A basic introduction to the field for MIE448 Cognitive Ergonomics

Slides:



Advertisements
Similar presentations
Thinking Like an Economist
Advertisements

Probability Unit 3.
Deductive Reasoning & Decision Making Dr. Claudia J. Stanny EXP 4507 Memory & Cognition Spring 2009.
Hawawini & VialletChapter 7© 2007 Thomson South-Western Chapter 7 ALTERNATIVES TO THE NET PRESENT VALUE RULE.
1 Intuitive Irrationality: Reasons for Unreason. 2 Epistemology Branch of philosophy focused on how people acquire knowledge about the world Descriptive.
Judgment and Decisions. Judgment: “how likely is that …?” Decision-Making (Choice): ‘should you take a coupon for $200 or $100 in cash, given that …”
BCOR 1020 Business Statistics Lecture 6 – February 5, 2007.
CHAPTER 14 Utility Axioms Paradoxes & Implications.
Decision making and economics. Economic theories Economic theories provide normative standards Expected value Expected utility Specialized branches like.
Judgment and Decision Making How Rational Are We?.
Cognitive Processes PSY 334 Chapter 11 – Judgment & Decision-Making.
Introduction to Probability and Risk in Financial Investment
1 Chapter 8 Subjective Probability. 2 Chapter 8, Subjective Probability Learning Objectives: Uncertainty and public policy Subjective probability-assessment.
Solved the Maze? Start at phil’s house. At first, you can only make right turns through the maze. Each time you cross the red zigzag sign (under Carl’s.
Uncertainty and Consumer Behavior
Introduction to Decision Analysis
Behavioral Economics Chapter 30. What Is Behavioral Economics? The study of choices actually made by economic decision makers in an effort to assess the.
1 Many people debate basic questions of chance in games such as lotteries. The Monty Hall problem is a fun brain teaser that Marilyn vos Savant addressed.
Heuristics and Biases. Normative Model Bayes rule tells you how you should reason with probabilities – it is a normative model But do people reason like.
Chapter 15 Decision Making and Organizational Learning
Reasoning with Uncertainty. Often, we want to reason from observable information to unobservable information We want to calculate how our prior beliefs.
Inductive Reasoning Bayes Rule. Urn problem (1) A B A die throw determines from which urn to select balls. For outcomes 1,2, and 3, balls are picked from.
Capital Budgeting Decision Tools 05/17/06. Introduction Capital Budgeting is the process of identifying, evaluating, and implementing a firm’s longer.
Decision Making and Reasoning
Cognitive Processes PSY 334 Chapter 10 – Reasoning & Decision-Making August 21, 2003.
Basic Tools of Finance Finance is the field that studies how people make decisions regarding the allocation of resources over time and the handling of.
Heuristics & Biases. Bayes Rule Prior Beliefs Evidence Posterior Probability.
BCOR 1020 Business Statistics
Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. Understanding Probability and Long-Term Expectations Chapter 16.
© 2008 McGraw-Hill Higher Education The Statistical Imagination Chapter 9. Hypothesis Testing I: The Six Steps of Statistical Inference.
Copyright ©2005 Brooks/Cole, a division of Thomson Learning, Inc. Understanding Probability and Long-Term Expectations Chapter 16.
Decision Analysis (cont)
Decision making Making decisions Optimal decisions Violations of rationality.
Heuristics and bias Dr Carl Thompson. Before we start… A quick exercise.
1/20 Remco Chang (Computer Science) Paul Han (Tufts Medical / Maine Medical) Holly Taylor (Psychology) Improving Health Risk Communication: Designing Visualizations.
Darlene Ringhand March 14,  Unit 2 review  Comments –Discussion  Comments – Homework  Unit 3 Discussion Expectations  Other  Good Evening!
