Reasoning with Uncertainty. Reasoning with Uncertainty. Bayes Rule. Often, we want to reason from observable information to unobservable information
|
|
- Darren Doyle
- 5 years ago
- Views:
Transcription
1 Reasoning with Uncertainty Reasoning with Uncertainty Often, we want to reason from observable information to unobservable information We want to calculate how our prior beliefs change given new available evidence Bayes rule tells us how to optimally reason with uncertainty. Do people reason like Bayes rule? Bayes Rule Prior probability Evidence Posterior Probability Bayes rule tells us how the available evidence should alter our belief in something being true 1
2 Difficulties in Reasoning with Uncertainty Problems reasoning with probabilities frequencies are easier to understand Problems understanding Conditional probability doctors need to calculate the probability of disease given the observed symptoms: P( disease symptoms ) Sometimes P( symptoms disease ) is used incorrectly when reasoning about the likelihood of a disease Why is this wrong? The base rate is important To get P( disease symptom ), you need to know about P( symptom disease ) and also the base rate -- prevalence of the disease before you have seen patient More intuitive example: what is the probability of being tall given you are player in the NBA? what is the probability of being a player in the NBA given that you are tall? P( NBA player tall ) P( tall NBA player ) Reasoning with base rates Suppose there is a disease that affects 1 out of 100 people There is a diagnostic test with the following properties: If the person has the disease, the test will be positive 98% of the time if the person does not have the disease, the test will be positive 1% of the time A person tests positive, what is the probability that this person has the disease? Frequent answer =.98 Correct answer.50 2
3 Are we really that bad in judging probabilities? According to some researchers (e.g., Gigerenzer), it matters how the information is presented and processed. Processing frequencies is more intuitive than probabilities (even it leads to the same outcome). A counting heuristic (in tree form) 10,000 people 100 have disease 9,900 do not 98 test positive 2 test negative 99 test positive 9801 test negative P( disease test positive ) = 98 / ( ).50 The same thing in words... Let s take 10,000 people. On average, 100 out of 10,000 actually have the disease and 98 of those will test positive (98% true positive rate) Among the 9,900 who do not have the disease, the test will falsely identify 1% as having it. 1% of 9,900 = 99 On average, out of 10,000 people: 98 test positive and they have the disease 99 test positive and they do not have the disease. Therefore, a positive test outcome implies a 98/(98+99) 50% chance of having the disease 3
4 Change the example What now if the disease affects only 1 out of 10,000 people? Assume same diagnosticity of test (98% true positive rate, 1% false positive rate) A person tests positive, what now is the probability that this person has the disease? A counting heuristic (in tree form) 1,000,000 people 100 have disease 999,900 do not have the disease 98 test positive 2 test negative 9999 test positive test negative P( disease test positive ) = 98 / ( ) =.0097 (smaller than 1%) Bayes Rule The previous example essentially is a simple way to apply Bayes rule: P ( positive disease) P( disease) P( disease positive) = P ( positive disease) P( disease) + P ( positive not disease) P( not disease) P( positive disease ) =.98 P( positive not disease ) =.01 P( disease ) =.0001 P( disease positive ) =
5 Normative Model Bayes rule tells you how you should reason with probabilities it is a prescriptive (i.e., normative) model But do people reason like Bayes? In certain circumstances, the base rates are neglected base rate neglect The Taxi Problem: version 1 A witness sees a crime involving a taxi in Carborough. The witness says that the taxi is blue. It is known from previous research that witnesses are correct 80% of the time when making such statements. What is the probability that a blue taxi was involved in the crime? The Taxi Problem: version 2 A witness sees a crime involving a taxi in Carborough. The witness says that the taxi is blue. It is known from previous research that witnesses are correct 80% of the time when making such statements. The police also know that 15% of the taxis in Carborough are blue, the other 85% being green. What is the probability that a blue taxi was involved in the crime? 5
6 Base Rate Neglect: The Taxi Problem Failure to take prior probabilities (i.e., base rates) into account In the taxi story, the addition of: The police also know that 15% of the taxis in Carborough are blue, the other 85% being green. has little influence on rated probability Base Rate Neglect (2) Kahneman & Tversky (1973). group A: 70 engineers and 30 lawyers group B: 30 engineers and 70 lawyers What is probability of picking an engineer in group A and B? Subjects can do this Provide some evidence Jack is a 45 year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies, which include home carpentry, sailing, and mathematical puzzles What now is probability Jack is an engineer? Estimates for both group A and group B was P =.9 6
