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Decision Making With Many Options

Dive into the complexities of decision-making with numerous options. Explore how reducing choices can enhance efficiency and satisfaction. Research findings, experimental design, and impacts of age on decision quality are examined in detail.

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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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