Background and notes from an experts discussion of well-being hosted by the Dubai Futures Foundation as part of the 2019 World Government Summit

1. Well-Being - A Sunset Conversation
Micah Altman
Director of Research
Center for Research in Equitable & Open Science
Massachusetts Institute of Technology
<escience@mit.edu>
Prepared for Dubai Future Foundation
World Government Summit 2019
Dubai
SLIDES: tiny.cc/dubaiwellbeing2019

2. Disclaimer
These opinions are my own. They are not the opinions of MIT our funders, or
our collaborators.
These thoughts are preliminary -- they represent an exploration not a
conclusion of current research.

3. Per Capita GDP does not predict happiness
Easterlin, Richard A., et al. "The happiness–income
paradox revisited." Proceedings of the National
Academy of Sciences (2010): 201015962.

4. Per-Capita GDP elides distribution
Davies, James, and Anthony
Shorrocks. "Comparing global
inequality of income and
wealth." United Nations
University-WIDER Working
Paper (2018).

5. Increasing Energy Consumption may not Increase Happiness
(especially in developed countries)
Okulicz-Kozaryn A, Altman M. The Energy
Paradox: Energy Use and Happiness. Working
Paper.

6. Two Kinds of Limits to GDP as a Measure
Technical limits
● exclusion of non-market transactions
● exclusion of negative externalities
● exclusion of economic public goods
● treats replacement of depreciated
capital as new capital
Ethical limitations -- factors excluded
● equality of distribution
● sustainability
● intergenerational equity
● exclusion of non-economic goods
○ Health
○ Life satisfaction
○ Meaningful choices (capacities)

7. A Case for Enlightened Indicators
1. Incorporating an intergenerational perspective that considers the long-term
sustainability of human progress
2. Acknowledging the diversity in wellbeing value functions across cultures,
communities, and individuals, and proposing a more inclusive and
participatory approach to governing the advancement of human progress
3. Unpacking the inequalities and patterns of segregation, which aggregate
indices and average measures risk glossing over
4. Adopting a holistic measure of social progress that considers
simultaneously individual, community, and societal levels of wellbeing and
their interrelatedness
5. Going beyond the elimination of harms and the acquiring of basic goods to
instead value people’s freedom to pursue the life they value, and their
enjoyment of a sense of hopeand confidence in a better future
6. Adopting and continuing to develop new methods for how new types of
data are collected,as well as how existing data and indicators are interpreted
to deepen our understandingof how social progress is promoted, sustained,
and made more inclusive “The Case for Extended Intelligence” 2019
https://engagestandards.ieee.org/rs/211-FYL-
955/images/CXI%20report.pdf

8. What does social science say about well-being?
Well-being has four main dimensions:
● Wealth
● Health (physical, psychological)
● Life satisfaction
● Choices
Well-being can be measured and predicted:
● Important events affect the entire life course
See: A harm-reduction framework for algorithmic fairness
M Altman, A Wood, E Vayena
IEEE Security & Privacy 16 (3), 34-45

9. Science and Well Being
Grand Challenges in Scholarly Communication
grandchallenges.mit.edu
Despite its deep and broad social benefits, science itself remains surprisingly constricted in a number of
fundamental aspects:
1. The benefits of science are unevenly distributed.[8]
2. Access to scientific data and scholarly communication, as well as STEM learning materials, has until
recently been limited almost exclusively to those inside research or university environments with the ability
to pay and fluency in English.[9]
3. Participation in our collective knowledge is limited to a small minority. The vast majority of research that
gets into mainstream scholarly publications is conducted in elite university settings in developed
countries.[10]
4. Even in those countries, participation in science is heavily skewed by gender, race, class, and language—
which affect the construction and evaluation of scientific knowledge.[11]
5. The evidence base is restricted—subjects (people), behaviors, languages, even forms of knowledge, and
the evidence base in many fields is shifting to new sources.[12]
6. The algorithms we use to interpret evidence in political and commercial systems embody unexamined
bias.[13]

10. Discussion questions
● Use cases
○ in what scenarios are non-economic
measures of well-being most needed?
○ What decisions or insights could such
measures support?
● Data
○ What data is being collected by someone
(individual, government, corporation), that
if made widely available would be most
useful for measuring well being?
○ How could this data be used to measure
well-being?
○ Where would such measures be used?
● Methods/challenges
○ What is the most challenging aspects of
measuring well-being?
○ Where do existing methodologies fall short?
● Ethics
○ Where are diverse measures of well-being
most needed to serve the world?
○ What are potential ethical concerns of
developing and using new measures?

11. Discussion notes: Use Cases & Desired Data Sources
Potential use cases
- Better understanding of political unrest
- Understanding doting behavior
- Measuring emotional state of students in learning
- Individual evaluation after satisfying life needs
- Understanding satisfaction with employment
- Understanding when knowledge makes people better
off -- for science of science policy
- Understanding sense of personal safety
Data sources
- Facial expression for use in short term subjective
measures (e.g. happiness)
- Facial expression/video for measure of durable
emotional states (e.g. anxiety/depression)
- More extensive collection of self-evaluative measure
(e.g. through quantified self systems, apps)
- Detecting life events from social profile, commercial,
administrative data (e.g. change jobs, relationships,
employment)
- Using always-on listening devices (e.g. alexa) to
measuring voice tone, voice emotion, conversational
patterns (behavioral health, social communication
patterns)
- Social media data for
- Movement variability for short term subjective well
being
- Personalized KPI’s , goals, goal achievement through
quantified self systems data

12. Discussion notes: Methodological Challenges, Ethics
Methodological challenges
- Tremendous resistance to knowing the answer
- Information Privacy
(control over learning about individuals)
- Information Agency
(consent, control over downstream use and purposes
thereof)
- Data access -- lack of repositories, unified access to
collections, unified data sharing processes
- Sample size -- especially for subpopulations
- Data fusion -- temporal integration; spatial integration;
combining subjective and objective measures;
measured at unit of individual and group; real time;
high granularity
- Data quality measures
- Causal inference methods and research designs
- Cross-cultural comparison validity
Ethics - Pressing needs
- Individual level
- Enable individuals to define their own happiness
measure
- Empower individuals to create and track their own
interventions
- Society/community level
- measuring freedom, agency and democracy
- Designing an education system that promotes
individual, community and societal well-being
Ethics - Concerns
- enhanced quantification could have a reductive effect on
definition of wellness and chill idiographic approaches
- Danger of embedding our own parochial values in meaures
- Neo-behaviorism - elide quale (qualitative sensation) when
measures are not based on sense of mind

13. Thanks to everyone for participating in this ad-hoc panel!