History of Psychology Session 11: Decision Science PDF

COURSE EVALUATION Please take 5 minutes and complete the course evaluation for History of Psychology! We will discuss the results next week. Image created with AI (ChatGPT 4o), November 2024...

COURSE EVALUATION Please take 5 minutes and complete the course evaluation for History of Psychology! We will discuss the results next week. Image created with AI (ChatGPT 4o), November 2024 THANK YOU! 1 # Date Topic Instructor 1 23.09.2024 Session 1: Introduction Tisdall 2 30.09.2024 Session 2: Pre-psychology Mata 3 07.10.2024 Session 3: The birth of psychology Mata 4 14.10.2024 Session 4: Psychoanalysis Mata 5 21.10.2024 Session 5: Behaviorism Mata 6 28.10.2024 Session 6: Gestalt psychology Mata 7 04.11.2024 Session 7: Cognitive psychology Mata 8 11.11.2024 Session 8: Psychology today Tisdall 9 18.11.2024 Session 9: Psychotherapy research Tisdall 10 25.11.2024 Session 10: Psychological testing Tisdall 11 02.12.2024 Session 11: Decision science Tisdall 12 09.12.2024 Session 12: What kind of science is psychology? Mata AGENDA Mock exam solutions: Please send us your questions IN ADVANCE by Friday (December 6) via ADAM! We will prepare answers and discuss during the last session (December 9) Recap: Last slides Psychological Testing (~15 minutes) Decision Science (~60 minutes) 3 RECAP: History of Psychology Session 10: Psychological testing Loreen Tisdall, Center for Cognitive and Decision Sciences November 25, 2024  Psychological testing and psychometrics: Four phases and major events PHASE I PHASE II PHASE III PHASE IV (1880s - 1890s) (1900s - 1910s) (1920s - 1940s) (1950s - present) The Origins: Modern Tests: Methodological and empirical The Birth of IQ: Standardisation and objectivity in Point scales, verbal, non-verbal, and advances, behavioral genetics, and Norms and predictive validity psychological testing ‘culture-free’ tests consensus 1883: Francis Galton publishes 1901: Applications of correlation by Clark 1921: Psychological Corporation Inquiries into Human Faculty and Its Wissler (Cattell’s student) suggest that founded by J.M. Cattell, R.B. Development “brass measures” have little predictive validity (e.g., for academic performance) Woodworth, & E.L. Thorndike 1984: Francis Galton administers a test 1904: Charles Spearman publishes The 1926: Scholastic Aptitude Test (SAT) to battery to thousands of volunteers at proof and measurement of the association predict college success (but only the International Health Exhibition, between two things (rank order!) normed later in 1941) London 1904: General Intelligence by Charles 1933: Publication of Vectors of Mind by 1890: James McKeen Cattell publishes Spearman, introduces Correlational psychology and g factor (psychometric g) L.L. Thurstone suggestion Mental Tests and Measurements reformulation of Spearman’s approach 1905: Binet-Simon scale, 30 ordered items 1896: Karl Pearson develops the 1935: Foundation of the Psychometric product-moment correlation 1914: William Stern publishes The Society psychological methods of testing intelligence 1938: Ravens’ progressive matrices 1914/1916: Stanford-Binet Scale through 1938: Wechsler-Bellevue Intelligence translation of Binet Scale by Terman, introduction of IQ. Test (published by Psych Corp.) 1917: Woodworth Personal Data Sheet (WPDS) 1917: Army Alpha & Beta, group tests for military recruits, Committee led by Yerkes (advisors, Terman, Cattell, Goddard) Gregory, R. J. (2004). Psychological testing: History, principles, and applications. Pearson Education India. Link to ebook Wasserman, J. D. (2012). A history of intelligence assessment: The un nished tapestry. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd 5 ed., pp. 3-55). Guilford Press. Link to chapter fi Wechsler Intelligence Scales The Wechsler scales were not revolutionary in the tests used (a lot of them were copied from other extant scales, as we have seen already)! The main innovation was the use of the point scale (rather than a chronological age scale as in Stanford-Binet scales) This allowed assigning points to each item and thus items to be grouped according to content The scale thus also allowed testers to obtain multiple scores for each individual. The point scale transformed how intelligence was measured and interpreted, providing a multidimensional perspective on cognitive abilities. What does this new scoring method mean for the interpretation of what IQ is or measures? -> IQ is fundamentally relative, it quantifies the deviation of an individual’s cognitive abilities from the statistical mean