The Tariff Standoff: Who Will Blink First in the U.S.– China Showdown?

By J. André Faust (April 08, 2025)

Asymmetric War of Attrition: US vs. China Trade War

Understanding the Game of Attrition

In game theory, a "war of attrition" is when two players compete not by direct confrontation, but by waiting each other out. Think of it like a staring contest—each side pays a cost for staying in, and the last one to quit takes the prize. It’s used to explain animal behavior, business strategy, and politics. In this case, we're applying it to the US-China trade war, where both nations are enduring economic strain hoping the other backs down first.

With Trump recently slapping a 104% tariff on Chinese EVs, the game has clearly entered a new phase. The stakes are high, and so are the risks. But is there really a winner in a game where the longer you stay, the less there is to win?

Fig. 1 – Asymmetric War of Attrition: US vs. China Trade War

This graph is generated from the Python code below and illustrates how both countries approach the trade war with different cost structures and reward decay rates:

import matplotlib.pyplot as plt
import numpy as np

# Simulation parameters
rounds = 20
initial_reward_us = 100
initial_reward_china = 100
reward_decay_us = 3
reward_decay_china = 2
cost_per_round_us = 4
cost_per_round_china = 2

# Arrays for plotting
x = np.arange(1, rounds + 1)
reward_us = np.maximum(initial_reward_us - reward_decay_us * (x - 1), 0)
reward_china = np.maximum(initial_reward_china - reward_decay_china * (x - 1), 0)
cumulative_cost_us = cost_per_round_us * x
cumulative_cost_china = cost_per_round_china * x
net_gain_us_if_china_concedes = reward_us - cumulative_cost_us
net_gain_china_if_us_concedes = reward_china - cumulative_cost_china

# Plotting
plt.figure(figsize=(12, 7))
plt.plot(x, reward_us, label='US: Remaining Reward ($)', linewidth=2)
plt.plot(x, reward_china, label='China: Remaining Reward ($)', linewidth=2)
plt.plot(x, cumulative_cost_us, label='US: Cumulative Cost ($)', linestyle='--', linewidth=2)
plt.plot(x, cumulative_cost_china, label='China: Cumulative Cost ($)', linestyle='--', linewidth=2)
plt.plot(x, net_gain_us_if_china_concedes, label='US: Net Gain if China Concedes ($)', linestyle=':', linewidth=2)
plt.plot(x, net_gain_china_if_us_concedes, label='China: Net Gain if US Concedes ($)', linestyle=':', linewidth=2)

plt.xlabel('Rounds of Trade War')
plt.ylabel('USD Value')
plt.title('Fig 1. Asymmetric War of Attrition: US vs. China Trade War')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Explanation: This figure shows how each side bears costs over time and how the potential reward they’re fighting for diminishes. The U.S. burns through more money per round, with rewards decaying faster, while China maintains a slower cost burn and a more gradual reward loss. This imbalance makes it an asymmetric war—each side is playing with different tools and different pain thresholds.

Fig. 2 – War of Attrition with Shrinking Reward

This second graph, generated by the code below, takes it a step further by showing what happens when the total reward shrinks over time—not just because of internal costs, but because of external factors like global economic downturns:

import matplotlib.pyplot as plt
import numpy as np

# Time axis
time = np.linspace(0, 10, 100)

# Reward decay function (e.g., exponential decay)
initial_reward = 10  # Starting reward value
decay_rate = 0.3
reward = initial_reward * np.exp(-decay_rate * time)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(time, reward, label="Shrinking Reward Over Time", linewidth=2)

# Labels and Title
plt.title("Fig 2. War of Attrition with Shrinking Reward", fontsize=14)
plt.xlabel("Time (Rounds of Trade War)", fontsize=12)
plt.ylabel("Reward Value (Billion $)", fontsize=12)
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

Explanation: Fig. 2 captures a bigger problem: what if the prize you’re fighting for is evaporating? Even if a country “wins” by outlasting its opponent, the final reward might be a fraction of what it once was. Global recessions, shifts in alliances, inflation—these can all chip away at the prize until there’s nothing left worth fighting for.

Closing Thoughts

Looking at Fig. 1, we see the broader picture: an asymmetric war of attrition between the U.S. and China. The U.S. enters the game with higher costs per round and a steeper reward decay, while China plays a more patient, lower-cost game. Each round that goes by chips away at the potential payoff. If either side drops out early, the other could win big—but if they both hold out too long, the prize may not be worth the fight.

Then there’s Fig. 2, where the game gets even trickier. Here, the reward itself shrinks over time, not just because of cost but due to external economic pressure—like global market contractions or public fatigue. It’s a warning: sometimes, there’s no glory in winning if the reward has already vanished.

Trump’s latest move—slapping a 104% tariff on Chinese EVs—might look tough on the surface. But from a game theory lens, it’s a signal. He’s testing China’s resolve, daring them to respond, all while trying to frame the narrative on his terms. The question isn’t just who wins—it’s when they decide the game isn’t worth playing anymore.

That’s the heart of a war of attrition. It’s not about who throws the biggest punch—it’s about who can outlast their opponent, and whether the prize is still worth it by the time someone gives in.

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Structural entanglements, layered systems, humble forecasts.