Bibliographic record
Abstract
In the mobile video games industry, a common in-app purchase is for additional “moves” or “time” in single-player puzzle games. We call these in-app purchases bonus actions. In some games, bonus actions can only be purchased in advance of attempting a level of the game (pure advance sales (PAS)), yet in other games, bonus actions can only be purchased in a “spot” market that appears when an initial attempt to pass the level fails (pure spot sales). Some games offer both advance and spot purchases (hybrid advance sales). This paper studies these selling strategies for bonus actions in video games. Such a question is novel to in-app tools selling in video games, and it cannot be answered by previous advance selling studies focusing on end goods. We model the selling of bonus actions as a stochastic extensive form game. We show how the distribution of skill among players (i.e., their inherent ability to pass the level) and the inherent randomness of the game influence selling strategies. For casual games, where low-skill players have a sufficiently high probability of success in each attempt, if the proportion of high-skill players is either sufficiently large or sufficiently small, firms should adopt PAS and shut down the “spot” market. Furthermore, the player welfare-maximizing selling strategy is to sell only in the spot market. Hence, no “win-win” strategy exists for casual games. However, PAS can be a win-win for hardcore games, where low-skill players have a sufficiently low success probability for each attempt. This paper was accepted by Hemant Bhargava, information systems. Funding: C. T. Ryan received funding from NSERC Discovery [Grant RGPIN-2020-06488]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.02348 .
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".