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Optimizing for Happiness in Personal Finance

Periodic Reporting for period 1 - OHPF (Optimizing for Happiness in Personal Finance)

Reporting period: 2015-09-30 to 2017-09-29

This project investigated for first time a computational model that enables forecasting of happiness – addressed in the project through a measure of quality of life known as subjective well-being (SWB) – that arises as a consequence of the individual purchases that people make.This provides through a computational model unprecedented insights into the relationship between consumption and cognitive judgments about life satisfaction.This insight offers the potential to develop interventions to influence individual behaviors or outcomes.
Our first objective was to design a predictor of affective values of new future purchases based on past emotions and moods associated with things and services bought by that person.The affective history was captured with self-reports. Moreover, we studied how information about affective experiences of other individuals with comparable psychological profiles who made similar purchases in the past can be used to improve the accuracy of forecasts.
We implemented a system (app) for managing personal finance that includes capabilities for keeping affective history with regard to spending. Such a prototype is essential for obtaining an evaluation of improvements in affective forecasting achieved through availability of guidance generated based on the historical data. Additionally, the prototype was used to track SWB of individuals.
We studied whether more precise affective forecasts lead to greater pleasure from purchases and an improvement in the overall SWB. The results of quantitative experiments showed that technological advancements in the area of personal finance have a potential to improve well-being of people.
A unique element of our work is that it was conducted in real-life settings where participants reported their purchases and SWB via a smartphone app. No restrictions, recommendations, or rewards were given for purchasing particular items,and subjects were asked to make purchases as they would normally do, ensuring that our data describes naturalistic behavior and that this data represents a realistic picture of typical spending patterns and their affective outcomes. It therefore offers a highly ecologically valid data set for building a rich and realistic model of SWB. The development of a model based on rich empirical data has led to a series of new insights into how spending and well-being interact.By presenting the computational model of subjective well-being for consumption that integrates real purchases and recalled SWB, our findings provide a rich space for other researchers to develop and test new hypotheses,as well as providing the financial technology industry and the general public with insights that they can appreciate,value, and use to motivate behavioural change.
Cross-platform app created during the project