7 Heuristics and Biases Tversky & Kahneman propose that people often do not follow rules of probability Instead, decision making may be based on heuristics Lower cognitive load but may lead to systematic errors and biases Example heuristics representativeness availability All the families having exactly six children in a particular city were surveyed. In 72 of the families, the exact order of the births of boys and girls was: G B G B B G What is your estimate of the number of families surveyed in which the exact order of births was: B G B B B B Answer: a) < 72 b) 72 c) >72 Representativeness Heuristic The sequence G B G B B G is seen as A) more representative of all possible birth sequences. B) better reflecting the random process of B/G 7
8 A coin is flipped. What is a more likely sequence? A) H T H T T H B) H H H H H H A) #H = 3 and #T = 3 (in some order) B) #H = 6 Gambler s fallacy: wins are perceived to be more likely after a string of losses Does the hot hand phenomenon exist? Most basketball coaches/players/fans refer to players having a Hot hand or being in a Hot zone and show Streaky shooting However, making a shot after just making three shots is pretty much as likely as after just missing three shots not much statistical evidence that basketball players switch between a state of hot hand and cold hand (Gilovich, Vallone, & Tversky, 1985) 8
9 Availability Heuristic Are there more words in the English language that begin with the letter V or that have V as their third letter? What about the letter R, K, L, and N? (Tversky & Kahneman, 1973) 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 anti-nuclear demonstrations. Rate the likelihood that the following statements about Linda are true: a) Linda is active in the feminist movement b) Linda is a bank teller c) Linda is a bank teller and is active in the feminist movement Rating C as more likely than B and A is a Conjunction Fallacy What to make of these results? One interpretation of Tversky & Kahneman s findings: people do not use proper probabilistic reasoning people use arbitrary mechanisms/ heuristics with no apparent rationale However, Gigerenzer and Todd show in their Fast and Frugal Heuristics research program that heuristics can often be very effective 9
10 Which city has a larger population? A) San Diego B) San Antonio 66% accuracy with University of Chicago undergraduates. However, 100% accuracy with German students. San Diego was recognized as American cities by 78% of German students. San Antonio: 4% With lack of information, use recognition heuristic (Goldstein & Gigerenzer, 2002) How to pick a stock Problem: what stocks to invest in? Solution 1 optimizing : Gather lots of info about many companies Process with sophisticated tools and choose Solution 2 the recognition heuristic: Purchase stocks from recognized companies (slide from Peter Todd) Paying for the name. (slide from Peter Todd) 10
11 Picking Stocks with Recognition Heuristic Borges et al. (1999) can ignorance beat the stock market? 180 German lay-people recognition of German stocks 6 month return on DAX 30: Dec 1996 Jun 1997 Market Index +34% Recognition Rate > 90% +47% Recognition Rate < 10% +13% Note: this result has not replicated in other studies (e.g., Boyd, 2001; Rakow, 2002) -- don t rush to use this heuristic on your own money! 11
Decision-making II judging the likelihood of events
Decision-making II judging the likelihood of events Heuristics and Biases Tversky & Kahneman propose that people often do not follow rules of probability Instead, decision making may be based on heuristics
More informationHeuristics & Biases:
Heuristics & Biases: The Availability Heuristic and The Representativeness Heuristic Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/29/2018: Lecture 10-2 Note: This Powerpoint presentation
More informationRepresentativeness Heuristic and Conjunction Errors. Risk Attitude and Framing Effects
1st: Representativeness Heuristic and Conjunction Errors 2nd: Risk Attitude and Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/30/2018: Lecture 10-3 Note: This Powerpoint
More informationFirst Problem Set: Answers, Discussion and Background
First Problem Set: Answers, Discussion and Background Part I. Intuition Concerning Probability Do these problems individually Answer the following questions based upon your intuitive understanding about
More information4/26/2017. Things to Consider. Making Decisions and Reasoning. How Did I Choose to Get Out of Bed...and Other Hard Choices
How Did I Choose to Get Out of Bed...and Other Hard Choices Judgments, Decision Making, Reasoning Things to Consider What kinds of reasoning traps do people get into when making judgments? What is the
More informationPerception Search Evaluation Choice
Decision Making as a Process Perception of current state Recognition of problem, opportunity, source of dissatisfaction Framing of problem situation and deciding how to decide Search Search for alternatives
More informationPrediction. Todd Davies Symsys 130 April 22, 2013
Prediction Todd Davies Symsys 130 April 22, 2013 Variable Prediction Independent variable (IV) predictor Dependent variable (DV) - target Judgment and Prediction Estimation predict a population statistic
More informationInside View Versus Outside View Irregularity Hypothesis Attribute Substitution
Finish: Next: Next: Inside View Versus Outside View Irregularity Hypothesis Attribute Substitution Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 10/24/2017: Lecture 05-1 Note: This
More informationReferences. Christos A. Ioannou 2/37
Prospect Theory References Tversky, A., and D. Kahneman: Judgement under Uncertainty: Heuristics and Biases, Science, 185 (1974), 1124-1131. Tversky, A., and D. Kahneman: Prospect Theory: An Analysis of
More informationAnswer the questions on the handout labeled: Four Famous Reasoning Problems. Try not to remember what you may have read about these problems!
