An Eight Component Decision-Making Model for Problem Gambling:A Systems Approach to Stimulate Integrative Research
Bibliographic record
Abstract
Problem Gambling (PG) represents a serious problem for affected individuals, their families and society in general. Previous approaches to understanding PG have been confined to only a subset of the psychobiological factors influencing PG. We present a model that attempts to integrate potential causal factors across levels of organization, providing empirical evidence from the vast literature on PG and complimentary literatures in decision-making and addiction. The model posits that components are arranged systematically to bias decisions in favor of either immediately approaching or avoiding targets affording the opportunity for immediate reward. Dopamine, Testosterone and Endogenous Opioids favor immediate approach, while Serotonin and Cortisol favor inhibition. Glutamate is involved in associative learning between stimuli and promotes the approach response through its link to the DA reward system. GABA functions to monitor performance and curb impulsive decision-making. Finally, while very high levels of Norepinephrine can induce arousal to an extent that is detrimental to sound decision-making, the reactivity of the Norepinephrine system and its effects of Cortisol levels can shift the focus towards long-term consequences, thereby inhibiting impulsive decisions. Empirical evidence is provided showing the effects of each component on PG and decision-making across behavioural, neuropsychological, functional neuroimaging and genetic levels. Last, an effect size analysis of the growing pharmacotherapy literature is presented. It is hoped that this model will stimulate multi-level research to solidify our comprehension of biased decision-making in PG and suggest pharmacological and psychological approaches to treatment.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".