The Psychology of Judgment

1
The Psychology ofJudgment Decision Making

• MIS 696A Readings in MIS (Nunamaker)
• 05 November 2003
• Cha / Correll / Diller / Gite / Kim / Liu /
  Zhong

2
SECTION IPerception, Memory Context

• Hoon Cha Jeff Correll

3
Chapter 1Selective Perception

• Hoon Cha

4
Define First and See?

• People selectively perceive what they expect and
  hope to see

5
Examples

• Any book which is published will have been read
  possibly hundreds of times, including by
  professional proof readers.
• And yet grammatical and other errors still get
  into print. Why?
• Because the mind is very kind and corrects the
  errors that our eyes see.

6
Lessons Learned

• Before conducting your research and interpreting
  your results
• Ask yourself what expectations you did bring into
  the situation?
• Consult with others who dont share your
  expectations and motives

7
Chapter 2Cognitive Dissonance
8

Are you a sexist person?

• People are motivated to reduce or avoid
  psychological inconsistencies.
• Cognitive dissonance
• People are in much the same position as an
  outside observer when making inferences.
• Self-Perception

9
Examples

• Smokers find all kinds of reasons to explain away
  their unhealthy habit.
• The alternative is to feel a great deal of
  dissonance.

10
Lessons Learned

• Change in behavior can influence change in
  attitude
• During your research, get other people to commit
  themselves to own the object, then they will form
  more positive attitudes toward an object.
• Use systems development as a research methodology

11
Chapter 3Memory Hindsight Biases
12
"I knew it all along "

• Memory is reconstructive, not a storage chest in
  the brain.
• Shattered memories
• It can be embarrassing when things happen
  unexpectedly. People tend to view what has
  already happened as relatively inevitable and
  obvious.
• Hindsight bias

13
Examples

• Just before the election, people tend to be
  uncertain about who will win but, after the
  election, they tend to point to signs that they
  now say had indicated clearly to them which
  candidate was going to win.
• In other words, they are likely to remember
  incorrectly that they had known all along who the
  winning candidate was going to be.

14
Lessons Learned

• During your research, explicitly consider how
  past events might have turned out differently.
• Keep in mind the value of keeping accurate notes
  and records of past events

15
Chapter 4Context Dependence

• Jeff Correll

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4 Illustrations of Context Effect

• Contrast Effect
• Primacy Effect
• Recency Effect
• Halo Effect

17
Contrast Effect

• Examples
• Experiment with 3 bowls of water
• Sports announcer standing next to basketball
  players vs. horse jockeys

• Only occurs among similar objects ex apparent
  size wont change if standing next to a large
  race horse (Ebbinghaus Illusion)

18
Primacy Effect

• Characteristics appearing early in a list
  influence impressions more strongly than those
  appearing later Asch (1946)
• The first entry is most important, but 2nd and
  3rd also show a primacy effect-Anderson(1965)
• This effect also occurs in many other situations
  involving sequential information

19
Recency Effect

• Sometimes the final presentation has more
  influence than the first
• Which is stronger? it depends (Miller and
  Campbell study - 1959)

• Hoch (1984) found similar results in human
  prediction experiments

20
Halo Effect

• People cant treat an individual as a compound
  of separate qualities and rate these qualities
  independent of the others
• Examples Army officer ratings, teacher
  evaluations, beauty halo, warm vs. cold,
  teacher expectations, etc.

21
Conclusion Context Dependence

• Everything is context-dependent
• Persuasion professionals exploit these effects
• Includes us as MIS Researchers!
• Contextual effects are limited

22
SECTION IIHow Questions Affect Answers
How the format of a problem can influence the way
people respond to it

• Jeff Correll

23
Chapter 5Plasticity

• Jeff Correll

24
Are you a gambler?

• Same choice in a different context can lead to
  very different answers
• A 100 chance of losing 50
• B 25 chance lose 200, 75 nothing
• Worded in sure loss language Risk-taking
• Worded in insurance languageRisk-averse

