The research, led by Duc Nghia Vu of VNU University of Economics and Business in Hanoi with colleagues Thi Hong Ngoc Nguyen and Thi Mai Huong Nguyen, introduces what the authors call a Historical Incentive Translation Framework. The idea is deceptively simple: take documented institutional rules from historical sanitation systems, convert them into parameterized non-cooperative games played by rational, utility-maximising agents, and identify the precise conditions under which cooperation was sustained. The historical stability of these systems suggests, the authors note, that the observed outcomes were Nash equilibria of the games they specify. Two complementary strategic archetypes anchor the analysis: common-pool resource management, examined through Vietnam’s double-vault composting tradition, and principal-agent moral hazard, examined through England’s gong farmer contracts.

Vietnam’s double-vault composting latrines are a striking example of decentralised, self-enforcing commons governance. Households shared collection points and composting facilities, and the fertiliser output was distributed only to households that contributed suitable organic matter, free of non-compostable trash and chemicals. The authors model this first as a threshold public good game, in which the compost is saleable only if the number of cooperating households meets a minimum threshold. Their formal analysis reveals a fundamental weakness: when benefits are non-excludable, universal cooperation is not a Nash equilibrium, because any cooperator can gain by defecting while still enjoying the shared benefit. Universal defection, meanwhile, is always an equilibrium. The historical system avoided this trap by making the benefit excludable, transforming the communal latrine into what game theorists call an excludable club good.

The mathematics shows why this modification was decisive. Under excludable benefits, a defector receives zero regardless of what others do, so universal cooperation becomes a Nash equilibrium whenever the per-household benefit of compost exceeds the private cost of cooperation. Even more striking is the repeated-game extension: with non-excludable benefits, sustaining cooperation requires households to be sufficiently patient, with a discount factor of at least the cost-to-benefit ratio. With excludable benefits, that patience requirement is eliminated entirely, because the temptation to free-ride is removed at the source. The authors argue this formalises Elinor Ostrom’s foundational design principles for sustainable commons governance: clear membership boundaries, proportional benefit sharing, and monitoring. The graduated sanction scheme of the Vietnamese system, warnings, fines and ultimately exclusion from the fertiliser share, maintained the excludability condition, and the model predicts the design should sustain cooperation even among relatively impatient households, consistent with the system’s persistence through political and economic upheaval.

England’s gong farmers, the night-soil collectors of pre-industrial towns, present the second archetype: a principal-agent contract under moral hazard with imperfect verification. A landlord values a legally emptied cesspit, but cannot directly observe whether the collector transported the waste to a designated farm or dumped it in a river. The historical solution was a receipt system: the collector was paid only upon presenting a farmer’s receipt certifying delivery, and failure to produce one meant no payment, often a fine, and sometimes exclusion from the guild. The authors formalise this with a binary verifiable signal, where the probability of a receipt given honest disposal is α and the probability of a false or forged receipt given illegal dumping is β, with β strictly below α. The incentive compatibility condition requires the payment to be at least the extra cost of honest disposal divided by the signal gap α minus β.

The comparative statics of this contract yield insights directly relevant to modern desludging operations. As verification worsens, the required nominal wage rises, but when the participation constraint binds, the principal’s expected payment stays constant, because the higher wage exactly offsets the lower probability of payment; the cost is instead borne through increased income variance for the agent. As forgery becomes more widespread, the incentive constraint tightens, and when the signal gap approaches zero the required wage diverges and the contract becomes infeasible. The historical receipt system worked because the receiving farmer was a reliable third party, making α close to one, while guild oversight and municipal licensing made forgery costly, keeping β close to zero. The same logic applies today to desludging trucks equipped with GPS or blockchain-enabled delivery verification, though modern systems introduce new failure modes such as signal loss in dense urban areas, spoofing, and collusion with plant operators.

