The skydiving scenario captures a phenomenon economists call moral hazard, the tendency of people to take on more risk when someone else bears part of the cost. A student without insurance faces the full financial consequences of a broken leg, while an insured student knows that a policy will absorb much of the bill. Neither faces any risk of death in the hypothetical, which strips the exercise down to its economic essentials. When the class compares choices, students can see directly how coverage alters behavior at the level of a single decision, and then trace how those decisions aggregate into claims, premiums, and the overall cost of care. Concepts that can feel remote when presented in a textbook chapter become immediate and personal, because the students have just made the kind of choice the theory describes.

Zane and his colleagues, including associate professor Debora Goetz Goldberg and doctoral students Shaan Muberra Khan and Sravya Vunnam, describe the approach in a new paper titled More Than a Game, published in a special issue of The Journal of Health Administration Education devoted to gamification. The paper documents how simulation-based gaming, anchored in the principles of game theory, helped students grasp complex health economics concepts and the dynamics of insurance markets. Course evaluations collected from 129 students between 2022 and 2025 suggest the method resonated strongly: roughly 89 percent of respondents found the class environment engaging, and 85 percent said the teaching methods enhanced their learning. In written feedback, students reported that the games clarified challenging concepts and exposed them to classmates’ different approaches to the same problem.

The origin of the experiment-based course was, fittingly, a problem of engagement itself. During a summer of online teaching in 2022, Zane found most of his students keeping their cameras off, their attention drifting somewhere beyond the screen. It is a topic that I love, but they didn’t seem to get it, he recalled. Many of the students in his Health Economics and Policy course arrived with little background in economics and, in some cases, little interest in it. Health administration programs attract students motivated by careers in hospitals, clinics, and health systems, not by abstract models of markets and incentives. Zane needed a way to make the economic machinery of health care visible and worth caring about.

Game theory offered the way in. The field examines how people make decisions when their outcomes depend on the choices of others, a situation that describes nearly everything in health care: insurers setting premiums while anticipating who will enroll, hospitals negotiating with payers, patients deciding whether to seek care, and governments designing rules that shape all of the above. Before entering academia, Zane had worked as an antitrust lawyer, where he used game-theoretic reasoning to help clients decide whether to settle lawsuits. That professional experience convinced him that strategic decision-making under interdependence could be taught through direct experience rather than lecture, and that students who played through the incentives themselves would understand them more deeply than students who merely read about them.

His first experiment was the ultimatum game, one of the most well-established exercises in behavioral economics. In the classic version, one party receives a financial windfall and proposes how to divide it with a second party, who can either accept the offer or reject it, leaving both with nothing. Rational self-interest predicts that the proposer should offer the smallest possible share and that the responder should accept anything above zero, since something beats nothing. In Zane’s classroom adaptation, Kit Kat candy bars stand in for cash. The results defy the cold logic of the model in a way students can watch unfold in real time: some students would rather walk away with nothing than accept a share they consider unfair. That outcome opens a discussion about why fairness considerations can outweigh personal gain, a lesson with direct relevance to health care, where perceptions of equity shape everything from patient trust to public support for insurance expansions.

From that starting point, Zane built a growing library of exercises, some adapted from existing game-theory models and others of his own design, refining them according to student feedback each term. One of his original creations is the orange juice game, which teaches market structure and price competition. Students are assigned to groups representing different levels of competition, ranging from a single seller holding a monopoly to a crowded market with many sellers. Zane tells each group that he is willing to pay up to $5 for a bottle of juice. The monopolist, facing no rivals, can hold its price near that ceiling. Sellers in competitive markets, by contrast, must undercut one another to win sales, and in the most crowded market the price can fall close to zero. The demonstration shows students how competition drives down prices, a foundational insight for understanding why economists worry about market concentration among hospitals, insurers, and pharmaceutical companies.

