Rationality and Heuristics: Part I
Rationality and Heuristics: Part I Soc 201
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This 3 page Class Notes was uploaded by Julia Caine on Tuesday September 20, 2016. The Class Notes belongs to Soc 201 at New York University taught by Blaine Robbins in Fall 2016. Since its upload, it has received 13 views. For similar materials see Social Psychology in Sociology at New York University.
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Date Created: 09/20/16
Rationality and Heuristics: Part I Rational choice theory o Based on four assumptions Preferred assumption Purposeful or intentional actions based on hierarchy of preference guide individual behavior o Based on wants and desires, not likes and dislikes Preference types o Egoistic You care about your own welfare more than other people’s o General Preferences that can span time and space o Fungibility and liquidity A product is has the same value as another unit of that product (fungibility) and can have the same value as another product (liquidity) Constraints assumption Anything that increases or decreases an individual’s ability by performing certain actions is a condition for performing these actions Utility maximization assumption Individuals choose those actions that satisfy their preferences to the greatest extent, taking into account the constraints Auxiliary assumption People are fully informed about their environment and about their own past and future behavior o Often not true o Narrow (thin) vs wide (thick) version Narrow Only egoistic preferences are relevant Only tangible constraints are relevant Subjects are fully informed Objective constraints are relevant Wide All kinds of preferences may be explanatory factors All kinds of constraints may govern human behavior Subject may, but need not, be fully informed Perceived as well as objective constraints may be relevant Issues of uncertainty o According to the narrow version: Within the limits of their constraint, guided by their preferences and fully informed beliefs, humans tend to choose their actions that maximize their preferences to greatest extent o Which components are unrealistic? Full information and maximization (to certain extent) Example There is a 50% chance of rain, so you have to choose whether or not you bring your umbrella E(U)= (.5*-6) + (.5*10) = 2 o If you bring your umbrella and it rains, the utility will be -6 o If you bring your umbrella and it doesn’t rain, the utility will be 10 E(!U) = (.5*-24) + (.5*12)= 6 o If you don’t bring your umbrella and it rains, the utility will be -24 o If you don’t bring your umbrella and it doesn’t rain, the utility will be 12 Satisficing o Perfect solutions and maximization are rare because: Uncertain and complex world Cognitive limitations We often filter out what we perceive is unnecessary stimuli Instead people satisfice Combination of satisfying preferences and sacrificing max utility o Settling for what is “good enough” o How do people arrive at “optimal” decisions with satisficing? People use heuristics, which are cognitive shortcuts that reduce complex problem solving to simpler operations, to meet the pressing demands of the environment In other words, a simplified mental strategy Heuristics o Heuristics are proxies for more optimal judgement strategies They provide a better basis for judgement than chance, but do not deliver the guarantee of 100% correct judgement that expected value theory or other algorithmic processing methods might deliver Heuristics: Representative Heuristic o Frequently as “What is the probability that…” “Object A belongs to category B?” “Event A originates from process B?” “Process B generates event A?” o When probabilities are evaluated by the degree to which A resembles (or is representative of) B o Issue with representative heuristics Often rely on our ideas and don’t get info that goes against it People ignore: Sample size o Smaller sample sizes have more variability and room for error than larger sample sizes Base rates o A prior probability, a factual description, or a statistical accounting that describes the situation o Explains bad decisions Why people assume their decisions are more valid then they really are Why people have problems in making estimates of chance events
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