To illustrate the framework’s practical power, the authors present a Monte Carlo numerical illustration calibrated with plausible cost ranges. Under idealized verification conditions, a receipt-based payment contract achieves full compliance across a wide parameter space, whereas status-quo payment structures produce substantially lower compliance, approximately 21 percent in the numerical illustration, under the same conditions. The authors are careful to stress that these results are conditional on the maintained assumptions of the models and serve as a proof-of-concept rather than empirical validation. Even so, the gap between roughly full compliance and roughly one-fifth compliance under identical conditions is a vivid demonstration of how payment structure, not just monitoring technology, determines whether circular sanitation works.

The framework extends beyond these two archetypes into market creation and norm dynamics. A coordination game with network effects captures the chicken-and-egg problem facing source-separating toilets: a collection and composting market is viable only above a critical participation threshold, and the game has two stable equilibria, universal participation and universal non-participation. Historical systems, the authors suggest, escaped the low-participation trap because the Vietnamese state acted as a large player that forced or heavily subsidised initial participation, pushing the population across the tipping point, after which full participation became self-enforcing and the intervention could be withdrawn. A guaranteed offtake contract from a municipal composting plant, offering a minimum participation bonus for early adopters, could in theory eliminate the same trap today. An evolutionary model of cooperative disposal norms, meanwhile, shows that proper disposal becomes evolutionarily dominant once the product of detection probability and penalty exceeds the gap between private cost and public benefit, explaining why historical systems collapsed when guilds and local courts weakened.

The framework’s ultimate contribution is decoupling historical incentive logic from historical technologies and re-embedding it within modern, WHO-compliant safety protocols. This yields two actionable design instruments: verified-delivery contracts for desludging operators, potentially structured as two-stage payments with 70 percent released upon verified delivery and 30 percent upon treatment quality confirmation, and community governance charters for shared composting facilities built on excludable benefits, graduated sanctions and clear membership. The authors also derive a bilateral bargaining model of night-soil pricing that explains documented historical price reversals, in which farmers paid collectors in times of fertiliser scarcity and collectors paid farmers when supply was overabundant, as shifts in fertiliser value, delivery cost and bargaining power. In modern terms, the nutrient loop closes spontaneously whenever the avoided disposal cost plus fertiliser value exceeds delivery cost.

The study, published open access on 04 October 2026 and citable under DOI 10.1007/s43621-026-04799-x, received no external funding and the authors declare no competing interests. Its implications reach well beyond sanitation engineering. If the incentive architectures that kept pre-industrial nutrient loops closed for centuries can be parameterized, tested and re-embedded in modern safety-compliant systems, cities may be able to design sanitation that is simultaneously safe, circular and self-enforcing, harnessing self-interest rather than external command. The authors propose a field experiment design that would require ethical review before implementation, and they call for future empirical work to estimate the key parameters, including fertiliser value, delivery cost, benefit-sharing ratios, participation thresholds and detection-penalty products, in contemporary settings. The message is provocative: the blueprints for closing today’s broken nutrient loop may already exist, written centuries ago in the institutional rules of Vietnamese villages and English market towns, waiting to be read with the tools of modern game theory.

Subject of Research: Game Theory

Article Title: Game theoretic translation of historical incentive architectures into modern circular sanitation contracts and commons governance in Vietnam and pre industrial England

Article References: Vu, D. N., Nguyen, T. H. N., & Nguyen, T. M. H. (2026). Game theoretic translation of historical incentive architectures into modern circular sanitation contracts and commons governance in Vietnam and pre industrial England. Discover Sustainability. https://doi.org/10.1007/s43621-026-04799-x

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04799-x

Keywords: circular economy, nutrient recovery, game theory, historical sanitation, principal-agent, common-pool resources, Vietnam, incentive design, sanitation contracts, commons governance, Game, theoretic

The starting point of the analysis is the fragmented state of AI governance itself. Scholars describe the current landscape as underdeveloped, unorganized and immature, populated by three broad categories of actors. Private sector giants such as Google, OpenAI, Microsoft, IBM and Meta have produced industry guidelines, ethics documents and voluntary commitments, often favoring self-regulation that does not slow the pace of development. Public sector actors, including national governments and intergovernmental bodies like the G20, the Organisation for Economic Co-operation and Development and the United Nations, have published national strategies and international agreements. Non-governmental organizations, professional bodies such as the IEEE, think tanks and advocacy groups round out the picture, setting standards and drawing attention to emerging risks. Yet despite this flurry of activity, there is still no generally recognized global governance mechanism for AI, and no consensus on what one should even look like.

