Application of Prisoner's Dilemma Theory in Corporate Competition: A Critical Review of Meituan and Ele. me's Subsidy-Driven Price War
Bibliographic record
Abstract
This article examines the Prisoner’s Dilemma in the context of competition among Chinese food delivery platforms, with a focus on its underlying mechanisms, manifestations, and impacts. In 2025, Meituan and Ele.me engaged in an aggressive price war of subsidies for food delivery fees, which led to varying degrees of losses and pressure on merchants, food delivery riders, and the enterprises themselves. Merchants are forced to bear part of the subsidy costs, which has led to a significant reduction in their profit margins. At the same time, delivery riders are also forced to work overtime due to the sudden increase in the number of takeout orders, and their risks have also risen sharply. Even the platform itself has fallen into a distorted profit structure and excessive growth in marketing expenses. Based on the Prisoner's Dilemma theory, the analysis reveals that the core reasons for this phenomenon include: decision-making mistakes caused by cognitive biases, incorrect market judgments due to information asymmetry, and the lack of reasonable and effective regulatory constraints. This study effectively demonstrates the explanatory power of the Prisoner's Dilemma theory for real business competition, and at the same time provides a reference for understanding and analyzing the irrational competitive behavior of enterprises and constructing a regulatory framework to prevent vicious competition.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".