Running on the Hedonic Treadmill: A Dynamical Model of Happiness Based on an Approach–Avoidance Framework
Bibliographic record
Abstract
Describing the dynamical nature of happiness is crucial for understanding why individuals are constantly running on a hedonic treadmill around set levels of well-being. Based on the self-centeredness branch of the ’self-centeredness/selflessness happiness model’, we present a dynamical model that focuses on unfolding the hedonic dimension of happiness dynamics through the use of the approach–avoidance framework. This numerical model enables us to understand and analyze emerging hedonic cycles caused by hedonic motivation and hedonic adaptation. In particular, hedonic motivation leads people to experience hedonic activities, which result in successes or failures and experiences of pleasure and afflictive affects; whereas hedonic adaptation causes individuals to return to a baseline level of pleasure and afflictive affects, more quickly for the former than the latter. The proposed dynamical model is based on the approach–avoidance framework that considers human behavior in two separate regulatory processes that contribute to homeostasis of individuals’ happiness. We analyze these two processes independently and conjointly in order to highlight their effect on happiness levels. The analysis shows how individual characteristics and their combination may result in hedonic cycles, afflictive affects, (dis-)pleasure, and particular happiness dynamics. We also discuss how such a numerical model enables us to perform a multifactorial analysis which is hardly feasible outside the context of a simulation and how it may help us to narrow and design relevant experimental surveys from these preliminary numerical results.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".