Optimal Play in Caverna: A Formal Proof of Weak Dominance

11 Sensitivity Analysis

The point-estimate payoff matrix proves weak dominance conditional on 64 exact values. This chapter strengthens the result: we replace each scalar entry with a closed interval bounding the true payoff, and prove that \(\text{FurnRush}{}\) remains weakly dominant for every matrix consistent with these bounds.

11.1 Interval payoff matrix

Definition 11.1 Payoff interval
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A payoff interval \([l, h]\) with \(l \le h\) bounds the true payoff for a particular (row, column) matchup. The point estimate from payoffMatrix satisfies \(l \le M_{i,j} \le h\).

Definition 11.2 Interval payoff matrix
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The interval payoff matrix assigns a \(\text{PayoffInterval}\) to each cell. Error bounds are asymmetric and per-cell:

  • Mirror matchups (diagonal): \(\varepsilon = 2\).

  • Cross-archetype matchups: \(\varepsilon = 5\).

  • Weapon rush vs. furnishing rush: \(\varepsilon = 3\).

Theorem 11.3 Point estimates contained

For all \(i, j\), the point estimate \(M_{i,j}\) lies within the interval \([\text{lo}(i,j),\, \text{hi}(i,j)]\).

11.2 Robust weak dominance

Theorem 11.4 Robust weak dominance
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For every column \(j\) and every non-furnishing alternative row \(i \ne 0\),

\[ \text{lo}(0, j) \; \ge \; \text{hi}(i, j). \]

Equivalently: for any true payoff matrix within the intervals, \(\text{FurnRush}{}\) weakly dominates every alternative.

Definition 11.5 Robust margin
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The robust margin in column \(j\) is \(\text{lo}(0,j) - \max _{i {\gt} 0}\, \text{hi}(i,j)\). A non-negative margin implies robust weak dominance in that column.

Theorem 11.6 Tightest margin
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The tightest robust margin is \(0\), occurring in column \(0\) (mirror matchup): \(\text{FurnRush}{}\) interval \([83, 87]\) meets \(\text{WeapRush}{}\) interval \([77, 83]\) at the boundary \(83 \ge 83\).

Theorem 11.7 All margins non-negative
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Every column has a non-negative robust margin.

Theorem 11.8 Non-mirror robustness
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All columns except the mirror (\(j \ne 0\)) have a robust margin of at least \(20\) points, making them resilient to substantially larger estimation errors.

11.3 Robust Nash equilibrium and welfare

Theorem 11.9 Robust Nash equilibrium
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\((\text{FurnRush}{}, \text{FurnRush}{})\) is a Nash equilibrium for every payoff matrix consistent with the interval bounds.

Theorem 11.10 Robust Nash welfare
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The Nash welfare lies in \([166, 174]\). The point estimate \(170\) is contained in this interval.

Theorem 11.11 Robust social optimum
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The social optimum candidate (\(\text{FurnRush}{}\) vs. \(\text{AnimHusb}{}\)) has welfare in \([200, 220]\).

Theorem 11.12 Robust price of anarchy

Even in the best case for Nash welfare (\(174\)) and worst case for the social optimum (\(200\)), the social optimum exceeds Nash welfare. The price of anarchy is robust: selfish play always costs welfare.

11.4 Error bound analysis

Theorem 11.13 Estimates within bounds
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Every point estimate differs from its interval boundary by at most \(\varepsilon _{\max } = 5\) points.

Theorem 11.14 Column 0 fragility
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If all error bounds were widened by \(1\), robust dominance would fail in column \(0\) (furnishing rush lower bound \(82\) vs. weapon rush upper bound \(84\)). This precisely quantifies the fragility of the mirror-column result.