Crucially, the games are only half the lesson. After each round, students explain the reasoning behind their choices and compare their logic with that of their classmates. These structured reflections serve a double purpose. They force students to articulate the economic principles guiding their behavior, converting an intuitive play experience into explicit understanding, and they give Zane a diagnostic window into whether the intended lesson actually landed. Reading the reflections, he said, it became pretty clear that most of the students were getting it. The debriefing stage reflects a broader consensus in education research that active learning exercises work best when paired with guided reflection, since experience alone does not guarantee that the underlying concept is extracted from the activity.

The published evidence, drawn from three years of course evaluations, positions the George Mason experiment as a case study in how gamification can transform the teaching of quantitative and strategic subjects. The stakes are considerable: health economics underpins policy debates on insurance design, provider consolidation, and cost containment, and the professionals who manage health systems often make decisions with billions of dollars and public health on the line. If future administrators internalize concepts like moral hazard, market power, and strategic interaction not as vocabulary to memorize but as forces they have personally experienced in a classroom game, the argument runs, they will carry that intuition into practice. What began as a desperate attempt to keep online students from turning off their cameras has matured into a documented pedagogy, one in which a free, perfectly safe skydive teaches more about insurance than a semester of slides ever could.

Subject of Research: Simulation-based game theory exercises for teaching health economics and insurance market dynamics to university students

Article Title: Study finds simulation game helps students grasp complex health economics concepts

Article References: Study finds simulation game helps students grasp complex health economics concepts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: health economics, game theory, gamification, simulation-based learning, health insurance, moral hazard, ultimatum game, market competition, health administration education, student engagement, George Mason University, active learning

The contradiction is written directly into Chinese planning documents. The Fourteenth Five-Year Plan calls for constructing a green ecological civilization and achieving carbon peaking and carbon neutrality, while simultaneously orienting the entire economy around digital infrastructure, big data and AI. The plan even instructs policymakers to promote the profound convergence of the internet, big data and artificial intelligence with all industries, and to forcefully advance the coordinated transformation of industrial digitization and greening. Checketts observes that many Chinese leaders, and their counterparts in Hong Kong, do not see these aims as oppositional at all. Hong Kong’s Research Grants Committee, for instance, lists both developing a sustainable environment and big data and artificial intelligence as priority funding areas for 2026 and 2027, presenting them as twin routes to the same broad goal rather than competing demands.

China is hardly alone in this posture. The European Union wants AI leadership and climate compliance at once, Korea and Japan chase IPCC targets while courting the AI elite, and only the United States, Checketts notes, has recently pursued one goal with single-minded disregard for the other. He traces the shared confusion to the ideology of sustainable development itself, pointing out that the United Nations pairs SDG 9, which promotes industrialization and innovation, with SDG 13, which demands urgent climate action, reinforcing the belief that industrialism can be commensurate with ecological harmony. The concept of AI for sustainability adds a further layer: the claim, voiced most enthusiastically by figures such as former Google CEO Eric Schmidt, that AI is not merely compatible with fighting climate change but may be the only solution to it.

Checketts argues that this claim functions like chemotherapy rhetoric, making the planet sicker in the hope that it will heal, and he finds it potentially suicidal rather than merely optimistic. The moral hazard argument, developed in climate research by Corner and Pidgeon in their work on geoengineering, holds that when a party feels insured against an undesired outcome, it takes greater risks and may neglect urgent action. Geoengineering debates show the pattern clearly: proposals to reflect sunlight with atmospheric particulates or to sequester carbon dioxide at scale can be perceived by the public as a license to continue carbon-intensive lifestyles, producing a rebound effect that makes emissions worse. Checketts transfers the same logic to AI. AI development is currently exacerbating the climate problem, he writes, and there is no substantiated reason to believe AI will find climate solutions more efficiently than existing policy already could.

The evidence for AI’s environmental toll is substantial. Data centers already consume up to 4.4 percent of all electricity in the United States, and global data center electricity demand is projected to more than double from 415 terawatt hours in 2024 to 945 terawatt hours by 2030. Total carbon emissions from AI are estimated at around 100 megatonnes per year, a figure often compared to the global air transit industry. Training costs scale geometrically with model size: GPT-4 produced roughly 40 times the carbon of GPT-3, which itself was estimated at 550 tonnes for training, about the annual consumption of 120 American homes. Routine use is also expensive, with AI-powered search running between 5 and 25 times more inefficient than simple search algorithms, and inference demands exceeding training demands over a model’s lifetime. Company self-reporting compounds the uncertainty, since swapping energy credits has allowed tech firms to report emission rates possibly seven times lower than they actually are.