The central obstacle, according to the study, lies in the divergent approaches of the three actors that lead AI development worldwide. The United States has published more AI strategies and policy reports than any other country and pursues a market-driven, less restrictive model, prioritizing sector-specific governance, granting autonomy to federal agencies and allowing industry to shape policy through consultation and voluntary commitments. There is no federal AI law; the most prominent federal action has been the Executive Order on Safe, Secure, and Trustworthy AI, while state legislatures pass a patchwork of regulations on applications ranging from elections to healthcare. China, by contrast, treats AI as an extension of its centrally planned political system, aligning development with national and socialist values through a rule-based approach while tolerating decentralized innovation at the local level. Its 2017 New Generation Artificial Intelligence Development Plan declares the ambition to become the world leader in AI by 2030. The European Union has carved out a third position, weaving the narrative of trustworthy, human-centered AI into global discourse and releasing the EU AI Act, the world’s first comprehensive AI law, which classifies systems into four risk categories with escalating regulatory requirements.

These three actors each want to lead globally, and their competing ambitions generate concrete barriers to cooperation. The EU’s risk-based regulation is perceived by the US as innovation-stifling, while the US preference for self-regulation clashes with both the EU’s legal approach and China’s state-led model. Geopolitics deepens the divide: decoupling technology supply chains, the ongoing chip war and trade tensions have made faster AI innovation a matter of national security for Washington and Beijing, while the EU pursues open strategic autonomy. Ideological differences compound the problem. The US and EU favor collaboration with democratic partners, whereas China’s use of AI for mass surveillance and social scoring has alienated Western states, pushing it toward partnerships with Russia and developing countries through the Belt and Road Initiative and an AI study group within BRICS. Even the preferred modes of cooperation differ, with the US and EU working through existing institutions while China builds new alternative ones, a dynamic that risks accelerating bloc formation in an already fragmented landscape.

The consequences of this non-cooperation are far from abstract. The study catalogs a series of risks that are amplified or directly generated by the standoff between the three powers. Heightened nationalism in AI development could divert attention from global concerns such as sustainability toward short-term economic interests, fostering protectionist policies that disrupt markets and widen inequalities between nations. The US-China rivalry could spill over into other international conflicts as more countries pursue AI purely for competitive advantage, concentrating power among leading nations while dispersing costs widely. A hollowed-out multilateral system could fragment into minilateral groupings, enabling forum shopping in which countries align with the least restrictive regime. Cross-border risks, from AI-driven cyberattacks to nefarious uses of open-source models, become harder to manage without joint research and coordination, and societal-scale risks from frontier AI models could go unassessed without an international consortium pooling expertise and regulatory resources.

Perhaps most alarming is the prospect of a race to the bottom, in which each actor prioritizes speed over safety, cutting corners to be first to achieve breakthroughs. Coordination would help establish AI safety standards and agree on red lines that should not be crossed, including military applications such as lethal autonomous weapons systems and bioweapons. A competitive race toward artificial general intelligence among the three most capable actors could expose humanity to existential and global catastrophic risks, the study warns, making collective management of long-term dangers as urgent as addressing immediate harms.