of a standardized norm-building sample. Raw score - Mean IQ = 100 + 15 × Standard Deviation 6  Psychological testing and psychometrics: Four phases and major events PHASE I PHASE II PHASE III PHASE IV (1880s - 1890s) (1900s - 1910s) (1920s - 1940s) (1950s - present) The Origins: Modern Tests: Methodological and empirical The Birth of IQ: Standardisation and objectivity in Point scales, verbal, non-verbal, and advances, behavioral genetics, and Norms and predictive validity psychological testing ‘culture-free’ tests consensus 1883: Francis Galton publishes 1901: Applications of correlation by Clark 1921: Psychological Corporation 1950: APA publishes Ethical standards Inquiries into Human Faculty and Its Wissler (Cattell’s student) suggest that for the distribution of psychological founded by J.M. Cattell, R.B. Development “brass measures” have little predictive tests and diagnostic aids validity (e.g., for academic performance) Woodworth, & E.L. Thorndike 1984: Francis Galton administers a test 1951: Lee Cronbach introduces the 1904: Charles Spearman publishes The 1926: Scholastic Aptitude Test (SAT) to battery to thousands of volunteers at alpha coef cient to measure proof and measurement of the association predict college success (but only the International Health Exhibition, between two things (rank order!) consistency (think subtests!) normed later in 1941) London 1954: Technical recommendations for 1904: General Intelligence by Charles 1933: Publication of Vectors of Mind by psychological tests and diagnostic aids 1890: James McKeen Cattell publishes Spearman, introduces Correlational psychology and g factor (psychometric g) L.L. Thurstone suggestion by APA (chaired by L. Cronbach) Mental Tests and Measurements reformulation of Spearman’s approach 1905: Binet-Simon scale, 30 ordered items 1955: Wechsler Adult Intelligence Scale 1896: Karl Pearson develops the 1935: Foundation of the Psychometric (WAIS) product-moment correlation 1914: William Stern publishes The Society psychological methods of testing 1963: Theory of uid and crystallized intelligence 1938: Ravens’ progressive matrices intelligence by R.B. Cattell (leads to C- H-C!!!) 1914/1916: Stanford-Binet Scale through 1938: Wechsler-Bellevue Intelligence translation of Binet Scale by Terman, introduction of IQ. Test (published by Psych Corp.) 1917: Woodworth Personal Data Sheet 1943: Minnesota Multiphasic (WPDS) Personality Inventory (MMPI) 1917: Army Alpha & Beta, group tests for military recruits, Committee led by Yerkes (advisors, Terman, Cattell, Goddard) Gregory, R. J. (2004). Psychological testing: History, principles, and applications. Pearson Education India. Link to ebook Wasserman, J. D. (2012). A history of intelligence assessment: The un nished tapestry. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd 7 ed., pp. 3-55). Guilford Press. Link to chapter fi fl fi Wechsler Intelligence Scales Developed by David Wechsler (1896-1981), American psychologist (worked with Cattell and Spearman) Several versions, including Wechsler Adult Intelligence Scale (WAIS), Wechsler Intelligence Scale for Children (WISC), and Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV) WAISC is a revision of the Wechsler-Bellevue Intelligence Scale (originally released in 1939), contains multiple tasks that are supposed to capture different latent constructs https://en.wikipedia.org/wiki/Wechsler_Adult_Intelligence_Scale Scale Abbreviation Factor Vocabulary v Similarities s Verbal Comprehension Information i Comprehension c Picture completion pc Bloc design bd Perceptual Organization Matrix reasoning mr Picture arrangement pa Arithmetic a Digit span ds Working Memory Letter-number sequencing ln Digit-symbol coding cd Processing Speed Symbol search ss ➡ In line with the Cattell-Horn-Carroll three-stratum model of intelligence (but: # second stratum factors varies)! Deary, I. J. (2001). Human intelligence di erences: a recent history. Trends in cognitive sciences, 5(3), 127-130. Link to paper 8 ff  Psychological testing and psychometrics: Four phases and major events PHASE I PHASE II PHASE III PHASE IV (1880s - 1890s) (1900s - 1910s) (1920s - 1940s) (1950s - present) The Origins: Modern Tests: Methodological and empirical The Birth of IQ: Standardisation and objectivity in Point scales, verbal, non-verbal, and advances, behavioral genetics, and Norms and predictive validity psychological testing ‘culture-free’ tests consensus 1883: Francis Galton publishes 1901: Applications of correlation by Clark 1921: Psychological Corporation 1950: APA publishes Ethical standards Inquiries into Human Faculty and Its Wissler (Cattell’s student) suggest that for the distribution of psychological founded by J.M. Cattell, R.B. Development “brass measures” have little predictive tests and diagnostic aids validity (e.g., for academic performance) Woodworth, & E.L. Thorndike 1984: Francis Galton administers a test 1951: Lee Cronbach introduces the 1904: Charles Spearman publishes The 1926: Scholastic Aptitude Test (SAT) to battery to thousands of volunteers at alpha coef cient to measure proof and measurement of the association predict college success (but only the International Health Exhibition, between two things (rank order!) consistency (think subtests!) normed later in 1941) London 1954: Technical recommendations for 1904: General Intelligence by Charles 1933: Publication of Vectors of Mind by psychological tests and diagnostic aids 1890: James McKeen Cattell publishes Spearman, introduces Correlational psychology and g factor (psychometric g) L.L. Thurstone suggestion by APA (chaired by L. Cronbach) Mental Tests and Measurements reformulation of Spearman’s approach 1905: Binet-Simon scale, 30 ordered items 1955: Wechsler Adult Intelligence Scale 1896: Karl Pearson develops the 1935: Foundation of the Psychometric (WAIS) product-moment correlation 1914: William Stern publishes The Society psychological methods of testing 1963: Theory of uid and crystallized intelligence 1938: Ravens’ progressive matrices intelligence by R.B. Cattell (leads to C- H-C!!!) 1914/1916: Stanford-Binet Scale through 1938: Wechsler-Bellevue Intelligence translation of Binet Scale by Terman, 1980: Applications of Item Response introduction of IQ. Test (published by Psych Corp.) Theory to Practical Testing Problems 1917: Woodworth Personal Data Sheet 1943: Minnesota Multiphasic by Frederic Lord (WPDS) Personality Inventory (MMPI) 1981: Familial studies of intelligence by 1917: Army Alpha & Beta, group tests for Bouchard & McGue in Science military recruits, Committee led by Yerkes (advisors, Terman, Cattell, Goddard) Gregory, R. J. (2004). Psychological testing: History, principles, and applications. Pearson Education India. Link to ebook Wasserman, J. D. (2012). A history of intelligence assessment: The un nished tapestry. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd 9 ed., pp. 3-55). Guilford Press. Link to chapter fi fl fi  Behavioral genetics “A summary of 111 studies identi ed in a survey of the world literature on familial resemblances in measured intelligence reveals a pro le of average correlations consistent with a polygenic mode of inheritance. There is, however, a marked degree of heterogeneity of the correlations within familial groupings, which is not moderated by sex of familial pairing or by type of intelligence test used.” Behavioral Genetics ==> the eld of study that examines the role of genetic and environmental in uences on animal (including human) behaviour. Behavioural geneticists study the inheritance of behavioural traits. In humans, this information is often gathered through the use of the twin study or adoption study. Heritability ==> a statistic used in breeding and genetics works that estimates how much variation in a phenotypic trait in a population is due to genetic variation among individuals in that population. Bouchard Jr, T. J., & McGue, M. (1981). Familial studies of intelligence: A review. Science, 212(4498), 1055-1059. Link to paper 10 fl fi fi fi  Psychological testing and psychometrics: Four phases and major events PHASE I PHASE II PHASE III PHASE IV (1880s - 1890s) (1900s - 1910s) (1920s - 1940s) (1950s - present) The Origins: Modern Tests: Methodological and empirical The Birth of IQ: Standardisation and objectivity in Point scales, verbal, non-verbal, and advances, behavioral genetics, and Norms and predictive validity psychological testing ‘culture-free’ tests consensus 1883: Francis Galton publishes 1901: Applications of correlation by Clark 1921: Psychological Corporation 1950: APA publishes Ethical standards Inquiries into Human Faculty and Its Wissler (Cattell’s student) suggest that for the distribution of psychological founded by J.M. Cattell, R.B. Development “brass measures” have little predictive tests and diagnostic aids validity (e.g., for academic performance) Woodworth, & E.L. Thorndike 1984: Francis Galton administers a test 1951: Lee Cronbach introduces the 1904: Charles Spearman publishes The 1926: Scholastic Aptitude Test (SAT) to alpha coef cient to measure battery to thousands of volunteers at proof and measurement of the association predict college