Classroom Experiment Answer the questions on the handout labeled: Four Famous Reasoning Problems Try not to remember what you may have read about these problems! Psych 466, Miyamoto, Aut '17 1 The Representativeness
More informationThe role of linguistic interpretation in human failures of reasoning
The role of linguistic interpretation in human failures of reasoning Salvador Mascarenhas The University of Oxford Ecole Normale Supérieure ESSLLI 2016, week 2, lecture #4 1 The conjunction fallacy Scandinavian
More informationExperimental Economics Lecture 3: Bayesian updating and cognitive heuristics
Experimental Economics Lecture 3: Bayesian updating and cognitive heuristics Dorothea Kübler Summer term 2014 1 The famous Linda Problem (Tversky and Kahnemann 1983) Linda is 31 years old, single, outspoken,
More informationRepresentativeness heuristics
Representativeness heuristics 1-1 People judge probabilities by the degree to which A is representative of B, that is, by the degree to which A resembles B. A can be sample and B a population, or A can
More informationProbabilistic judgment
Second-Year Advanced Microeconomics: Behavioural Economics Behavioural Decision Theory: Probabilistic judgment, Hilary Term 2010 Vincent P. Crawford, University of Oxford (with very large debts to Matthew
More informationWhen Intuition. Differs from Relative Frequency. Chapter 18. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.
When Intuition Chapter 18 Differs from Relative Frequency Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc. Thought Question 1: Do you think it likely that anyone will ever win a state lottery
More informationBehavioural Issues: Heuristics & Biases; Level-k Reasoning
Decision Making in Robots and Autonomous Agents Behavioural Issues: Heuristics & Biases; Level-k Reasoning Subramanian Ramamoorthy School of Informatics 5 March, 2013 The Rational Animal The Greeks (Aristotle)
More informationSlide 1. Slide 2. Slide 3. Is implies ought: Are there ethical cognitive biases? What are cognitive biases? categories
Slide 1 Is implies ought: Are there ethical cognitive biases? 1 Slide 2 What are cognitive biases? cognitive biases are empirically discoverable and predictable deviations from normative standards of reasoning,
More informationOct. 21. Rank the following causes of death in the US from most common to least common:
Oct. 21 Assignment: Read Chapter 17 Try exercises 5, 13, and 18 on pp. 379 380 Rank the following causes of death in the US from most common to least common: Stroke Homicide Your answers may depend on
More informationIn what follows, we will assume the existence of a context without stating it.
4 Confidence 4.1 Probability The development of probability theory below follows Kolmogorov (1933), as elaborated upon by authors such as Krantz, Luce, Suppes, and Tversky (1971) and Kreps (1988). We consider
More informationCS 5306 INFO 5306: Crowdsourcing and. Human Computation. Lecture 10. 9/26/17 Haym Hirsh
CS 5306 INFO 5306: Crowdsourcing and Human Computation Lecture 10 9/26/17 Haym Hirsh Infotopia, Chapter 3 Four Big Problems for Deliberating Groups Four Big Problems for Deliberating Groups Amplifying
More informationPsychological. Influences on Personal Probability. Chapter 17. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.
Psychological Chapter 17 Influences on Personal Probability Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc. 17.2 Equivalent Probabilities, Different Decisions Certainty Effect: people
More informationIs implies ought: Are there ethical cognitive biases?
