On the Market Power of Network Coding in P2P Content Distribution Systems
Bibliographic record
Abstract
Network coding is emerging as a promising alternative to traditional content distribution approaches in P2P networks. By allowing information mixture in peers, it simplifies the block scheduling problem, resulting in more efficient data delivery. Existing protocols have validated such advantages assuming altruistic and obedient peers. In this paper, we develop an analytical framework that characterizes a coding based P2P content distribution market where peers selfishly seek for individual payoff maximization. Through virtual monetary exchanges, agents in the market buy the coded blocks from others and resell their possessions to those in need. We model such transactions as decentralized strategic bargaining games, and derive the equilibrium prices between arbitrary pairs of agents when the market enters the steady state. We identify the traditional P2P content distribution approach as a special case of network coding, and characterize the relations between coding complexity and market performance metrics, including agents' entry price and expected payoff, thus providing operation guidelines for a real P2P market. Our analysis reveals that the major power of network coding lies in its ability to maintain stability of the market with impatient and selfish agents, and to incentivize agents with lower price and higher payoff, at the cost of reasonable coding complexity.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".