Tagged articles

price elasticity

4 articles · Page 1 of 1
Model Perspective
Model Perspective
Jul 14, 2026 · Industry Insights

Why Are Shared Bike Services Raising Prices Simultaneously?

Shared bike operators in Beijing, Nanjing and Zhengzhou have lifted the base fare from 1.5 yuan per 30 minutes to about 1.9 yuan per hour, a roughly 30% increase that, according to market saturation, product homogeneity and short‑term supply rigidity, reflects tacit collusion and will mainly deter low‑frequency, short‑trip users.

bike sharingmarket saturationmembership lock‑in
0 likes · 9 min read
Why Are Shared Bike Services Raising Prices Simultaneously?
Model Perspective
Model Perspective
May 10, 2026 · Industry Insights

Why Coffee Drinking Is Surging: A Quantitative Look at Labor Intensity

The article quantifies the rapid rise in coffee consumption in China by linking price reductions, addiction mechanisms, and increasing work intensity into a feedback loop, while also contrasting coffee's market dynamics with tea's declining everyday relevance.

Market Trendsaddictionbeverage industry
0 likes · 7 min read
Why Coffee Drinking Is Surging: A Quantitative Look at Labor Intensity
Xiaolong Cloud Tech Team
Xiaolong Cloud Tech Team
Oct 30, 2025 · Artificial Intelligence

Why Making Agents Move Is the Next Critical Step: Explainability, Tool Creation, and 100% Accuracy

The interview with Sheet0.com founder Wang Wenfeng explores why the next phase for AI agents hinges on explainability, real‑time data integration, and tool creation, emphasizing 100% accuracy, trust, and feedback loops as essential for turning agents from interns into truly intelligent assistants.

AI AgentsReal-Time Dataexplainability
0 likes · 15 min read
Why Making Agents Move Is the Next Critical Step: Explainability, Tool Creation, and 100% Accuracy
DataFunSummit
DataFunSummit
Mar 19, 2024 · Artificial Intelligence

Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling

This article explores the challenges of estimating hotel occupancy in online booking platforms and presents four comprehensive approaches—background analysis, demand‑function based quantity‑price modeling, causal‑inference modeling, and multi‑scenario joint modeling—highlighting novel models, datasets, and experimental results for dynamic pricing optimization.

Demand ModelingMachine Learningcausal inference
0 likes · 11 min read
Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling