Choice Manipulation Through Comparability in Markets with Verifiable Multi-Attribute Products
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
We illustrate how an information sender may use unverifiable signals regarding a set of substitute products located in an alternative market to manipulate the choices made by uninformed but perfectly rational decision makers (DMs) within the verifiable market where the information sender operates. We do so by defining an optimal information gathering structure for rational DMs who acquire information sequentially from a set of multidimensional products. The resulting strategic signaling environment delivers two main results that are illustrated numerically. First, in order for the sender to successfully manipulate the information gathering and choice behavior of DMs, he should release signals on characteristics that differ from their most preferred ones. Second, the capacity of the sender to manipulate the behavior of DMs depends negatively on his reputation regarding the expected value of the unobserved characteristics guaranteed to DMs within the market where he operates. Normative applications to online search environments conditioned by the provision of strategic reviews in social media are presented.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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