game

A

– decision-makers have to divide a good between themselves and another Person
– if the other person rejects the proposed division, both leave empty-handed

• Game-theoretic prediction
– A gives minimum positive amount possible
– B accepts because any positive amount is better than Zero

• Empirical findings
– higher offers than minimum are made
– variations occur, but even in very different societies, gametheoretic prediction is not supported

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11
Q

Dictator Game

A

– decision-makers have to divide a good between themselves and another person
– the other person has to accept the proposed division

• Game-theoretic prediction
– A gives nothing

Empirical findings
– many people give something to the other person; on average, 28% of the amount

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12
Q

Prisoner’s dilemma

A

– two decision-makers independently have to decide between cooperation and defection
– while from an individual perspective defection(Überlauf->Geständnis) is the rational solution, the overall outcome is better if both cooperate

• Game-theoretic prediction
– both players defect

• Empirical findings
– many people cooperate, trusting that the other person will also cooperate

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13
Q

Backward induction

A

– analysis of repeated decision problems
– starts from considering rational solution in last round
– from that solution, rational solution in previous round can be derived, and so on

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14
Q

Decision anomalies

A
Result from:
– information processing (Speziell: misunderstanding from potential growth)
 – emotions
 – time 
– heuristics
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15
Q

Monty Hall Dilemma

A

– decision-makers have to decide whether to revise a previous decision when presented with additional information
– demonstrates that people have difficulties understanding probabilities

• Maximization strategy
– switch

• Empirical findings
 – very few people switch from their first choice
 – explanations: 
• misunderstanding of probabilities 
• regret
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16
Q

Emotions

A

Feeling and anticipated emotions are relevant for the decisions

17
Q

Affective forecasting

A

– prediction of emotional reactions to future events

• Four components(Wilson & Gilbert, 2003) 
– Valence (Wertigkeit)
 – specific emotions
 – intensity 
– duration 

Impact bias:
– duration and intensity of emotional reactions to future events often overestimated

projection bias:
tendency to assume that one’s future feelings and preferences will be similar to the current feelings and preferences

18
Q

Delayed gratifications ( Time)

A

– people have trouble deferring rewards, even when future reward is significantly larger than present reward
– “Marshmallow experiments” (Mischel, 1974)
– particularly difficult when System 1 is dominant

19
Q

Time discounting ( Time)

A

– preference for immediate utility vis-à-vis delayed utility in decisions where consequences occur at different points in time

Problems:
– short-term benefits vs long-term Costs
– discounting often inconsistent

20
Q

Melioration (Time)

A

– people often choose the alternative that puts them in a better position for the time being
– in the long run, melioration can counteract maximisation

• Law of relative effect (‘matching law’)
– selection ratio of various behavioural alternatives is proportional to the subjective value of the reinforcements attached to these alternatives
– and inversely proportional to the time that passes between the behaviour and its reinforcement

21
Q

Distorted Memories (Better-than-average effect )

A

– people believe they are better than others
– positive traits more developed, negative traits less developed than those of other people

  • Often higher for low Performers
  • Serves to enhance self-worth
22
Q

Distorted Memories (Peak and End rule)

A

– in retrospect, an experience is often not judged by its duration and the sum of all its elements, but is strongly influenced by outstanding elements and by the elements at the end

23
Q

Hindsight(Rückblick) bias

A

– after learning an outcome, people tend to claim to have known that the event would turn out that way

• Causes:
– adaption of memories to enhance self-worth
– not remembering, but new prediction processes with outcome information as anchor and “motivated” answers

24
Q

Mood-as-Information heuristic

A

– judgments are based on the current mood

• Can lead to errors because relevant aspects are not considered and mood may be caused by unrelated events

25
Q

Representativeness heuristic

A

judgments are based on the similarity between an outcome and a model
• Relevant for judgements of category, frequency, probability, as well as evaluations or decisions based on these
• Can be correct because objects within a category share Attributes

• Can lead to errors because similarity alone is not sufficient, and people neglect other important statistical rules

• Gambler’s fallacy
– mistaken belief that, after a sequence of one outcome, the other outcome becomes more likely

• Hot hand fallacy
– mistaken belief that, after a sequence of successes, another success becomes more likely

• Conjunction fallacy
– people do not take into account that combined events cannot be more likely than the constituting single events

26
Q

Fast and frugal heuristic

A

decision-making aids that allow a complex problem to be solved in a short period of time, using only a few pieces of information

• Recognition heuristic
– decisions are based on familiarity with objects
– when only one of two objects is recognised, that object is seen as more important

• Less-is-more effect
– in some cases, less knowledge can lead to better decisions because it allows using the recognition heuristic

• Take-the-best heuristic
– decisions are based on the one characteristic of options that seems especially relevant

• Elimination heuristic
– decisions are based on sequential elimination of alternatives