World Solve

The problem of mass unemployment and underemployment continues to affect hundreds of millions of people and undermines progress on multiple Sustainable Development Goals. Existing responses remain fragmented, under-resourced and poorly adapted to local contexts. A strong solution must operate effectively under severe constraints of skilled personnel, infrastructure, electricity and connectivity. It should define clear measurable outcomes, identify possible unintended harms, and demonstrate how the intervention can be sustained by local institutions long after initial funding ends.

The problem of unequal treatment of boys and girls in schools and homes continues to affect hundreds of millions of people and undermines progress on multiple Sustainable Development Goals. Existing responses remain fragmented, under-resourced and poorly adapted to local contexts. A strong solution must operate effectively under severe constraints of skilled personnel, infrastructure, electricity and connectivity. It should define clear measurable outcomes, identify possible unintended harms, and demonstrate how the intervention can be sustained by local institutions long after initial funding ends.

Addressing widespread diseases caused by contaminated water is critical for achieving the 2030 Agenda. Current efforts fall short in scale, speed and equity, leaving the most vulnerable behind. Any viable solution must function in low-resource settings with limited specialists, transport, laboratory capacity and broadband. Proposals should specify quantifiable biological, social or environmental outcomes, openly discuss potential risks, and present a realistic pathway for long-term maintenance and local ownership beyond short research pilots.

The problem of lack of clean and safe drinking water continues to affect hundreds of millions of people and undermines progress on multiple Sustainable Development Goals. Existing responses remain fragmented, under-resourced and poorly adapted to local contexts. A strong solution must operate effectively under severe constraints of skilled personnel, infrastructure, electricity and connectivity. It should define clear measurable outcomes, identify possible unintended harms, and demonstrate how the intervention can be sustained by local institutions long after initial funding ends.

The problem of children going to bed hungry every night continues to affect hundreds of millions of people and undermines progress on multiple Sustainable Development Goals. Existing responses remain fragmented, under-resourced and poorly adapted to local contexts. A strong solution must operate effectively under severe constraints of skilled personnel, infrastructure, electricity and connectivity. It should define clear measurable outcomes, identify possible unintended harms, and demonstrate how the intervention can be sustained by local institutions long after initial funding ends.

Digital consultation can produce large volumes of participation while still failing to influence decisions. The challenge is creating governance processes where consultation changes outcomes and does not become symbolic theater. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Frontier AI may become essential infrastructure controlled by a small number of governments or companies. The unresolved task is developing ownership, access, competition, and public-interest models that prevent private or geopolitical concentration from determining global outcomes. The design must remain functional when standards, trade routes, data access, and diplomatic cooperation are unreliable. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Utility data are fragmented, pressure changes are poorly localized, and repairs compete with emergency work. Low-cost sensing, probabilistic fault localization, and outcome-based maintenance could let local operators prioritize the few failures that matter most. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with measure uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Vendors operate with thin margins and little historical data. Lightweight forecasting, shared cold storage, and cooperative purchasing could convert neighborhood-level signals into practical decisions without requiring enterprise software. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with allocate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

The technical problem is coupled with a social allocation problem: production varies, meters may be imperfect, and residents need rules they trust. Tariff design, optimization, and transparent settlement tools could make shared generation investable. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with forecast uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Frontline workers often face incomplete records, unreliable connectivity, and high consequences for missed deterioration. Decision-support models must be calibrated locally, auditable, and paired with referral logistics rather than treated as standalone AI. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with verify uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Teachers cannot manually inspect every error pattern, while generic adaptive apps often optimize clicks rather than mastery. Compact knowledge models and teacher-controlled workflows could make personalization affordable and pedagogically accountable. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with coordinate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Good firms can be rejected because financial statements lag reality or assets are hard to pledge. Cash-flow inference, purchase-order verification, and risk-sharing products could widen credit while limiting fraud and over-borrowing. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with measure uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Patients waste time visiting multiple shops, while inventory data are private and substitution rules are complex. Privacy-preserving stock signals, route optimization, and pharmacist-led protocols could reduce search costs without creating a dominant intermediary. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with route uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Curb demand changes by hour and street, but regulations are often static and enforcement is inconsistent. Auctions, reservation systems, and fairness constraints could improve throughput while protecting accessibility and local commerce. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with preserve uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

The bottleneck is not only parts supply but identification, dimensions, safety, and liability. Open component ontologies, machine-vision matching, and verified refurbishment networks could create new revenue for local technicians. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with repair uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Many consultation processes collect comments but provide no traceable response or rationale. Structured argument maps, versioned policy records, and privacy-aware participation analytics could improve accountability without reducing deliberation to popularity. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with coordinate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Materials may be valuable but poorly documented, contaminated, or unavailable at the needed time. Standardized audits, uncertainty-aware marketplaces, and logistics coordination could make reuse competitive with disposal. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with measure uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Competence is often real but invisible to employers because certificates measure course completion rather than performance. Practical assessments, portable evidence, and trusted local validators could improve matching without forcing workers into expensive retraining. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with allocate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Maps rarely capture broken lifts, blocked sidewalks, steep surfaces, or staff-dependent access. Community sensing plus uncertainty-aware routing could produce useful guidance while clearly communicating confidence and risk. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with forecast uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.