Abstract
Subjective well-being, in contrast to other commonly used performance metrics such as gross domestic product, appears to offer a way to directly measure what society aims to achieve. Subjective well-being modeling to date has been restricted to regression analysis. This paper synthesizes and critiques existing literature and case studies to examine the challenges and opportunities presented by more advanced computations of well-being, including spatial, optimizing and spatial-optimizing models, which may well be created by researchers in the future if current policy level interest in well-being continues to grow. Subjective well-being is a promising measure, especially in light of recent research that shows reliable correlations with objective measures. However, the issue of individual adaptation means that excessive focus on subjective well-being may discriminate against groups with lower expectations and higher ability and/or willingness to adapt. Alternative approaches such as equivalent income may address this issue, at the expense of being harder to measure. Through an examination of four case studies and one thought experiment, we find that modeling challenges include nonlinearity, interaction, spatial sorting and extrapolation beyond valid limits. A significant research gap is found in how individual well-being scores should be aggregated to a collective one; this is a normative question although descriptive ethics would appear to offer a practical approach.
Highlights
Governments and urban planners have long used quantitative models to inform the decision-making process, including optimizing models in transport and spatial models in economics, while academic research experiments more widely with these model types
It still seems reasonable to take the increase of well-being as an aim, if not our overall one: even climate change can be framed as a well-being issue, including the well-being of other species if so desired
We have no current data to describe how UK citizens would cope with, for instance, disbanding all cities: a valid portion of parameter space for an optimizing model to explore, but which—if the spatial agglomeration–wealth link holds—would risk dramatic changes including loss of the revenue stream that funds health services currently taken for granted. This serves to highlight the importance of the capabilities-based alternative to well-being modeling discussed in Section 4, which may provide an approach to evaluating the extreme edge cases reachable during optimization
Summary
Governments and urban planners have long used quantitative models to inform the decision-making process, including optimizing models in transport and spatial models in economics, while academic research experiments more widely with these model types. We will not discuss these examples further, save to suggest that neither need undermine the well-being agenda, but both outline some of the limits to the context in which it is valid Within these limits, it still seems reasonable to take the increase of well-being as an aim, if not our overall one: even climate change can be framed as a well-being issue, including the well-being of other species if so desired (and in any case, shifting from economic growth to well-being as an outcome metric is likely to be a win-win for both well-being and climate). From a modeler’s perspective, until a more precise emissions-vs.-future-well-being relationship can be established, we can only conclude that enormous effort is required to meet scientifically agreed targets for emissions, and it is likely necessary to present climate risk and current well-being as dual outputs within a multi-objective framework These frameworks discard sub-optimal (lose-lose) solutions while maintaining a set of non-dominated (win-lose) solutions, which cannot, within the confines of the model, be ranked as objectively better or worse than one another. They are the natural choice for unifying quantitative urban models [1,2,3,4]; and with thoughtful presentation offer valuable opportunities for public engagement [36]. (Multi-criteria optimization is rightly known in some literature as Pareto optimization; the current paper avoids this term to prevent confusion with the notion of Pareto optimality in economics, which while mathematically related, is a distinct application in a political context not relevant to our discussion)
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