Water is the second cost. Data centers pull water from local sources to cool their heat, and while the water is not destroyed, its removal from the water table has real consequences for ecosystems and human populations. In 2023, Google alone was responsible for evaporating around 23 billion liters of water, more than PepsiCo used in the same year, and current projections estimate that by 2027 data centers will consume the equivalent of half the entire water use of the United Kingdom. Li and colleagues estimate that 10 to 50 medium-length GPT-3 responses consume around half a liter of water. In China, data centers cluster around Shanghai, Beijing, Guangzhou and Shenzhen, cities each numbering over 15 million people, and Beijing and Shanghai already face high levels of water stress. With 75 percent of Asia water insecure and countries hosting more than 90 percent of the region’s population confronting an imminent water crisis, adding thirsty data centers to the equation is a serious gamble.

The third cost is hardware, and here China’s exposure is uniquely severe. Sixty percent of all rare earth elements traded globally come from China, most from the Bayan Obo mining and refining site in Inner Mongolia. Open-pit mining there leaves elevated rare earths and heavy metals in soil up to 60 kilometers from the site, and refining requires huge amounts of water and open-air baths of sulfuric or hydrochloric acid in an arid region. Julie Klinger has described the consequences as radioactive rivers, cancer villages, acute chronic arsenic toxicity and an environmental and epidemiological crisis so grave that addressing it is now viewed as a matter of national security. Electronics manufacturing concentrated in southern China adds toxic fumes, soil pollution and groundwater poisoning, with fine particulate air pollution contributing to one-fifth of all premature deaths worldwide and rates as high as 26 percent in South and East China. Components in data centers last only 3 to 5 years, yet only around 20 percent of electronics are properly recycled, leaving an estimated 63 million tonnes of e-waste globally in 2022, much of it historically routed through southern China and now increasingly to South Asia.

What makes China’s situation distinctive is that its own doctrine appears to condemn precisely this pattern. The Ten Definites articulated by Xi Jinping state that the principal contradiction in China’s society in the New Era is between the people’s growing need for a better life and unbalanced and inadequate development, demanding a people-centered development ideology. The Scientific Outlook on Development warns against economic growth relying mainly on expanded investment scale, resulting in excessive resource and environmental costs, and calls for a path characterized by high technological content, low resource consumption and minimal environmental pollution. The Fourteen Commitments include improving people’s well-being as the primary goal of development, and the ninth commitment insists that lucid waters and lush mountains are invaluable assets and that the ecological environment must be treated as life itself. Checketts argues that AI growth pursued at all costs, with proliferating data centers, stressed water supplies and expanding pollution, constitutes unbalanced development that these commitments were designed to prevent.

His prescription is not to abandon AI but to discipline it. China should prioritize hardware efficiency and slimmer AI models over data center proliferation, direct development toward specific applications in agriculture, energy efficiency and healthcare rather than a blanket promotion of the technology, and avoid the moral hazard of trusting AI for sustainability as insurance against the climate crisis. The report that DeepSeek required far fewer resources than comparable large language models is significant on this front, though Checketts notes that benchmarking work suggests DeepSeek may actually consume the most energy in real-world performance, and that overuse of LLMs for mundane purposes constitutes significant waste regardless. He points to signs of a different path: China has promised to expand renewable energy sources sixfold by 2035, aims to peak carbon emissions before 2030, and appears more focused on practical AI applied to national solutions than on the promethean artificial general intelligence project pursued by American companies.

