Data Science and Measuring Happiness

GNHIs it possible to measure happiness? Can we compare countries on the basis of a universal yardstick for collective happiness similar to Gross National Product (GNP), the accepted measure for a country’s material well-being?  How could data science contribute to practical applications of happiness analysis?

The Kingdom of Bhutan, a 770,000-strong nation landlocked between China and India at the eastern end of the Himalayas, has been developing what it calls Gross National Happiness (GNH) and applying it to government policies for more than 40 years. Influenced by Buddhism, happiness as measured by GNH is different from the way it is perceived in the West in two ways. It is “multidimensional – not measured only by subjective well-being,” according to the Short Guide to GNH. Furthermore, it is “not focused narrowly on happiness that begins and ends with oneself and is concerned for and with oneself.”

A new data science workshop, to take place November 6th to 11th in Bhutan, will discuss and debate questions related to the measurement of happiness  with experts in data science, Buddhist leadership and Gross National Happiness. The Data Happy conference and workshop will involve a high level of participatory process, collaboratively exploring ways by which individuals can contribute to the measurement of Gross National Happiness on a daily basis.

Troy Sadkowsky, the lead organizer of the event and the founder of Data Scientists Pty Ltd and creator of DataScientists.Net, says: “Developing a more holistic and accurate measure of wealth that includes more than just financial aspects would benefit us all. This could be a powerful tool for monitoring a healthy global growth in sustainable prosperity. Data Science is purpose-built for exploring this new terrain.”

The multiple dimensions of happiness and its collective or community orientation are reflected in the 9 domains that comprise the GNH index: psychological wellbeing, time use, community vitality, cultural diversity, ecological resilience, living standards, health, education, and good governance. These are in turn comprised of 33 clustered indicators, each one of which is composed of several variables, for a total of 124 variables.

The government of Bhutan administers every four years a survey based on the GNH index and respondents rate themselves on each of the variables. The 2010 survey found that 10.4% of the population is ‘unhappy’ (defined as achieving sufficiency in 50% or less of the weighted indicators), 48.7% were found to be ‘narrowly happy, ’ 32.6% were ‘extensively happy,’ and  8.3% of the population was identified as ‘deeply happy’ (showing sufficiency in 77% or more of the weighted indicators).

At the Data Happy conference, participants will discuss a proposed system for going beyond the periodical paper-based survey to an online process that will run continuously and will be integrated into other services in Bhutan. Says Sadkowsky: “We will be looking to introduce as much new technology as feasible to help increase accessibility and usability.  A major goal is to convert attitudes around the GNH measurement tools from something that people feel they have to do, like mandatory census surveys, to something that they want to do.“

Sadkowsky hopes that participants in the Data Happy conference will contribute to designing a tool for measuring Gross Individual Happiness that can be integrated into the daily lives of the people of Bhutan. Find out more about the program and registration here.

Originally published on Forbes.com

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About GilPress

I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/
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One Response to Data Science and Measuring Happiness

  1. Pingback: Data Science and Gross National Happiness | Data Science Thailand Blog

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