25
Order Effects

• Order of questions/alternatives also influence
  responses
• Example Schuman and Pressers 1981 survey on
  freedom of the press
• Recency effect is the most common response order
  effect
• Example Survey question about divorce

26
Pseudo-Opinions

• People will offer an opinion on a topic about
  which they have no real opinion
  (pseudo-opinion) 25 to 35
• Multiple humorous examples
• Common in issues involving foreign and military
  policy
• Must be separated through filtering

27
Inconsistency

• Discrepancy between two related attitudes
  (attitude-attitude) or an attitude and a
  corresponding behavior (attitude-behavior)
• Attitude-attitude inconsistency Attitudes about
  abstract propositions are often unrelated to
  attitudes about specific applications of the same
  proposition!
• Attitude-behavior inconsistency People can hold
  abstract opinions which have little or nothing to
  do with their actual behavior!

28
Inconsistency Continued

• Ultimate example of attitude-behavior
  inconsistency Darley and Batsons 1973
  experiment on seminary students
• Should we abandon the idea of attitudes
  altogether (Wicker)?
• Revisionist attitude researchers say no -
  attitudes are consistent with behavior, provided
  certain conditions are met (Atzen et al 1977)

29
Conclusion Plasticity

• Russian Proverb
• Going through life is not so simple as crossing
  a field
• Translation to Judgment and Decision-Making
• Measuring an attitude, opinion, or preference
  is not so simple as asking a question
• We as MIS researchers must pay close attention
  to the structure and context of our survey
  questions!

30
Chapter 6Effects of Wording Framing

• Jeff Correll

31
Question Wording

• Small changes in wording can equal big changes
  in how people answer
• Example Does your countrys nuclear weapons
  make you feel safe? (40 yes, 50 no, 10 no
  opinion) vs. safer? (50 yes, 36 no, 14 no
  opinion)
• Potential pitfalls in question wording
• Forced Choice questions (no middle category)
• Questions with a middle category
• Open vs. Closed Questions - Schuman and Scott
  (1987)

32
Response Scales / Social Desirability / Allow vs.
Forbid

• Differences in response scales also influence
  results (ex reported TV usage)

• In the absence of a firm opinion on an issue,
  respondents typically cling to catch phrases
  that point them in a socially desirable direction
• Are you for or against a freeze in nuclear
  weapons? (one question equated it with Russian
  nuclear superiority, the other with world
  peace)

• Varying the words Allow and Forbid leads to very
  different responses (Rugg -1941)

33
Framing

• People respond differently to losses than to
  gains (Tversky and Kahneman-1981)
• A Sure gain of 240, or
• B 25 chance to gain 1000, 75 chance to gain
  0
• 84 chose A over B (people tend to be risk
  averse with gains)
• C Sure loss of 750, or
• D 75 chance to lose 1000, 25 chance to lose
  0
• 87 chose D over C (people tend to be risk
  seeking w/losses)

34
Framing Continued

• Interesting point A and D are chosen together
  73 of time, yet B and C together has a higher
  expected value outcome
• Concept has similar application to Medical
  Decision Making
• Asian Disease question (1981)
• Lung cancer treatment decision experiment

35
Psychological Accounting

• Decision makers also frame the outcomes of their
  choices
• Main issue Is the outcome framed in terms of
  the direct consequences of an act (minimal
  account) or is it evaluated with respect to a
  previous balance (inclusive account)?
• Price to see a play is 10. As you enter
  theatre, you realize youve lost a 10 bill.
  Would you still pay 10 for a ticket to the play?
  (88 said yes)
• Same situation, but this time youve lost your
  10 ticket (which youve already paid for and
  cant replace). Would you pay 10 for another
  ticket? (only 46 said yes!)

