MediMath Science

Shin Jin-seo's recent victory  (2026-07-21) over KataGo demonstrates a subtle but profound distinction between optimal decision making and winning against a particular opponent. The result suggests that maximizing an objective function is not always equivalent to maximizing the probability of defeating an adversary whose own decision process is constrained by optimization. One moment in the third and final game was particularly striking. Shin intentionally accepted a slight local loss in the corner, but in return secured outside thickness by steering the game into an essentially single-path sequence that even KataGo was forced to follow.  Although this exchange was locally suboptimal, it significantly simplified the subsequent game and ultimately became one of the decisive factors in his victory.  KataGo is designed to maximize its expected winning probability. Formally, it selects an action $a\in A$ by solving $a^*=\arg\max_{a\in A}\mathbb{E}[U(a)]$, where $U(a)$ d...

Recently (July 2026), financial markets have exhibited behavior that appears irrational over short time horizons. In particular, memory semiconductor stocks have experienced unusually large price fluctuations despite exceptionally strong earnings. Leading companies such as Samsung Electronics, SK hynix, and Micron have traded at only 5--7 times forward P/E , a low valuation given their record profitability.  The apparent contradiction reflects uncertainty about the appropriate valuation multiple. The market is caught between two opposing forces: robust earnings growth driven by AI infrastructure investment and the possibility that aggressive capital expenditures may eventually create excess capacity, leading to oversupply and weaker future profitability. Consequently, the key investment question is no longer, "How large are today's earnings?" Instead, the market asks, "Can today's earnings be sustained over many years?" Equivalently, investors focus less on ...

 In this blog, I provide a brief introduction to the famous Black–Scholes partial differential equation and examine it from Charlie Munger's critical perspective. While the equation revolutionized option pricing and became a cornerstone of modern quantitative finance, its assumptions may limit its usefulness when applied to long-term stock investing. Before proceeding, I should note that I am not an expert in this field. The Black–Scholes framework begins with the assumption that a stock price follows a stochastic process, $dS=\mu Sdt+\sigma S dW$, where $S$ denotes the stock price, $\mu$ is the expected growth rate, $\sigma$ represents volatility, and $dW$ is Brownian motion. In essence, stock prices are modeled as evolving continuously through a combination of deterministic growth and random uncertainty. Using stochastic calculus, one arrives at $\frac{\partial V}{\partial t}+\frac{1}{2}\sigma^2S^2\frac{\partial^2V}{\partial S^2}+rS\frac{\partial V}{\partial S}-rV=0,$ where $V$ i...

I have watched a few recent UFC fights, and the bout between Ilia Topuria and Justin Gaethje in mid-June 2026 was particularly memorable. Most people predicted a victory for Ilia, but the outcome was different from expectations. Both fighters displayed exceptional stamina and endurance, suggesting that they had invested a tremendous amount of time in training. However, some analysts believe that although Ilia preferred high-intensity training camps, he may have absorbed fewer realistic punches in sparring because his training partners were reluctant to engage him at full intensity due to the risk of being knocked out by his formidable punching power. Many critics dismiss mixed martial arts as little more than a modern version of Roman gladiatorial combat.  (The comparison, however, is imperfect. Gladiators were often forced into combat, whereas UFC fighters voluntarily dedicate years of their lives to earning a place among the sport's elite. Countless athletes train relentlessly fo...

This blog examines the AI-related stock market, which has delivered exceptional returns over the past several years and whose long-term prospects remain promising. Despite this favorable outlook, AI-related stocks have recently experienced significant volatility. Much of this reflects the tension between exceptionally high market expectations and uncertainty surrounding AI monetization, compounded by elevated valuations, interest-rate sensitivity, geopolitical risks, and export controls. The objective of this blog is to develop a framework for understanding the long-term evolution of the AI investment landscape. Before proceeding, I should note that I am not a professional financial analyst and have been actively investing for only about one year. My professional background lies instead in mathematics, artificial intelligence, engineering, medical imaging, and related quantitative disciplines. These perspectives, together with discussions with friends and extensive use of ChatGPT as a ...

Before beginning this blog, I should acknowledge that I do not have the ability to speak with complete confidence about the current confrontation between the U.S. and Iran (March 2026). Those who are not direct participants in the conflict cannot fully understand the deeper motives, internal calculations, and hidden tensions between the parties. For that reason, as an outside observer, I am not in a position to make definitive judgments, since much of the true structure of the conflict is neither visible nor easily knowable. Most media reports portray the current confrontation between the U.S. and Iran as a struggle between good and evil, or as the product of the greed of a few actors. While this framing attracts attention, it is analytically weak. Consequently, individual investors influenced by such narratives may misinterpret the prospects for war and make costly financial decisions. A recurring reality is that individual investors who simply follow sensational news headlines of...