Contract-Theoretic Pricing for Security Deposits in Sharded Blockchain With Internet of Things (IoT)
Why this work is in the frame
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Bibliographic record
Abstract
A sharded blockchain with the Proof-of-Stake (PoS) consensus protocol has advantages in increasing throughput and reducing energy consumption, enabling the resource-limited participants to manage transactions and in a decentralized way and obtain rewards at a lower cost, e.g., Internet-of-Things (IoT) users. However, the latest PoS (e.g., Casper) requires a steep security deposit, which is the key to provide more robust security guarantees than Proof of Work, but not practical for the owners of heterogeneous IoT devices. This article considers any individual and institute who owns the IoT devices as the potential participant and focuses on designing the proper security deposits in a practical scenario with hidden information and hidden action. To bridge blockchain and the IoT users, we study the problem of balancing the security incentive and the economic incentive under two cases: 1) stake oriented and 2) effort oriented. We propose two joint models under the contract theory framework to efficiently address the problems: 1) joint adverse selection and moral hazard and 2) joint adverse selection and tournament. Both optimal contracts can provide a maximized profit for blockchain. The optimal rewards and security deposits for different types of participants can be determined accordingly. Simulations indicate that the proposed models can overcome asymmetric information and offer feasible contracts. Moreover, it demonstrates that both joint models can provide an economic incentive for the participants without reducing security incentives for the sharded blockchain.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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