Quantifying the Emotional Value of Goods and Services: Values of Hate and Love and Everything in between
Bibliographic record
Abstract
Conventional economic analysis treats goods and services as bundles of functional attributes whose value is revealed by prices and choices. Yet real-world demand is pervasively shaped by feelings—joy, disgust, pride, nostalgia, envy, comfort, belonging. This paper formalizes emotional value as a measurable component of consumer welfare, distinct from (but interacting with) functional utility and monetary cost. Building on affective science, neuro-economics, marketing, and information systems, I propose a composite Emotional Value Index (EVI) that integrates (i) self-report psychometrics, (ii) linguistic and behavioral traces, (iii) psychophysiology (e.g., HRV, EDA, pupil and gaze), (iv) neural evidence, and (v) digital footprints (search, clickstreams, reviews). The paper details measurement, validation, and computation of EVI, including methods to infer affect from online data rather than questionnaires alone. I illustrate applications for platform firms (Google/YouTube, Amazon, Apple, Netflix), discuss how love (positive valence, high identity alignment) and hate (negative valence, high arousal/identity threat) sit at opposite poles of an effect space, and show how EVI can slot into cost–benefit analysis, hedonic pricing, discrete-choice models, and computable general equilibrium. I conclude with a governance blueprint (privacy law, dark-pattern avoidance, differential privacy) for ethically harnessing emotion. The approach reframes “value” to include how goods make us feel, not just what they do. Key empirical and theoretical anchors are cited at the end
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".