Navigating Uncertainty: China-Russia Arctic Security Cooperation in a Game-Theoretic Framework

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Arctic troops of the Russian Federation in 2016. Photo: Ministry of Defence of the Russian Federation

The Arctic Institute China Series 2025

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Long insulated by geography and climate, the Arctic is no longer a passive backdrop in international affairs. It has become a region where environmental volatility intersects with shifting global power structures. Melting sea ice is not simply a climate phenomenon—it is a catalyst for reconfiguring trade routes, resource access, and strategic calculations.¹⁾Lynch AH & Norchi H. C & Li X (2022) The interaction of ice and law in Arctic marine accessibility. PNAS, 21 May, https://www.pnas.org/doi/epub/10.1073/pnas.2202720119. Accessed on 6 May 2025 Amid this transformation, Russia and China have emerged as central players, albeit with asymmetrical roles shaped by their differing Arctic positions and capabilities. Russia anchors its Arctic presence through history, geography, and military infrastructure. China, though external to the region, has pursued an Arctic policy grounded in scientific collaboration, economic investment, and normative engagement.²⁾Kauppila L & Kopra S (2022) The war in Ukraine as a Critical Juncture: China, Russia, and Arctic collaboration up to 2035. Arctic Yearbook 2022: 233-248.

Their Arctic postures are shaped by overlapping interests but divided by strategic ambiguity. Both actors stand to benefit from cooperation on infrastructure development and access to emerging maritime routes. Yet, uncertainties—about climate trajectories, resource availability, and external pressures from actors such as NATO—create diverging incentives. These uncertainties are compounded by the absence of robust enforcement mechanisms and the fragile state of trust between the two powers.³⁾Chen C (2023) China-Russia Arctic cooperation in the context of a divided Arctic. Arctic Institute, 04 April, https://www.thearcticinstitute.org/china-russia-arctic-cooperation-context-divided-arctic/. Accessed on 9 May 2025

In this context, game theory provides a useful framework for analyzing possible trajectories of China–Russia interaction. Unlike narrative scenarios, a formal model can capture how strategies shift in response to changes in payoffs, environmental conditions, and belief systems. This article introduces a simplified yet structured game-theoretic analysis, comparing strategic choices under two environmental regimes: Stable Ice and Rapid Melt. The model incorporates Bayesian learning and stochastic environmental change, showing how belief updates and dynamic uncertainty influence cooperation or defection over time.

By quantifying strategic incentives and identifying tipping points, the analysis clarifies under what conditions cooperation may emerge, erode, or transform. This approach aims to inform Arctic policy by making the mechanics of uncertainty explicit and offering insight into the evolving logic of Sino-Russian alignment in the circumpolar north.

Game-Theoretic Framework

Players, Strategies, and Payoffs

Russia and China must choose a strategic posture in the Arctic. Broadly speaking, a country can cooperate with the other in joint security and development efforts, or defect (act independently/competitively). Cooperation could mean joint infrastructure projects, shared scientific missions, or coordinated patrols. Defection represents pursuing interests unilaterally, perhaps securing resources alone or maintaining exclusive military control. Payoffs measure the utility (strategic and economic value) each player gains from an outcome. These depend on both choices and the external environment. For example, under Stable Ice conditions (limited new resources), cooperation yields moderate benefits, while defection by one side reduces the cooperator’s share. Consider this illustrative payoff matrix for a Stable Ice scenario:

Table 1. Payoff Matrix for Russia and China under Stable Ice Conditions

Russia/China	Cooperate (C)	Defect (D)
Cooperate (C)	(8,6)	(4,7)
Defect (D)	(7,4)	(3,3)

As ice melts more rapidly, the stakes grow. In a Rapid Melt scenario with abundant new routes and resources, joint gains are higher but so is temptation to exploit. For instance:

Table 2. Payoff Matrix for Russia and China under Rapid Melt Conditions

Russia/China	Cooperate (C)	Defect (D)
Cooperate (C)	(10,10)	(5,12)
Defect (D)	(12,5)	(2,2)

Here both cooperating yields high payoffs (10,10). If Russia cooperates and China defects, Russia gets 5 while China gets 12 (China takes a bigger share alone); vice versa for (12,5). Mutual defection is very poor (2,2).

