# Presentation: 163358

## Emotional Adaptation and the End of Economics

- Peter A. Ubel, M.D.
- Center for Behavioral and Decision Sciences in Medicine
- Ann Arbor VAMC
- University of Michigan Health System

## Slide 2

## The answer depends on who you ask

- TTO utility of ESRD (0-1 scale)
-  Patients = .56
-  Community = .39
- Moods (-2 to +2 scale)
-  Patients = .66
-  Community prediction of patients = -.17

## Economic Importance of This Discrepancy

- Whose utilities should we include in cost effectiveness analyses?

## As if the “who” question wasn’t enough!

- We need to figure out _what_ question to ask
- Specifically:  should policy decisions be based on
-  decision utility or
-  experience utility

## A Quick and Inaccurate History of Economics

- Economics = Science of utility maximization
- Original notion of utility
- Jeremy Bentham
- Balance of pleasure & pain = Experience utility
- More recent view of utility
- Revealed preferences
- Rational people’s free choices lead to utility maximization
- 			= Decision utility

## Decision Utility and Revealed Preferences

## Illustration of distinction between Experience and Decision Utility

## What happened in this study?

- Experience utility –
-  The 60 second bucket was better than the 90
- Decision utility –
-  The 90 second bucket was better
- People _misremembered_ their 2 experiences, causing them to make a bad decision

## Goals of Talk

- Potential flaw of decision utility
- Based on mispredictions and misrememberings of experience utility
- Potential flaw of experience utility
- Goals of healthcare go beyond mood maximization
- Point to future research directions
- Empirical
- Normative

## Global versus Momentary QoL: source of patient overestimation?

**Global versus Momentary QoL: source of patient overestimation?**

## Global versus Momentary Reports of Well-being (SWB)

- People have difficulty describing average emotions over time
- There may be a discrepancy between
**moment to moment SWB **
**general evaluation of SWB**

## No pain, no gain

**Pain**

**High**

## It’s the little things in life

## Imagine a Dialysis Patient’s Quality of Life 

- How happy are you right now (0 - 10)?
**6**
**5**
**5**
**6**
**8**
- How happy do you feel generally?
**7**

## Entry:  Percentage of time, during a typical week, that you are in each of the following moods?

- _Entry_:  Percentage of time, during a typical week, that you are in each of the following moods?

**Notes:**
Easily compared to EMA

## Palm Week

**Notes:**
Same responses as for the estimation task
happy, joyful, pleased, enjoyment/fun
depressed/blue, unhappy, frustrated, angry/hostile, worried/anxious 

## Predicted Moods

**Notes:**
Average ... +2 to -2 scale. .. (would be .63 and .66 on a 0 to 1 scale)
Power at .8, we can detect differences of .3.
None of the demographics were significant covariates, so we used the full sample.
Another commonly used technique to demonstrate power is to show that the group predictor is significant if a covariate is added.

## Predicted vs. Actual Mood 

**Predicted Mood**

## Specific Moods (0-6 scale)- Palm Data -

## Imagining life on Dialysis

|  | Actual Mood | Dialysis Mood |
| --- | --- | --- |
| Patients | .66 |  |
| Controls | .80 | -.17 |

- P< .001

## Imagining perfect Health(Never had kidney problems. . .)

|  | Actual Mood | Dialysis Mood | Healthy Mood |
| --- | --- | --- | --- |
| Patients | .66 |  | 1.10 |
| Controls | .80 | -.17 |  |

- P< .028

## A Whole Lot of Mispredicting Going On

- Patients
-  Mispredict life without kidney disease
- General public
-  Mispredict life with kidney disease
- These mispredictions of _experience utility_ could influence _decision utility_

## Theory Driven Recall Bias: an alternative to scale recalibration

## Hope I die before I get old!

- Common belief (among rock stars, at least)
- Getting old stinks
- But happiness data suggest that
- Happiness relatively stable across adult years
- Not much change between 30 and 70
- If anything, less negative affect with age
- What do real people believe?
- < 40 year olds
- > 60 year olds
- And how do beliefs compare to “reality”?