The deeper question the paper raises is whether climate change is fundamentally a technological problem at all. Checketts, drawing on Herbert Marcuse, suggests that in economies prioritizing consumption over balance, technological fixes may only pave the way for new demands, and that what is truly needed is a different orientation toward material production and consumption. As a communist power whose founding tenets prioritize economic development for the people rather than for the sake of growth, China has, in his view, a real opportunity to demonstrate that AI can be developed within specific parameters and limitations rather than for unchecked profit. Whether it seizes that opportunity remains uncertain, but the stakes, for China and for the world watching its model, could hardly be higher.

Subject of Research: The environmental costs of AI development in China and the contradiction with Chinese sustainability policy

Article Title: The sustainable AI development contradiction: Chinese policy on sustainability and the costs of AI development

Article References: Checketts, L. (2026). The sustainable AI development contradiction: Chinese policy on sustainability and the costs of AI development. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03296-z

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03296-z

Keywords: artificial intelligence, China, climate change, sustainability, data centers, moral hazard, carbon emissions, water consumption, rare earth elements, e-waste, Chinese policy, AI & Society

To understand why this is more than wordplay, it helps to start with the physics. Quantum energy teleportation, first proposed by Masahiro Hotta in 2008, is a protocol that allows energy to be extracted at a distant location by exploiting entanglement in the ground state of a many-body quantum system. In an ordinary quantum system, the ground state is the lowest-energy configuration, and a local region of it can appear as a so-called passive state, one from which no work can be extracted by local operations alone. Yet the region is not isolated: it is entangled with its surroundings. If a distant party performs a measurement on their part of the system and communicates the result over a classical channel, the recipient can apply a conditional operation tailored to that outcome. The measurement injects energy locally at the sender’s site, and the conditional operation at the receiver’s site then unlocks energy that was previously inaccessible, effectively delivering usable energy without any physical carrier traveling between the two points.

The protocol respects all the usual physical constraints. No energy travels faster than light, because the classical communication of the measurement outcome is essential, and the sender must pay an energy cost that, as Ikeda proves rigorously in the paper, is always at least as large as the total energy delivered to the receivers. The proof relies on the fact that the ground state minimizes the total energy, so any manipulation that raises the energy of the sender’s region must account for the energy extracted elsewhere. What makes the protocol economically interesting is precisely this structure: a supplier pays a cost, consumers receive a benefit, and the accounting between them is governed by the laws of quantum mechanics rather than by contract law. Ikeda demonstrated the protocol experimentally on IBM superconducting quantum processors, using pairs of qubits on the ibm_strasbourg, ibm_sherbrooke and ibm_kyiv devices, with modern error mitigation techniques such as matrix-free measurement mitigation and zero-noise extrapolation to extract clean signals from noisy hardware.

With this physical substrate in place, the paper builds economic machinery on top of it. The central move is to treat teleported energy as a commodity and the entangled ground state as a shared resource, then to ask what happens to classical game-theoretic models when players can additionally choose quantum strategies. In classical game theory, following John von Neumann, Oskar Morgenstern and John Nash, a game is defined by players, strategies and payoffs, and an equilibrium is a set of strategies from which no player benefits by deviating alone. Ikeda extends this framework by allowing strategies to be quantum operations, including measurements and conditional unitary gates, which means the space of possible actions is vastly larger than in the classical setting. New equilibria appear that have no classical counterpart, and in some cases the quantum strategies allow players to escape dilemmas, such as the free-rider problem, that are unavoidable when strategies are restricted to classical choices.

The paper works through a remarkable range of standard industrial organization models. Ikeda constructs quantum versions of the Bertrand and Cournot duopoly models, the two canonical frameworks for describing competition between firms. In Cournot competition, firms choose quantities and the market price adjusts; in Bertrand competition, firms choose prices and consumers buy from the cheapest supplier. When firms can share or trade entangled resources and deploy quantum strategies, the resulting equilibria differ from the classical ones, offering unconventional ways to divide the market or to extract surplus. The analysis extends to monopoly, to Hotelling-style location games in which firms choose where to position themselves along a market, to contestable markets where the threat of entry disciplines incumbents, and to perfect competition. In each case the quantum layer does not simply reproduce the classical results with extra steps; it introduces genuinely new strategic possibilities rooted in the correlation structure of entangled states.

