The temporal phase structure of team interaction under asymmetric information distribution: The solution fixation trap
Bibliographic record
Abstract
Summary Organizations facing dynamic environments typically use teams of individuals to collect and share information in order to make timely and accurate joint decisions. The mature body of prior research concerning team information processing indicates a consistent bias in team decision‐making under conditions that asymmetrically distribute information across team members; this body of research, however, focuses mainly on identifying significant relationships between static inputs and decision outcomes. As a result, little is known regarding the actual team processes that may influence decision outcomes. We introduce the notion of the temporal phase structure of team behaviors to the asymmetric information distribution research stream and identify relevant phase characteristic variables that show significant differences between lower‐ and higher‐performing teams in a team decision‐making simulation. We find evidence suggesting that higher‐performing teams are more able than other teams to prolong productive discussion of information without falling into a pattern of solution fixation. Finally, we identify specific behaviors that are likely to trigger beneficial and detrimental phase shifts. We close with a discussion of this evidence and suggestions for related future research.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".