Insights, knowledge, and enjoyment from a mathematical modeling researcher and educator. Hosted by Haihua Wang, a modeling instructor and author of "Clever Use of Chat for Mathematical Modeling", "Modeling: The Mathematics of Thinking", "Mathematical Modeling Practice: A Hands‑On Guide to Competitions", and co‑author of "Mathematical Modeling: Teaching Design and Cases".
This article analyzes the sudden surge in demand for Forward Deployed Engineers (FDEs) at AI firms like ByteDance and Moonshot, explaining the role's origins at Palantir, the 'last mile' problem in AI adoption, 42x job growth data, and the critical distinction between product flywheel and outsourcing trap outcomes.
The article adapts Aswath Damodaran's valuation model—cash flow, growth, and risk—to everyday choices like hiring, project selection, and parenting, illustrating why growth must exceed its cost of capital, why market price often diverges from intrinsic value, and how to test narratives with numbers.
The article argues that AI's greatest value in learning isn't providing answers but precisely calibrating difficulty to maintain an 85% success rate, citing the '85% rule' from a 2019 Nature Communications paper and detailing six evidence-based techniques like productive failure, teaching virtual students, error detection, confidence-based practice, and interleaved problem sets.
This article explores Nassim Taleb's first book, Dynamic Hedging, revealing how its practical treatment of options Greeks, dynamic hedging costs, liquidity black holes, path dependence, and model boundaries laid the technical foundation for his later ideas on black swans and antifragility.
This tutorial covers Tencent's WorkBuddy AI agent, which directly modifies Word, Excel, and PPT files, explaining installation, three operation modes based on risk, prompt engineering with five key elements, four practical examples for file organization, data merging, research presentations, and scheduled reports, plus tips on skills, connectors, credit management, and verification habits.
The 2026 Nobel Prize in Economics highlights why economists struggle to predict crises: forecasts that are believed change behavior, making them self-defeating, while successful prevention erases the evidence of accuracy. The article distinguishes spotting fragility from timing crashes, citing Hall's consumption random walk, Schularick-Taylor credit signals, Rajan's 2005 warning, and Woodford's forward guidance.
Analysis of Japan's simultaneous record corporate bankruptcies and acute labor shortages reveals that rising wage demands eliminate firms unable to afford market-rate labor, while productive firms gain advantage; a simple cost model shows how higher employment costs squeeze marginal businesses, and BOJ research suggests wage flexibility alone cannot instantly reallocate workers across skills and regions.
Santa Fe Institute research shows cities follow universal scaling laws: socioeconomic outputs like GDP and patents grow superlinearly with population, while infrastructure needs grow sublinearly, making larger cities faster-paced, more innovative, but also more stressful.
The article argues that honest people suffer losses not because they are honest but because they are easily deceived; using game theory, evolutionary dynamics, and Axelrod's tournaments, it shows that being 'hard to fool' — detecting deception, retaliating, and forgiving — reduces exploitation, and that lowering the proportion of gullible people makes deception unprofitable.
The article analyzes the true costs of buying ultra-cheap homes in Yitulihe, a declining forestry town in Inner Mongolia, revealing that the purchase price is negligible compared to heating, healthcare, and the need for external income, making such purchases a lifestyle arbitrage rather than a bargain.
