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Modeling Risky Decision-Making Annie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd, Ph.D. Self-Report Questionnaires: Personality measures of preference/aversion to risk; 10 different scales with corresponding questions 10 scales: Weber, Rational-Experiential Inventory (REI), Barrett Impulsiveness, Maximization (MAX), Temperament Character Inventory (TCI), Helping-Orientation Questionnaire (HOQ), Regret, Brief Sensation Seeking (BSS), Behavioral Inhibition/Behavioral Approach (BIS/BAS), Interpersonal Reactivity Index (IRI) Demographic Surveys: Age, gender, ethnicity, etc. Risk Premium: Percent change in the expected value of the risky or ambiguous decision and that of the certain decision i.e. how much higher expected value of risky or ambiguous bet must be in order to choose that bet over the certain bet 0 = risk neutral | + = risk averse | - = risk seeking Risky Certain Trial 1: Trial n: . . . Y X Loss Aversion: Percent change in the potential gain of an always 50/50 gamble; possible to lose money i.e. how much higher potential gain must be in order to take gamble

Modeling Risky Decision-Making Annie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd, Ph.D. Self-Report Questionnaires: Personality

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Page 1: Modeling Risky Decision-Making Annie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd, Ph.D. Self-Report Questionnaires: Personality

Modeling Risky Decision-MakingAnnie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd,

Ph.D.

Self-Report Questionnaires: Personality measures of preference/aversion to risk; 10 different scales with corresponding questions

10 scales: Weber, Rational-Experiential Inventory (REI), Barrett Impulsiveness, Maximization (MAX), Temperament Character Inventory (TCI), Helping-Orientation Questionnaire (HOQ), Regret, Brief Sensation Seeking (BSS), Behavioral Inhibition/Behavioral Approach (BIS/BAS), Interpersonal Reactivity Index (IRI)

Demographic Surveys: Age, gender, ethnicity, etc.

Risk Premium: Percent change in the expected value of the risky or ambiguous decision and that of the certain decisioni.e. how much higher expected value of risky or ambiguous bet must be in order to choose that bet over the certain bet

0 = risk neutral | + = risk averse | - = risk seeking

RiskyCertain

Trial 1:

Trial n:

. . .

Y X

Loss Aversion: Percent change in the potential gain of an always 50/50 gamble; possible to lose moneyi.e. how much higher potential gain must be in order to take gamble

Page 2: Modeling Risky Decision-Making Annie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd, Ph.D. Self-Report Questionnaires: Personality

Machine Learning Analysis (Risk Premium)

Linear Discriminant Analysis (LDA)0

0.10.20.30.40.50.60.70.80.9

1

Classification Rates of Linear Discriminant Analysis (Full Model)

PremiumChoice

Risk Premium Median Split (0.35)

Ambiguous Premium Median Split (1.375)

Other Machine Learning Classifiers Used:Logistic Regression

Support Vector Machine (SVM)Naïve Bayes

Classification Trees & Random Forest k- nearest neighbors

Decision Tree Regression (Loss Aversion)

LDA Classification Full Reduced Difference

Classification Rate 0.612 0.698 +8.6%

True Negative 0.727 0.717 -1.0%

True Positive 0.612 0.689 +7.7%

As supported by existing literature, women were found to be more loss averse than men.

Page 3: Modeling Risky Decision-Making Annie Tang, Ethan Levine, Brandon Ho, Jonathan Winkle, Ph.D., Emma Wu Dowd, Ph.D. Self-Report Questionnaires: Personality

How do people make decisions in the face of uncertainty?

RiskPremium ~ WeberC2 + WeberD2 + WeberE2 + REI.ET + TCI.NS

Weber Scale: assesses risk taking in five content domains: financial decisions, health/safety, recreational, ethical, and social decisions

Rational Experiential Inventory: measures two independent processing modes, intuitive experiential and analytical rational thinking

Temperament Character Inventory: personality survey that looks at both temperament and character

*70% classification rate with LDA model based on 5 features