Each payoff table can be encoded as a utility function. For example, in the Stable Ice case let X_R=1 if Russia cooperates (0 if defects) and X_C ​ likewise for China. Then Russia’s payoff is:

U_R = 8X_RX_C + 4X_R (1–X_C) + 7(1–X_R) X_C+3(1–X_R) (1–X_C),

China’s payoff is:

U_C = 6X_RX_C + 7X_R (1–X_C) + 4(1–X_R) X_C+3(1–X_R) (1–X_C),

These formulas yield the numbers in the table when X_R, X_C ​ take 0 or 1. In the Rapid Melt scenario, the coefficients would change to (10,10,12,2) accordingly. More generally, one could define continuous strategy variables (e.g. levels of investment or fleet presence) and write linear or nonlinear utilities, but our discrete illustration captures the main incentives.

Stochastic Transitions Between States

The Arctic environment can change over time. We model climate state as a simple two-state process: S (Stable Ice) and R (Rapid Melt). Let

p=P(S→R), q=P(R→S)

be the one-period transition probabilities. The Markov transition matrix is:

P=1-P p q 1-q

For example, if p=0.2 and q=0.1, each period has a 20% chance the Arctic shifts from stable to rapid melt (e.g. due to accelerated warming), and 10% to revert (say an unusually cold spell). Over time the long-run fraction of periods in the Rapid state is p/(p + q).

Players choose strategies each period, then the state may switch according to these probabilities. Expected payoffs combine both cases. For instance, if π=P (Rapid next period), Russia’s expected payoff from cooperating (if China cooperates) is (1− π) × 8 + π × 10 in our examples. The chance of state changes also means strategies can adapt over time.

Uncertainty and Belief Updating

China and Russia may not know future conditions with certainty. Each can update beliefs as new information arrives. For example, suppose China’s prior probability of Rapid Melt next year is π. It observes a climate “signal” s (e.g. a record-warm summer), which has likelihood Pr(s∣Rapid) if the Rapid state is true and Pr(s∣Stable) if not. By Bayes’ Rule, the updated probability of Rapid Melt is

Prs = ^PrRapidπ_{PrRapidπ+PrStable1-π}

If the signal is much more likely under rapid-melt conditions, this increases Pr(Rapid). A higher belief in rapid warming makes cooperation more attractive (since cooperative payoffs in the Rapid scenario are higher). For instance, if China initially believed π=0.3, and receives a signal with Pr(s∣Rapid)=0.8 and Pr(s∣Stable) = 0.2, then Pr(Rapid∣s) ≈ 0.63. This boosted probability raises expected cooperative payoffs.

Belief updating works similarly if China is uncertain about Russian intent or the credibility of agreements. Observing Russia’s actions or communications would update China’s belief in Russia’s cooperativeness. Over repeated interactions, such Bayesian learning can shift strategy choices.

Scenario Outcomes and Strategic Insights

Stable Ice Scenario

Under a Stable Ice outlook (low ice melt probability), payoffs from Arctic ventures are moderate. Referring to our Stable-Ice matrix, mutual cooperation gives (8,6), but each player tempts unilateral defection to get 7 instead of 8. In fact, analysis of that payoff matrix shows the Nash equilibria are the two asymmetric outcomes: one country cooperates while the other defects. Mutual cooperation is not an equilibrium because, for example, China would prefer to defect if Russia cooperates (increasing China’s payoff from 6 to 7). In practice this implies a “trust gap” scenario: one side may cooperate expecting reciprocation, but the other has an incentive to withhold.

Policy implications are that with limited immediate gains, neither side fully trusts the other. Russia might offer joint projects hoping China will invest, but worry China will then push Russia out. China might hold back in fear Russia will renege on resource sharing. The game-theoretic model highlights the need for confidence-building (such as legal agreements) before stable conditions.

Rapid Melt Scenario

If the Arctic warms rapidly, the total gains grow (as in the Rapid-Melt matrix). In this scenario, both cooperating yields a payoff of (10,10), which is large. However, each still has a temptation to defect: e.g. if China expects Russia to cooperate, China could defect and get 12 instead of 10. In our example payoffs, the only Nash equilibria remain the asymmetric ones. Even with greater joint potential, mutual cooperation can fail without credible enforcement.