Risk, Probability and Judgment. The Harnessed AtomRisk, Probability, and Judgment 2 Today’s Topics What is risk? How do we perceive risk? How do we measure.
Copyright © 2004 South-Western 27 The Basic Tools of Finance.
● Uncertainties abound in life. (e.g. What's the gas price going to be next week? Is your lottery ticket going to win the jackpot? What's the presidential.
Understanding Probability and Long-Term Expectations Example from Student - Kalyani Thampi I have an example of "chance" that I thought about mentioning.
Information Retrieval Evaluation and the Retrieval Process.
©2004 Prentice Hall Business Publishing Introduction to Financial Accounting, 3e by Werner/Jones2 - 1 Chapter 2 Economic Decision Making.
How People make Decisions
The Psychologist as Detective, 4e by Smith/Davis © 2007 Pearson Education Chapter Seven: The Basics of Experimentation II: Final Considerations, Unanticipated.
RISK BENEFIT ANALYSIS Special Lectures University of Kuwait Richard Wilson Mallinckrodt Professor of Physics Harvard University January 13th, 14th and.
Managerial Economics Demand Estimation & Forecasting.
Bayesian vs. frequentist inference frequentist: 1) Deductive hypothesis testing of Popper--ruling out alternative explanations Falsification: can prove.
Lecture 15 – Decision making 1 Decision making occurs when you have several alternatives and you choose among them. There are two characteristics of good.
Copyright Catherine M. Burns
Uncertainty Management in Rule-based Expert Systems
Decision Making Chapter 7. Definition of Decision Making Characteristics of decision making: a. Selecting a choice from a number of options b. Some information.
Past research in decision making has shown that when solving certain types of probability estimation problems, groups tend to exacerbate errors commonly.
Ch2b: Decisions &Decision Makers Decision Support Systems in the 21 st Century by George M. Marakas.
Chapter 8 McGraw-Hill/IrwinCopyright © 2010 The McGraw-Hill Companies, Inc. All rights reserved.
1 DECISION MAKING Suppose your patient (from the Brazilian rainforest) has tested positive for a rare but serious disease. Treatment exists but is risky.
Decision Making. Reasoning & Problem Solving A. Two Classes of Reasoning I. Deductive Reasoning II. Inductive Reasoning.
The Nonrational Escalation of Commitment The Nonrational Escalation of Commitment Presented by: Hamid Shekari Omid Keivanloo.
6. Population Codes Presented by Rhee, Je-Keun © 2008, SNU Biointelligence Lab,
Medical Decision Making Although decision making is a prime activity of physicians, little time is spent on this subject in medical school curricula This.
The Law of Averages. What does the law of average say? We know that, from the definition of probability, in the long run the frequency of some event will.
Decision Making ET 305, Spring 2016
A. Judgment Heuristics Definition: Rule of thumb; quick decision guide When are heuristics used? - When making intuitive judgments about relative likelihoods.
The Psychology of Inductive Inference Psychology 355: Cognitive Psychology Instructor: John Miyamoto 5/26/2016: Lecture 09-4 Note: This Powerpoint presentation.
The Representativeness Heuristic then: Risk Attitude and Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/1/2016: Lecture.
Uncertain Judgements: Eliciting experts’ probabilities Anthony O’Hagan et al 2006 Review by Samu Mäntyniemi.
Psychology and Personal Finance
The Foreign Language Effect on The Sunk-Cost Fallacy
Poor Decision Making Mental Bias Answer Sheet.
Decision Making and Reasoning
CS 594: Empirical Methods in HCC Introduction to Bayesian Analysis
Presentation transcript:

A basic introduction to the field for MIE448 Cognitive Ergonomics Decision Making A basic introduction to the field for MIE448 Cognitive Ergonomics

References Arkes, H. R., and Blumer, C. The Psychology of Sunk Cost, Organizational Behavior and Human Decision Processess 35, (1985), 124-140. Kahneman, D., and Tversky, A. Subjective Probability: A Judgement of Representativeness, Cognitive Psychology 3, (1972), 430-454. Tversky, A., and Kahneman, D. Availability: A Hueristic for Judging Frequency and Probability, Cognitive Psychology 5, (1973), 207-232. Tversky, A., and Kahneman, D. Rational Choice and the Framing of Decisions, Journal of Business 59, 4, pt2 (1986), S251-S277. Wickens, C. D. Engineering Psychology and Human Performance, Second ed. HarperCollins Publishers Inc., 1992). Wickens, C. D., and Hollands, J. G. Engineering Psychology and Human Performance, Third ed. (Upper Saddle River, New Jersey: Prentice Hall, 2000).

What this lecture hopes you walk away with ... An understanding of what the field of decision making is and who is interested in it A familiarity with some of the better known decision making biases and heuristics Potentially- an understanding of how people make judgments and decisions and to design appropriately

Who is interested in the study of judgement & decision making? Economists, mathematics, business - normative models how people should make decisions cognitive psychologists, marketing- descriptive models of how people do make decisions Other fields: medical diagnosis, fault diagnosis, weather forecasting, judicial procedures (weighing evidence), military, university admission procedures

Decision making tasks refers to which … The decision maker must select one of a number of possibilities (sometimes many options). The time to make the decision is relatively long as compared to manual control tasks. The decision often must be made under uncertain conditions (stimulus information may of may not be reliable)

Human Limits People are bad at making absolute judgments, (but are very good at making relative judgments) Have a poor understanding of extreme values (< 0.05 and > 0.95) Can not easily extrapolate growth functions

Extrapolating Growth Functions

Three causes for conservatism in extrapolation Exponential growth functions are more complex conservatism true life exponential growth are rare Therefore, when possible use a predictive display to assist, or a transformation to create a linear display.

In the beginning… Gamblers were asking statisticians how much should they be willing to pay for different gambles? (Coin flip: heads win $1, tails win $0.) Simple answer (normative rule): compute the expected value. (50%*1 + 50%*0 = bet up to 50 cents)

Basic Steps In Rational Decision Making 1. Identify all possible options (including doing nothing). 2. Quantify the value (or cost) of consequences which may arise if each course of action is adopted 3. Assess the likelihood of each consequence actually happening. 4. Integrate across all possibilities.

Unfortunately, this is too simple Quantifying cost and value, and computing all options is cognitively expensive. Isn’t descriptive. It doesn’t explain how people do make decisions- or allow for the prediction of how people make decisions. Expected value doesn’t take into account things like “subjective utility”. (Lottery: odds of winning 1/13 million, jackpot = 50 million $, therefore bet the house …)

Too simple… continued Decision making using an expected value model would mean there would be no reason to ever buy insurance. (instead- minimize maximum loss)

Bayes’ Rule The ultimate optimal statistical model for making decisions under uncertainty- starting point for all comparisons. People tend to use only the likelihood ratio in making decisions. P(H1 | D): Conditional probability that event H1 will occur, given that D has occurred.

Bayes’ Rule Posterior Odds = Prior Odds * Likelihood Ratio D is a data statement (an observation, a piece of data) H1 and H2 are two exclusive and exhaustive possibilities

Heuristics & Biases The Salience Bias The “as if” Heuristic The Representativeness Heuristic The Availability Heuristic Overconfidence Anchoring and Adjustment The Confirmation Bias The Framing Effect Sunk Costs

The Salience Bias We are hardwired to filter sensory information based on the saliency of the stimuli in the following order: loud sounds bright lights motion spatial position This means people are biased toward salient information even if salient cues contain less information

The “As If” Heuristic Information extracted from the stimuli in the world may be thought of as having different levels of importance, i.e. different weights However, people tend to treat all different data cue “as if” they had equal weights Decisions are made based more on the total number of data cues- without regard to their reliability or importance

The “As If” Heuristic

The Representativeness Heuristic The subjective probability of an event is determined by the degree to which it is similar in essential characteristics to its parent population reflects the salient features of the process by which it is generated sample size is irrelevant and prior probabilities are ignored (cf. Bayes’ Rule)

The Representativeness Heuristic All families of six children in a city were surveyed. In 72 families the exact order of births was GBGBBG. What is your estimate of the number of families surveyed in which the exact order of births was BGBBBB?

The Representativeness Heuristic Both birth sequences are equally likely However, the two sequences do not appear equally representative. (5 boys and 1 girl do not reflect the proportion of boys and girls in the population.) Median response was 30 in Kahneman and Tversky’s experiment.

The Availability Heuristic The probability of events is evaluated by the ease with which relevant instances come to mind. In general, frequent events are easier to recall or imagine than infrequent ones.

The Availability Heuristic Consider the letter K. Is K more likely to appear in The first position of a word? The third position of a word?

