惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
腾讯CDC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
N
Netflix TechBlog - Medium
有赞技术团队
有赞技术团队
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
P
Proofpoint News Feed
L
LangChain Blog
aimingoo的专栏
aimingoo的专栏
GbyAI
GbyAI
B
Blog
F
Fortinet All Blogs
T
Tailwind CSS Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
G
Google Developers Blog
A
About on SuperTechFans
C
Check Point Blog
Microsoft Security Blog
Microsoft Security Blog
MyScale Blog
MyScale Blog
B
Blog RSS Feed

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Business Mode Selection in Digital Content Markets
Shaolei Ren, Jaeok Park, Mihaela van der Schaar · 2011-04-25 · via cs.SI updates on arXiv.org

In this paper, we consider a two-sided digital content market, and study which of the two business modes, i.e., Business-to-Customer (B2C) and Customer-to-Customer (C2C), should be selected and when it should be selected. The considered market is managed by an intermediary, through which content producers can sell their contents to consumers. The intermediary can select B2C or C2C as its business mode, while the content producers and consumers are rational agents that maximize their own utilities. The content producers are differentiated by their content qualities. First, given the intermediary's business mode, we show that there always exists a unique equilibrium at which neither the content producers nor the consumers change their decisions. Moreover, if there are a sufficiently large number of consumers, then the decision process based on the content producers' naive expectation can reach the unique equilibrium. Next, we show that in a market with only one intermediary, C2C should be selected if the intermediary aims at maximizing its profit. Then, by considering a particular scenario where the contents are not highly substitutable, we prove that when the intermediary chooses to maximize the social welfare, C2C should be selected if the content producers can receive sufficient compensation for content sales, and B2C should be selected otherwise.