The difference is context: with high stakes, policymakers may be more willing to invest in trust and enforcement. For instance, long-term contracts or joint ventures can raise the cooperative payoffs or penalize defectors, shifting the equilibrium. In our model terms, such measures would increase the (C, C) cell or decrease the payoff of (D, anything). If, for example, an international insurance scheme compensated a cheated cooperator, Russia and China might both find cooperation worthwhile.

In a dynamic setting, repeated interaction also matters. If Russia and China interact over many periods, each can punish the other’s defection (e.g. by withholding future cooperation). This can sustain cooperation even if a one-shot game would predict defection. Formally, with a high enough discount factor δ, the infinite-horizon game admits cooperative equilibria. In practice, this means that stable, long-term Arctic partnerships or repeated joint exercises can help overcome short-term temptations.

Role of External Pressure

External actors like NATO/Western states influence the game indirectly. Suppose NATO increases Arctic naval exercises and security presence. This adds external “threat pressure.” In game terms, this can be modeled by increasing the payoff to mutual cooperation (e.g. a security bonus) or increasing the cost of defection (vulnerability). Qualitatively, greater NATO activity tends to push Russia and China closer together, because both then have more to lose from acting alone.⁴⁾Andersson P (2024) Sino-Russian cooperation in the Arctic: Implications for Nordic countries and recommended policy responses. Swedish Institute of International Affairs, 22 October, https://www.ui.se/globalassets/ui.se-eng/publications/other-publications/sino-russian-cooperation-in-the-arctic_implications-for-nordic-countries-and-recommended-policy-responses.pdf. Accessed on 10 May 2025

For example, if an Arctic Treaty under NATO safeguards grants joint rights to cooperating states, Russia and China would gain extra value by banding together. In our framework, the (C,C) payoff cells might increase by a constant T when facing a common challenge. Western policymakers should note aggressive posturing in the Arctic could inadvertently make Russia and China collaborate more tightly. Combining pressure with dialogue (e.g. inviting them to Arctic council discussions) might avoid unintended incentives.⁵⁾Staalesen A (2016) Norway’s new Arctic oil licences on the table. Independent Barents Observer, 17 May, https://www.thebarentsobserver.com/industry-and-energy/norways-new-arctic-oil-licenses-on-the-table/143683. Accessed on 20 May 2016

Belief Dynamics and Strategy Shift

Throughout these scenarios, shifting beliefs can trigger strategy changes. Initially, if both nations deem a stable ice regime likely, they may adopt low-cooperation, self-reliant strategies. But as evidence of rapid warming accumulates, Bayesian updating raises π=P(Rapid), altering expected payoffs. For example, if Russia moves from a 50% to a 70% belief in imminent rapid melting, its expected payoff from cooperating (given China cooperates) rises from 0.5×8+0.5×10=9 to 0.3×8+0.7×10=9.4. Meanwhile, the defection’s payoff goes from 9.5 to 10.5. Defection still dominates in this example, but the gap shrinks as π increases. Only when π is very high, or if cooperative gains are institutionally boosted, does cooperation become clearly superior.

This illustrates threshold effects: policymakers can compute the critical probability where cooperation pays off. In practice, it means investing in monitoring (like shared climate data) can push beliefs past that threshold, facilitating joint strategies.

Policy Implications

The formal analysis of China–Russia Arctic cooperation through a game-theory lens offers more than abstract insight. It reveals concrete leverage points for policy design, strategic forecasting, and risk mitigation. As environmental and geopolitical uncertainty shape state behavior, strategic outcomes increasingly depend on how actors perceive and respond to evolving information.⁶⁾Szukp M (2020) Sentiments, strategic uncertainty, and information structures in coordination games. Games and Economic Behavior, Elsevier 124: 534-553. The following section outlines six interrelated policy implications emerging from the model: the centrality of information-sharing, the potential to engineer incentives, the role of scenario-based planning, the influence of external actors, the value of multilateral forums, and the utility of incremental cooperation in security.

