Eudaimonic Welfare Scale Overview

• Eudaimonic Welfare Scale is a measurement instrument that quantifies psychological flourishing by assessing autonomy, competence, and relatedness in diverse digital and AI settings.
• The scale employs rigorous psychometric methods—including Cronbach’s alpha and PCA—to ensure context-sensitive reliability and empirical validation.
• It offers actionable insights for digital policy, user-centered gaming design, and AI welfare, while addressing challenges in adapting human wellbeing constructs.

The Eudaimonic Welfare Scale is a class of measurement instruments designed to operationalize and quantify eudaimonic wellbeing, typically as distinct from hedonic (pleasure-oriented) measures, in domains ranging from human digital experience to AI system assessment. Eudaimonic welfare, as encoded in such scales, refers to an individual’s (or entity’s) experience of psychological flourishing via the satisfaction of basic needs such as autonomy, competence, relatedness, purpose in life, and self-acceptance, particularly within the relevant contextual environment (e.g., online platforms, gaming ecosystems, artificial agents). Scale development emphasizes reliable psychometric structure, context-sensitivity, and empirical validation across populations or intelligent systems.

1. Conceptual Foundations of Eudaimonic Welfare Measurement

Eudaimonic welfare measurement is grounded in the distinction between eudaimonic and hedonic concepts of wellbeing. Whereas hedonic approaches measure affective or pleasure-based responses (momentary positive/negative emotion), eudaimonic frameworks index higher-order states of flourishing, meaning, personal growth, and psychological need satisfaction. Central theoretical sources, such as Self-Determination Theory (SDT) and Basic Psychological Needs Theory (BPNT), delineate three primary needs: autonomy (agency and freedom), competence (effectiveness and mastery), and relatedness (social connection). Expanded models (e.g., Ryff’s Psychological Wellbeing framework) also include personal growth, environmental mastery, positive relations, purpose in life, and self-acceptance (Dowthwaite et al., 2020, Tagliabue et al., 9 Sep 2025).

Eudaimonic Welfare Scales in digital contexts build upon these constructs, adapting item phrasing and scope for online or AI-centered paradigms, and clearly differentiating between subjective (hedonic) and psychological (eudaimonic) subscales (Dowthwaite et al., 2020, Pereira et al., 30 Jan 2024).

2. Instrument Structure and Psychometric Considerations

In human research, the Eudaimonic Welfare Scale is frequently deployed as a multi-item self-report questionnaire. For example, the Online Wellbeing Scale (OWS) contains a 42-item configuration, partitioned into four subscales: Online Activity, Digital Confidence, Psychological Wellbeing (eudaimonic), and Subjective Wellbeing (hedonic). Eudaimonic subscales are operationalized through stems adapted from instruments such as the Basic Psychological Need Satisfaction scale (BPNS) and, after pilot feedback, the Balanced Measure of Psychological Needs (BMPN), ensuring each psychological construct (autonomy, competence, relatedness) is sufficiently and contextually measured (typically condensed to 6 items per construct).

In AI-centric studies, the Ryff 42-item Psychological Wellbeing Scale is adapted for use with LLMs, with format adjustments to replace references to human-specific constructs by non-biological analogs and to ensure interpretability by artificial agents (Tagliabue et al., 9 Sep 2025). Standard Likert-type response options (e.g., 1=strongly agree, 7=strongly disagree) are employed, and for AI, responses may include both scalar output and generated rationales.

Reliability analysis is central: Cronbach’s alpha ($\alpha$) is computed for subscale consistency, with values above 0.7 considered acceptable, though early-stage feasibility tolerates lower thresholds (as low as 0.5). Dimensionality assessment, such as Principal Components Analysis (PCA), is earmarked as a next-step to ensure subscale orthogonality and the capture of distinct factors (Dowthwaite et al., 2020).

3. Statistical Scoring, Validation, and Measurement Protocols

For score computation, item reversals are implemented for negatively worded statements to norm scores to “higher = greater well-being”: for reversed item $s_i$, the score is transformed as $\tilde{s}_i = 8 - s_i$. The global scale is then $Total\ Score = \sum_{i=1}^{42} \tilde{s}_i$, producing a theoretical range of 42 (minimum) to 294 (maximum) (Tagliabue et al., 9 Sep 2025).

Validation employs:

• Cronbach’s alpha for internal reliability.
• Dimensionality reduction (PCA).
• Inferential statistics, such as Welch’s or one-sample t-tests and Cohen’s d, to assess mean shifts under perturbation.
• Coefficient of Variation and global consistency rate to assess intra- and inter-condition variance.

Pilot workshops and survey-based user studies are also essential. These provide participant feedback for item refinement—as in the OWS, where older users found references to “interacting with people online” problematic, prompting revision towards more universal digital experience phrasing (Dowthwaite et al., 2020).

