Bibliographic record
Abstract
We show how prospect theory uncovers critical decision-making insights in the design of sequential experiences by formulating a general framework and applying it to three experience design settings. First, we study the problem of releasing a piece of good news versus bad news, where a firm may incrementally reveal the news over a preemptive period. We characterize the optimal release strategy for both types of news and show that when the ultimate news is good (resp., bad) and the audience is sufficiently gain-seeking, it is optimal first to release information of a negative (resp., positive) sentiment. Second, we consider the problem of organizing an event such as a concert with performances of known valuations, where an event organizer needs to arrange the sequence of all performances. We show that for both loss-averse and gain-seeking audiences, interior peaks can be optimal, where pleasant and aversive performances are arranged to alternate throughout the event. Lastly, we investigate the problem of simultaneous versus sequential release of a series, such as songs or TV episodes, where a content provider does not know a priori the audience's exact valuation of each item. We show that if the audience's sensitivity to losses is sufficiently small (resp., large), the optimal strategy is to release all items in the series sequentially (resp., simultaneously). Across all of the settings, we show that the audience's sensitivity to losses relative to a reference point is a critical factor that governs how to design and manage the audience's evolving experience dynamics.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".