Value of Shared Information

The model demonstrates that belief asymmetry—where actors assign different probabilities to future environmental states—contributes to suboptimal outcomes. When Russia and China operate with divergent expectations about Arctic melting or each other’s intentions, cooperation becomes riskier.⁷⁾Andersson (2025) Polar Partners: What Sino-Russian Arctic cooperation means for Europe. China Observers, 25 February, https://chinaobservers.eu/polar-partners-what-sino-russian-arctic-cooperation-means-for-europe/. Accessed on 9 May 2025 Enhancing transparency in climate data and strategic intent is therefore a low-cost, high-impact intervention.

Joint satellite monitoring initiatives, shared sea ice projections, and climate signal reporting mechanisms can increase alignment in beliefs. For example, the Arctic and Antarctic Research Institute (AARI) in Russia and China’s Polar Research Institute could co-produce seasonal forecast bulletins. Such efforts would reduce the likelihood of misperception-driven defections, allowing strategies to converge around a shared understanding of environmental probability distributions. In Bayesian terms, increasing access to credible and shared signals tightens posterior convergence and enables more stable equilibria in repeated games.⁸⁾Hill S J. (2017) Learning together slowly: Bayesian Learning about political facts. The Journal of Politics 79(4): 1403-1418.

Incentive Engineering and Institutional Design

The payoff matrices suggest that even under high-potential conditions (e.g., rapid melt scenarios), defection can dominate unless cooperative payoffs are institutionally reinforced. This points to the utility of “incentive engineering”—structuring agreements and institutions to shift the game’s equilibrium toward cooperation.

Bilateral agreements on cost-sharing for Arctic infrastructure (e.g., LNG terminals, deep-sea ports), third-party arbitration mechanisms, and pre-negotiated revenue-sharing formulas can reduce the risk that one party will renege after initial investments. China’s investment in Yamal LNG, a project led by Novatek and supported by the Russian state, illustrates the potential for cooperative ventures to succeed when backed by clear legal protections and mutual economic benefit.⁹⁾Humpert M (2025) China looking to buy more Russian Arctic LNG as EU aims to announce plans in May to Phase out. High North News, 21 April, https://www.highnorthnews.com/en/china-looking-buy-more-russian-arctic-lng-eu-aims-announce-plans-may-phase-out-imports. Accessed on 10 May 2025 From a game-theory perspective, such agreements effectively increase the utility of the (C, C) cell while penalizing unilateral deviation, narrowing the gap between short-term incentives and long-term trust.

Scenario Planning for Strategic Flexibility

The model’s stochastic transitions and probabilistic payoffs highlight the importance of scenario planning. Because strategic incentives are conditional on environmental regimes—Stable Ice versus Rapid Melt—policy approaches must be adaptive to changing climatic signals.¹⁰⁾Goodman SH & Guy K & Maddox M & Hansen V & Sending O & Winther I (2021) Climate Change and security in the Arctic. Council on Strategic Risks (CSR), and The Norwegian Institute of International Affairs(NUPI), January, https://climateandsecurity.org/wp-content/uploads/2021/01/Climate-Change-and-Security-in-the-Arctic_CCS_NUPI_January-2021-1.pdf. Accessed on 10 May 2025 Governments should develop flexible Arctic strategies tied to key thresholds. For instance, if projected ice-free days along the Northern Sea Route surpass a specific threshold for three consecutive years, this could trigger joint Sino-Russian investment in maritime rescue infrastructure or shipping corridor management.

The framework allows policymakers to calculate “tipping probabilities”—threshold belief levels at which cooperation becomes the dominant strategy. Such data can inform the timing and nature of diplomatic engagement, helping states prioritize either confidence-building under uncertainty or formal institutionalization when beliefs converge.

