More Dynamic Than You Think: Hidden Aspects of Decision-Making
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.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".