36
Conclusion Question Wording and Framing

• Can significantly affect how people respond
• In our studies, we as MIS researchers must
  consider how respondents answers might have
  changed based on all of the previous factors
• Furthermore, we should probably qualify
  interpretations of results until multiple
  variations in wording/framing can be tested
• If multiple procedure results are consistent,
  there may be some basis for trusting the
  judgment otherwise further analysis required
  (Slovic, Griffin, and Tversky 1990)

37
SECTION IIIModels of Decision Making

• Chris Diller

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Chapter 7Expected Utility Theory
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Classic Utility Theory

• Example Self-Test Question 30
• The "St. Petersburg Paradox"
• Question initially posed by Nicolas Bernoulli
  (1713)
• "Solution" provided by Daniel Bernoulli
  (1738/1754)

40
Expected Utility Theory

• Expected Utility Theory
• Developed by von Neumann Morganstern (1947)
• The value of money DECLINES with the amount won
  (or already possessed)
• Normative NOT descriptive!

41
Expected Utility Theory

• "Rational Decision Making" Assumptions
• Ordering Preferred alternatives or indifference
• Dominance Alternative with better outcome(s)
• "Weakly" dominant vs. "Strongly" dominant
• Cancellation Ignore identical
  factors/consequences
• Transitivity If A gt B and B gt C then A gt C
  !
• Continuity Prefer gamble to sure thing (odds!)
• Invariance Unaffected by way alt's are
  presented
• A Major Paradigm with Many Extensions

42
Chapter 8Paradoxes in Rationality
43
The Allais Paradox

• Example Self-Test Question 28
• Maurice Allais (1953)
• Showed how the Cancellation Principle is violated
• The addition of equivalent consequences CAN lead
  people to make different (irrational?) choices

44
Ellsberg's Paradox

• Daniel Ellsberg (1961)
• Also showed how Cancellation Principle is
  violated
• People to make different (irrational?) choices in
  order to avoid uncertain probabilities
• Example Urn with 90 balls (R/B/Y)

45
Intransitivity

• "Money Pump"
• Decision makers with intransitive preferences
• A lt B ? B lt C ? A gt C
• Amos Tversky (1969)
• Harvard study 1/3 of subjects displayed this!
• "Committee Problem" Example
• Choose between three applicants
• Leader frames vote to avoid direct comparisons

46
Preference Reversals

• Sarah Lichtenstein Paul Slovic (1971)
• Preferences can be "reversed" depending upon how
  they are elicited
• High payoff vs. High probability
• Choosing between a PAIR of alternatives involves
  different psychological processes than bidding
  on a particular alternative separately
• Exist even for experienced DMs in real life!
• Example Study of Las Vegas bettors dealers

47
Conclusions

• Violations of EUT are not always irrational!
• Approximations simplify difficult decisions
• Increase efficiency by reducing cognitive effort
• Lead to decisions similar to optimal strategies
• Assume that the world is NOT designed to take
  advantage of the approximation efforts utilized
• A decision strategy that can not be defended as
  logical may be rational if it yields a quick
  approximation of a normative strategy that
  maximizes utility.

48
Chapter 9Descriptive Models of DM
49
Satisficing

• Herb Simon Blows Up EUT (1956)
• Simplifying assumptions make the problems
  tractable
• DMs are assumed to have complete information
• DMs are assumed to understand and USE this
  information
• DMs are assumed to compare calculations
  maximize utility
• Simon says People "satisfice" rather than
  optimize
• "People often choose a path that satisfies their
  most important needs, even though the choice may
  not be ideal or optimal."
• Humans' adaptive nature falls short of economic
  maximization

50
Prospect Theory

• Daniel Kahneman Amos Tversky (1979)
• Prospect Theory differs from EUT in two big ways
• Replace "Utility" with "Value" (net wealth vs.
  gains/losses)
• The value function for losses is different than
  the one for gains

51
Prospect Theory

• George Quattrone Amos Tversky (1988)
• Introduced notion of "loss aversion" its
  results
• Political ramifications Incumbent re-elections
• Commercial ramifications Bargaining
  negotiation
• Personal ramifications "The Endowment Effect"
• Losses are felt much more strongly than gains!