Managing the Role of External Actors

External powers, particularly NATO members and Arctic Council states, exert indirect pressure on China–Russia Arctic dynamics. The model illustrates that an increase in perceived external threat (modeled as an exogenous change in payoffs) can reinforce cooperation between China and Russia, even in the absence of deep trust. For example, expanded NATO naval exercises in the Barents or Norwegian seas could be interpreted by Russia as a signal of encirclement, thereby increasing the relative value of bilateral alignment with China.¹¹⁾Boulegue M & Alander M & Collen C & Lucas E & Sendak C & Viksnins K (2024) up North: Confronting Arctic insecurity implications for the United States and NATO. The Center for European Policy Analysis (CEPA), 05 December, https://cepa.org/comprehensive-reports/up-north-confronting-arctic-insecurity-implications-for-the-united-states-and-nato/. Accessed on 7 May 2025

However, this alignment may not necessarily serve the long-term interests of regional stability or transparency. Western policymakers must therefore calibrate Arctic engagement carefully. A dual-track approach that balances presence with inclusion—such as offering Russia continued roles in Arctic Council science working groups and supporting China’s observer status—can blunt the incentive for exclusive alignment while preserving deterrence.¹²⁾Rumer E & Sokolsky R & Stronski P (2021) Russia in the Arctic-A critical examination. Carnegie Endowment for International Peace, 29 March, https://carnegieendowment.org/research/2021/03/russia-in-the-arctica-critical-examination?lang=en. Accessed on 10 May 2025 In effect, external actors shape the structure of the game indirectly and must be aware of how their moves alter other players’ perceived payoffs.

Multilateral Norm-Building and Commitment Devices

Repeated interaction in multilateral forums adds another layer to the repeated game model. When China and Russia engage regularly in institutions like the Arctic Council or the International Maritime Organization (IMO), they internalize shared rules and expectations. These institutions serve as informal commitment devices, raising the reputational cost of unilateral deviation.

Moreover, multilateral engagement introduces third-party monitoring and norm-setting that enhances the credibility of cooperative strategies. Rules on maritime traffic control, environmental regulation, or fisheries management—when negotiated in multilateral forums—can embed both countries in a broader regime of mutual obligations.¹³⁾Koivurova T & Molenaar E.J (2009) International Governance and regulation of the Marine Arctic. WWF International Arctic Programme, January, https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/4e4mgcfnx2_International_Governance_and_Regulation_of_the_Marine_Arctic_Three_Reports_Prepared_for_the_WWF_International_Arctic_Program.pdf. Accessed on 11 May 2025 This reduces reliance on bilateral enforcement mechanisms and helps to anchor long-term cooperation, especially in a domain where hard enforcement is difficult.

Cooperative Security as an Incremental Strategy

While high-level strategic trust remains elusive, the model suggests that cooperation can be built incrementally through lower-risk, operationally useful security initiatives. Joint search-and-rescue (SAR) operations, coordinated maritime traffic systems, or port facility sharing arrangements represent “low politics” cooperation with “high payoff” symbolic and practical value. These initiatives not only increase the value of the (C, C) outcome in payoff terms, but also generate observable signals of intent, feeding into belief-updating mechanisms in future iterations of the game.

One example is the Arctic Coast Guard Forum, which brings together maritime agencies from Arctic states, including Russia, for cooperative SAR exercises. Though China is not a member, similar bilateral mechanisms could be explored to build institutional routines and test cooperative reliability in narrow but meaningful domains.

Conclusion

The game-theory analysis of China–Russia Arctic cooperation underlines how uncertainty—whether environmental, strategic, or informational—drives divergent behavior. It also identifies specific mechanisms through which that uncertainty can be reduced or managed: transparent information-sharing, incentive-structuring agreements, flexible scenario-based planning, calibrated external engagement, multilateral norms, and incremental security cooperation.

By formalizing payoffs and belief dynamics, the framework offers policymakers a clearer view of the strategic logic governing Arctic interactions. Rather than assuming inevitability—of either alignment or conflict—the model reveals that the outcomes are conditional and contingent. The future of Arctic cooperation will not likely depend on fixed interests, but on how states respond to evolving information, shared risks, and shifting incentives.

In a region defined by rapid transformation, policy must be strategically grounded and dynamically adaptive, prepared to respond to emerging tipping points and capable of recalibrating as conditions evolve. Game-theory tools can play a crucial role in supporting this adaptability, offering structured insight into a highly fluid geopolitical space.

Abbas Qaidari is an International Security Researcher and Member of the Canadian International Council.