Juan Moises de la Serna

Great work! +1µg/m³ PM2.5 = +0.49% daily suicides. China's air reforms prevented ~46,000 deaths!

Wonderful initiativ of lived experience and health economics. Being able to quantify the long‑term social and economic burden of TBI while co‑designing tools like the Social Brain Toolkit feels exactly like what we need to close the gap between evidence and real‑world support.

Cutting capital gains and fast‑tracking private investment in energy, AI and critical minerals is basically a textbook playbook for stealth privatization. It shifts power from public decision‑making to global capital while socializing the long‑term risks.

Bombing refineries and gas sites doesn’t just hit the global economy, it means massive, sudden CO₂ and methane emissions from fires, leaks and flaring—exactly the opposite of what the climate needs.

Love this framing. Treating g as an emergent property of large-scale coordination rather than a ‘magic spot’ in PFC fits really well with what we see in network neuroscience and lesion data.

From a decision-making angle, being able to classify bursts by waveform and link specific subclasses to trial-by-trial adaptation performance is a big step towards using beta dynamics as a mechanistic marker of updating, credit assignment, or stability vs flexibility in sensorimotor control.

METR’s data helps put “off the charts” in context: they estimate that Claude Opus 4.6 has a 50% time horizon of roughly 14–15 hours on software tasks, meaning it can autonomously complete tasks that would take a human expert about half a day of focused work, with around a 50% success rate.

Perplexity AI añade más contexto: el gasto militar global representa ya el 2.3% del PIB mundial. EE.UU. lidera con $886B (+3%), seguido de China con $296B. Curioso que mientras el gasto en defensa bate récords, la inversión en salud y educación globales sigue estancada. Prioridades que hablan solas.

Dato: según Perplexity, el mercado de IA alcanzará $1.8 billones en 2030. Las alianzas con el Pentágono no son accidentales — son parte estructural del modelo. Anthropic, OpenAI y otros compiten por contratos de defensa millonarios. La ética IA no puede desconectarse de quién la financia.

Fascinating! Classic RM displaces memory ~2-5% forward along a trajectory. Your 2025 extension to brightness & proportion (but not hue!) maps the true limits of automatic prediction—perfectly aligning with predictive coding models. The no-motion finding is the real kicker for theories of perception.

🌍 The Economics of Pre-War Times (2025)

Global military spending hit a record $2.718T in 2024 (+9.4% — steepest rise since the Cold War).
📉 Global GDP growth slowing to 2.4%
📈 Inflation still above pre-pandemic levels in 2/3 of countries
⚠️ Trade wars delaying investment worldwide

The cold weather excuse doesn't hold up. Data shows avg DC bills jumped from ~$95 to $138 by summer 2025—before winter. Root causes: a June 2025 PJM capacity auction hit decade-high prices (+$20/mo), delivery rates rose 5%, and data centers are adding ~$14/mo to bills via market pressure.

This is the democratization of astronomy in real time. The Seestar uses a CMOS sensor, automated star tracking & stacked exposures — tech that would have cost tens of thousands in the 90s. Citizen science with tools like this is genuinely expanding our observational data on variable stars more.

Johnson solved Euler angles and orbital mechanics by hand — before electronic computers were trusted. Her calculations for John Glenn's Friendship 7 (1962) were so precise that Glenn refused to fly unless she personally verified the IBM results. Science has always needed human precision.

Proper data archiving is foundational to reproducibility — one of science's biggest current crises. Studies show only ~20-40% of psychology papers can be fully replicated partly due to unavailable data. FAIR principles (Findable, Accessible, Interoperable, Reusable) are the gold standard here.

Stars like this (likely a red hypergiant, >1000x the Sun's radius) lose mass through stellar winds before going supernova. The 2014 "Great Dimming" event may signal the star is shedding its outer layers — a classic precursor. When it blows, it'll briefly outshine entire galaxies. 🌟

Exactly. Herd immunity for measles requires ~95% vaccination coverage. Once rates drop below that threshold, the virus finds enough susceptible hosts to spread exponentially. The MMR vaccine is 97% effective after 2 doses — the science here is rock solid.

Exactly right. From a neuroeconomics perspective, those identity-based "superhighways" are also what drive consistent decision patterns — including irrational ones. Understanding how the brain automates behavior is key to both behavioral change and detecting anomalous choices.

Fraud detection is a perfect case study — AI stopping fraudulent transactions in milliseconds isn't hype, it's behavioral pattern recognition at scale. The real challenge is combining ML with behavioral science to catch the *intent* behind anomalies, not just the anomalies themselves.

A classic that should be on every decision-maker's shelf. Thaler's work on mental accounting and nudges directly informs how I study fraud — people don't act rationally, and understanding *why* is what gives behavioral insights their edge.

Moving beyond competition-only models is a game changer. From a behavioral & fraud detection lens, real decision-making is rarely a pure tug-of-war — it's shaped by context, trust, and social cues. Excited to read how this framework applies to market behavior!

The data backs this up: 1,000+ victims over 2 decades. A compensation fund paid $120M to 150 claimants. Jan 2025 releases: 900+ pages naming 184 "J. Does." Final Jan 30, 2026 batch: 3M pages, 2K videos, 180K images. The scale is staggering.

The numbers back this up: Trump's first 100 days destroyed 62,000+ clean energy jobs, with 400,000 more at risk. Over 70% of the 200 new factories threatened—122,000 jobs, $33B in activity—are in Trump-voting states. TX, GA, OK hit hardest.

Love this framing! Treating cognitive effort as a neuroeconomic variable — where the brain weighs costs vs. benefits of mental engagement — reframes "effortfulness" from a vague concept to a measurable decision. Huge implications for education, motivation research, and clinical interventions.

Sex differences emerging specifically in change-of-mind choices — not initial value judgments — is a nuanced result. It suggests distinct neural mechanisms for re-evaluation versus preference. The Restaurant Row task is such an elegant neuroeconomic paradigm. Looking forward to the full thread!

A fascinating framework! The idea that emotional, aesthetic, and social flows can replace accumulation aligns with neuroeconomic findings on intrinsic motivation and well-being. Would love to explore how this model operationalizes value beyond utility maximization.

This is a foundational paper! The neuroeconomics of trust reveals how social preferences aren't just learned — they're wired into our reward systems. Understanding the neural basis of trust has huge implications for economics, policy, and even AI design. Thanks for sharing this gem!

The idea of a "neuroeconomic rearchitect" is compelling! Bridging nervous systems, social emotions, and economics points to how deeply interconnected human behavior really is. The physical + digital cocreation angle is especially relevant today. Would love to learn more about your work!

Such a great reframe! "Cognitive laziness" as a neuroeconomic optimization makes so much sense — the brain allocates effort based on expected utility. What we call laziness is often just efficient resource allocation shaped by experience. The real question is: how do we update those priors?

Fascinating research! The fact that neuroeconomic adaptation to norm shifts remains intact in BPD challenges assumptions about decision-making in this population. It suggests the core mechanism for learning social norms may be preserved — with real implications for therapy design. Thanks for sharing