decision making for all: leaders, followers, partners, loners, and more!

November 10, 2016

Decision Making for AllLeaders, Followers, Partners, Loners, and more!

Vincci KwongIndiana University South Bend

2016 Indiana Library Federation Annual Conference


How do you make a decision?• For a recent purchase• For a project at work

November 10, 2016


Bad decision

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Critical thinking and evaluation

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ClearAccurate

Relevant / important

Sufficient

Purpose / Goal

Question / Issue

AssumptionsInformation /

Data / Evidence

Reasoning

Conclusions / Implications

Alternatives

Source: Variation on Gerald Nosich model


Potential biases on inputs• Perception• Memory• Numerical data

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Perception

November 10, 2016

Factors contribute to different perception includes: self-concept, values, experience, beliefs, and culture.


Biases related to perception• Confirmation effect

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• Expectation bias• Self-serving biases


Memory

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Biases related to memory

• Subsequent information

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• Moral evaluation


Numerical data

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Issues related to numerical data• Response bias• Representativeness• Framing

Pseudo opinions Answer sets Response scales Social desirability Allowing vs. Forbidding

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Ways to avoid biases• Be aware• Actively look for other perspectives• Find objective data

Perception specific:• Focus on questions that both agree with and challenge

our thinking• Try to take different point of views intentionally

Memory specific:• Keep notes

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Heuristics

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A mental shortcut:Solve problems and make judgements quickly and efficiently


Issues related to heuristics

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• Availability• Representativeness• Anchoring


Availability heuristic

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Representativeness heuristic• A cognitive bias in which an individual categorizes a

situation based on a pattern of previous experiences or beliefs about the scenario.

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Women VegetarianWomen

&Vegetaria

n

Question: Polly went to the store and bought tofu, eggplant, broccoli, and frozen meatless lasagna. Is it more likely that Mary is a woman or a woman who is a vegetarian?

Gambler’s Fallacy

Conjunction Fallacy


Anchoring• Use an initial piece of information to

make subsequent judgments

November 10, 2016

How would you answer these two questions?

1. Is the population of Shanghai greater than 20 million?

2. What’s your best estimate of Shanghai’s population?


Avoiding heuristics pitfall• Recognize when you are using heuristic• Beware of biases associated with

representativeness

November 10, 2016


Informal fallacies• Defects found in the content of the

arguments. There are many ways arguments can be defective. Fallacies of Relevance Fallacies of ambiguity Fallacies of the complex question Fallacies of weak induction

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Fallacies of relevance

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Ad Hominem

• Straw man• Appeal to tradition• Argument from the club• Sunk cost fallacy• Appeal to pity

Argument from popularity

Appeal to authority


Fallacies of ambiguity

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Interested in more fallacies?• http://www.iep.utm.edu/fallacy/• http://

utminers.utep.edu/omwilliamson/ENGL1311/fallacies.htm

November 10, 2016

http://www.iep.utm.edu/fallacy/

http://www.iep.utm.edu/fallacy/

http://utminers.utep.edu/omwilliamson/ENGL1311/fallacies.htm




Questions?• Please feel free to contact me

Vincci [email protected]

November 10, 2016

mailto:[email protected]

decision making for all: leaders, followers, partners, loners, and more!

Leadership & Management