Is implies ought: Are there ethical cognitive biases? 1 What are cognitive biases? cognitive biases are empirically discoverable and predictable deviations from normative standards of reasoning, observable
More informationROLE OF HEURISTICS IN RISK MANAGEMENT
ROLE OF HEURISTICS IN RISK MANAGEMENT Kuwait Enterprise Risk Management Conference 4 th Edition, 2017 Abhishek Upadhayay CFPS, MIRM its about time We use heuristics to simplify choices in relation to risk.
More informationReasoning about probabilities (cont.); Correlational studies of differences between means
Reasoning about probabilities (cont.); Correlational studies of differences between means Phil 12: Logic and Decision Making Fall 2010 UC San Diego 10/29/2010 Review You have found a correlation in a sample
More informationChance. May 11, Chance Behavior The Idea of Probability Myths About Chance Behavior The Real Law of Averages Personal Probabilities
Chance May 11, 2012 Chance Behavior The Idea of Probability Myths About Chance Behavior The Real Law of Averages Personal Probabilities 1.0 Chance Behavior 16 pre-verbal infants separately watch a puppet
More informationManagerial Decision Making: Session 6
Representativeness Review Managerial Decision Making: Session 6 Classic Heuristics: Representativeness (continued) and Availability Kent L. Womack, 2003, all rights reserved. Please do not share outside
More informationBiases and [Ir]rationality Informatics 1 CG: Lecture 18
Biases and [Ir]rationality Informatics 1 CG: Lecture 18 Chris Lucas clucas2@inf.ed.ac.uk Why? Human failures and quirks are windows into cognition Past examples: overregularisation, theory of mind tasks
More informationDo Causal Beliefs Influence the Hot-Hand and the Gambler s Fallacy?
Do Causal Beliefs Influence the Hot-Hand and the Gambler s Fallacy? Giorgio Gronchi (giorgio.gronchi@gmail.com) Università di Firenze, Dipartimento di Psicologia, via della Cittadella 7 50144 Firenze,
More informationLecture 10: Psychology of probability: predictable irrationality.
Lecture 10: Psychology of probability: predictable irrationality. David Aldous October 5, 2017 Here are two extreme views of human rationality. (1) There is much evidence that people are not rational,
More informationLecture 10: Psychology of probability: predictable irrationality.
Lecture 10: Psychology of probability: predictable irrationality. David Aldous March 7, 2016 Here are two extreme views of human rationality. (1) There is much evidence that people are not rational, in
More informationDETECTING STRUCTURE IN ACTIVITY SEQUENCES: EXPLORING THE HOT HAND PHENOMENON
DETECTING STRUCTURE IN ACTIVITY SEQUENCES: EXPLORING THE HOT HAND PHENOMENON A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science By TALERI HAMMACK B.S., University
More information8/23/2017. ECON4260 Behavioral Economics. 1 st lecture Introduction, Markets and Uncertainty. Practical matters. Three main topics
ECON4260 Behavioral Economics 1 st lecture Introduction, Markets and Uncertainty Kjell Arne Brekke Practical matters. Most lectures here at this time (Wednesday 12-14) Lecture 2 and 4 in Auditorium 6 Lecture
More informationBiases and [Ir]rationality Informatics 1 CG: Lecture 18
Why? Biases and [Ir]rationality Informatics 1 CG: Lecture 18 Chris Lucas cl u cas2 @ i n f. ed. ac. u k Human failures and quirks are windows into cognition Past examples: overregularisation, theory of
More informationRecognizing Ambiguity
Recognizing Ambiguity How Lack of Information Scares Us Mark Clements Columbia University I. Abstract In this paper, I will examine two different approaches to an experimental decision problem posed by
More informationEVIDENCE AND INVESTIGATION: Booklet 1
EVIDENCE AND INVESTIGATION: Booklet 1 NAME: Key Questions: What is a detective? Detective: What is Forensic Science or Forensic Investigation: How can we use information and evidence to fight crime? Evidence:
More informationMITOCW conditional_probability
MITOCW conditional_probability You've tested positive for a rare and deadly cancer that afflicts 1 out of 1000 people, based on a test that is 99% accurate. What are the chances that you actually have
More informationProbability: Psychological Influences and Flawed Intuitive Judgments
Announcements: Discussion this week is for credit. Only one more after this one. Three are required. Chapter 8 practice problems are posted. Homework is on clickable page on website, in the list of assignments,
More informationLecture 15. When Intuition Differs from Relative Frequency
Lecture 15 When Intuition Differs from Relative Frequency Revisiting Relative Frequency The relative frequency interpretation of probability provides a precise answer to certain probability questions.
