Decision Making for All: Leaders, Followers, Partners, Loners, and More!
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This document discusses decision making and common cognitive biases that can influence decisions. It notes that perception, memory, and numerical data can be affected by biases like confirmation bias, availability heuristic, and representativeness heuristic. The document provides examples of these biases and suggests ways to avoid them, such as being aware of potential biases, looking for multiple perspectives, and finding objective data. Overall, the document aims to educate about cognitive biases that can undermine critical thinking and lead to flawed decisions.
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Decision Making for All: Leaders, Followers, Partners, Loners, and More!
- 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. How do you make a decision? • For a recent purchase • For a project at work November 10, 20162016 Indiana Library Federation Annual Conference
- 3. Bad decision November 10, 20162016 Indiana Library Federation Annual Conference
- 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. Potential biases on inputs • Perception • Memory • Numerical data November 10, 20162016 Indiana Library Federation Annual Conference
- 6. Perception November 10, 20162016 Indiana Library Federation Annual Conference Factors contribute to different perception includes: self-concept, values, experience, beliefs, and culture.
- 7. Biases related to perception • Confirmation effect November 10, 20162016 Indiana Library Federation Annual Conference • Expectation bias• Self-serving biases
- 8. Memory November 10, 20162016 Indiana Library Federation Annual Conference
- 9. Biases related to memory • Subsequent information November 10, 20162016 Indiana Library Federation Annual Conference • Moral evaluation
- 10. Numerical data November 10, 20162016 Indiana Library Federation Annual Conference
- 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. 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. Heuristics November 10, 20162016 Indiana Library Federation Annual Conference A mental shortcut: Solve problems and make judgements quickly and efficiently
- 14. Issues related to heuristics November 10, 20162016 Indiana Library Federation Annual Conference • Availability • Representativeness • Anchoring
- 15. Availability heuristic November 10, 20162016 Indiana Library Federation Annual Conference
- 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. 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. Avoiding heuristics pitfall • Recognize when you are using heuristic • Beware of biases associated with representativeness November 10, 20162016 Indiana Library Federation Annual Conference
- 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. 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. Fallacies of ambiguity November 10, 20162016 Indiana Library Federation Annual Conference
- 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. Questions? • Please feel free to contact me Vincci Kwong vkwong@iusb.edu 574-520-4444 November 10, 20162016 Indiana Library Federation Annual Conference