Inside View Versus Outside View Irregularity Hypothesis Attribute Substitution

Size: px
Start display at page:

Download "Inside View Versus Outside View Irregularity Hypothesis Attribute Substitution"

Transcription

1 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 Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren t needed to view the slides. You can disable or delete the macros without any change to the presentation.

2 Outline Irregularity Hypothesis - People believe that random events are patternless. Inside view versus the outside view Attribute substitution Psych 466, Miyamoto, Aut '17 Representativeness Heuristic Has Two Aspects - Similarity & Irregularity 2

3 Representativeness Heuristic, Similarity & Irregularity Representativeness heuristic had two aspects: Similarity Hypothesis Irregularity Hypothesis (a.k.a. Misconceptions of Chance) After defining the irregularity hypothesis, I will give some examples of misconceptions of chance Independent, Stationary Model for Random Events Psych 466, Miyamoto, Aut '17 3

4 Independent, Stationary Model for Random Events Independent stationary model: Independent what happens in the past has no influence over what happens next. Stationary the probability of the event doesn't change over time. Examples: Chance of heads when flipping a coin is independent and stationary. Chance of rolling a "2" with a die is independent and stationary. Chance of rain on successive days is not independent (rain or dry on day 1 is correlated with rain or dry on day 2) and not stationary (chance of rain varies depending on the season). Subjective Perception of Event Clusters Psych 466, Miyamoto, Aut '17 4

5 Subjective Perception of Event Clusters People sometimes think that airplane crashes happen in clusters (bunched together in time). Vaught & Dawes tested whether airplane crashes happen in clusters. Best fitting model is independent and stationary. V&D's result does not deny the existence of clusters they deny that clusters are caused by karma, fate or some systematic cause like deteriorating quality control. Definition Gambler's Fallacy Psych 466, Miyamoto, Aut '17 5

6 Gambler's Fallacy Gambler's Fallacy: The chances in a random sequence get smaller or larger in response to a recent history of large or small outcomes. ( Supporting Evidence: People attempting to flip a coin mentally: Mental coin flips are too patternless; they have fewer patterns than real coin flips. Gambler's Fallacy in an Italian Lottery Psych 466, Miyamoto, Aut '17 6

7 Gambler's Fallacy in an Italian Lottery Italian National Lotto (weekly): You can bet on any digit from 1 to numbers are drawn at random in 10 different Italian cities. Between May 2003 to February 2005, the number 53 was NOT drawn in the Venice lottery (152 consecutive lotteries). Pr(No "53" in 100 draws) = (about 1 in 170). Pr(No "53" in 150 draws) = (about 1 in 2000) Frenzy of betting on "53" in the Venice lotto. Woman drowned herself; she left a note stating that she had lost her family's saving on "53. A man in Signa shot his wife and son before killing himself. Four deaths, other physical violence, and many personal bankruptcies attributed to "53" madness. Gambler's Fallacy in an Italian Lottery - Consequences Psych 466, Miyamoto, Aut '17 7

8 Gambler's Fallacy in an Italian Lottery (cont.) Total of 3.5 billion Euros (about 4.2 billion dollars) was bet on "53". $27.5 million was spent per month before the February appearance of "53." $806 million was spent in the month of January before the February appearance of "53." (Almost 30 times the average monthly betting on "53.") The eventual winner who bet on "53" got about $768 million. Hot Hand in Basketball Psych 466, Miyamoto, Aut '17 8

9 Hot Hand in Basketball (Gilovich, Vallone & Tversky) Independent & stationary (I&S) model for a player shooting baskets: Player has a constant p-chance of making a shot. This chance is the same for every shot in a sequence of shots. If a player gets a "hot hand," then he/she has a higher chance of making a basket when he/she is hot than when he/she is cold. The "hot hand" hypothesis implies that shooting baskets is not independent and stationary 91% of basketball fans (Stanford & Cornell games) believed that a player has a better chance of making a shot after making 2 or 3 than after missing 2 or 3. The illusion of a hot hand in basketball has been thoroughly researched. Most sports fans are prone to seeing the illusion, but if you have studied JDM, you don t have to believe in it. Psych 466, Miyamoto, Aut '17 Runs Test for "Hot Hand" (Non-Independent & Non-Stationary Distribution) 9

10 Analysis of Runs A run is a succession of H or a succession of misses. H H M H M M M H. 5 runs The Wald-Wolfowitz runs test is a test for stationarity (low p-value on the test means that stationarity is violated). Rejection of null hypothesis (p <.01) was found for 1 of 9 players (Daryl Dawkins), i.e., most players satisfy stationary. Analysis of shots grouped into four successive shots. Stationarity predicts the number of groups with 0 shots made, 1 shot made,..., 4 shots made. Stationarity predictions are supported for all players. Also found no evidence for game by game variation in the chance of making a shot. Remarkable: All teams have equally good defense? Conclusions re Hot Hand Psych 466, Miyamoto, Aut '17 10

