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Leveraging Cognitive Bias in Social Design
 

Leveraging Cognitive Bias in Social Design

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My d.Construct talk from Friday, September 5, 2008.

My d.Construct talk from Friday, September 5, 2008.

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    Leveraging Cognitive Bias in Social Design Leveraging Cognitive Bias in Social Design Presentation Transcript

    • Leveraging Cognitive Bias in Social Design Rationality be damned. Joshua Porter Purveyor: bokardo.com UX guy: chi.mp d.Construct 2008 author of
    • Q: Which restaurant would you choose?
    • http:// ickr.com/photos/elgrandee/1819133411/
    • http:// ickr.com/photos/albedo/110329014/
    • Bandwagon E ect the observation that people often do and believe things because many other people do and believe the same things
    • Heuristics When people are faced with a complicated judgment or decision, they often simplify the task by relying on heuristics, or general rules of thumb. In many cases, these shortcuts yield very close approximations to the “optimal” answer, that which results from purely rational thinking. Gather all information necessary Uncertainty for rational judgment Heuristic (shortcut) Make decision
    • Cognitive Biases In certain situations, heuristics lead to predictable biases and inconsistencies. Gather all information necessary Uncertainty for rational judgment Heuristic (shortcut) Bias Make decision
    • A few design-related biases Lake Wobegon e ect the phenomenon that a supermajority of people report themselves as above average in desirable qualities Self-serving bias the tendency to claim more responsibility for successes than failures Optimism bias the systematic tendency to be over-optimistic about the outcome of planned actions Not invented here the tendency to ignore that a product or solution already exists, because its source is seen as an quot;enemyquot; or as quot;inferiorquot; Hindsight bias also known as the ‘I-knew-it-all-along e ect’ Prediction bias the tendency to underestimate the time and cost of work Fun reading: http://en.wikipedia.org/wiki/List_of_cognitive_biases
    • Judgment under Uncertainty: Heuristics and Biases (1974) Daniel Kahneman & Amos Tversky Proposed theory: people do not make fully rational decisions, they use rules of thumb (heuristics) instead
    • Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. Please check o the most likely alternative. Linda is a bank teller. Linda is a bank teller and is active in the feminist movement.
    • Feminist Bank Tellers Bank Tellers Feminists ~90% choose “feminist bank tellers”, although that cannot be the case.
    • Representation Bias As the amount of detail in a scenario increases, its probability can only decrease steadily, but its representativeness and hence its apparent likelihood may increase.
    • Leveraging the representation bias (people will assume that these examples are representative of the group)
    • Leveraging the representation bias (people will assume that these examples are representative of the group)
    • Would you accept this bet? 50% chance of gaining £100 50% chance of losing £100
    • Would you accept this bet? 50% chance of gaining £200 50% chance of losing £100
    • Would you accept this bet? 50% chance of gaining £300 50% chance of losing £100
    • Loss Aversion Losses loom larger than gains
    • Feature described in terms of gain:
    • Feature described in terms of loss: josh.mp
    • Feature described in terms of future savings: Would it be more e ective framed as an immediate loss? (e.g. Don’t lose the ability to track your package)
    • What is more valuable to you? a) A co ee mug on sale at the local convenience store b) The co ee mug that your future husband/ wife gave you during that amazing autumn morning last year when they proposed to you. http:// ickr.com/photos/studiosmith/2559034807/
    • Ownership Bias People value things more when they feel a sense of ownership
    • The Sign-up Problem (the 9x e ect) Because of loss aversion Because of the ownership and the ownership e ect, e ect and the optimism people tend to overvalue bias, software makers the software they 3x 3x tend to overvalue what currently use by about a they o er by about a 9x factor of 3. factor of 3. Harvard Business Review, June 2006 further reading Eager Sellers, Stony Buyers by John T. Gourville
    • Thank you. Contact? http://twitter.com/bokardo porter@bokardo.com