World Solve

Wars, droughts, and shipping disruptions can send food prices soaring almost overnight, hitting poorer households hardest. A more resilient global food system could soften these shocks before they cause real hunger.

Despite regulation added after every major crash, financial systems keep finding new ways to become unstable. We still don't have a reliable way to prevent the next one before it happens.

Depression, anxiety, and other mental health conditions often go undetected until symptoms are already severe. Earlier, more reliable prediction could allow prevention instead of just treatment after the fact.

Droughts, floods, and shifting seasons are making traditional farming less reliable in many regions. Crops that can handle this unpredictability are essential to keeping global food supply stable.

Even the best robots struggle with uneven terrain, sudden obstacles, or tasks requiring fine motor control that animals handle instinctively. Closing this gap would open up robotics for disaster response, farming, and elder care.

Hurricanes and tornadoes can still shift path or intensity with little warning, leaving communities scrambling to evacuate. More precise, longer-range forecasting could save lives and billions in property damage every year.

Some volcanoes give warning signs, but many erupt with little notice, putting nearby populations at serious risk. Better prediction could prevent mass casualties near active volcanoes worldwide.

Seismologists can identify risky zones but not the exact moment a quake will strike. A few minutes of reliable warning could save thousands of lives in earthquake-prone cities.

A persistent gap exists in realizing practical, scalable solutions for governance frameworks for the ethical use of predictive analytics in criminal justice and social services. Scientific understanding, engineering readiness and institutional capacity remain insufficient to translate promising concepts into durable impact under real-world constraints of cost, skills and governance.

A persistent gap exists in realizing practical, scalable solutions for predictive analytics for anticipating and preventing cascading infrastructure failures. Scientific understanding, engineering readiness and institutional capacity remain insufficient to translate promising concepts into durable impact under real-world constraints of cost, skills and governance.

Create validated organoid, organ-on-chip, computational, and regulatory approaches that better predict human outcomes while reducing animal use.

Develop flexible attendance, assessment, housing, healthcare, and academic-support models for students whose conditions fluctuate unpredictably.

Develop ethical forecasting methods that identify areas likely to experience forced mobility from climate stress without stigmatizing populations or enabling exclusion.

Create predictive maintenance and equipment-upgrade strategies for overloaded local grids serving growing cities and newly electrified rural areas.

Improve forecasting by combining meteorology, vegetation, field observations, and community reporting so control efforts can occur before swarms devastate crops.

Design clinically reliable decision-support tools using basic vitals, history, and low-cost tests to identify high-risk pregnancies before preventable postpartum hemorrhage occurs.

The absence of robust, context-adapted solutions for real-time systems for predicting and mitigating the cascading impacts of climate extremes creates cascading risks across interconnected human and natural systems. Key missing elements include durable performance, low lifetime cost, and governance arrangements capable of sustaining impact beyond pilot stages.

The absence of robust, context-adapted solutions for sensor networks and models for predicting urban flood risk at street-level resolution creates cascading risks across interconnected human and natural systems. Key missing elements include durable performance, low lifetime cost, and governance arrangements capable of sustaining impact beyond pilot stages.

Dengue surveillance in Brazil is largely retrospective, relying on confirmed case counts reported weeks after transmission has already accelerated, rather than integrating mosquito density, rainfall, and mobility data into a forward-looking model.

Current approaches to real-time systems for predicting and mitigating the impacts of extreme precipitation events either underperform under variable field conditions or generate externalities that undermine long-term sustainability and equity. Closing the gap requires coordinated advances in science, systems design and enabling institutions.