11 Conclusion re the Hot Hand in Basketball Human intuition assumes that randomness looks even more random than real randomness. Therefore real randomness looks like there are non-random patterns in the data. We are prone to infer causal forces where none exist. In the case of the "hot hand", the inferred causal force is physical or psychological process that changes the probability of success on the next shot. Website devoted to research into sports streaks (Alan Reifman): Illusion of Control Psych 466, Miyamoto, Aut '17 11

12 Comments re Next Topic The illusion of control is the hypothesis that normal people perceive themselves to have control over success or failure in a task with random rewards even when they do not have any control. WARNING: There is current controversy whether the evidence for an illusion of control is based on improper use of statistics. Psych 466,, Miyamoto, Aut '17 Illusion of Control 12

13 Illusion of Control Ellen Langer demonstrated that people perceive themselves to have control over outcomes even when they have no control. Experiment varied contingency between subjects' responses and the probability of "winning." High contengency subjects' strategy had a big effect on chance of "winning" Low contingency subjects' strategy had a small effect on chance of "winning" No contingency - subjects' strategy had NO effect on chance of "winning" Subjects in ALL conditions reported that their strategies affected their chances of reward, i.e., they could do something to give themselves a better chance of reward. Illusion of control subjects in NO contingency condition still thought their strategies had some efficacy (improved their chances). Comment re Illusion of Control in a Simulated Computer Investments Psych 466, Miyamoto, Aut '17 13

14 Comments re Next Slide The next slide documents a finding that stock traders who are more influenced by the illusion of control have less success in their stock trading decisions. In other words, being prone to seeing patterns where they do not exist is correlated with making poor investment decisions. This makes sense because a person who easily sees patterns where they do not exist will be too quick to buy a stock that starts to rise in value or too quick to sell a stock that starts to fall in value. Psych 466,, Miyamoto, Aut '17 Illusion of Control in Simulated Stock Environment 14

15 Illusion of Control in Simulated Investments Fenton-O'Creevy, M., Nicholson, N., Sloane, E., & Willman, P. (2003). Trading on illusions: Unrealistic perceptions of control and trading performance. Journal of Occupational and Organizational Psychology, 76, Traders from four British investment banks played a computer game. Traders make fictional investments which may or may not influence the value of an investment index (measure of a stock's value). Computer program was rigged so that the value of the investment index was independent from the traders' actions. The traders reported that they were able to influence the investment index to varying degrees. (In reality, they had no influence.) Traders with the greater illusion of control earned less on the average than other traders in the investment game. Their managers rated them as being lower on risk management and analytical ability. Conclusions re Misperception of Randomness Psych 466, Miyamoto, Aut '17 15

16 Conclusions re the Perception of Randomness Human intuition assumes that randomness looks even more random than real randomness. Therefore real randomness looks like there are non-random patterns in the data. Therefore we are prone to infer causal forces where none exists. Inside View Versus Outside View Psych 466, Miyamoto, Aut '17 16

17 Psych 466, Miyamoto, Aut '17 Inside View vs Outside View in Planning Judgments 17 The Inside View and the Outside View "Inside View" and "Outside View": Two different ways to look at a decision or judgment problem. Inside View: Look closely at the particular case. What do you see that would guide you towards a prediction? Outside View: Place the particular case in the context of many other similar cases. What usually happens in this population of similar cases?

18 Psych 466, Miyamoto, Aut '17 Inside View vs Outside View in the Lawyer/Engineer Problem 18 Inside View vs Outside View in Planning Judgments Inside View: Think about your plans. What do you need to get this job done? How long will each component take? Extrapolate to the entire project. Outside View: Think about your past experience with projects that are similar in complexity. Do people complete these projects on time? What has caused delays in the past? Is the current project likely to be different from past experiences with similar projects?

19 Inside View of the Lawyer/Engineer Problem DESCRIPTION OF JACK: 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. High Base Rate Condition: Jack's description was drawn at random from a set of 70 engineers and 30 lawyers. Low Base Rate Condition: Jack's description was drawn at random from a set of 30 engineers and 70 lawyers. INSIDE VIEW OF LAWYER/ENGINEER PROBLEM: What do you know about Jack that would make you think that he is a lawyer or an engineer? Outside View of Lawyer/Engineer Problems (Left Side - High Base Rate Only) Psych 466, Miyamoto, Aut '17 19

20 Example: Outside View of the Lawyer/Engineer Problem High Base Rate Condition Low Base Rate Condition 30 Lawyers 70 Engineers 70 Lawyers 30 Engineers Sounds like Jack Sounds like Jack Sounds like Jack Sounds like Jack Low Base Rate Condition (Left Side Hidden) Psych 466, Miyamoto, Aut '17 20

