Culture and Decision Making: Influence of Analytic Versus Holistic Thinking Style on Resource Allocation in a Fort Game
Bibliographic record
Abstract
People have to make different decisions every day, in which culture affects their strategies. This research examined the role of analytic versus holistic thinking style on resource allocation across cultures. We expected that, analytic thinking style, which refers to a linear view about the world where objects’ properties remain stable and separate, would make people concentrate their resource allocation corresponding to the current demand, whereas holistic thinking style, which refers to a nonlinear view that people perceive change to be a constant phenomenon and the universe to be full of interconnected elements, would encourage people to spread out their resource allocation. In Study 1, Hong Kong Chinese, a representative group of holistic cultures, and European Canadians, a representative group of analytic cultures, completed a resource allocation task (i.e., fort game). The results showed that the allocation pattern of European Canadians was more concentrated than that of Hong Kong Chinese and holistic thoughts predicted a less concentrated allocation pattern. To test causality, thinking styles were manipulated in Study 2, in which mainland Chinese were primed with either holistic thinking style or analytic thinking style. The results showed that the allocation pattern was more concentrated in the analytic condition than that in the holistic condition, which was explained by greater perceived predictability in the analytic condition. Implications of these findings on cross-cultural decision-making research and applied research were discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".