Opportunity-Cost-Driven Reward Mechanisms for Crowd-Sourced Computing Platforms
Bibliographic record
Abstract
This paper introduces a game-theoretic model tailored for reward distribution on crowd-sourced computing platforms. It explores a repeated game framework where miners, as computation providers, decide their computation power contribution in each round, guided by the platform’s designed reward distribution mechanism. The reward for each miner in every round is based on the platform’s randomized task payments and the miners’ computation transcripts. Specifically, it defines Opportunity-Cost-Driven Incentive Compatibility (OCD-IC) and Dynamic OCD-IC (DOCD-IC) for scenarios where strategic miners might allocate some computation power to more profitable activities, such as Bitcoin mining. The platform must also achieve Budget Balance (BB), aiming for a non-negative total income over the long term. This paper demonstrates that traditional Pay-Per-Share (PPS) reward schemes require assumptions about task demand and miners’ opportunity costs to ensure OCD-IC and BB, yet they fail to satisfy DOCD-IC. The paper then introduces Pay-Per-Share with Subsidy (PPSS), a new reward mechanism that allows the platform to provide subsidies to miners, thus eliminating the need for assumptions on opportunity cost to achieve OCD-IC, DOCD-IC, and long-term BB.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".