21 Example: Outside View of the Lawyer/Engineer Problem High Base Rate Condition Low Base Rate Condition 30 Lawyers 70 Engineers 70 Lawyers 30 Engineers Sounds like Jack Sounds like Jack Sounds like Jack Sounds like Jack Show Both Sides of This Slide Psych 466, Miyamoto, Aut '17 21

22 Example: Outside View of the Lawyer/Engineer Problem High Base Rate Condition Low Base Rate Condition 30 Lawyers 70 Engineers 70 Lawyers 30 Engineers Sounds like Jack Sounds like Jack Sounds like Jack Sounds like Jack is higher. is lower. Why Does the Inside View Promote Base-Rate Neglect? Psych 466, Miyamoto, Aut '17 22

23 Why Does the Inside View Promote Base-Rate Neglect? Inside view focuses on information about the particular case. o Tom W Problem: Focus on what Tom is like,... on what computer scientists are like,... on what business majors are like,... on what social science students are like,... etc.. o Lawyer/Engineer Problem: Focus on what Jack is like. Focus on what engineers are like. Focus on what lawyers are like. Outside view interprets the current problem as one example of many similar problems. o How many different outcomes can this problem have? o How often do these different outcomes occur? Base-rate is not part of the inside view. It tends to get ignored. Psych 466, Miyamoto, Aut '17 Inside View and Outside View of Planning Problems 23

24 Conclusions re Inside View versus Outside View Both perspectives are useful and informative. Learn to switch back and forth between these two views. Both views have their strengths and weaknesses. Psych 466, Miyamoto, Aut '17 Introduction to Attribute Substition 24

25 Attribute Substitution in Probability Judgment Target attribute: Probability of an event, E.g., Will the U.S. have a large military presence in Afghanistan in 2020? Heuristic attribute: Easy-to-judge attribute that is related to the probability of the event. E.g., similarity to other political situations or availability of analogous political situations. A.k.a. the proxy attribute. Hypothesis that motivated Kahneman & Tversky s research: People substitute similarity or availability for probability. Similarity & availability System 1 Probability theory System 2 Psych 466, Miyamoto, Aut '17 When Does Attribute Substitution Occur? 25

26 When is Attribute Substitution Likely to Occur? (K&F) K&F: Attribute substitution is more likely when: 1. The target attribute is relatively inaccessible, i.e., hard to evaluate or unfamiliar; 2. A semantically and associatively related attribute is highly accessible (heuristic attribute); 3. The substitution of the heuristic attribute in the judgment is not rejected by critical operations of System 2. Note to JM: Explain System 1 and System 2 Psych 466, Miyamoto, Aut '17 Repeat Slide with Examples of Attribute Substitution 26

27 When is Attribute Substitution Likely to Occur? (K&F) K&F: Attribute substitution is more likely when: 1. the target attribute is relatively inaccessible, i.e., hard to evaluate or unfamiliar; 2. a semantically and associatively related attribute is highly accessible (heuristic attribute); 3. the substitution of the heuristic attribute in the judgment is not rejected by critical operations of System 2. Examples of common attribute substitutions: Substitution of similarity for probability (representativeness) Substitution of availability for probability (availability) Substitution of fluency for recollective memory processes Affective response for more cognitive evaluations like worth or virtue. Psych 466, Miyamoto, Aut '17 Recipe for Demonstrating Attribute Substitution: Method I 27

28 How to Demonstrate Occurrence of Attribute Substitution Method I Collect judgment data for the heuristic attribute and the target attribute. Show that judgments of the heuristic attribute are highly correlated with judgments of the target attribute. The demonstration is especially strong when there are other objective considerations that should reduce the correlation between the heuristic attribute and the target attribute. Tom W problem is an example of Method 1. Psych 466, Miyamoto, Aut '17 Tom W problems exemplifies this strategy 28

29 Tom W Problem - Results Correlation between judged base rate and probability rank = Correlation between similarity rank and probability rank = Psych 466, Miyamoto, Aut '17 Scatter Plots of the Data in this Table 29

30 Rank of Judged Probability Rank of Judged Probability Graph of Tom W Results Similarity versus Judged Probability Base Rate versus Judged Probability Similarity Rank Rank of Judged Base Rate Heuristic attribute (similarity) and target attribute (probability) are highly correlated (+.97). Rule-based System 2 requires attention to base rate. Subjects appear to disregard base rate (correlation = -.65). Psych 466, Miyamoto, Aut '17 How to Demonstrate Occurrence of Attribute Substitution: Method II 30

31 How to Demonstrate Occurrence of Attribute Substitution Method 2 Experimentally manipulate the strength of the heuristic attribute. Show that judgments of the target attribute are influenced in the predicted direction by variation of the heuristic attribute. Again, the demonstration is especially strong when there are other objective considerations that should reduce the correlation between the heuristic attribute and the target attribute. Manipulations of ease of recall exemplify Method 2. Ease of Recall & Availability Exemplifies this Method Psych 466, Miyamoto, Aut '17 31

32 Psych 466, Miyamoto, Aut '17 32 Summary of Attribute Substitution Example: Ease of Recall Affects Judgment Making it easy to remember examples where subject has been assertive makes subject think she is more assertive. Easier to recall 6 examples than 12 examples. Easier to recall examples when smiling than when frowning. Target attribute: Evaluation of how assertive you are. Heuristic attribute: Evaluation of how easy it is to recall the examples of being assertive. In this example, the experimenter manipulates the heuristic attribute (makes recall easier or harder for different subjects), and shows that the attribution (self-rating of assertiveness) increases in the predicted direction.