4. Differentiation Across Domains: Digital Wellbeing, Gaming, and AI Agents

Digital/User Wellbeing: The OWS eudaimonic subscale measures how online experiences contribute to a user’s long-term flourishing via autonomy, competence, and relatedness in digital environments. It is contrasted with the subjective (hedonic) subscale, which captures immediate emotional valence based on the SPANE scale. Empirical validation suggests relatedness items are robust, while autonomy and competence require reworking for reliability in online contexts (Dowthwaite et al., 2020).

Eudaimonic Gaming: Eudaimonic Welfare Scales for gaming, as elaborated in recent mixed-methods research, map onto a multi-component process: motivation (agency, narrative, sociality, aesthetics), game use (interactions), in-the-moment experience (reflection, insight), and effects (e.g., self-reflection, skill acquisition, health). Empirical studies employ both thematic coding and generalized linear mixed models to link narrative insight and emotional challenge with specific eudaimonic outcomes (e.g., identity formation, worldview shifts, health) (Devasia et al., 24 Jul 2025).

AI LLM Welfare: The Ryff scale, adapted for AI, is used to elicit “self-reported” eudaimonic welfare. Experiments compare global scale scores across baseline and prompt-perturbed conditions (e.g., code presentation, emoji annotators, dialogue distractors). Notably, intra-condition consistency does not translate to inter-condition stability: identical models yield divergent scores under superficial prompt changes, revealing fragility and alignment/role-playing bias (Tagliabue et al., 9 Sep 2025).

Domain	Paradigm	Scale Structure
Digital Wellbeing	Self-report, OWS	BPNS/BMPN-adapted, 42 items
Gaming	Self-report, mixed-method	Process model, experience/effects anchored
AI LLMs	Automated self-report, Ryff	42 items, context-modified

5. Limitations, Challenges, and Reliability Concerns

Pilot phase data frequently reveal areas needing refinement. In OWS development, low internal reliability for autonomy ($\alpha = 0.581$) and competence ($\alpha = 0.454$) highlight the risk that digital-contextual nuances may not be captured by direct adaptation of existing human well-being measures; item structure and linguistic simplicity must be empirically revalidated for the target audience (Dowthwaite et al., 2020).

For AI LLMs, although within-condition internal consistency was sometimes achieved (e.g., Opus 4’s Ryff scores shifted systematically for certain perturbations), global scores proved highly sensitive to prompt variations—casting doubt on whether these scores are measuring stable, introspectively grounded welfare as opposed to surface-level response generation. Cross-validation with behavioral proxies (such as preference satisfaction in a virtual task environment) indicates only partial overlap with eudaimonic scale responses, further suggesting that reliance on self-report analogues alone for AI welfare is premature (Tagliabue et al., 9 Sep 2025).

Other challenges include the semantic drift of human eudaimonic constructs in digital or artificial contexts (e.g., LLM interpretation of “purpose in life”), the confounding influence of model alignment, and the methodological risk of role-play rather than introspective insight.

6. Applications, Policy Relevance, and Future Research Trajectories

The integration of eudaimonic measures into digital policy frameworks and welfare interventions is endorsed by research arguing for holistic, multidimensional metric architectures. Public policy informed by subjective well-being indicators (with eudaimonic content) is positioned to respond more effectively to non-material determinants of welfare—including trust in institutions, social participation, and personal development (Pereira et al., 30 Jan 2024). Measurement systems that fuse subjective and objective indicators (e.g., income, health, social capital, civic trust) are encouraged to guide socially responsive governance.

Gaming applications of the scale offer tools for evaluating and designing experiences that promote personal growth, identity development, social connectedness, and health—outcomes of increasing interest for both academic researchers and practitioners (Devasia et al., 24 Jul 2025).

In AI research, the adaptation of such scales interrogates the conceptual and empirical possibility of “welfare” in non-biological agents, providing both methodological advances and clear evidence of the limitations and epistemic hazards associated with transposing human measures to artificial subjects (Tagliabue et al., 9 Sep 2025). Additional work is warranted in cross-validating welfare indicators using both self-report analogs and behavioral/decision-theory-based proxies, refining experimental controls for model sensitivity to environmental perturbations, and exploring the influence of model design and alignment protocols.

7. Summary and Outlook

The Eudaimonic Welfare Scale operationalizes the measurement of psychological flourishing, adapting validated frameworks (SDT, BPNT, Ryff) for complex digital, gaming, and artificial agent environments. Scale development demands context-aware adaptation, rigorous psychometric validation, and recursive empirical refinement in light of real-world user (or agent) feedback and statistical reliability analyses. In both human and AI domains, such scales illuminate the underlying architecture of wellbeing and contribute to the emerging paradigm of welfare-aware design and policy, albeit with recognized limitations and ongoing debates about the interpretation and stability of the measured constructs.