As the Arctic continues to lose ice, scientists are studying not only how quickly the region is changing but also what countries can do to slow the damage. Ryka Chopra, a 17-year-old student from Fremont, California, has approached the problem from an unusual angle: mathematics, game theory and artificial intelligence.A student at Mission San Jose High School, Ryka developed a mathematical framework that models how countries’ conservation decisions can influence Arctic ice loss. Her project, titled “A Dynamic Graph-Game-RL Framework for Incentive-Compatible Conservation in the Arctic Global Commons,” has earned her a place among the 40 finalists of the 2026 Regeneron Science Talent Search.
A mathematical approach to the Arctic’s changing ice
Arctic ice is not only an environmental indicator but also an important part of the global climate system. As ice disappears, it can contribute to broader changes in Earth’s climate.According to Ryka’s research, CryoSat-2 satellite data show that Arctic ice thickness has been declining by 12.5% per decade. Her project explores an important question: What happens when the decisions made by individual countries are considered part of the same system as the environmental changes they cause?Many existing models can estimate how Arctic ice melts, but Ryka’s approach attempts to incorporate human decision-making directly into the model.She created a two-graph framework that connects two sides of the problem. One represents the actions and interactions of countries and regions, while the other represents environmental impacts. Linking the two allows the model to examine how decisions by one country could influence both other countries and the Arctic environment.
How game theory enters the climate problem
Countries often have competing economic interests. Investing in conservation may benefit the global environment, but an individual country could also face immediate economic costs.This creates a classic problem in game theory: how can multiple players with different interests be encouraged to cooperate?Ryka used game theory to simulate the potential conservation decisions of nations and regions connected to the Arctic. Rather than assuming that every country would automatically choose the environmentally best option, her model considers strategic decision-making.She then added reinforcement learning (RL) to the framework.Reinforcement learning is a type of machine learning in which an agent learns by trying different actions and receiving feedback or rewards. In Ryka’s model, it allowed countries to repeatedly adjust their conservation strategies while seeking favourable economic outcomes.The result was a model that could examine not just one decision but how strategies might evolve over many time periods.
What her simulations found
Ryka’s simulations examined interactions involving 25 Arctic-related nations and regions across multiple time periods.The model showed that countries could potentially cooperate to reduce Arctic ice loss when the right incentives were introduced.This is an important part of her research because simply asking countries to make environmental sacrifices may not be enough to produce long-term cooperation. Nations have different economic priorities, resources and interests.By incorporating those competing incentives into her mathematical model, Ryka’s research attempts to identify conditions under which environmental cooperation could also make economic sense for participating countries.Her work therefore moves beyond asking how much Arctic ice could disappear and explores another question: How can human behaviour be incorporated into climate models to find strategies that encourage cooperation?
Bringing mathematics, economics and AI together
Ryka’s project sits at the intersection of several disciplines.Graph theory provides a way to represent relationships between countries and environmental systems. Game theory helps model strategic decisions between countries, while reinforcement learning allows those strategies to evolve through repeated interactions.Together, these tools create a framework for studying a complicated real-world problem in which environmental and economic decisions are closely connected.The research does not claim to provide a ready-made international climate policy. Instead, it offers a mathematical framework that could potentially help researchers examine different incentive structures and understand how countries might respond to them.That approach could be particularly relevant to global environmental challenges, where no single country can solve the problem alone.
A finalist among the US’s top young scientists
Ryka is one of 40 finalists selected for the 2026 Regeneron Science Talent Search, a Society for Science programme that recognises exceptional high school research.The finalists were selected from more than 2,600 entrants representing 826 high schools across 46 states, Washington, D.C., Puerto Rico, the Northern Mariana Islands and 16 countries.Their research spans areas including science, mathematics, engineering, medicine, social sciences and environmental research. The finalists will compete for a total of $1.8 million in awards.Ryka’s project stands out for bringing together mathematical modelling and artificial intelligence to examine a problem that affects countries far beyond the Arctic itself.
Beyond the climate model
Ryka’s interests extend well beyond mathematics and environmental research.She serves as president of the history club at Mission San Jose High School. She is also a pianist and music composer in the Pre-College division of the San Francisco Conservatory of Music.Her involvement in music extends to student leadership as well. She serves as the conservatory’s student council secretary, where she helps arrange concerts.Ryka is also part of her school’s yearbook editorial team and works as a photographer, documenting student activities.Her interests therefore span subjects that might initially appear very different—from history and music to mathematics, artificial intelligence and climate science.
Can incentives change the future of Arctic conservation?
The Arctic presents a particularly difficult conservation challenge because its future is shaped by both natural processes and human decisions.Ryka Chopra’s research explores what happens when those two sides are brought together in the same mathematical framework.Her model suggests that cooperation does not necessarily have to mean countries simply accepting economic losses for the sake of conservation. With carefully designed incentives, countries may be able to pursue strategies that offer economic benefits while also reducing environmental damage.The research is still a mathematical and computational model rather than a tested international policy. But by combining game theory, reinforcement learning and environmental modelling, the 17-year-old has offered a fresh way to examine one of the world’s most complex climate challenges.From a high school classroom in California, Ryka is using mathematics to ask a question with global consequences: Can better incentives persuade countries to work together before more Arctic ice disappears?Disclaimer: This article has been prepared using information and project details shared by Society for Science as part of the 2026 Regeneron Science Talent Search. The scientific findings and observations mentioned are based on the student’s research and information provided by Society for Science and have not been independently verified by The Times of India.