33 Summary Attribute substitution is a common form of heuristic reasoning. Attribute substitution = judging a target attribute in terms of a more accessible heuristic attribute. There can be more or less direct conflicts between heuristic reasoning and rule-governed or theory-governed reasoning. Heuristic reasoning is often context sensitive Different contexts may suggest different heuristics. Different contexts may produce different emphases on heuristic versus rule-governed reasoning. Heuristic reasoning is sometimes good and sometimes bad (for the person doing the reasoning). Psych 466, Miyamoto, Aut '17 Class discussion of attribute substitution 33

34 Class Discussion Can we generate examples of attribute substitution in everyday life? Example: Target Attribute: Will my relationship with XXX last? Heuristic Attribute: How well am I getting along with XXX? Example: Target Attribute: Will a career doing YYY be rewarding for me over the long run? Heuristic Attribute: How much fun do people have when doing YYY? Psych 466, Miyamoto, Aut '17 END 34

Heuristics & Biases:

Heuristics & 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 information

Reasoning with Uncertainty. Reasoning with Uncertainty. Bayes Rule. Often, we want to reason from observable information to unobservable information

Reasoning with Uncertainty. Reasoning with Uncertainty. Bayes Rule. Often, we want to reason from observable information to unobservable information 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

More information

Representativeness Heuristic and Conjunction Errors. Risk Attitude and Framing Effects

Representativeness 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 information

The Psychology of Inductive Inference

The 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 information

When Intuition. Differs from Relative Frequency. Chapter 18. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.

When 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 information

Answer the questions on the handout labeled: Four Famous Reasoning Problems. Try not to remember what you may have read about these problems!

Answer 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 information

Representativeness heuristics

Representativeness 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 information

Experimental Economics Lecture 3: Bayesian updating and cognitive heuristics

Experimental 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 information

Lecture 15. When Intuition Differs from Relative Frequency

Lecture 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 information

Do Causal Beliefs Influence the Hot-Hand and the Gambler s Fallacy?

Do 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 information

Psychology 466: Judgment & Decision Making

Psychology 466: Judgment & Decision Making Psychology 466: Judgment & Decision Making Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 09/28/2017: Lecture 01-2 Note: This Powerpoint presentation may contain macros that I wrote

More information

The Case Against Deliberative Decision Making

The Case Against Deliberative Decision Making The Case Against Deliberative Decision Making Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 11/30/2017: Lecture 10-2 Note: This Powerpoint presentation may contain macros that I

More information

Decision-making II judging the likelihood of events

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 information

Psychological. Influences on Personal Probability. Chapter 17. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.

Psychological. 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 information

References. Christos A. Ioannou 2/37

References. 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 information

Prediction. Todd Davies Symsys 130 April 22, 2013

Prediction. 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 information

Oct. 21. Rank the following causes of death in the US from most common to least common:

Oct. 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 information

An Understanding of Role of Heuristic on Investment Decisions

An 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 information

Reasoning about probabilities (cont.); Correlational studies of differences between means

Reasoning 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 information

Chance. May 11, Chance Behavior The Idea of Probability Myths About Chance Behavior The Real Law of Averages Personal Probabilities

Chance. 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 information

Inductive arguments, inductive fallacies and related biases

Inductive arguments, inductive fallacies and related biases Fino PhD Lectures 2018 Genoa, 23 February 2018 Inductive arguments, inductive fallacies and related biases Margherita Benzi Università del Piemonte Orientale Plan of the seminar 1 Induction 1.1 Inductive

More information

Managerial Decision Making: Session 6

Managerial 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 information

Perception Search Evaluation Choice

Perception 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 information

Introduction to Preference and Decision Making

Introduction to Preference and Decision Making Introduction to Preference and Decision Making Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 10/31/2017: Lecture 06-1 Note: This Powerpoint presentation may contain macros that I

More information

Chapter 11 Decision Making. Syllogism. The Logic

Chapter 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 information

to Cues Present at Test

to Cues Present at Test 1st: Matching Cues Present at Study to Cues Present at Test 2nd: Introduction to Consolidation Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/03/2018: Lecture 06-4 Note: This Powerpoint

More information

ROLE OF HEURISTICS IN RISK MANAGEMENT

ROLE 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 information

Myers Psychology for AP* David G. Myers PowerPoint Presentation Slides by Kent Korek Germantown High School Worth Publishers, 2010