Perhaps the most provocative applications concern public goods and incentive problems. Moral hazard, the situation in which an agent’s effort cannot be perfectly observed by a principal, has been a cornerstone of contract theory since Bengt Holmström’s 1979 work on observability. Free riding on public goods, analyzed classically by Paul Samuelson and others, similarly arises because individual contributions are hard to verify or reward. Ikeda shows that quantum energy teleportation offers a lens on these problems: because the measurement and feedback operations in the protocol are verifiable parts of a physical process, and because entanglement creates correlations that cannot be replicated classically, agents can be given incentives that depend on quantum information. The paper unveils strategies that transcend classical limits, suggesting that in a future quantum economy, the design of contracts and contribution mechanisms might exploit resources that classical mechanism design cannot even describe.

The study also reaches into matching theory, the branch of economics founded on the Gale-Shapley algorithm that underlies everything from school choice to organ exchange. Ikeda incorporates entangled resources into matching models and proves that stable and optimal allocations can be achieved in settings where the participants’ payoffs are tied to quantum states. In his formalism, a matching corresponds to a configuration whose total payoff is encoded in a Hamiltonian, and a stable matching is the ground state of that Hamiltonian, with penalty terms ensuring that no participant has an incentive to block the outcome. The proofs in the paper’s appendix show that the ground state satisfies individual rationality, meaning no participant prefers to opt out, which is exactly the stability condition that market designers care about. This connection between quantum many-body physics and market stability is one of the paper’s most elegant technical contributions.

None of this is pure abstraction. Ikeda’s earlier work demonstrated quantum energy teleportation on real superconducting hardware in 2023, and the new paper includes a detailed appendix on the experimental methods, showing how mid-circuit measurements and conditional operations are implemented, how noise is mitigated, and how the latest Eagle-generation processors run the protocol ten to one hundred times faster than the devices used two years earlier. The programming code for the simulations and experiments is publicly available under an MIT license on the author’s GitHub repository. Related results by other groups, including experimental activation of strongly passive states and proposals for quantum key distribution based on teleportation protocols, indicate a rapidly maturing research area in which the physics of entanglement and the engineering of quantum processors advance together.

The intellectual lineage of the paper is also worth noting. The idea that information is physical traces back through Maxwell’s demon, Leo Szilard’s analysis of measurement and entropy, and the work of Rolf Landauer and Charles Bennett. Quantum money, proposed by Stephen Wiesner in the 1980s and developed by later researchers, showed that quantum states could serve as unforgeable economic objects. Ikeda’s own 2022 paper with Shumpei Aoki on quantum games and quantum economic behavior laid the groundwork for the present synthesis. What is new here is the scale of the ambition: rather than treating one economic object quantum mechanically, the paper rebuilds an entire toolkit of microeconomics, from auctions and mechanism design to repeated games and cooperative solution concepts like the Shapley value, on quantum foundations.

Caveats remain, as they must in any nascent field. The models are theoretical, the demonstrations are small-scale, and no one knows whether entangled energy markets will ever exist outside the laboratory. Yet the paper makes a serious case that the question is worth asking. If quantum networks eventually distribute entanglement as routinely as fiber optics distribute bandwidth, then the economics of that distribution, including who pays for measurements, who profits from teleported energy, and how stable allocations are reached, will need a theory. Quantum Information Economics, as sketched in this work, is an early attempt to provide one, and it suggests that the strategic landscape of a quantum-enabled economy may be richer, and stranger, than anything classical game theory has imagined.

Subject of Research: Quantum energy teleportation as a commodity in quantum game theory and economic models

Article Title: Quantum games and economics through teleportation

Article References: Ikeda, K. (2026). Quantum games and economics through teleportation. Quantum Information Processing, 25(10), Article 331. https://doi.org/10.1007/s11128-026-05341-8

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05341-8

Keywords: quantum energy teleportation, quantum game theory, Quantum Information Economics, entanglement, Bertrand competition, Cournot duopoly, moral hazard, public goods, matching theory, market design, superconducting qubits, quantum networks