More informationAn Understanding of Role of Heuristic on Investment Decisions
International Review of Business and Finance ISSN 0976-5891 Volume 9, Number 1 (2017), pp. 57-61 Research India Publications http://www.ripublication.com An Understanding of Role of Heuristic on Investment
More informationLecture III. Is s/he an expert in the particular issue? Does s/he have an interest in the issue?
1 Introduction to Critical Thinking Lecture III 2 Appeal to Authority I m becoming a vegetarian. I.B. Singer said it is the ethical thing to do, and he won the Nobel Prize! I m buying a Bumpster mountain
More informationTHE CONJUNCTION EFFECT AND CLINICAL JUDGMENT
GARB CLINICAL JUDGMENT Journal of Social and Clinical Psychology, Vol. 25, No. 9, 2006, pp. 1048-1056 THE CONJUNCTION EFFECT AND CLINICAL JUDGMENT HOWARD N. GARB Wilford Hall Medical Center Judgments made
More informationMS&E 226: Small Data
MS&E 226: Small Data Lecture 10: Introduction to inference (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 17 What is inference? 2 / 17 Where did our data come from? Recall our sample is: Y, the vector
More informationFAQ: Heuristics, Biases, and Alternatives
Question 1: What is meant by the phrase biases in judgment heuristics? Response: A bias is a predisposition to think or act in a certain way based on past experience or values (Bazerman, 2006). The term
More informationPercep&on of Risk. Content of the Lectures. Topic 2. Peter Wiedemann
Percep&on of Risk Topic 2 Content of the Lectures Topic 1: Risk concept Topic 2: Percep&on of risks Topic 3: Risk communica&ons Topic 4: Trust and credibility Topic 5: Labeling risks Topic 6:Par&cipatory
More informationStatistics. Dr. Carmen Bruni. October 12th, Centre for Education in Mathematics and Computing University of Waterloo
Statistics Dr. Carmen Bruni Centre for Education in Mathematics and Computing University of Waterloo http://cemc.uwaterloo.ca October 12th, 2016 Quote There are three types of lies: Quote Quote There are
More information2/25/11. Interpreting in uncertainty. Sequences, populations and representiveness. Sequences, populations and representativeness
Interpreting in uncertainty Human Communication Lecture 24 The Gambler s Fallacy Toss a fair coin 10 heads in a row - what probability another head? Which is more likely? a. a head b. a tail c. both equally
More informationUnderstanding Probability. From Randomness to Probability/ Probability Rules!
Understanding Probability From Randomness to Probability/ Probability Rules! What is chance? - Excerpt from War and Peace by Leo Tolstoy But what is chance? What is genius? The words chance and genius
More informationAre Humans Rational? SymSys 100 April 14, 2011
Are Humans Rational? SymSys 100 April 14, 2011 Anderson s Rational Approach to Cognition What underlies the regularities that we see in human behavior? One answer: Because of characteristics of the mechanisms
More informationHeuristics as beliefs and as behaviors: The adaptiveness of the "Hot Hand" Bruce D. Burns. Michigan State University
1 To appear in Cognitive Psychology Heuristics as beliefs and as behaviors: The adaptiveness of the "Hot Hand" Bruce D. Burns Michigan State University Short title: The adaptiveness of the "Hot Hand" Address
More informationResearch Strategies: How Psychologists Ask and Answer Questions. Module 2
Research Strategies: How Psychologists Ask and Answer Questions Module 2 How Do Psychologists Ask and Answer Questions? The Scientific Method Goals and Tools of Psychology Description Correlation Experimentation
More informationPsychological Perspectives on Visualizing Uncertainty. Barbara Tversky Stanford University
Psychological Perspectives on Visualizing Uncertainty Barbara Tversky Stanford University Two catalogs Reasoning under uncertainty Perception & cognition of visualizations First catalog Reasoning under
More informationChapter 1 Review Questions
Chapter 1 Review Questions 1.1 Why is the standard economic model a good thing, and why is it a bad thing, in trying to understand economic behavior? A good economic model is simple and yet gives useful
More informationChapter Eight. Language and Thought. Language Problem Solving Probabilistic Reasoning
Chapter Eight Language and Thought Language Problem Solving Probabilistic Reasoning Part One: Language Though second nature to native speakers, languages are complex in their content, structure, and diversity.
