The Psychology ofJudgment & Decision Making

MIS 696A – Readings in MIS (Nunamaker)05 November 2003

[Cha / Correll / Diller / Gite / Kim / Liu / Zhong]

SECTION I:Perception, Memory & Context

Hoon Cha & Jeff Correll

Chapter 1:Selective Perception

Hoon Cha

Define First and See?

• People selectively perceive what they expect and hope to see

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.

Lessons Learned

• Before conducting your research and interpreting your results– Ask yourself what expectations you did bring

into the situation?– Consult with others who don’t share your

expectations and motives

Chapter 2:Cognitive Dissonance

• 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

Are you a sexist person?

Examples

• Smokers find all kinds of reasons to explain away their unhealthy habit.

• The alternative is to feel a great deal of dissonance.

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

Chapter 3:Memory & Hindsight Biases

"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

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.

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

Chapter 4:Context Dependence

Jeff Correll

• Contrast Effect

• Primacy Effect

• Recency Effect

• Halo Effect

4 Illustrations of Context Effect

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 won’t change if standing next to a large race horse (Ebbinghaus Illusion)

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

Primacy 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

Recency Effect

People can’t 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.

Halo Effect

Everything is context-dependent

Persuasion professionals exploit these effects

• Includes us as MIS Researchers!

Contextual effects are limited

Conclusion – Context Dependence

SECTION II:How Questions Affect Answers

How the format of a problem can influence the way people respond to it

Jeff Correll

Chapter 5:Plasticity

Jeff Correll

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 language’=Risk-averse

Are you a ‘gambler’?

Order of questions/alternatives also influence responses

Example: Schuman and Presser’s 1981 survey on freedom of the press

Recency effect is the most common response order effect

Example: Survey question about divorce

Order Effects

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

Pseudo-Opinions

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!

Inconsistency

Ultimate example of attitude-behavior inconsistency: Darley and Batson’s 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)

Inconsistency – Continued

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!

Conclusion – Plasticity

Chapter 6:Effects of Wording & Framing

Jeff Correll

Small changes in wording can equal big changes in how people answer:

• Example: Does your country’s 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)

Question Wording

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)

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

Response Scales / Social Desirability / Allow vs. Forbid

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)

Framing

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

Framing – Continued

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 you’ve lost a $10 bill. Would you still pay $10 for a ticket to the play? (88% said yes)

• Same situation, but this time you’ve lost your $10 ticket (which you’ve already paid for and can’t replace). Would you pay $10 for another ticket? (only 46% said yes!)

Psychological Accounting

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)

Conclusion – Question Wording and Framing

SECTION III:Models of Decision Making

Chris Diller

Chapter 7:Expected Utility Theory

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)

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!

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 > B and B > C … then A > C !

– Continuity = Prefer gamble to sure thing (odds!)

– Invariance = Unaffected by way alt's are presented

• A Major Paradigm with Many Extensions

Chapter 8:Paradoxes in Rationality

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

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)

Intransitivity

• "Money Pump"– Decision makers with intransitive preferences

• A < B B < C A > 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

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

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.

Chapter 9:Descriptive Models of DM

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

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

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!

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

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!

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

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)

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)

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!

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

Break

SECTION IV:Heuristics & Biases

Sanghu Gite, Iljoo Kim & Jun Liu

Heuristics and Biases

Sanghmitra Gite

He loves me…he loves me not…

HOW?

WHICH?

WHEN? WHERE?

IF?

WHY?

Heuristics or Hueristics?

General Rules of Thumb

Reduce time and effort

Fairly good estimate

Leads to predictable biases

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.”

The Representativeness Heuristic

Tversky and Kahneman—“People often judge probabilities …by the degree to which A resembles B”.

Don’t be misled by highly detailed scenarios!

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 movementExample #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.

“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.”

The Law of Small Numbers

Remember that chance is not self-correcting!

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: Gambler’s 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.”

Neglecting Base Rates

Whenever possible, pay attention to 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

Nonregressive Prediction

“Regression to the mean is the phenomena in which high or low scores tend to be followed by more average scores…The 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, disease…or 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…”

Don’t misinterpret regression toward the mean

The Availability HeuristicTversky 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?

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.”

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)

It’ll never happen to me…or will it? The degree to which an outcome is viewed as positive or negative positive outcomes viewed as more probable than negative outcomes

Compound Events

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

URN 12 colored marbles and

18 white marbles

URN 210 colored marbles and

10 white marbles

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

Take away this…

• Maintain accurate records

• Beware of wishful thinking

• Break compound events into simple events

Importance in Your research

o Use heuristics and probability measures carefully

o Be aware of biases arising from each type of heuristic

o Apply corrective measures to your data to “undo” the effect of biases

o Don’t let your “desire” for accuracy sway you towards inaccurate data

Chapter 13:Anchoring & Adjustment

“To reach a port, we must sail – sail, not tie at anchor – sail, not drift.”

Franklin Delano Roosevelt

Iljoo Kim

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”.

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”

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

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

Chapter 14:The Perception of Randomness

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

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!

Chapter 15:Correlation, Causation & Control

Ch 15. Correlation, Causation, and Control

• Correlation Assessments are not easy (Survey #14)

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

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

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

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”

SECTION V:The Social Side

Jun Liu

Chapter 17:Social Influences

Social Facilitation

• What change in an individual’s 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.

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

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.

Lessons Learned Three monks’ story

New version of three monks’ story

• Conclusion:

- Diffusion of responsibility leads to group failures

- Explicitly assign responsibility to group members

Chapter 18:Group Judgments & Decisions

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.

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

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

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

SECTION VI:Common Traps

Mike Zhong

Chapter 19:Overconfidence

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)

Overconfidence

• Description– Occurring when a subject’s confidence in the estimated

accuracy surpasses the real accuracy.

– Correlation between overconfidence and accuracy:

Overconfidence

Accuracy

50% 80%

Overconfidence

• Overconfidence in Research

Literature review

Accuracy didn’t increased accordingly

Confidence increased

Overconfidence

Information increased

Overconfidence

• Remedy– Extensive literature review is not enough itself;– stop to consider reasons why your judgment

might be wrong;– because of the subject’s confirmation bias,

opinions from other researchers are valuable.

Confirmation Bias

• Example– Have we bought a bargain?

It’s a real bargain !

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 strategyAre all insects have 6 legs?

Chapter 20:Self-Fulfilling Prophecies

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”

Self-fulfilling Prophecies

• Using it– Affecting a person’s behavior.

• Defending – Questioning their assumptions about you if you

do not wish to be pushed in this direction.

Chapter 21:Behavioral Traps

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

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)

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.

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!

• Don’t 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!