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November 10, 2016
Decision Making for All
Leaders, Followers, Partners, Loners, and more!
Vincci Kwong
Indiana University ...
How do you make a decision?
• For a recent purchase
• For a project at work
November 10, 20162016 Indiana Library Federati...
Bad decision
November 10, 20162016 Indiana Library Federation Annual Conference
Critical thinking and evaluation
November 10, 20162016 Indiana Library Federation Annual Conference
Purpose /
Goal
Questio...
Potential biases on inputs
• Perception
• Memory
• Numerical data
November 10, 20162016 Indiana Library Federation Annual ...
Perception
November 10, 20162016 Indiana Library Federation Annual Conference
Factors contribute to different perception i...
Biases related to perception
• Confirmation effect
November 10, 20162016 Indiana Library Federation Annual Conference
• Ex...
Memory
November 10, 20162016 Indiana Library Federation Annual Conference
Biases related to memory
• Subsequent information
November 10, 20162016 Indiana Library Federation Annual Conference
• Mor...
Numerical data
November 10, 20162016 Indiana Library Federation Annual Conference
Issues related to numerical data
• Response bias
• Representativeness
• Framing
 Pseudo opinions
 Answer sets
 Response...
Ways to avoid biases
• Be aware
• Actively look for other perspectives
• Find objective data
Perception specific:
• Focus ...
Heuristics
November 10, 20162016 Indiana Library Federation Annual Conference
A mental shortcut:
Solve problems and make j...
Issues related to heuristics
November 10, 20162016 Indiana Library Federation Annual Conference
• Availability
• Represent...
Availability heuristic
November 10, 20162016 Indiana Library Federation Annual Conference
Representativeness heuristic
• A cognitive bias in which an individual categorizes a
situation based on a pattern of previ...
Anchoring
• Use an initial piece of information to
make subsequent judgments
November 10, 20162016 Indiana Library Federat...
Avoiding heuristics pitfall
• Recognize when you are using heuristic
• Beware of biases associated with
representativeness...
Informal fallacies
• Defects found in the content of the
arguments. There are many ways
arguments can be defective.
 Fall...
Fallacies of relevance
November 10, 20162016 Indiana Library Federation Annual Conference
Ad Hominem
• Straw man
• Appeal ...
Fallacies of ambiguity
November 10, 20162016 Indiana Library Federation Annual Conference
Interested in more fallacies?
• http://www.iep.utm.edu/fallacy/
• http://utminers.utep.edu/omwilliamson/EN
GL1311/fallacie...
Questions?
• Please feel free to contact me
Vincci Kwong
vkwong@iusb.edu
574-520-4444
November 10, 20162016 Indiana Librar...
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Decision Making for All: Leaders, Followers, Partners, Loners, and More!

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This presentation provides a general framework for critical thinking, together with common fallacies and biases.

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Decision Making for All: Leaders, Followers, Partners, Loners, and More!

  1. 1. November 10, 2016 Decision Making for All Leaders, Followers, Partners, Loners, and more! Vincci Kwong Indiana University South Bend 2016 Indiana Library Federation Annual Conference
  2. 2. How do you make a decision? • For a recent purchase • For a project at work November 10, 20162016 Indiana Library Federation Annual Conference
  3. 3. Bad decision November 10, 20162016 Indiana Library Federation Annual Conference
  4. 4. Critical thinking and evaluation November 10, 20162016 Indiana Library Federation Annual Conference Purpose / Goal Question / Issue Assumptions Information / Data / Evidence Reasoning Conclusions / Implications Alternatives Source: Variation on Gerald Nosich model
  5. 5. Potential biases on inputs • Perception • Memory • Numerical data November 10, 20162016 Indiana Library Federation Annual Conference
  6. 6. Perception November 10, 20162016 Indiana Library Federation Annual Conference Factors contribute to different perception includes: self-concept, values, experience, beliefs, and culture.
  7. 7. Biases related to perception • Confirmation effect November 10, 20162016 Indiana Library Federation Annual Conference • Expectation bias• Self-serving biases
  8. 8. Memory November 10, 20162016 Indiana Library Federation Annual Conference
  9. 9. Biases related to memory • Subsequent information November 10, 20162016 Indiana Library Federation Annual Conference • Moral evaluation
  10. 10. Numerical data November 10, 20162016 Indiana Library Federation Annual Conference
  11. 11. Issues related to numerical data • Response bias • Representativeness • Framing  Pseudo opinions  Answer sets  Response scales  Social desirability  Allowing vs. Forbidding November 10, 20162016 Indiana Library Federation Annual Conference
  12. 12. 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 November 10, 20162016 Indiana Library Federation Annual Conference
  13. 13. Heuristics November 10, 20162016 Indiana Library Federation Annual Conference A mental shortcut: Solve problems and make judgements quickly and efficiently
  14. 14. Issues related to heuristics November 10, 20162016 Indiana Library Federation Annual Conference • Availability • Representativeness • Anchoring
  15. 15. Availability heuristic November 10, 20162016 Indiana Library Federation Annual Conference
  16. 16. Representativeness heuristic • A cognitive bias in which an individual categorizes a situation based on a pattern of previous experiences or beliefs about the scenario. November 10, 20162016 Indiana Library Federation Annual Conference Women Vegetarian Women & Vegetarian 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
  17. 17. Anchoring • Use an initial piece of information to make subsequent judgments November 10, 20162016 Indiana Library Federation Annual Conference 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?
  18. 18. Avoiding heuristics pitfall • Recognize when you are using heuristic • Beware of biases associated with representativeness November 10, 20162016 Indiana Library Federation Annual Conference
  19. 19. 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 November 10, 20162016 Indiana Library Federation Annual Conference
  20. 20. Fallacies of relevance November 10, 20162016 Indiana Library Federation Annual Conference Ad Hominem • Straw man • Appeal to tradition • Argument from the club • Sunk cost fallacy • Appeal to pity Argument from popularity Appeal to authority
  21. 21. Fallacies of ambiguity November 10, 20162016 Indiana Library Federation Annual Conference
  22. 22. Interested in more fallacies? • http://www.iep.utm.edu/fallacy/ • http://utminers.utep.edu/omwilliamson/EN GL1311/fallacies.htm November 10, 20162016 Indiana Library Federation Annual Conference
  23. 23. Questions? • Please feel free to contact me Vincci Kwong vkwong@iusb.edu 574-520-4444 November 10, 20162016 Indiana Library Federation Annual Conference

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