Decision Making With Many Options

Tibor Besedes

Cary Deck

Sudipta Sarangi

Mikhael Shor

October 2007

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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

)max()max( YXX

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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

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Limitations of Prior Studies

Past studies examine either satisfaction with choice or whether a choice was madeFewer choices made does not imply that

the average choice is worseSatisfaction with choice does not imply

objectively good decision-making

We want to know whether a choice is optimal

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.

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Medicare Part D

Private insurers offer prescription drug plans

A person may see

as many as 140 competing plans

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Motivating Example

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Motivating Example

Motivating Example

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Motivating Example

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Motivating Example

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Nature of Decisions

Physician Office Visit

Preventive Care

Urgent Care Service

Emergency Room Service

Hospital Expenses (inpatient)

Hospital Expenses (outpatient)

Diagnostic Services

Available Plans

A B C D

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 realizedNo “cost” of options If chosen option covers the state that occurs,

subject receives payment

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Experimental Decision Problem

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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%)

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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)

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Results

Selection of optimal option4 options 13 options

PDF 1(all states likely, new options all bad)

6 attributes 41% 29%10 attributes 45% 24%

PDF 2 (low prob. states, new option better)

6 attributes 50% 50%10 attributes 52% 36%

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Increasing options reduces frequency of optimal choice

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

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Impact of Age: First Order Effects

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Optimal decision-making decreases with age

Age Group18-40 41-60 61+

Frequency of Optimal Choice

52% 42% 31%

Nearly Optimal Choice (within 10%)

72% 66% 59%

Improvement over random choice

61% 49% 36%

Frequency of “dominated” options

0% 5% 18%

Impact of Age: Second Order Effects

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Decision complexity interacts with age

Age Group18-40 41-60 61+

Relative Frequency 82%

18%

66%

34%

47%

53%

decision)complex (least choice optimal %

decision)complex (most choice optimal %

Results

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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

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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