Myers Psychology for AP* David G. Myers PowerPoint Presentation Slides by Kent Korek Germantown High School Worth Publishers, 2010 Myers Psychology for AP* David G. Myers PowerPoint Presentation Slides by Kent Korek Germantown High School Worth Publishers, 2010 *AP is a trademark registered and/or owned by the College Board, which

More information

Probabilistic judgment

Probabilistic 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 information

#28 A Critique of Heads I Win, Tails It s Chance: The Illusion of Control as a Function of

#28 A Critique of Heads I Win, Tails It s Chance: The Illusion of Control as a Function of 101748078 1 #28 A Critique of Heads I Win, Tails It s Chance: The Illusion of Control as a Function of the Sequence of Outcomes in a Purely Chance Task By Langer, E. J. & Roth, J. (1975) Sheren Yeung Newcastle

More information

Introduction to Categorization Theory

Introduction to Categorization Theory Introduction to Categorization Theory (Goldstein Ch 9: Knowledge) Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/15/2018: Lecture 08-2 Note: This Powerpoint presentation may contain

More information

Heuristics as beliefs and as behaviors: The adaptiveness of the "Hot Hand" Bruce D. Burns. Michigan State University

Heuristics 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 information

FAQ: Heuristics, Biases, and Alternatives

FAQ: 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 information

Chapter 1 Review Questions

Chapter 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 information

ABSOLUTE AND RELATIVE JUDGMENTS IN RELATION TO STRENGTH OF BELIEF IN GOOD LUCK

ABSOLUTE AND RELATIVE JUDGMENTS IN RELATION TO STRENGTH OF BELIEF IN GOOD LUCK SOCIAL BEHAVIOR AND PERSONALITY, 2014, 42(7), 1105-1116 Society for Personality Research http://dx.doi.org/10.2224/sbp.2014.42.7.1105 ABSOLUTE AND RELATIVE JUDGMENTS IN RELATION TO STRENGTH OF BELIEF IN

More information

ASSIGNMENT 2. Question 4.1 In each of the following situations, describe a sample space S for the random phenomenon.

ASSIGNMENT 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 information

Behavioral Finance 1-1. Chapter 6 Overconfidence

Behavioral 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 information

What is the problem?

What is the problem? The Hidden Illness What is the problem? Actually betting the farm off is a serious problem (psychology today). Role modeling gambling behavior to our children at a very young age. How old were you when

More information

Apply Your knowledge of the Psychology of Learning

Apply 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 information

Scatter Plots and Association

Scatter Plots and Association ? LESSON 1.1 ESSENTIAL QUESTION Scatter Plots and Association How can you construct and interpret scatter plots? Measurement and data 8.11.A Construct a scatterplot and describe the observed data to address

More information

Maps and Models & Why They Matter

Maps and Models & Why They Matter Maps and Models & Why They Matter An Introduction to NLP Part 1: The Premise by Charles Faulkner Maps and Models & Why They Matter An Introduction to NLP Part 1: The Premise by Charles Faulkner The Map

More information

Introduction to Behavioral Economics Like the subject matter of behavioral economics, this course is divided into two parts:

Introduction 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 information

How has attribution theory been studied in the past? How might it be studied in the future? Psychology 1

How has attribution theory been studied in the past? How might it be studied in the future? Psychology 1 How has attribution theory been studied in the past? How might it be studied in the future? Psychology 1 Psychology 2 Human beings can explain anything. No matter the cause, we have a strong need to understand

More information

Introduction to Attention and Theories of Selective Attention

Introduction to Attention and Theories of Selective Attention Introduction to Attention and Theories of Selective Attention Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/09/2018: Lecture 03-1 Note: This Powerpoint presentation may contain macros

More information

PSYCHOLOGY : JUDGMENT AND DECISION MAKING

PSYCHOLOGY : JUDGMENT AND DECISION MAKING PSYCHOLOGY 4136-100: JUDGMENT AND DECISION MAKING http://psych.colorado.edu/~vanboven/teaching/psyc4136/psyc4136.html Monday, 1:00-3:30, Muenzinger D156B Instructor Assistant Dr. Leaf Van Boven Jordan

More information

Introduction to Long-Term Memory

Introduction to Long-Term Memory Introduction to Long-Term Memory Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/26/2018: Lecture 05-4 Note: This Powerpoint presentation may contain macros that I wrote to help me create

More information

t-test for r Copyright 2000 Tom Malloy. All rights reserved

t-test for r Copyright 2000 Tom Malloy. All rights reserved t-test for r Copyright 2000 Tom Malloy. All rights reserved This is the text of the in-class lecture which accompanied the Authorware visual graphics on this topic. You may print this text out and use

More information

Probabilities and Research. Statistics

Probabilities 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 information

1. What is the difference between positive and negative correlations?

1. What is the difference between positive and negative correlations? 1. What is the difference between positive and negative correlations? 2. Can correlations make predictions? 3. Can correlations prove causation? 4. What are illusory correlations? We can take data from

More information

History of JDM Linear Judgment Models

History of JDM Linear Judgment Models Finish: Begin: History of JDM Linear Judgment Models Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 10/03/2017: Lecture 02-1 Note: This Powerpoint presentation may contain macros

More information

In what follows, we will assume the existence of a context without stating it.