52
Prospect Theory's Certainty Effect

• Amos Tversky Daniel Kahneman (1981)
• Reductions in probability have variable impacts
• Zeckhauser Russian Roulette 4 to 3 bullets vs.
  1 to 0 bullets
• People would rather eliminate risk than just
  reduce it
• Probabilistic Insurance Kahneman Tversky
  (1979)
• Small probabilities often "overweighted,"
  inflating the importance of improbable events
• Example Self-Test Question 23

53
Prospect Theory's Pseudocertainty

• Amos Tversky Daniel Kahneman (1981)
• Similar to Certainty Effect, this effect deals
  with apparent certainty rather than real
  certainty (Framing)
• Slovic, Fischhoff, Lichtenstein (1982)
• Example Vaccinations
• People prefer the option that appeared to
  eliminate risk!
• Other Examples Marketing Tactics
• Buy two, get one FREE (preferred) versus 33
  off!

54
Regret Theory

• Prospect Theory's Premise
• Compare gains losses relative to a reference
  point
• However, some compare imaginary outcomes!
• "Counterfactual Reasoning"
• Dunning Parpal (1989) The basis of Regret
  Theory
• Compare decisions with what MIGHT have happened
• Same as Prospect Theory's Risk Aversion but
• "Regret variable" is added to the new utility
  function
• Accounts for many previously-mentioned paradoxes

55
Multi-Attribute Choice

• Einhorn Hogarth (1981)
• Consistency of goals/values, not objective
  optimality
• Research HOW (not how well) decisions are made
• Compensatory Strategies (John Payne, 1982)
• Used primarily for simple choices, few
  alternatives
• Trade off low high values on different
  dimensions
• Linear Model (All attributes weighted ? index
  score)
• Additive Differences Model (Only the different
  attributes weighted)
• Ideal Point Model (Evaluate attributes on their
  distance from the ideal)

56
Noncompensatory Strategies

• R.M. Hogarth (1987)
• Used primarily for complex choices, many
  alternatives
• These do NOT allow for making trade-offs!
• Most well-known examples include
• Conjunctive Rule (Satisficing! Criterion ranges
  ? acceptance/rejection)
• Disjunctive Rule (Each alternative is measured
  by its BEST attribute)
• Lexicographic Strategy (Step-wise evaluation of
  attributes ? superior)
• Elimination-By-Aspects (Step-wise evaluation of
  attributes ? inferior)

57
The More Important Dimension

• Slovic (1975)
• "Given a choice between two equally-valued
  alternatives, people tend to choose the
  alternative that is superior on the more
  important dimension."
• Example Baseball players' statistics
• Results indicate that people DO NOT choose
  randomly!

58
Applications to MIS Academia

• Normative vs. Descriptive Approaches
• Importance of Framing
• Understanding "Rationality" in DM
• Departmental budget "battles"
• Competition for research funding
• Analysis of technology adoption
• Personnel decisions
• Selling "transitioned" research products/tools

59
Break
60
SECTION IVHeuristics Biases

• Sanghu Gite, Iljoo Kim Jun Liu

61
Heuristics and Biases

• Sanghmitra Gite

62
He loves mehe loves me not
HOW?
WHY?
WHICH?
IF?
WHEN?
WHERE?
63
Heuristics or Hueristics?
Tversky and Kahneman When people are faced
with a complicated decision, they often simplify
the task by relying on heuristics,In many cases,
these shortcuts yield very close approximations
to the optimal In certain situations, though,
heuristics lead to predictable biases and
inconsistencies.
General Rules of Thumb
Fairly good estimate
Reduce time and effort
Leads to predictable biases
64
The Representativeness Heuristic
Tversky and Kahneman People often judge
probabilities by the degree to which A resembles
B.
As the amount of detail in a scenario increases,
its probability can only decrease steadily, but
its representativeness and hence its apparent
likelihood may increase.

• Example 1 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
  antinuclear demonstrations. Which is most likely?
• Linda is a bank teller.
• Linda is a bank teller and is active in the
  feminist movement
• Example 2 Which of the scenarios is more
  likely?
• Scenario 1 An all-out nuclear war between the
  United States and Russia
• Scenario 2 a situation in which neither country
  intends to attack the other side with nuclear
  weapons but an all-out nuclear war between the
  U.S. and Russia is triggered by the actions of a
  third country such as Iraq, Libya or Pakistan.