To diagnose the problem, Olugbade turns to the collective action literature, a field transformed by Mancur Olson’s 1965 book The Logic of Collective Action and later by the Nobel laureate Elinor Ostrom. Collective action problems arise when group members pursue short-term individual interests at the expense of long-term shared interests, leaving everyone worse off than if they had cooperated. The key to classifying such problems lies in the nature of the goods at stake, defined by two properties: excludability, whether access can be limited, and rivalry, whether use by one diminishes availability for others. Global AI governance, the study argues, is non-excludable, since any of the three actors can currently influence AI’s direction, but rivalrous, because dominance by one precludes equivalent influence by the others. That combination makes global AI governance a common good, placing it in the same analytical category as the common-pool resources Ostrom famously studied, from fisheries to irrigation systems.

Ostrom distilled eight design principles from long-enduring institutions that successfully managed common-pool resources, and the study applies each to AI governance. Clearly defined boundaries would mean identifying who has a right to do what, using multistakeholder mechanisms while granting the three leading powers special privileges of influence as incentives to participate. Rules must be congruent with local conditions, since centrally determined universal rules are unlikely to survive the ideological differences among the three actors; regional and local mechanisms must complement global ones. Collective-choice arrangements should allow stakeholders at every level to modify rules as the technology evolves. Monitoring and graduated sanctions are deemed crucial, addressing the enforcement weakness that has undermined initiatives like the UNESCO Recommendation on the Ethics of AI and the OECD AI Principles. Mutual monitoring between the US and China, each interested in the other’s activities, could be leveraged, with sanctions escalating progressively based on compliance history. Conflict-resolution mechanisms, minimal recognition of the rights of governance bodies to organize, and nested enterprises complete the framework.

The study also draws on the analytical framework for large-scale collective action developed by Sverker Jagers and colleagues, which identifies facilitators such as trust, reciprocity and reputational stake, and stressors such as anonymity, lack of accountability, heterogeneity, risk and uncertainty. Applied to AI governance, the facilitators include a shared perspective among all three actors on using AI to solve global challenges and a common willingness to work with international organizations, with China engaging the UN and the US and EU supporting the OECD. These glimmers of common ground, the study suggests, could serve as entry points around which global AI governance is organized, with international organizations such as the UN, the OECD and the Global Partnership on AI acting as third-party interventions with the capacity and legitimacy to generate cooperation, including through the newly proposed International Scientific Panel on AI.

The study’s central conclusion challenges the many proposals for a single centralized global AI authority, such as a G20 coordinating committee, an International Artificial Intelligence Organization or an Intergovernmental Panel on AI modeled on the climate body. Such centralized mechanisms, the analysis finds, are less likely to succeed than a polycentric, multilevel arrangement in which interconnected but independent governance institutions operate at global, regional, national and subnational scales, each determined by the stakeholders at that level and guided by the principle of subsidiarity. Polycentric systems allow learning from experimentation, build trust, respond to local contexts and reduce opportunistic behavior. Enforcement, supported by monitoring, sanctioning, information provision and conflict resolution, remains the linchpin of implementation. The author acknowledges that the paper focuses on institutional design considerations rather than specifying exact institutions, and calls for future research on how polycentric arrangements could be implemented and how information provision should be structured. But the message is clear: any global AI governance proposal that ignores the non-cooperation of the United States, China and the European Union is likely doomed to fail, and the path forward runs not through a single world AI authority but through a resilient mosaic of overlapping, mutually adjusting institutions.

Subject of Research: Global governance of artificial intelligence analyzed through collective action theory

Article Title: In search of a global governance mechanism for Artificial Intelligence (AI): a collective action perspective

Article References: Olugbade, O. (2025). In search of a global governance mechanism for Artificial Intelligence (AI): a collective action perspective. Global Public Policy and Governance, 5(2), 139-161. https://doi.org/10.1007/s43508-025-00113-z

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00113-z

Keywords: artificial intelligence, global governance, collective action, common-pool resources, Elinor Ostrom, polycentric governance, EU AI Act, US-China rivalry, institutional design, AI regulation, international organizations, frontier AI risks