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Decision Making for All Leaders, Followers, Partners, Loners, and more!
Vincci Kwong Indiana University South Bend 2016 Indiana Library Federation Annual Conference November 10, 2016
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How do you make a decision?
For a recent purchase For a project at work 2016 Indiana Library Federation Annual Conference November 10, 2016
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Bad decision 2016 Indiana Library Federation Annual Conference
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Critical thinking and evaluation
Purpose / Goal Question / Issue Assumptions Information / Data / Evidence Reasoning Conclusions / Implications Alternatives Clear Accurate Relevant / important Sufficient 2016 Indiana Library Federation Annual Conference Source: Variation on Gerald Nosich model November 10, 2016
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Potential biases on inputs
Perception Memory Numerical data 2016 Indiana Library Federation Annual Conference November 10, 2016
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Perception Factors contribute to different perception includes:
self-concept, values, experience, beliefs, and culture. 2016 Indiana Library Federation Annual Conference November 10, 2016
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Biases related to perception
Confirmation effect Self-serving biases Expectation bias 2016 Indiana Library Federation Annual Conference November 10, 2016
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Memory 2016 Indiana Library Federation Annual Conference
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Biases related to memory
Subsequent information Moral evaluation 2016 Indiana Library Federation Annual Conference November 10, 2016
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Numerical data 2016 Indiana Library Federation Annual Conference
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Issues related to numerical data
Response bias Representativeness Framing Pseudo opinions Answer sets Response scales Social desirability Allowing vs. Forbidding 2016 Indiana Library Federation Annual Conference November 10, 2016
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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 2016 Indiana Library Federation Annual Conference November 10, 2016
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Heuristics A mental shortcut:
Solve problems and make judgements quickly and efficiently 2016 Indiana Library Federation Annual Conference November 10, 2016
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Issues related to heuristics
Availability Representativeness Anchoring 2016 Indiana Library Federation Annual Conference November 10, 2016
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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. 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 Women Vegetarian & Conjunction Fallacy 2016 Indiana Library Federation Annual Conference November 10, 2016
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Anchoring Use an initial piece of information to make subsequent judgments How would you answer these two questions? Is the population of Shanghai greater than 20 million? What’s your best estimate of Shanghai’s population? 2016 Indiana Library Federation Annual Conference November 10, 2016
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Avoiding heuristics pitfall
Recognize when you are using heuristic Beware of biases associated with representativeness 2016 Indiana Library Federation Annual Conference November 10, 2016
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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 2016 Indiana Library Federation Annual Conference November 10, 2016
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Fallacies of relevance
Ad Hominem Argument from popularity Straw man Appeal to tradition Argument from the club Sunk cost fallacy Appeal to pity Appeal to authority 2016 Indiana Library Federation Annual Conference November 10, 2016
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Fallacies of ambiguity
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Interested in more fallacies?
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Questions? Please feel free to contact me Vincci Kwong vkwong@iusb.edu
2016 Indiana Library Federation Annual Conference November 10, 2016
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