success (but only consistency (think subtests!) the International Health Exhibition, between two things (rank order!) normed later in 1941) London 1954: Technical recommendations for 1904: General Intelligence by Charles 1933: Publication of Vectors of Mind by psychological tests and diagnostic aids 1890: James McKeen Cattell publishes Spearman, introduces Correlational L.L. Thurstone suggestion by APA (chaired by L. Cronbach) Mental Tests and Measurements psychology and g factor (psychometric g) reformulation of Spearman’s approach 1955: Wechsler Adult Intelligence Scale 1905: Binet-Simon scale, 30 ordered items 1896: Karl Pearson develops the 1935: Foundation of the Psychometric (WAIS) product-moment correlation 1914: William Stern publishes The Society 1963: Theory of uid and crystallized psychological methods of testing intelligence intelligence by R.B. Cattell (leads to C- 1938: Ravens’ progressive matrices H-C!!!) 1914/1916: Stanford-Binet Scale through 1938: Wechsler-Bellevue Intelligence 1980: Applications of Item Response translation of Binet Scale by Terman, introduction of IQ. Test (published by Psych Corp.) Theory to Practical Testing Problems by Frederic Lord 1917: Woodworth Personal Data Sheet 1943: Minnesota Multiphasic (WPDS) Personality Inventory (MMPI) 1981: Familial studies of intelligence by Bouchard & McGue in Science 1917: Army Alpha & Beta, group tests for military recruits, Committee led by Yerkes 1997: 52 intelligence researchers sign (advisors, Terman, Cattell, Goddard) editorial on a consensus about intellgence and mental testing Gregory, R. J. (2004). Psychological testing: History, principles, and applications. Pearson Education India. Link to ebook Wasserman, J. D. (2012). A history of intelligence assessment: The un nished tapestry. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd 11 ed., pp. 3-55). Guilford Press. Link to chapter fi fl fi  25-point consensus on intelligence and testing “1. Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it re ects a broader and deeper capability for comprehending our surroundings—“catching on,” “making sense” of things, or “ guring out” what to do.” “2. Intelligence, so de ned, can be measured, and intelligence tests measure it well. They are among the most accurate (in technical terms, reliable and valid) of all psychological tests and assessments. They do not measure creativity, character, personality, or other important differences among individuals, nor are they intended to.” … Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1), 13-23. Link to paper 12 fi fi fl More consensus on intelligence and testing ➡ Mental tests (i.e., general mental ability) have predictive validity in the real world, such as in job performance (average correlation ~ 0.5)! Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of 13 research ndings. Psychological bulletin, 124(2), 262. Link to paper fi Summary Psychological testing: initially fuelled by eugenics and need for objective measurement; dual focus on theory of psychological faculties and applications to real-world problems (selection) Measurement: researchers rst emphasised standardisation of measurement conditions and later standardisation of scoring; introduction of points and homogenisation across batteries (i.e., IQ) facilitated determining (age appropriate) norms, and distinction of components (e.g., verbal vs. non-verbal) Methods: development of statistical methods to quantify association between variables (e.g., Pearson correlation) and perform dimensionality reduction (e.g., factor analysis/principal component analysis) Conceptual issues: theoretical consensus by the end of the 20th century, some agreement about biological (behavioural genetics) and environmental bases (still debate about the nature of g, neural basis, etc.). Main applications: personnel selection/job performance, academic achievement 14 fi History of Psychology Session 11: Decision science Loreen Tisdall, Center for Cognitive and Decision Sciences December 2, 2024 Learning Objectives for Today Identify utility as an important concept in Psychology and Economics Understand key differences between models of choice under uncertainty 16 Your turn! What is the main aim of decision science? Which disciplines feed into this field? What is its relevance? Image created with AI (Bing), February 2024 ~ 1 minute 17 Decision science: useful terminology Field of study into how decisions should optimally (rationally) be made, are made, and how to bridge the two Multi / interdisciplinary eld that includes