In 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 information

Behavioral Game Theory

Behavioral 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 information

Lecture 5. Social Cognition. Social cognition the process of thinking about and making sense of oneself and others

Lecture 5. Social Cognition. Social cognition the process of thinking about and making sense of oneself and others Lecture 5 Social Cognition Social cognition the process of thinking about and making sense of oneself and others Part I: The Social Thinker Part II: Conserving Mental Effort Part II: Managing Self Image

More information

in Cognitive Neuroscience

in Cognitive Neuroscience Finish: History of Cognitive Psychology Then: Physiological Measures. in Cognitive Neuroscience Psychology 355: Cognitive Psychology Instructor: John Miyamoto 03/29/2018: Lecture 01-4 Note: This Powerpoint

More information

Consolidation of Memories. Memory in the Real World

Consolidation of Memories. Memory in the Real World Finish: Consolidation of Memories. Begin: Memory in the Real World Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/08/2018: Lecture 07-2 Note: This Powerpoint presentation may contain

More information

Chapter 5 & 6 Review. Producing Data Probability & Simulation

Chapter 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 information

Brook's Image Scanning Experiment & Neuropsychological Evidence for Spatial Rehearsal

Brook's Image Scanning Experiment & Neuropsychological Evidence for Spatial Rehearsal Brook's Image Scanning Experiment & Neuropsychological Evidence for Spatial Rehearsal Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/24/2018: Lecture 05-2 Note: This Powerpoint presentation

More information

Structuring and Behavioural Issues in MCDA: Part II: Biases and Risk Modelling

Structuring and Behavioural Issues in MCDA: Part II: Biases and Risk Modelling Structuring and Behavioural Issues in : Part II: Biases and Risk Modelling Theodor J Stewart Department of Statistical Sciences University of Cape Town Helsinki Part II 1 / 45 and Helsinki Part II 2 /

More information

Behavioural models. Marcus Bendtsen Department of Computer and Information Science (IDA) Division for Database and Information Techniques (ADIT)

Behavioural 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 information

4/26/2017. Things to Consider. Making Decisions and Reasoning. How Did I Choose to Get Out of Bed...and Other Hard Choices

4/26/2017. 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 information

Desirability Bias: Do Desires Influence Expectations? It Depends on How You Ask.

Desirability Bias: Do Desires Influence Expectations? It Depends on How You Ask. University of Iowa Honors Theses University of Iowa Honors Program Spring 2018 Desirability Bias: Do Desires Influence Expectations? It Depends on How You Ask. Mark Biangmano Follow this and additional

More information

Kids Booklet 5 & on Autism. Create an autism awareness ribbon! Tips for parents & teachers. Activities puzzles

Kids Booklet 5 & on Autism. Create an autism awareness ribbon! Tips for parents & teachers. Activities puzzles Kids Booklet on Autism Create an autism awareness ribbon! Tips for parents & teachers 5 & Activities puzzles Take a look at what s inside! Questions and Answers About Autism page 2 Brothers and Sisters

More information

Homework Assignment Section 2

Homework Assignment Section 2 Homework Assignment Section 2 Carlos M. Carvalho DMBA McCombs School of Business Problem 1 I am interested in building a portfolio of stocks and bonds... a very convenient way is to invest in two ETFs

More information

Readings: Textbook readings: OpenStax - Chapters 1 11 Online readings: Appendix D, E & F Plous Chapters 10, 11, 12 and 14

Readings: Textbook readings: OpenStax - Chapters 1 11 Online readings: Appendix D, E & F Plous Chapters 10, 11, 12 and 14 Readings: Textbook readings: OpenStax - Chapters 1 11 Online readings: Appendix D, E & F Plous Chapters 10, 11, 12 and 14 Still important ideas Contrast the measurement of observable actions (and/or characteristics)

More information

HOW TO INSTANTLY DESTROY NEGATIVE THOUGHTS

HOW TO INSTANTLY DESTROY NEGATIVE THOUGHTS HOW TO INSTANTLY DESTROY NEGATIVE THOUGHTS Groundbreaking Technique Revealed Author Dian Winter YES!! You Can Become a Positive, Confident Person This is a FREE report brought to you by: Dian Winter http://www.destroythedemonwithin.com

More information

Are We Rational? Lecture 23

Are 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 information

Probability: Psychological Influences and Flawed Intuitive Judgments

Probability: 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 information

Pooling Subjective Confidence Intervals

Pooling Subjective Confidence Intervals Spring, 1999 1 Administrative Things Pooling Subjective Confidence Intervals Assignment 7 due Friday You should consider only two indices, the S&P and the Nikkei. Sorry for causing the confusion. Reading