More informationASSIGNMENT 2. Question 4.1 In each of the following situations, describe a sample space S for the random phenomenon.
ASSIGNMENT 2 MGCR 271 SUMMER 2009 - DUE THURSDAY, MAY 21, 2009 AT 18:05 IN CLASS Question 4.1 In each of the following situations, describe a sample space S for the random phenomenon. (1) A new business
More informationThe Assisted Decision-Making (Capacity) Act 2015 and the Decision Support Service
The Assisted Decision-Making (Capacity) Act 2015 and the Decision Support Service Inclusion Ireland AGM 9/6/2018 Áine Flynn Director of the Decision Support Service 1 Assisted Decision-Making Capacity
More informationBEHAVIORAL. About the todays class. About Behavioral Finance. Bias
International Week OBUDA UNIVERSITY About the todays class During the course BEHAVIORAL FINANCE Elona SHEHU, PhD Candidate elona.shehu@uet.edu.al By the end of the course OR People in standard finance
More information15.301/310, Managerial Psychology Prof. Dan Ariely Lecture 2: The Validity of Intuitions
Introduction: 15.301/310, Managerial Psychology Prof. Dan Ariely Lecture 2: The Validity of Intuitions We are beginning recitation sections this week. Topic: generating ideas for your project. We have
More informationBayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
Bayes theorem Bayes' Theorem is a theorem of probability theory originally stated by the Reverend Thomas Bayes. It can be seen as a way of understanding how the probability that a theory is true is affected
More informationMath HL Chapter 12 Probability
Math HL Chapter 12 Probability Name: Read the notes and fill in any blanks. Work through the ALL of the examples. Self-Check your own progress by rating where you are. # Learning Targets Lesson I have
More information15.301/310, Managerial Psychology Prof. Dan Ariely Recitation 8: T test and ANOVA
15.301/310, Managerial Psychology Prof. Dan Ariely Recitation 8: T test and ANOVA Statistics does all kinds of stuff to describe data Talk about baseball, other useful stuff We can calculate the probability.
More informationWhat can statistical theory tell us about human cognition? Dan Navarro University of Adelaide
What can statistical theory tell us about human cognition? Dan Navarro University of Adelaide Wouter Voorspoels Sean Tauber Amy Perfors Drew Hendrickson Simon De Deyne Wai Keen Vong Keith Ransom The cognitive
More informationOn the Conjunction Fallacy in Probability Judgment: New Experimental Evidence
On the Conjunction Fallacy in Probability Judgment: New Experimental Evidence June 6, 2008 Abstract This paper reports the results of experiments designed to test whether and to what extent individuals
More informationThe Human Side of Science: I ll Take That Bet! Balancing Risk and Benefit. Uncertainty, Risk and Probability: Fundamental Definitions and Concepts
The Human Side of Science: I ll Take That Bet! Balancing Risk and Benefit Uncertainty, Risk and Probability: Fundamental Definitions and Concepts What Is Uncertainty? A state of having limited knowledge
More informationBehavioural models. Marcus Bendtsen Department of Computer and Information Science (IDA) Division for Database and Information Techniques (ADIT)
Behavioural models Cognitive biases Marcus Bendtsen Department of Computer and Information Science (IDA) Division for Database and Information Techniques (ADIT) Judgement under uncertainty Humans are not
More informationImproving statistical estimates used in the courtroom. Precis. Bayes Theorem. Professor Norman Fenton. Queen Mary University of London and Agena Ltd
Improving statistical estimates used in the courtroom Professor Norman Fenton Queen Mary University of London and Agena Ltd Address: Queen Mary University of London School of Electronic Engineering and
More informationWe will do a quick review before we get into the content of this week s lecture.
We will do a quick review before we get into the content of this week s lecture. Brain function is modular: specialized and localized, different areas of the brain are responsible for different functions.