psychologists, engineers, economists, mathematicians, philosophers, statistics, marketing, amongst others A key aspect in decision science is trying to understand decision-making under uncertainty (uncertainty regarding outcome magnitudes, probabilities, directions, temporal aspects) ➡ Distinction between risk (outcomes and their probabilities are given or can be ascertained; e.g., roll of a die) versus Knightian uncertainty (ambiguity; outcomes and their probabilities are not given and cannot be ascertained, e.g., impact of Arti cial General Intelligence) ➡ Addresses deviations from rational models when navigating uncertainty (e.g., how cognitive limitations, heuristics, and biases in uence decision-making when faced with incomplete or ambiguous information) Busemeyer, J. R. (2015). Cognitive science contributions to decision science. Cognition, 135, 43-46. https://www.sciencedirect.com/science/article/pii/ S0010027714002303 Hertwig, R. (2015). Decisions from experience. The Wiley Blackwell handbook of judgment and decision making, 2, 239-267. https://onlinelibrary.wiley.com/doi/ 18 10.1002/9781118468333.ch8 fl fi fi  Normative, descriptive, and prescriptive models NORMATIVE: How should ultra-intelligent, super-rational people make decisions? ‣ Sets out how to make optimal (instead of actual) decisions ‣ The profession of economists DESCRIPTIVE: How do people actually make decisions? ‣ Account for and explain decisions ‣ The profession of psychologists PRESCRIPTIVE: How can better decisions be made? ‣ Integration of descriptive and normative perspectives (creating a bridge!) ‣ The profession of practitioners and engineers Image created with AI (Bing), February 2024 Rai a, H. (2007). Negotiation analysis: The science and art of collaborative decision making. Cambridge, Massachusetts: Harvard University Press. 19 ff From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, Blaise Pascal and Pierre de Fermat propose EVT as one of the earliest mathematical approaches to decision-making under uncertainty Includes the notion of expected value, EV: a choice was thought rational if it maximized EV EV = “the product of the probability of an outcome and the value of that outcome” (sum of products for multiple outcomes) Image created with AI (Bing), February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 20 Ore, O. (1960). Pascal and the invention of probability theory. The American Mathematical Monthly, 67(5), 409-419. Link to paper Integration of value and probability Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices Example for 2 options EV = p1*x1 + p2*x2 EV = 0.5*100 + 0.5*0 = 50 Value function x is linear Expected Value 2000 1500 utility) (invalue” “subjective 1000 Money 500 Image created with AI (Bing), February 2024 0 0 500 1000 1500 2000 Money (in “objective $) value” 21 From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, 1738: Daniel Bernoulli publishes Exposition of a New Blaise Pascal and Pierre de Fermat propose EVT as Theory on the Measurement of Risk, the foundation one of the earliest mathematical approaches to for EUT, but originally received little attention decision-making under uncertainty Includes the notion of expected value, EV: a choice was thought rational if it maximized EV EV = “the product of the probability of an outcome and the value of that outcome” (sum of products for multiple outcomes) Image created with AI (Bing), February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 22 Daniel Bernoulli Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 23 From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, 1738: Daniel Bernoulli publishes Exposition of a New Blaise Pascal and Pierre de Fermat propose EVT as Theory on the Measurement of Risk, the foundation one of the earliest mathematical approaches to for EUT decision-making under uncertainty EUT ‘solves’ the St. Petersburg paradox rst Includes the notion of expected value, EV: a choice proposed by his cousin Nicolas Bernoulli in 1713 by was thought rational if it maximized EV replacing the notion of expected value (EV) with expected utility (EU) EV = “the product of the probability of an outcome and the value of that outcome” “There is no doubt that a gain of one thousand (sum of products for multiple outcomes) ducats is more signi cant to a pauper than to a rich man.” (Daniel Bernoulli) EU = “the product of the probability of an outcome and the utility of that outcome” (sum of products for multiple outcomes) Image created with AI (Bing), February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 24 fi fi Integration of value and probability Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices Example for 2 options Example for 2 options EV = p1*x1 + p2*x2 EU = p1*u(x1) + p2*u(x2) EV = 0.5*100 + 0.5*0 = 50 EU = 0.5*80 + 0.5*0 = 40 Value function x is linear Value function u(x) is concave Expected Value Expected Value versus Expected Utility 2000 2000 1500 1500 Money (in utility) utility) (invalue” “subjective 1000 1000 Money 500 500 Image created with AI (Bing), February 2024 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Money (in “objective $) value” Money (in $) How do we nd the ‘U’ in EU? What experimental approach would be suitable? 