Dont be misled by highly detailed scenarios!
65
The Law of Small Numbers
The Author says ...a belief that random
samples of a population will resemble each other
and the population more closely than statistical
sampling theory would predict.

• Examples
• Gamblers fallacy
• the belief that a successful outcome is due
  after a run of bad luck
• The Hot Hand
• a streak shooter in basketball or an athelete
  on a roll
• Tversky and Kahneman
• tendency to view chance as self correcting is a
  bias resulting from the representativeness
  heuristic, because samples are expected to be
  highly representative of their parent population.

Remember that chance is not self-correcting!
66
Neglecting Base Rates
The author says In some instances, a reliance
on representativeness leads people to ignore base
rate information ( the relative frequency with
which an event occurs)

• Example
• More description tends to ignore base rates

Whenever possible, pay attention to base rates
67
Nonregressive Prediction

• Regression to the mean is the phenomena in
  which high or low scores tend to be followed by
  more average scoresThe tendency to overlook
  regression leads to critical errors of judgment.
• Examples
• Baseball Magic
• Sports Illustrated Jinx
• Nisbett and Ross
• measures designed to stem a crisis ( sudden
  increase in crime, diseaseor a sudden decrease
  in sales, rainfall, or Olympic gold medal
  winners) will, on the average, seem to have
  greater impact than there actually has been

Dont misinterpret regression toward the mean
68
The Availability Heuristic

• Tversky and Kahneman
• Assess the frequency of a class or the
  probability of an event by the ease with which
  instances or occurrences can be brought to mind.
• Example Which is a more likely cause of death in
  the U.S. being killed by falling airplane parts
  or sharks?
• The Author notes that
• Some events are more available than others not
  because they tend to occur frequently or with
  high probability, but because they are inherently
  easier to think about, because they have taken
  place recently, because they are highly
  emotional, and so forth.
• Main questions
• What are the instances in which availability
  heuristic leads to biased judgments?
• Do decision makers perceive an event as more
  likely after they have imagined it
  happening?
• How is vivid information different from any
  other information?

69
The Limits of Imagination

• Availability is a misleading indicator of
  frequency
• Biased judgments when examples of one event are
  inherently more difficult to generate than
  examples of another event.
• availability is linked with the act of imagining
  an event
• Extremely negative outcomes
• Vividness
• refers to how concrete or imaginable something
  is?
• The Legal Significance of Guacamole the power
  of vividness
• but beware !
• The important thing
• explicitly compare over- and underestimated
  dangers with threats that are misperceived in the
  opposite directions.

70
Probability and Risk

• The Game show problem
• Conditional Probability
• Bayes Theorem Probability of an event, given
  some evidence of a relevant event
• P(A/B) P(A) . P(B/A)
• -------------------
• P(B)
• Itll never happen to meor will it?
• The degree to which an outcome is viewed as
  positive or negative
• positive outcomes viewed as more probable than
  negative outcomes

71
Compound Events
URN 1 2 colored marbles and 18 white marbles
URN 2 10 colored marbles and 10 white marbles

• Example
• choose between
• simple bets e.g. drawing a colored marble
  randomly from urn 1
• Compound bets e.g. consecutively drawing 4
  colored marbles from urn 2 (replacing marbles
  after each drawing)
• Reliance of outcome on multiple events
• decision makers tend to get anchored or stuck
  on the probabilities of the simple events
  making up the compound event

72
Conservatism and the Perception of Risk
The author says - once people have formed a
probability, estimate, they are often quite slow
to change the estimate when presented with new
information Stone Yates - Perception are
highly subjective, and the value people on
preventive behaviors depends in part upon the way
a particular risk is presented and the type of
risk it is. Risk perception is extremely
important but often complicated. Do Accidents
Make Us Safer? Perceptions of risk are strongly
biased in the direction of preexisting views
73
Take away this

• Maintain accurate records
• Beware of wishful thinking
• Break compound events into simple events
• Importance in Your research
• Use heuristics and probability measures carefully
• Be aware of biases arising from each type of
  heuristic
• Apply corrective measures to your data to undo
  the effect of biases
• Dont let your desire for accuracy sway you
  towards inaccurate data