More informationPsychological Factors Influencing People s Reactions to Risk Information. Katherine A. McComas, Ph.D. University of Maryland
Psychological Factors Influencing People s Reactions to Risk Information Katherine A. McComas, Ph.D. University of Maryland What This Tutorial Covers Reasons for understanding people s risk perceptions
More informationIntroduction to Behavioral Economics Like the subject matter of behavioral economics, this course is divided into two parts:
Economics 142: Behavioral Economics Spring 2008 Vincent Crawford (with very large debts to Colin Camerer of Caltech, David Laibson of Harvard, and especially Botond Koszegi and Matthew Rabin of UC Berkeley)
More informationExamples of Feedback Comments: How to use them to improve your report writing. Example 1: Compare and contrast
Examples of Feedback Comments: How to use them to improve your report writing This document contains 4 examples of writing and feedback comments from Level 2A lab reports, and 4 steps to help you apply
More informationTesting boundary conditions for the conjunction fallacy: Effects of response mode, conceptual focus, and problem type
Available online at www.sciencedirect.com Cognition 107 (2008) 105 136 www.elsevier.com/locate/cognit Testing boundary conditions for the conjunction fallacy: Effects of response mode, conceptual focus,
More informationSupplementary notes for lecture 8: Computational modeling of cognitive development
Supplementary notes for lecture 8: Computational modeling of cognitive development Slide 1 Why computational modeling is important for studying cognitive development. Let s think about how to study the
More informationCognitive and computational limitations and bounded rationality
and bounded April 12, 2018 1/54 1 2 3 4 5 2/54 Ambiguous objects 3/54 Kanisza s triangle 4/54 Muller s arrows 5/54 Size from context 6/54 Shepard s tables 7/54 Beau Deeley s illusions 8/54 Esher s waterfall
More informationProbabilities and Research. Statistics
Probabilities and Research Statistics Sampling a Population Interviewed 83 out of 616 (13.5%) initial victims Generalizability: Ability to apply findings from one sample or in one context to other samples
More informationThe Psychology of Inductive Inference
The Psychology of Inductive Inference Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/24/2018: Lecture 09-4 Note: This Powerpoint presentation may contain macros that I wrote to help
More informationHeuristics. Close enough for government work
Heuristics Close enough for government work Heuristics Shortcut recipes for solving problems, making decisions, estimating quantities Product of evolution; generally work well But are also a source of
More informationProbability and Sample space
Probability and Sample space We call a phenomenon random if individual outcomes are uncertain but there is a regular distribution of outcomes in a large number of repetitions. The probability of any outcome
More informationHeuristics: Bias vs. Smart Instrument. An Exploration of the Hot Hand
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2013 Heuristics: Bias vs. Smart Instrument. An Exploration of the Hot Hand Jehangir Cooper Wright State
More informationReview: Conditional Probability. Using tests to improve decisions: Cutting scores & base rates
Review: Conditional Probability Using tests to improve decisions: & base rates Conditional probabilities arise when the probability of one thing [A] depends on the probability of something else [B] In
More informationArticle from. Forecasting and Futurism. Month Year July 2015 Issue Number 11
Article from Forecasting and Futurism Month Year July 2015 Issue Number 11 Thinking, Fast and Slow Review by Tyson Mohr As a reader of this newsletter, you ve almost certainly heard of Daniel Kahneman
More informationWhy are people bad at detecting randomness? A statistical analysis
Why are people bad at detecting randomness? A statistical analysis Joseph J. Williams and Thomas L. Griffiths Department of Psychology University of California, Berkeley Word count: 11 681 Address for
More informationApproximate calories used by a 154 pound man (Joe) Moderate physical activities: In 1 hour In 30 minutes
Station 1: Healthy Eating 101 via choosemyplate.gov The choices you make about the food you eat has a major impact on your body. Loading up on foods filled with high amounts of fat and sugar can lead to
More informationBehavioral Game Theory
School of Computer Science, McGill University March 4, 2011 1 2 3 4 5 Outline Nash equilibria One-shot games 1 2 3 4 5 I Nash equilibria One-shot games Definition: A study of actual individual s behaviors
More informationThe Role of Causality in Judgment Under Uncertainty. Tevye R. Krynski & Joshua B. Tenenbaum
Causality in Judgment 1 Running head: CAUSALITY IN JUDGMENT The Role of Causality in Judgment Under Uncertainty Tevye R. Krynski & Joshua B. Tenenbaum Department of Brain & Cognitive Sciences, Massachusetts