## Study Design

- Internet study of people
- 21 – 40 (n = 264; median age = 31)
- 60 and up (n = 255; median age = 68)
- Asked to:
- Report current happiness (0 – 10 scale)
- Estimate happiness of typical
- 30 year old
- 70 year old

## Results: Self reported happiness

**10**
**0**

**Happiness**

- < 40                                        > 60

- 6.6

- 7.3

## Results: Predictions of 21–40 year olds

**10**
**0**

**Happiness**

- < 40                                        > 60

- 6.6

- 7.3

- 7.0

- 6.2

## Results: Predictions of old people

**10**
**0**

**Happiness**

- < 40                                        > 60

- 6.6

- 7.3

- 7.0

- 6.2

- 7.6

- 6.3

## A whole lot of mispredicting and misremembering going on

- Young people mispredict happiness as they age
- Old people misremember happiness of their youth
- People are out of touch with their own experience

## Looking forward to a kidney transplant

## Misestimating the benefits of kidney transplantation

- Surveyed patients waiting for kidney transplant at University of Pennsylvania
-  Measured QoL
-  Asked them to predict QoL 1 year after successful transplant
- Resurveyed them after transplant
-  Measured QoL
-  Asked them to remember pre-transplant QoL

## Mispredictions

- Domain

- Pre-tx

- Prediction for Post-tx

- Actual  Post-tx

- 83

- 12

- 15

- 4.3

- QoL (0-100)		    66             	91

- Travel (days/yr)	     9            	20

- Work (hrs/wk)	    12             	32

- Energy (1-5)		   3.2             	5.1

## More mispredictions

- Domain

- Pre-tx

- Prediction for Post-tx

- Actual  Post-tx

- 3.5

- Happy w/ life		    2.8             3.9

- 3.1

- ...std. of lvg.                 2.6             3.7

- 3.5

- ...love life	               3.5             3.8

- ...health	               2.4             3.9

- 3.6

- ...work	               2.5             3.6

- 3.1

- ...family life	               3.8          	 3.8

- 3.8

- ...social life	               3.5          	 3.8

- 3.6

- ...spiritual life	    3.4          	 3.7

- 3.6

- ...leisure life	               3.0          	 3.8

- 3.3

## Misremembering ESRD

- Time

- Pre-tx	              	        65             	  91

- Pre-tx QoL

- Post-tx QoL

- Post-tx:
-     Immediate                    57                       78

-     6 months	                   55             	   80

-     12 months                    48             	   83

## What’s it like to have a colostomy?

## Colostomy Patient Survey

- Surveyed people who have received colostomies at UM within last 5 years
- some had permanent and some had reversed colostomies
- 94 permanent
- 100 reversed
- What do these two groups think of life with a colostomy?

## Adapting to life with a Colostomy

- Overall quality of life (0-100)
- permanent = 67
- reversed = 71
- Overall positive affect positive mood (0-4)
- permanent = 3.1
- reversed = 3.1
- Overall negative mood (0-4)
- permanent = 1.8
- reversed = 1.9

- So ... little to no difference in mood or quality of life

## How Happy:  Now & 5 yrs. ago

- Now                                     5 yrs. ago

- 6.1

- 6.8

- 6.4

- 6.0

- 7

- 6

- 5

## How bad do these groups think it is to have a colostomy?

- Time tradeoff (TTO) utility question
- Imagine you will live 10 years with a colostomy then die.
- How many months (0-120) would you give up to get rid of the colostomy?
- permanent = 18 months

- reversed = 44 months

## Slide 42

## Decision Utility and Revealed Preferences

## Rational decision making and revealed preferences

- Ua = P1U1 + P2U2 + ...
- Ub = P7U7 + P8U8 + ...
- If I chose A over B
- Then Ua > Ub
- Similarly, if I’m willing to pay
- $10 for X
- $15 for Y
- Then Uy > Ux

