Federico Bordonaro, Ph.D.
Foreign & Security Policy Specialist | Geopolitics | Political Officer @ Embassy of Canada 🇨🇦 to ITA | Ambassade du Canada 🇨🇦 |

Political decisions on tariffs, sanctions, and coercive foreign policy are not random; they follow a fairly systematic logic once incentives and power structures are mapped. The rational-choice, game-theoretic model developed by scholars like Bruce Bueno de Mesquita, Alastair Smith, and others works well in these domains because it forces analysts to specify, quantify, and then iterate those incentives until a politically sustainable outcome emerges.

In volatile markets, political decisions regarding tariffs, sanctions, and foreign policy often appear erratic or driven by ideological whims. However, a deeper analysis reveals that these moves are rarely random; they follow a systematic logic rooted in the survival instincts of leadership.

To cut through the noise, sophisticated political risk analysis turns to the game-theoretic models developed by authors like Bruce Bueno de Mesquita (2003, 2011), De Mesquita and Lalman (1992), De Mesquita and Smith (2011), and others like C. Signorino, R. Siverson, or J. Morrow. By forcing analysts to specify and quantify incentives, this rational-choice framework offers a powerful tool for predicting outcomes in the high-stakes worlds of energy and commodities.

The Core Logic: Leaders, Not Countries, Make Choices

The fundamental insight of De Mesquita’s approach is deceptively simple: leaders care primarily about staying in power. Policy decisions—whether declaring war, imposing tariffs, or ignoring climate pledges—are not crafted for an abstract “national interest.” Rather, they are calculated to maximize the leader’s probability of political survival.

To survive, a leader must maintain the loyalty of a specific winning coalition—the essential group of generals, party bosses, regional barons, or donors whose support is non-negotiable. The size of this coalition determines the strategy of governance. Leaders answering to a small, loyal clique can rule through private rewards, patronage, and repression. In contrast, those beholden to a massive electorate must deliver broader public goods, such as economic growth, low inflation, and affordable energy.

For the political risk analyst, this distinction is crucial. If we can map how trade and energy decisions affect the personal welfare of these key coalition members, we can predict which policies are sustainable and which are doomed to fail.

Quantifying the Game

In his work The Predictioneer’s Game, De Mesquita translates this theory into an “expected-utility bargaining model”—a method often used to forecast national security and business outcomes. Unlike vague commentary on “tensions,” this model demands precision.

The process begins by defining the issue on a distinct scale (e.g., a tariff rate from 0 to 100 percent) and identifying every actor with a stake in the outcome. Crucially, “The United States” is not treated as a unitary actor; instead, the model parses the President, specific industrial lobbies, ruling party factions, and opposition leaders as distinct players.

Each actor is assigned three values: their position (what they want), their salience (how much they care), and their clout (their ability to influence the result). The model then simulates a series of negotiations. Actors apply pressure, build coalitions, or threaten escalation based on the expected utility of fighting versus compromising. Through iteration, the model reveals the point where further conflict becomes too costly—the predicted political equilibrium.

The “Rationality” of Trade Wars

Trade policy serves as a perfect testing ground for this logic. In the rational-choice framework, tariffs are less about national competitiveness and more about wealth redistribution to solidify a coalition.

Recent U.S. tariff escalations illustrate this dynamic clearly. Post-2024, tariffs on energy-linked sectors—steel, aluminum, solar equipment—surged, with some rates exceeding 100 percent. While downstream manufacturers and consumers paid the price, the beneficiaries were highly concentrated: primary metal producers and industrial unions in pivotal election regions.

This alignment fits the model perfectly. The leader prioritized the concentrated benefits for a cohesive, electorally vital coalition over the dispersed costs borne by the general public. The market response was immediate and rational: U.S. physical premiums for aluminum spiked, and trade partners adjusted their energy strategies, with the EU linking LNG purchases to trade negotiations. As the model predicts, such tariffs persist as long as the political payoff from key sectors outweighs the electoral backlash from inflation.

Sanctions and the Resilience of Regimes

The application of economic sanctions, particularly in the energy sector, is equally illuminated by this framework. Sanctions are designed to alter the payoffs for a target’s winning coalition, forcing them to choose between policy change and losing their wealth. However, empirical history shows they often fail if the regime can shield its core supporters.

The post-2022 sanctions on Russia provide a textbook case. Western “sender” governments, answerable to large electorates, needed to demonstrate moral resolve without causing domestic economic ruin. This led to calibrated measures like price caps rather than full embargoes. Conversely, the Russian leadership, reliant on a small coalition tied to state resources, focused on re-routing energy rents. By utilizing “shadow fleets” and pivoting to Asian markets, the regime could continue feeding its key supporters, even while the broader population suffered.

For energy markets, this stalemate implies a long-term fragmentation of global trade rather than a quick resolution. The model correctly anticipates that as long as alternative buyers exist to generate rents, sanctions are unlikely to force immediate capitulation.

Aggression as a Rational Strategy

Perhaps the most chilling insight is that aggressive foreign policy—wars, blockades, and coercive threats—is often a rational gambit. Leaders escalate conflicts not out of madness, but when the expected utility of aggression exceeds that of the status quo.

This is particularly relevant in commodity markets. If a leader’s survival depends on military loyalty or resource rents, securing control over oil fields or transit routes becomes an existential imperative. Recent data confirms that such geopolitical risks drive significant shifts in energy consumption and investment, forcing nations to prioritize security of supply over efficiency.

Practical Implications for Market Analysis

For investors and analysts, adopting a game-theoretical approach transforms political risk from a guessing game into a structured discipline. It requires moving beyond headlines to answer fundamental questions about power:

• Map the Coalition: Identify exactly who keeps the leader in office—not the voters in the abstract, but the specific elites or swing groups.
• Trace the Rents: Determine where their wealth comes from. Is it oil exports? Industrial protectionism?
• Calculate the Thresholds: Estimate the breaking point. How much economic pain can the coalition endure before it becomes rational to defect?

By integrating these political realities with market data, analysts can better price “tail events” and anticipate the durability of disruptions. In an era of “de-globalization” and volatility, the rational-choice model provides a rigorous, empirically grounded lens for navigating the intersection of power and price.

References

• Bueno de Mesquita, B., A. Smith, J. Morrow, and R. Siverson (2003), The Logic of Political Survival, MIT Press.
• Bueno de Mesquita, B. (2011), The Predictioneer’s Game, Random House.
• Bueno de Mesquita, B. and D. Lalman (1992), War and Reason, Yale.
• Bueno de Mesquita, B., and A. Smith (2011), The Dictator’s Handbook, PublicAffairs.
• Source: https://www.linkedin.com/in/federicobordonaro/