74
Chapter 13Anchoring Adjustment
To reach a port, we must sail sail, not tie at
anchor sail, not drift. Franklin Delano
Roosevelt

• Iljoo Kim

75
Anchoring and Adjustment

• The insufficient adjustment up or down from an
  original starting value, or anchor
• Ex) Number estimates after a spin
• Anchoring is a robust phenomenon in which the
  size of the effect grows with the discrepancy
  between the anchor and the pre-anchor estimate.

76
What I really mean is?

• Arbitrary numerical references may have
  unintended effects
• - Would you support a U.S. attempt to build a
  defensive system against nuclear missiles and
  bombers if it were able to shoot down 90 of all
  Soviet nuclear missiles and bombers?
• - A defense that can protect against 99 of the
  Soviet nuclear arsenal may be judged as not good
  enough, given the destructive potential of the
  weapons that could survive

77
Power in a real-world

• Real Estate Agents Case
• - All agents given different figures about same
  information (e.g., info. about nearby properties)
• - Significant evidence of anchoring shown
• What we can see
• - Experts are not immune to it
• - Hard to realize
• - Powerful in real world

78
Things we learned

• Try to be free from the previous results or the
  existing perception
• Be aware of any suggested values that seem
  unusually high or low
• Generate an alternate anchor value that is
  equally extreme in the opposite direction
• Realize that a discussion of best- or worst-case
  scenarios can lead to unintended anchoring
  effects
• Worth considering multiple anchors before making
  final estimate

79
Chapter 14The Perception of Randomness
80
Ch. 14 The Perception of Randomness

• There are Coincidences out there
• People tend to see patterns in the randomness
• Which one is randomly selected?
• wwbbbwbwbbwbwww / wbwbwbwwbbwbwbw
• People saw randomness when there was actually a
  pattern, and saw patterns when the sequence was
  actually random

81
Things we learned

• Decision makers have a tendency to over-interpret
  chance events
• Researchers should resist the temptation to view
  short runs of the same outcome as meaningful
  Distinguish between a pattern and a coincidence!
• Try! Try! And Try!

82
Chapter 15Correlation, Causation Control
83
Ch 15. Correlation, Causation, and Control

• Correlation Assessments are not easy (Survey 14)

84
Illusory Correlation

• The mistaken impression that two unrelated
  variables are correlated
• e.g., Draw-A-Person test
• Hard to eliminate
• Usually from Stereotype, Longtime Perception
  Availability Explanation / Representativeness
  theory

85
Invisible Correlations

• Failing to see a correlation that does exist
• Difficult to detect in frequency
• Usually from the absence of an expectation
• e.g., correlation between smoking and lung
  cancer

86
Causation

• Correlation ! Causation
• Just as correlation need not imply a causal
  connection, causation need not imply a strong
  correlation
• Illusion of Control
• Belief of having more control over chance
  outcomes
• from Illusory Correlation and Causation

87
Things we learned

• Researchers should focus on more than confirming
  and positive cases of a relationship
• Take away biases
• Judgments from Observation or Expectation?
• Remember,
• Correlation ! Causation

88
SECTION VThe Social Side

• Jun Liu

89
Chapter 17Social Influences
90
Social Facilitation

• What change in an individuals normal performance
  occurs when other people are present?
• - Performance of simple, well-learned
  responses is enhanced while the performance of
  complex, unmastered skills tends to be impaired.
• 
  VS.

91
Social Loafing Bystander Intervention

• People do not work as hard in groups as they work
  alone.
• Decision to intervene is heavily influenced by
  the presence of others.
• Possible cause diffusion of responsibility

92
Social Comparison Theory

• People evaluate their opinion and abilities by
  comparing themselves with others.
• People tend to take cues from those who are
  similar
• Social analgesia social comparisons can
  influence perceptions.

