Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systems
Not on the map. Why?
Abstract (arXiv)
Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, efficiency, productivity, and financial return. These criteria are necessary but insufficient because they do not establish whether increasingly powerful systems improve or degrade human and planetary well-being. Through an integrative conceptual synthesis, we argue that human flourishing should serve as a primary success criterion for artificial intelligence, the global race to develop increasingly capable AI systems, and prospective post-AGI economic systems. We make three contributions. First, Flourishing Metrics provides an extensible framework spanning physical, emotional, financial, relational, spiritual, and planetary well-being, combining validated subjective measures with representative behavioural, organisational, community, and environmental indicators. Second, Return on Flourishing (RoF) extends return on investment by evaluating the counterfactual contribution of interventions, policies, and AI systems to flourishing relative to their resources, risks, and opportunity costs. Third, we develop distribution-sensitive safeguards and show how RoF could guide AI-enabled work redesign, institutional appraisal, assurance, and post-deployment monitoring through business pilots. We formalise flourishing as a dynamic system variable while emphasising the need for democratic specification, empirical calibration, independent validation, and protection against unacceptable losses within particular dimensions or stakeholder groups. RoF is proposed not as a universal reward function, but as a value-accounting and decision architecture for assessing whether intelligence, automation, and economic transformation generate durable human and planetary progress.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 63 references that are in the map. First posted on arXiv 31 Jul 2026.
Builds on
13 of 63 references.
References · 13
- 2016Concrete Problems in AI Safetycited by 1,582
- 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 115
- 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
- 2024Towards interactive evaluations for interaction harms in human-AI systemscited by 27
- 2025How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Studycited by 21
- 2024Scenarios for the Transition to AGIcited by 13
- 2026Sycophantic AI makes human interaction feel more effortful and less satisfying over timecited by 11
- 2025Harmful Traits of AI Companionscited by 6
- 2026Positive Alignment: Artificial Intelligence for Human Flourishingcited by 5
- 2025Measuring AI Alignment with Human Flourishingcited by 3
- 2025Human-AI Interactions: Cognitive, Behavioral, and Emotional Impactscited by 3
- 2025When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Beingcited by 3
1 more
- 2026Artificial Intelligence Index Report 2026cited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026A New Theory of Value for Post-AGI Economicscited by 0
- 2026A New Theory of Value for Post-AGI Economics2 Aug 2026