## Flaws with revealed preference assumptions

- People mispredict utilities
- As I’ve shown already
- Even given utilities
- People don’t always integrate p’s and u’s in rational manner
- Here’s an example

## Imagine you have Colon Cancer 

**Surgery A**
**80% cure without complications**
**16% die of disease**
**1% colostomy **
**1% intermittent bowel obstruction **
**1% wound infection **
**1% diarrhea**
**Surgery B**
**80% cure without complications **
**20% die**
**Which surgery would you choose?**

## Slide 48

## Give me colostomy or give me death!

- When we ask people to choose  between being dead or having one of these four complications
- >90% prefer each of the four complications to death
- To be consistent with these preferences
- <10% should choose the uncomplicated surgery

## Our Survey says . . .

- 50-60% of people choose surgery B,  the uncomplicated surgery

## Kahneman’s case against Decision Utility

- Based on
-  mispredictions of utility
-  poor integration of problem and utility
-  If we want to maximize utility, we should measure _experience utility_ and devise policies/practices that maximize it

## Narrowing the scope of debate

- Lots of problems with utilitarianism
- consequentialism:  ends over means
- maximizes average “utility” without regard to distribution
- etc. etc.
- But consequences _do_* *matter to decisions
- choose dinner at restaurant
- buy a movie ticket
- invest in a health care system
- Surely we want to understand distinction between decision and experience utility

## Advantages of experience utility as welfare criterion

- People generally want to be happy
- But they are often unaware of what would make them happy
- Recent advances allow for more accurate measures of happiness, mood and other experiences
- Ecological Momentary Assessment
- Experience Sampling
- DRM
- Policy should be informed by
- Actual experience
- Not mispredicted experience

## Current approach to experience utility

- Focus = mood
- Outcomes = maximization of mean mood
- Integral of momentary affect
- Thus, for example
- -3, -3, 4, 4, 4 is better than
- 1, 1, 1, 1, 1

## Limitations of experience utility as welfare criterion

## A thought experiment

- Imagine that you are about to receive a below the knee amputation (BKA)
- You will recover, physically, quickly
- You will receive a top-of-the-line prosthesis
- _Physical function_ – almost normal
- Able to play sports
- Sprinting and jumping mildly reduced

## A thought experiment - continued

- Imagine also that you completely adapt emotionally
- Mood indistinguishable, on average, from prior to BKA
- Some pangs of
- Loss
- Stigma
- Balanced by positive emotions from lessons learned

## A question about our thought experiment

- How much would you pay to avoid BKA?

## Hedonic Adaptation

- Our claim
- Adaptation to positive and negative circumstances is not
- Immediate
- Universal
- Complete
- But is often
- Rapid
- Common
- And substantial

## Relevance of adaptation to debate about experience utility

- If moods largely return to normal after good and bad circumstances
- Then policies based on experience utility
- Won’t care too much about people’s circumstances

## Counterargument

- Experience utility consists of things other than mood
- Our reply
- We agree, partially
- Thus, the job of experience utility proponents should be to better capture the aspects of experience that people care about
- But we also expect that people care about things that go beyond what would ever qualify as experience utility

## What is missing from experience utility?

- Mill’s higher and lower pleasures
- Better to be an unhappy person than a happy pig
- Consider: wine connoisseur
- Meaning and purpose
- Raising young children
- Evaluation of experiences matters
- Consider two movies

## What is missing from experience utility?

- Capabilities
- Walking in the woods with your children
- Brief episodes
- Death of a loved one
- Self-identity
- My mini-van
- Moral considerations
- I do it even though it won’t make me happy

## Empirical exploration of distinction between experience and decision utility

- Developed intervention to help people take account of adaptation when making affective forecasts
- Think of bad event from more than 6 months ago
- more or less upsetting than predicted
- emotions stronger or weaker over time?
- List the 2 most upsetting things about becoming paraplegic
- Do you think these 2 things would become more or less upsetting over time?

