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Decision Making With Many Options

Decision Making With Many Options. Tibor Besedes Cary Deck Sudipta Sarangi Mikhael Shor October 2007. Motivation. Life is full of choices Many important life decisions are made from an often overwhelming number of options Mathematical truism: Psychological perspective:

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Decision Making With Many Options

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  1. Decision Making With Many Options Tibor Besedes Cary Deck Sudipta Sarangi Mikhael Shor October 2007

  2. Motivation • Life is full of choices • Many important life decisions are made from an often overwhelming number of options • Mathematical truism: • Psychological perspective: • Information Overload • People “give up” when facing too many options • Cognitive perspective: • Brain “processing power” is limited

  3. Evidence on Information Overload • Fewer people join a 401(k) retirement plan when more savings options are presented • Iyengar, Jiang and Huberman 2004 • Physicians are less likely to prescribe any drug when more drugs are available • Redelmeier and Shafir 1995, Roswarski and Murray 2006 • Total amount of recycling decreases when people are offered multiple recycling options. • Greater “choice satisfaction” when choosing among six Godiva chocolates than among 30 • Iyengar and Lepper 2000 • I don’t like long restaurant menus

  4. Limitations of Prior Studies • Past studies examine either satisfaction with choice or whether a choice was made • Fewer choices made does not imply that the average choice is worse • Satisfaction with choice does not imply objectively good decision-making We want to know whether a choice is optimal

  5. Research Hypotheses • When faced with a large set of options, individuals make inefficient and suboptimal decisions. • Older individuals, will suffer a greater deterioration of decision accuracy as decision complexity increases. • Reducing the complexity of the task makes decision-making more efficient.

  6. Motivating Example • Medicare Part D • Private insurers offer prescription drug plans • A person may see as many as 140 competing plans

  7. Motivating Example

  8. Motivating Example

  9. Motivating Example

  10. Motivating Example

  11. Nature of Decisions • Available Plans • A • B • C • D • Physician Office Visit • Preventive Care • Urgent Care Service • Emergency Room Service • Hospital Expenses (inpatient) • Hospital Expenses (outpatient) • Diagnostic Services

  12. Nature of Decisions • There exist unknown future states of nature • I’ll be healthy or sick. I’ll need what drug? • States have associated probabilities • Options “cover” some states but not others • Choice is a maximization over states • Simplified: • Exactly one state is realized • No “cost” of options • If chosen option covers the state that occurs, subject receives payment

  13. Experimental Decision Problem

  14. Experimental Design • 2 x 2 x 2 (+ 1) within-subject design • Number of states: Either 6 or 10 • Number of options: Either 4 or 13 options • Probability distribution • 10 state problems equivalent to 6 state problems • Options “expanded” (all check marks preserved) • For probability distribution 1: • All states are rather likely • Going from 4 options to 13 by introducing suboptimal options • For probability distribution 2: • Several states have very low probability • 4 to 13: one new option is much better (96% v. 71%)

  15. Methodology • Random order of • Decision problems • Options • States • 125 subjects recruited online • Paid $1 for every successful state, plus $3 • Collected demographics: • Age, sex, education • Dependent variables: • Frequency of optimal decisions • Efficiency of decisions (how suboptimal is suboptimal)

  16. Results Increasing options reduces frequency of optimal choice

  17. Results • How suboptimal are the choices? • 42% of all choices were the optimal option • 66% of all choices were within 10% of optimal • Average efficiency loss was 13% • Subjects were half-way between optimal choice and random choice

  18. Impact of Age: First Order Effects Optimal decision-making decreases with age

  19. Impact of Age: Second Order Effects Decision complexity interacts with age

  20. Results Regressions Chance of selecting optimal option: Decreases with age Increases with education Does not depend on sex Parameter magnitudes 11 years of age offsets an education category 3 education categories offsets having more options

  21. Implications • With reasonable a priori knowledge about optimal options, presenting fewer options is better • AT&T knows this, government does not • For older people, fewer may be better even without any a priori knowledge • Best of any 4 better than random of 13 • Future investigation of choice presentations • Default “suggested” options • Break up big decisions into smaller ones • Recommender systems

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