An optimal sequential information acquisition model subject to a heuristic assimilation constraint
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
Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach – The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings – The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs. Originality/value – The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it