25 fi Repeated choices in lotteries 0 CHF 50 CHF vs. 100 CHF 26 From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, 1738: Daniel Bernoulli publishes Exposition of a New Blaise Pascal and Pierre de Fermat propose EVT as Theory on the Measurement of Risk, the foundation one of the earliest mathematical approaches to for EUT decision-making under uncertainty EUT ‘solves’ the St. Petersburg paradox rst Includes the notion of expected value, EV: a choice proposed by his cousin Nicolas Bernoulli in 1713 by was thought rational if it maximized EV replacing the notion of expected value (EV) with expected utility (EU) EV = “the product of the probability of an outcome and the value of that outcome” “There is no doubt that a gain of one thousand (sum of products for multiple outcomes) ducats is more signi cant to a pauper than to a rich man.” (Daniel Bernoulli) EU = “the product of the probability of an outcome and the utility of that outcome” (sum of products for multiple outcomes) 1943: John Von Neumann and Oskar Morgenstern publish The Theory of Games and Economic Behavior, formalizing and thus cementing EUT Four axioms of EUT that de ne a rational decision maker: completeness; transitivity; independence of irrelevant Image created alternatives; with AI (Bing), and continuity February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 27 https://en.wikipedia.org/wiki/Von_Neumann–Morgenstern_utility_theorem fi fi fi From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, 1738: Daniel Bernoulli publishes Exposition of a New 1953: Maurice Allais provides counter-example to Blaise Pascal and Pierre de Fermat propose EVT as Theory on the Measurement of Risk, the foundation EUT by showing violation of independence (Allais one of the earliest mathematical approaches to for EUT paradox) decision-making under uncertainty EUT ‘solves’ the St. Petersburg paradox rst 1979: Daniel Kahneman and Amos Tversky Includes the notion of expected value, EV: a choice proposed by his cousin Nicolas Bernoulli in 1713 by publish Prospect Theory: An analysis of decisions was thought rational if it maximized EV replacing the notion of expected value (EV) with under risk expected utility (EU) EV = “the product of the probability of an 2002: Daniel Kahneman receives the Nobel outcome and the value of that outcome” “There is no doubt that a gain of one thousand Memorial prize in Economic Sciences “for having (sum of products for multiple outcomes) ducats is more signi cant to a pauper than to a rich integrated insights from psychological research into man.” (Daniel Bernoulli) economic science, especially concerning human judgment and decision-making under uncertainty” EU = “the product of the probability of an outcome and the utility of that outcome” (sum of products for multiple outcomes) 1943: John Von Neumann and Oskar Morgenstern publish The Theory of Games and Economic Behavior, formalizing and thus cementing EUT Four axioms of EUT that de ne a rational decision maker: completeness; transitivity; independence of irrelevant Image created alternatives; with AI (Bing), and continuity February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 28 fi fi fi Integration of value and probability Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices Example for 2 options Example for 2 options Example for 2 options EV = p1*x1 + p2*x2 EU = p1*u(x1) + p2*u(x2) V = v(x1)*w(p1) + v(x2)*w(p2) EV = 0.5*100 + 0.5*0 = 50 EU = 0.5*80 + 0.5*0 = 40 Value function x is linear Value function u(x) is concave Value function v(x) is concave for gains and convex Expected Value Expected Value versus Expected Utility for losses 2000 2000 1500 1500 Money (in utility) utility) (invalue” “subjective 1000 1000 Money 500 500 Image created with AI (Bing), February 2024 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Money (in “objective $) value” Money (in $) 29 Integration of value and probability Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices Example for 2 