The Sovereo Diagnostic recommends countries the way decision scientists structure complex, high-stakes choices: by measuring conditions that the evidence links to wellbeing, eliciting a person's true priorities, and combining the two with a transparent, axiomatically grounded method. This page sets out what it measures, how it decides, what is established, and what remains to be tested.
The Diagnostic does not rank "nice places." It scores the social determinants of wealth and health, the conditions surrounding a person that decades of epidemiology link to how long and how well a life is lived. The Whitehall studies of British civil servants established a stepwise social gradient in mortality driven not only by income but by control and autonomy; the World Health Organization's Commission on Social Determinants of Health (2008), chaired by Sir Michael Marmot, formalised these as the "causes of the causes" of health outcomes. The six dimensions the Diagnostic uses map directly onto this framework and onto the domains of the OECD Better Life Index, the reference multi-domain wellbeing instrument used across governments.
| Diagnostic dimension | Established framework it maps to |
|---|---|
| Income & opportunity | SDOH economic stability; OECD income & jobs domains |
| Education | SDOH education access & quality; OECD education domain |
| Health & longevity | SDOH health care; life expectancy as the summary health outcome |
| Environment | SDOH neighbourhood & built environment; OECD environment domain |
| Lifestyle & belonging | SDOH social & community context; OECD community & safety domains |
| Legacy & sovereignty | SDOH distribution of power & resources; governance and rule of law |
Choosing where to live is a multi-criteria decision under conflicting objectives, the exact problem class that decision theory solves with Multi-Criteria Decision Analysis (MCDA) and its theoretical core, Multi-Attribute Utility Theory (MAUT). Keeney and Raiffa's 1976 work Decisions with Multiple Objectives provided the axiomatic foundation for additive and multiplicative utility models that combine performance across attributes with a decision maker's own weights. MCDA is not a marketing device; it is the established methodology for structured decisions in health technology assessment, codified by the ISPOR MCDA Emerging Good Practices Task Force, and applied across medicine, policy and operations research.
The Diagnostic follows the method's required steps: define non-overlapping, preference-independent criteria; score each country on each criterion from objective data; elicit the person's weights; and combine them into a transparent fit score, with hard constraints (budget, visa pathway, language) applied as the method prescribes for non-compensatory factors. Crucially, the recommendation is driven by fit to the person's stated priorities and constraints, not by an average ranking, which is why a low-tax remote earner and a safety-first family receive materially different countries.
The weights that drive the result come from the respondent, captured with stated-preference techniques, discrete-choice experiments, conjoint analysis and best-worst scaling, that are the standard tools for measuring preferences in health economics and market research. Systematic reviews comparing these formats find good measurement validity for eliciting priorities. The Diagnostic uses forced trade-offs and constrained choices rather than simple importance ratings, because trade-offs reveal what a person will actually sacrifice, the property that makes the output discriminating rather than flattering.
Country scores are not editorial estimates. Each factor is computed from an established, peer-reviewed or institutional dataset and normalised to a common scale:
The instrument is validated through qualitative, logical and expert evidence rather than a quantitative field trial. This is deliberate. A study that measured outcomes on real respondents would require collecting and processing sensitive personal data, with the legal and ethical burden that follows under GDPR and similar regimes. Instead, credibility is established by evidence a reviewer can audit directly, and the instrument is engineered so that it collects no personal data to begin with.
Established. The instrument rests on validated components: a determinant framework grounded in the social-determinants-of-health literature, a decision method (MCDA/MAUT) with axiomatic foundations and codified good practice, preference-elicitation formats with demonstrated validity, and source data from recognised indices. Its construct logic is transparent and fully reproducible, and known-groups behaviour is demonstrated by simulation: distinct profiles produce theoretically expected, divergent recommendations.
Privacy by design. The instrument never requests a declared net worth, infers financial position indirectly, and is built to score on the respondent's own device without retaining identifying data. Because it does not collect or store sensitive personal data, it sits outside the heaviest processing obligations of GDPR and CCPA. It is an educational decision aid, structured guidance rather than a guarantee. We state this plainly because credibility is the product.
Run the Sovereo Relocation Diagnostic and watch the method produce three countries chosen for your life, each with its reasoning and its single biggest caveat.
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