More informationBayes Theorem Application: Estimating Outcomes in Terms of Probability
Bayes Theorem Application: Estimating Outcomes in Terms of Probability The better the estimates, the better the outcomes. It s true in engineering and in just about everything else. Decisions and judgments
More informationIntroduction: Statistics and Engineering
Introduction: Statistics and Engineering STAT:2020 Probability and Statistics for Engineering and Physical Sciences Week 1 - Lecture 1 Book Sections 1.1-1.2.4, 1.3: Introduction 1 / 13 Where do engineering
More informationApply Your knowledge of the Psychology of Learning
LP 9A applying operant cond 1 Apply Your knowledge of the Psychology of Learning You should start relating the psychology of learning to your list of occupations and/or social issues. Where do you see
More informationSingle-case probabilities and content-neutral norms: a reply to Gigerenzer
P.B.M. Vranas / Cognition 81 (2001) 105±111 105 COGNITION Cognition 81 (2001) 105±111 www.elsevier.com/locate/cognit Discussion Single-case probabilities and content-neutral norms: a reply to Gigerenzer
More informationAre We Rational? Lecture 23
Are We Rational? Lecture 23 1 To Err is Human Alexander Pope, An Essay on Criticism (1711) Categorization Proper Sets vs. Prototypes and Exemplars Judgment and Decision-Making Algorithms vs. Heuristics
More informationUnit 1 History and Methods Chapter 1 Thinking Critically with Psychological Science
Myers PSYCHOLOGY (7th Ed) Unit 1 History and Methods Chapter 1 Thinking Critically with James A. McCubbin, PhD Clemson University Worth Publishers Fact vs. Falsehood 1. Human intuition is remarkably accurate
More informationChapter 11 Decision Making. Syllogism. The Logic
Chapter 11 Decision Making Syllogism All men are mortal. (major premise) Socrates is a man. (minor premise) (therefore) Socrates is mortal. (conclusion) The Logic Mortal Socrates Men 1 An Abstract Syllogism
More informationChapter 5 & 6 Review. Producing Data Probability & Simulation
Chapter 5 & 6 Review Producing Data Probability & Simulation M&M s Given a bag of M&M s: What s my population? How can I take a simple random sample (SRS) from the bag? How could you introduce bias? http://joshmadison.com/article/mms-colordistribution-analysis/
More informationBehavioral Finance 1-1. Chapter 6 Overconfidence
Behavioral Finance 1-1 Chapter 6 Overconfidence 1 Overconfidence 1-2 Overconfidence Tendency for people to overestimate their knowledge, abilities, and the precision of their information, or to be overly
More informationIn press, Organizational Behavior and Human Decision Processes. Frequency Illusions and Other Fallacies. Steven A. Sloman.
In press, Organizational Behavior and Human Decision Processes Nested-sets and frequency 1 Frequency Illusions and Other Fallacies Steven A. Sloman Brown University David Over University of Sunderland
More informationUnit 3: EXPLORING YOUR LIMITING BELIEFS
Unit 3: EXPLORING YOUR LIMITING BELIEFS Beliefs and Emotions Bring to mind a negative belief you hold about money. Perhaps it is I don t believe I can win with money or Money is hard to come by. While
More informationThe Mirror on the Self: The Myers- Briggs Personality Traits
Lastname 1 Maria Professor L. Irvin English 1301-163 25 November 2014 The Mirror on the Self: The Myers- Briggs Personality Traits Isabel Brigg Myers said, It is up to each person to recognize his or her
More informationProbability: Judgment and Bayes Law. CSCI 5582, Fall 2007
Probability: Judgment and Bayes Law CSCI 5582, Fall 2007 Administrivia Problem Set 2 was sent out by email, and is up on the class website as well; due October 23 (hard copy for in-class students) To read
More informationTEACHING YOUNG GROWNUPS HOW TO USE BAYESIAN NETWORKS.
TEACHING YOUNG GROWNUPS HOW TO USE BAYESIAN NETWORKS Stefan Krauss 1, Georg Bruckmaier 1 and Laura Martignon 2 1 Institute of Mathematics and Mathematics Education, University of Regensburg, Germany 2
More informationTeaching Statistics with Coins and Playing Cards Going Beyond Probabilities
Teaching Statistics with Coins and Playing Cards Going Beyond Probabilities Authors Chris Malone (cmalone@winona.edu), Dept. of Mathematics and Statistics, Winona State University Tisha Hooks (thooks@winona.edu),
More informationConcepts and Connections: What Do You Expect? Concept Example Connected to or Foreshadowing
Meaning of Probability: The term probability is applied to situations that have uncertain outcomes on individual trials but a predictable pattern of outcomes over many trials. Students find experimental
More informationPolitical Science 15, Winter 2014 Final Review
Political Science 15, Winter 2014 Final Review The major topics covered in class are listed below. You should also take a look at the readings listed on the class website. Studying Politics Scientifically
More information