options Example for 2 options Example for 2 options EV = p1*x1 + p2*x2 EU = p1*u(x1) + p2*u(x2) V = v(x1)*w(p1) + v(x2)*w(p2) EV = 0.5*100 + 0.5*0 = 50 EU = 0.5*80 + 0.5*0 = 40 Value function x is linear Value function u(x) is concave Value function v(x) is concave for gains and convex Probability function Expected p is linear Value Probability Expected functionValue p isversus linearExpected Utility for losses, probability function w(p) is weighted 2000 2000 1500 1500 Money (in utility) utility) (invalue” “subjective 1000 1000 Money 500 500 Image created with AI (Bing), February 2024 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Money (in “objective $) value” Money (in $) 30  From normative to descriptive models Expected Value Expected Utility Prospect Theory Theory (EVT) Theory (EUT) (PT) Normative Origins and Rational Foundations Expected Utility and Risk Preferences Behavioral Insights and Real-World Choices 1654: Attempting to solve the “problem of points”, 1738: Daniel Bernoulli publishes Exposition of a New 1953: Maurice Allais provides counter-example to Blaise Pascal and Pierre de Fermat propose EVT as Theory on the Measurement of Risk, the foundation EUT by showing violation of independence (Allais one of the earliest mathematical approaches to for EUT paradox) decision-making under uncertainty EUT ‘solves’ the St. Petersburg paradox rst 1979: Daniel Kahneman and Amos Tversky Includes the notion of expected value, EV: a choice proposed by his cousin Nicolas Bernoulli in 1713 by publish “Prospect Theory: An analysis of decisions was thought rational if it maximized EV replacing the notion of expected value (EV) with under risk” expected utility (EU) EV = “the product of the probability of an 2002: Daniel Kahneman receives the Nobel outcome and the value of that outcome” “There is no doubt that a gain of one thousand Memorial prize in Economic Sciences “for having (sum of products for multiple outcomes) ducats is more signi cant to a pauper than to a rich integrated insights from psychological research into man.” (Daniel Bernoulli) economic science, especially concerning human judgment and decision-making under uncertainty” EU = “the product of the probability of an outcome and the utility of that outcome” Behavioral choice patterns that are in violation of (sum of products for multiple outcomes) EUT but are in line with PT: 1943: John Von Neumann and Oskar Morgenstern Certainty effect publish The Theory of Games and Economic Re ection effect Behavior, formalizing and thus cementing EUT Framing effect Four axioms of EUT that de ne a rational decision maker: completeness; transitivity; independence of irrelevant Image created alternatives; with AI (Bing), and continuity February 2024 Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. Link to chapter 31 fl fi fi fi Summary Utility is a central concept in decision science that represents a subjective quantity of value or worth; rst formalised in 18th century by Daniel Bernoulli, it has since been a central concept in both economics and psychology (as the causal principle underlying choices) and is central to concepts such as motivation, happiness and well-being Amongst other factors, models of choice under uncertainty differ in how outcomes and probabilities are treated: Expected Value Theory evaluates choices using objective monetary values and linear probabilities; Expected Utility Theory incorporates subjective utility to re ect risk preferences while maintaining linear probabilities; and Prospect Theory accounts for psychological biases by both a value and a probability weighting function. The history of decision science is characterised by transitions from purely normative to more descriptive models of choice —> fruitful marriage between empirical research and theoretical work! 32 fi fl Questions ??? 33 Key reading Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2022). Decision quality and a historical context. In Straight choices (3rd ed., pp. 12). Psychology Press. https://doi.org/ 10.4324/9781003289890 (download via ADAM: https://adam.unibas.ch/goto_adam_ le_1940530_download.html) 34 fi Additional reading (optional) Ruggeri, K., Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A.,... & Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature human behaviour, 4(6), 622-633. https://www.nature.com/articles/s41562-020-0886-x.pdf Comment by Kellen (2020). The Limited Value of Replicating Classic Patterns of Prospect Theory. https://communities.springernature.com/posts/the-limited-value-of-replicating- classic-patterns-of-prospect-theory 35