The Availability Heuristic It is a lot easier to think of words which start with the letter K than of words where K is in the third position. However, a typical selection of text contains twice as many words in which K is in the third position than words which start with K.

Overconfidence People (novices & experts) are in general, much more confident about their decisions than is reasonable given the environment in which they are making their decisions. Potential to close off the search for answers before all available evidence can be collected because of overconfidence.

Anchoring and Adjustment Under time pressure, estimate 8*7*6*5*4*3*2*1 1*2*3*4*5*6*7*8 2,250 vs 512 (40,320) Strategy to make a decision and stick with it- use future information only to make slight adjustments to original judgment

Confirmation Bias changing hypothesis requires greater cognitive effort than maintaining the same hypothesis harder to deal with negative information than positive information self-fulfilling prophecy (teacher influencing the student, experimenter influence data)

The Framing Effect (Survival Frame) You are a patient with lung cancer. Which of the following two option would you prefer? Surgery: Of 100 people having surgery 90 live through the post-operative period, 68 are alive at the end of the first year and 34 are alive at the end of five years. Radiation Therapy: Of 100 people having radiation therapy all live through the treatment, 77 are alive at the end of one year and 22 are alive at the end of five years.

The Framing Effect (Mortality Frame) You are a patient with lung cancer. Which of the following two option would you prefer? Surgery: Of 100 people having surgery 10 die during surgery or the post-operative period, 32 die by the end of the first year and 66 die by the end of five years. Radiation Therapy: Of 100 people having radiation therapy none die during treatment, 23 die by the end of one year and 78 die by the end of five years.

The Framing Effect Results: respondents who favored radiation therapy rose from 18% to 44%- no difference when subjects were patients or physicians. How a problem is worded (or framed), either in terms of cost or value effects how people make decisions. Difference between a perceived loss or perceived gain

The Sunk Cost Effect As the president of an airline company, you have invested 10 billion dollars of the company’s money into a research project. The purpose was to build a plane that would not be detected by conventional radar, in other words, a radar-blank plane. When the project is 90% completed, another firm begins marketing a plane that cannot be detected by radar. Also, it is apparent that their plane is much faster and far more economical that the plane your company is building. The question is: should you invest the last 10% of the research funds to finish your radar-blank plane?

The Sunk Cost Effect As the president of an airline company, you have received a suggestion from one of your employees. The suggestion is to use the last 1 billion dollars of your research funds to develop a plane that would not be detected by conventional radar, in other words, a radar-blank plane. However, another firm has just begun marketing a plane that cannot be detected by radar. Also, it is apparent that their plane is much faster and far more economical that the plane your company could build. The question is: should you invest the last billion dollars of your research funds to build the radar-blank plane proposed by your employee?

The Sunk Cost Effect As the owner of a printing company, you must choose whether to modernize your operation by spending $200,000 on a new printing press or on a fleet of new delivery trucks. You chose to buy the trucks, which can deliver your products twice as fast as your old trucks at about the same cost as your old trucks. One week after your purchase of the new trucks, one of your competitors goes bankrupt. To get some cash in a hurry, he offers to sell you his computerized printing press for $10,000. This press works 50% faster than your old press at about one-half the cost. You know that you will not be able to sell your old press to raise this money since it was built specifically for your needs and cannot be modified. However, you do have $10,000 in savings. The question is should you buy the computerized press from you bankrupt competitor?

The Sunk Cost Effect As the owner of a printing company, you must choose whether to modernize your operation by spending $200,000 on a new printing press or on a fleet of new delivery trucks. You chose to buy the press, which works twice as fast as your old press at about the same cost as the old press. One week after your purchase of the new press, one of your competitors goes bankrupt. To get some cash in a hurry, he offers to sell you his computerized printing press for $10,000. This press works 50% faster than your new press at about one-half the cost. You know that you will not be able to sell your new press to raise this money since it was built specifically for your needs and cannot be modified. However, you do have $10,000 in savings. The question is should you buy the computerized press from you bankrupt competitor?

The Sunk Cost Effect Sunk costs are irrelevant to current decisions- instead, only incremental costs should influence future decisions. Sunk costs have already been paid- you can’t get that cost back. Strategy used by US nuclear energy program.