## Thinking about adaptation changed QoL estimates 

- _	__	__	__	__	__	__QoL Rating (0 - 100)_
- _Disability__	__	__N __	__Before __	__After __	__P_
- Paraplegia     123
- Paraplegia 	 56

- 47
-  -

- 52
- 62

- .003
- .001

## Thinking about adaptation changed policy recommendations

- Given choice between saving the lives of
- 100 people who can be returned to perfect health
- X people who would experience onset of paraplegia
- X = 1000
- When given same choice after thinking about adaptation
- X = 101

## Thinking about adaptation di not change decision utility

- Standard gamble to elicit utility of paraplegia
- What chance of death would you take to be cured of paraplegia
- Time Tradeoff elicitation
- Imagine you will live 10 more years
- How many months of that time would you give up to be cured of paraplegia
- Adaptation exercise
- Did not influence responses to either elicitation

## These 3 studies suggest that

- Thinking about adaptation changes
- QoL estimates
- And potentially experience utility estimates
- But does not change
- People’s decision utility

## What criteria should guide public policy?

## What criteria should guide public policy?

- Two candidates, with limitations
- Decision utility/revealed preferences
- Experience utility/ moment-to-moment mood
- Given that neither option is ideal
- What should we use?
- We propose two answers
- Because there are two types of policies that require a welfare criterion

## Two kinds of policy

- Policies that are designed to directly affect the behavior of **individuals**
- Tax laws promoting IRAs
- FDA rules requiring food labels
- Seat belt laws
- Policies that operate at a level **higher than the individual**
- Local government deciding how much to spend on schools vs. cops
- Federal government corporate taxation policy
- Distinction between these two types of policy is not sharp

## Policies that guide individual behavior

- Standard approach
- Policies should enable people to make free, informed choices
- e.g. FDA food labels
- A step further
- Libertarian paternalism, soft paternalism, pruned decision utility...
- Set up decisions so that people are more likely to make optimal decision
- e.g. Default options for insurance
- e.g. Physician recommendations

## But what criteria/on should guide things like default options?

- Fact = People influenced by status quo bias/defaults options bias in choosing insurance
- So how do you chose the proper defaults?
- Our proposal
- Base policy on experience utility
- A maximally thick account of experience
- Inform people about experience when feasible
- And let them decide

## Why experience utility?

- Because experience utility _does_ matter to people
- And without “help”, they’ll mispredict it and make potentially bad decisions
- And because they can still chose to base their decision on non-affective or non-experiential issues

## Policies that operate above the level of the individual

- We don’t have a simple answer
- No single, appropriate welfare criterion
- Policy makers need to consider experience utility
- But need to take account of other things that matter to people
- Some hybrid of
- Decision utility
- Experience utility

## Possible approaches

- Deliberative democracy
- Informed decision utility
- But I’d like to see experimental studies evaluation those approaches

## Backing up our claim

- Test of global vs. momentary affect
- Dialysis study: EMA design
- Test of conversational norms
- Parkinson’s study
- Test of scale recalibration
- Diabetes and emphysema study

## Diabetes example

| Rate | Patients
N=367 | Non-patients
N=201 | Significance |
| --- | --- | --- | --- |
| diabetes | M=73.6
SD=24.1 | M=64.0
SD=24.2 | t(566)=4.51
p<.001 |
| lung disease | M=54.9
SD=24.7 | M=55.6
SD=23.6 | t(566)=.33
p=.74 |
| all items | M=51.1
SD=16.0 | M=51.6
SD=16.0 | t(566)=.36
p=.72 |
| health items only | M=47.1
SD=16.7 | M=47.4
SD=16.4 | t(566)=.22
p=.83 |
| non-health items only | M=52.6
SD=17.0 | M=54.1
SD=17.1 | t(566)=1.00
p=.32 |

## Acknowledgements

- Dylan Smith
- George Loewenstein
- Heather Pond Lacey
- Laura Damschroder
- Chris Jepson
- Harv Feldman
- Norbert Schwarz

- Ryan Sherriff
- Brian Zikmund-Fisher
- Jason Riis
- NIH
- Dept. of Veterans Affairs