Value of a clear desk: sequencing decisions when decision capacity is limited
Bibliographic record
Abstract
Postponing a decision may allow a better decision later. However, depending on what else comes up, there may never be a moment when it makes sense to come back to a postponed decision, leaving potential gains unrealized. We develop a model of an agent with limited decision-making capacity who faces a sequence of decisions that vary stochastically in their importance and improvability. In each period he can either act on the newly-arrived opportunity or return to one carried from earlier. The prospect of future congestion in decisions generates an incentive to make prompt decisions, to "keep a clear desk". The strength of that imperative is: (1) increasing in the expected importance of future opportunities but decreasing in the dispersion of their importance; (2) decreasing in the expected improvability of future opportunities, but ambiguously influenced by the dispersion of that improvability. The analysis illuminates some decision practices that would otherwise be hard to rationalize. Multiple equilibria in some cases rationalize persistently different behaviour by two agents facing (almost) identical sequences of choices. The setting allows for a generalization of the concept of option value to congested decision environments and, by accounting for a plausible cost to postponement of action, offers a counter-force to the precautionary principle.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".