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.
Call now : 9892124323 Nalasopara Beautiful Call Girls Vasai virar Best Call G...
Decision Making Insights
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
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
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