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 and randomized block selection, 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 rational agents seek for individual payoff maximization. Unlike existing game theoretical models, we focus on a decentralized resale market-through virtual monetary exchanges, agents 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 further characterize the relations between coding complexity and market properties including agents' entry price and expected payoff, thus providing guidelines for strategic operations in a real P2P market. Our analysis reveals that the major power of network coding lies in maintaining stability of the market with impatient agents, and incentivizing agents with lower price and higher payoff, at the cost of reasonable coding complexity. Since the traditional P2P content distribution approach is a special case of network coding, our model can be generalized to analyze the equilibrium strategies of rational agents in decentralized resale markets.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".