More Dynamic Than You Think: Hidden Aspects of Decision-Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Decision-making is a multifaceted, socially constructed, human activity that is often non-rational and non-linear. Although the decision-making literature has begun to recognize the effect of affect on decisions, examining for example the contribution of bodily sensations to affect, it continues to treat the various processes involved in coming to a decision as compartmentalized and static. In this paper, we use five theories to contribute to our understanding of decision-making, and demonstrate that it is much more fluid, multi-layered and non-linear than previously acknowledged. Drawing on a group experience of deciding, we investigate the intrapersonal, interpersonal, and collective states that are at play. These states are shown to be iterative: each being reinforced or dampened in a complex interaction of thought, affect, social space and somatic sensations in a dynamic flux, whilst individuals try to coalesce on a decision. This empirical investigation contributes to theory, method and practice by suggesting that Volatility, Uncertainty, Complexity and Ambiguity (VUCA) is a human condition. VUCA permeates and impacts decision-making in a multitude of ways, beyond researchers’ previous understanding. The innovation generated through this paper resides in a set of propositions that will accelerate progress in the theory, method, and practice of decision-making.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it