More information

REVIEW FOR THE PREVIOUS LECTURE

REVIEW FOR THE PREVIOUS LECTURE Slide 2-1 Calculator: The same calculator policies as for the ACT hold for STT 315: http://www.actstudent.org/faq/answers/calculator.html. It is highly recommended that you have a TI-84, as this is the

More information

Heuristics: Bias vs. Smart Instrument. An Exploration of the Hot Hand

Heuristics: 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 information

Trading Success Overcoming Fear In Trading. By Lyle Wright

Trading Success Overcoming Fear In Trading. By Lyle Wright Trading Success Overcoming Fear In Trading By Lyle Wright lylewright@gmail.com 1 A Little Background First... Name Is Lyle Wright (aka The IT Guy ) Education Mathematics, Computer Science, Psychology

More information

Feature Integration Theory

Feature Integration Theory Feature Integration Theory Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/12/2018: Lecture 03-4 Note: This Powerpoint presentation may contain macros that I wrote to help me create the

More information

Strategic Decision Making. Steven R. Van Hook, PhD

Strategic Decision Making. Steven R. Van Hook, PhD Strategic Decision Making Steven R. Van Hook, PhD Reference Textbooks Judgment in Managerial Decision Making, 8th Edition, by Max Bazerman and Don Moore. New York: John Wiley & Sons, 2012. ISBN: 1118065700

More information

Christopher J. R. Roney a & Lana M. Trick b a King's University College at the University of Western

Christopher J. R. Roney a & Lana M. Trick b a King's University College at the University of Western This article was downloaded by: [University of Guelph] On: 02 May 2014, At: 09:18 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer

More information

Rational Order Effects in Responsibility Attributions

Rational Order Effects in Responsibility Attributions Rational Order Effects in Responsibility Attributions Tobias Gerstenberg (t.gerstenberg@ucl.ac.uk), David A. Lagnado (d.lagnado@ucl.ac.uk), Maarten Speekenbrink (m.speekenbrink@ucl.ac.uk), Catherine Lok

More information

Reinforcement Learning : Theory and Practice - Programming Assignment 1

Reinforcement Learning : Theory and Practice - Programming Assignment 1 Reinforcement Learning : Theory and Practice - Programming Assignment 1 August 2016 Background It is well known in Game Theory that the game of Rock, Paper, Scissors has one and only one Nash Equilibrium.

More information

Substance Abuse Training for Supervisors

Substance Abuse Training for Supervisors Substance Abuse Training for Supervisors The following template can be used to cover the requirements set forth in MCO 5300.17, chap 2, par 1c(2) and par 1d. 1 Substance Abuse Training for Supervisors

More information

The role of linguistic interpretation in human failures of reasoning

The 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 information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Please note the page numbers listed for the Lind book may vary by a page or two depending on which version of the textbook you have. Readings: Lind 1 11 (with emphasis on chapters 5, 6, 7, 8, 9 10 & 11)

More information

PSYCHOLOGICAL RESEARCH DESIGN & METHODS AP PSYCHOLOGY: CHAPTER 2

PSYCHOLOGICAL RESEARCH DESIGN & METHODS AP PSYCHOLOGY: CHAPTER 2 PSYCHOLOGICAL RESEARCH DESIGN & METHODS AP PSYCHOLOGY: CHAPTER 2 Bellwork: Why do we need Psychological Research? What were your thoughts.. Hindsight Bias I knew it all along phenomenon Examples.. 2 groups..

More information

British Journal of Science 7 September 2011, Vol. 1 (1) The Use of Base Rates in Bayesian Inference Prediction

British Journal of Science 7 September 2011, Vol. 1 (1) The Use of Base Rates in Bayesian Inference Prediction British Journal of Science 7 The Use of Base Rates in Bayesian Inference Prediction Brown J lee Washington State University, Fullerton and Decision Research Center Email: mbirnbaum@fullerton.edu Introduction

More information

Heuristics. Close enough for government work

Heuristics. 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 information

MTAT Bayesian Networks. Introductory Lecture. Sven Laur University of Tartu

MTAT Bayesian Networks. Introductory Lecture. Sven Laur University of Tartu MTAT.05.113 Bayesian Networks Introductory Lecture Sven Laur University of Tartu Motivation Probability calculus can be viewed as an extension of classical logic. We use many imprecise and heuristic rules

More information

AN OVEVIEW OF GAMBLING ADDICTION

AN OVEVIEW OF GAMBLING ADDICTION AGAINST ALL ODDS AN OVEVIEW OF GAMBLING ADDICTION Brian L. Bethel, M.Ed., PCC-S, LCDC III, RPT-S Reproduction of training material without consent of Brian Bethel is prohibited. 1 Activity #1 Values Something

More information

FEEDBACK TUTORIAL LETTER

FEEDBACK TUTORIAL LETTER FEEDBACK TUTORIAL LETTER 1 ST SEMESTER 2017 ASSIGNMENT 2 ORGANISATIONAL BEHAVIOUR OSB611S 1 Page1 OSB611S - FEEDBACK TUTORIAL LETTER FOR ASSIGNMENT 2-2016 Dear student The purpose of this tutorial letter

More information

Understanding Probability. From Randomness to Probability/ Probability Rules!

