A recent Taiwan government warning against Amap turned into a viral boost, exposing the app’s superior traffic‑light countdown, massive data‑driven network effects, and the underlying reverse‑propagation model that explains why the ban accelerated downloads rather than suppressing them.
The article examines how shortcut tools that boost efficiency can reduce expressive diversity by lowering choice costs, creating path‑dependence, reinforcing the Matthew effect, and leveraging cognitive anchoring and network effects, ultimately eroding cultural creativity and value rationality.
The article examines how modern platform monopolies act as digital landlords extracting rent from service providers, analyzes the network effects that cement their power, and explores how blockchain and smart contracts could create decentralized, trustless marketplaces that reduce fees, improve data sovereignty, and challenge existing platform dominance.
This article examines the hidden complexities of A/B experiments in short‑video recommendation feeds, explains why traditional designs produce biased results due to learning, double‑sided, and network effects, and presents practical double‑sided and community‑randomized experiment frameworks to obtain unbiased strategy evaluations.
This article explores why returns in business, knowledge, and influence often grow faster than linearly, attributing the phenomenon to exponential growth, threshold effects, and network dynamics, and offers practical strategies for identifying and leveraging such superlinear opportunities.
This article examines how leading two‑sided platforms such as Uber, Lyft, and DoorDash design and run scientific experiments—ranging from time‑space slice A/B tests to random‑saturation and continuous bandit trials—to accurately measure and improve surge‑pricing strategies despite network‑effect biases.
This article discusses experimental design challenges in two-sided markets, particularly in advertising scenarios, and presents various methods including four-table experiments, counterfactual interleaving, and contingency table joint sampling to address issues like network effects and competition between supply and demand sides.
This article explains how to design and evaluate incentive strategies in two‑sided platform experiments, covering problem background, challenges such as spillover and SUTVA violations, and proposing solutions like gradual scaling, small‑world partitioning, and ranking‑fusion approaches, while outlining key metrics for assessment.
This article presents a graph‑neural‑network approach to predict user influence, cluster users with distributed Louvain methods, and conduct network‑aware A/B experiments that accurately evaluate large‑scale marketing campaigns despite strong network effects.
The article analyzes platform product growth by examining Douyin's user expansion tactics and JD's e‑commerce strategies, covering platform value, growth strategies, network effects, single‑ and double‑sided drives, local network effects, growth challenges, and corresponding solutions.
In this talk, Tsinghua professor Li Daokui uses three personal anecdotes to reveal core economic ideas—eliminating excess options, investing in one's true goals as human capital, and leveraging networks—to guide young people toward decisive, fulfilling career paths.
