Addiction as a Disruption of the Markov Blanket: A Hyper-Precision of Momentary Rewards Over Long-Term Adaptation
Bibliographic record
Abstract
This opinion piece proposes that addiction can be conceptualized as a disruption of an agent’s Markov blanket - the statistical boundary separating internal states from the external environment - under active inference. Addiction arises when the Markov blanket becomes overly rigid, assigning excessive precision to a narrow set of sensory inputs and active states associated with the addictive stimulus. This leads to compulsive behaviors aimed at immediate rewards while sacrificing the exploration of flexible action policies that would reduce uncertainty and optimize the agent’s long-term fit with the environment. The addicted agent becomes trapped in a limited behavioral repertoire, unable to adapt to volatility, thus eroding its own resilience. Rigid, compulsive patterns of interaction can propagate outwards, undermining the resilience and sustainability of the wider ecosystem. This perspective offers an integrative framework for understanding addictive disorders and their broader impacts, suggesting interventions aimed at pulling the Markov blanket away from problematic attractors and promoting adaptive exploration.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".