Understanding 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 information

Psychological 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 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 information

Confidence in Sampling: Why Every Lawyer Needs to Know the Number 384. By John G. McCabe, M.A. and Justin C. Mary

Confidence in Sampling: Why Every Lawyer Needs to Know the Number 384. By John G. McCabe, M.A. and Justin C. Mary Confidence in Sampling: Why Every Lawyer Needs to Know the Number 384 By John G. McCabe, M.A. and Justin C. Mary Both John (john.mccabe.555@gmail.com) and Justin (justin.mary@cgu.edu.) are in Ph.D. programs

More information

Article from. Forecasting and Futurism. Month Year July 2015 Issue Number 11

Article 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 information

Your Brain Is Primed To Reach False Conclusions

Your Brain Is Primed To Reach False Conclusions 1 of 8 2/21/2015 7:11 PM Your Brain Is Primed To Reach False Conclusions Paul Offit likes to tell a story about how his wife, pediatrician Bonnie Offit, was about to give a child a vaccination when the

More information

Jane L. Risen Research Statement

Jane L. Risen Research Statement Jane L. Risen Research Statement I am interested in how people form judgments to help them negotiate our complicated, uncertain world. In particular, I focus on the deliberate vs. automatic nature of belief

More information

Lost it. Find it. Already Have. A Nail A Mirror A Seed 6/2/ :16 PM. (c) Copyright 2016 Cindy Miller, Inc. (c) Copyright 2016 Cindy Miller, Inc.

Lost it. Find it. Already Have. A Nail A Mirror A Seed 6/2/ :16 PM. (c) Copyright 2016 Cindy Miller, Inc. (c) Copyright 2016 Cindy Miller, Inc. Already Have A Nail A Mirror A Seed Lost it Find it 1 Missed it Don t know you were made for Nike tells us just to DO In golf, a do over. In life, a second chance. WHY? WHAT? HOW? 2 Burned Out Freedom

More information

Education. Patient. Century. in the21 st. By Robert Braile, DC, FICA

Education. Patient. Century. in the21 st. By Robert Braile, DC, FICA Patient Education 21 st in the21 st Century By Robert Braile, DC, FICA Thealthcare marketplace. We also here are a few things we need to recognize relative to how chiropractic is perceived in the need

More information

fmri: What Does It Measure?

fmri: What Does It Measure? fmri: What Does It Measure? Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/02/2018: Lecture 02-1 Note: This Powerpoint presentation may contain macros that I wrote to help me create

More information

ORIENTATION SAN FRANCISCO STOP SMOKING PROGRAM

ORIENTATION SAN FRANCISCO STOP SMOKING PROGRAM ORIENTATION SAN FRANCISCO STOP SMOKING PROGRAM PURPOSE To introduce the program, tell the participants what to expect, and set an overall positive tone for the series. AGENDA Item Time 0.1 Acknowledgement

More information

BEHAVIORAL. About the todays class. About Behavioral Finance. Bias

BEHAVIORAL. 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 information

Reflections on the Results of the 4 th. Italian prevalence study

Reflections on the Results of the 4 th. Italian prevalence study Reflections on the Results of the 4 th Italian prevalence study Claudio Barbaranelli Sapienza University of Rome Department of Psychology -CIRMPA claudio.barbaranelli@uniroma1.it 1 OVERVIEW * Framework,

More information

Motivation Motivation

Motivation Motivation This should be easy win What am I doing here! Motivation Motivation What Is Motivation? Motivation is the direction and intensity of effort. Direction of effort: Whether an individual seeks out, approaches,

More information

How is ethics like logistic regression? Ethics decisions, like statistical inferences, are informative only if they re not too easy or too hard 1

How is ethics like logistic regression? Ethics decisions, like statistical inferences, are informative only if they re not too easy or too hard 1 How is ethics like logistic regression? Ethics decisions, like statistical inferences, are informative only if they re not too easy or too hard 1 Andrew Gelman and David Madigan 2 16 Jan 2015 Consider

More information

The effect of anchoring on dishonest behavior. Hiromasa Takahashi a, Junyi Shen b,c

The effect of anchoring on dishonest behavior. Hiromasa Takahashi a, Junyi Shen b,c The effect of anchoring on dishonest behavior Hiromasa Takahashi a, Junyi Shen b,c a Faculty of International Studies, Hiroshima City University, 3-4-1 Ozuka-Higashi, Asa-Minami, Hiroshima 731-3194, Japan

More information