93
Lessons Learned Three monks story
94
New version of three monks story

• Conclusion
• - Diffusion of responsibility leads to group
  failures
• - Explicitly assign responsibility to group
  members

95
Chapter 18Group Judgments Decisions
96
Group Errors and Biases

• Group-serving bias group members make
  dispositional attributions for group successes
  and situational attributions for group failures
• Outgroup homogeneity bias groups perceive
  their own members as more varied than members of
  other groups.

97
Are several heads better than one?

• Groups usually perform somewhat better than
  average individuals
• Groups performs worse than the best individual in
  a statistical aggregate of people
• Brainstorming is most effective when conducted by
  several people independently rather than in a
  group session

98
The Benefits of Dictatorship

• The best member of a group often outperforms the
  group
• The dictatorship technique outperforms other
  types of decision techniques (consensus,
  delphi, collective, etc.)
• An good leader encourages all members to express
  an opinion

99
Lessons learned

• Three cobblers with their wits combined equal
  Zhuge Liang the master mind.
• It is more important to put heads together
• Implications to MIS Researchers

100
SECTION VICommon Traps

• Mike Zhong

101
Chapter 19Overconfidence
102
Overconfidence

• Example
• Attack on Pearl Harbor
• Columbia Challenger disasters (The estimated
  launch risk was 1 catastrophic failure in 100,000
  launches equivalent to launching a shuttle once
  per day and expecting to see only one accident in
  three centuries)

103
Overconfidence

• Description
• Occurring when a subjects confidence in the
  estimated accuracy surpasses the real accuracy.
• Correlation between overconfidence and accuracy

104
Overconfidence

• Overconfidence in Research

Literature review
Information increased
Accuracy didnt increased accordingly
Confidence increased
Overconfidence
105
Overconfidence

• Remedy
• Extensive literature review is not enough itself
• stop to consider reasons why your judgment might
  be wrong
• because of the subjects confirmation bias,
  opinions from other researchers are valuable.

106
Confirmation Bias

• Example
• Have we bought a bargain?

Its a real bargain !
107
Confirmation Bias

• Confirmation Bias in Research
• Focusing on things which will confirm our new
  ideas or hypothesis, while ignoring the negative
  sides.
• Remedy
• Negative testing strategy
• Are all insects have 6 legs?

108
Chapter 20Self-Fulfilling Prophecies
109
Self-fulfilling Prophecies

• Example
• Robert Rosenthal and Lenore Jacobson s test,
  1968.This is also known as the Pygmalion Effect.
• Description
• The self-fulfilling prophecy is, in the
  beginning, a false definition of the situation
  evoking a new behavior which makes the originally
  false conception come true

110
Self-fulfilling Prophecies

• Using it
• Affecting a persons behavior.
• Defending
• Questioning their assumptions about you if you do
  not wish to be pushed in this direction.

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Chapter 21Behavioral Traps
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Behavioral Traps

• Description
• A course of action appears to be promising when
  embarked on, but later becomes undesirable and
  difficult to escape from.
• Traps Counter-traps

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Behavioral Traps

• Taxonomy
• Time delay traps (short-term vs long-term)
• Ignorance traps (unforeseen negative effects)
• Investment traps (sunk cost effects)
• Deterioration traps (changing benefits and cost)
• Collective traps (self-interests leads to
  negative consequences for whole)

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Behavioral Traps

• Avoiding behavioral traps in MIS Research
• To avoid time delay traps, balance short-term and
  long-term goals ( design vs implementation)
• To avoid ignorance traps, conduct comprehensive
  literature review before plunge into research
  work.
• To avoid collective traps, do not always depend
  on others in group research/work, do as good as
  you can when working alone.

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Summary / Key Take-Aways

• Changes in behavior can influence change in
  attitude
• Framing of questions/alternatives is important
• Understand the rationality of DM (e.g.
  satisficing)
• Be aware of biases arising from heuristics
  apply corrective measures!
• Dont over-interpret chance events
  distinguish between patterns and coincidence!
• The superior performance of groups is a function
  of not only having more heads than one but
  of putting those heads together!
• Avoid time-delay traps balance S-T and L-T
  goals!