MaxMax Strategy in DEX Arbitrage, Voting & Lambda Calc

• MaxMax Strategy is a unified framework that maximizes outcomes across multiple choices in domains like decentralized finance, voting, and lambda calculus evaluation.
• In DEX arbitrage, it calculates optimal token swap sizes using root-finding techniques to maximize monetized profit in cyclic trading loops.
• In voting and lambda calculus, it provides tight performance bounds and robust mechanisms, ensuring optimal selection even under worst-case conditions.

The MaxMax strategy refers to a class of approaches, found in several areas of theoretical computer science, decentralized finance, and social choice, unified by their focus on maximizing the worst-case or most favorable outcome across multiple alternatives or input choices. The following exposition provides a technical and methodical account of MaxMax strategies in three contexts: arbitrage in decentralized exchanges, λ-calculus evaluation strategies, and distributed voting mechanisms.

1. MaxMax in Blockchain Arbitrage: Formal Definition and Optimization

The MaxMax strategy, as formulated in cyclic arbitrage on decentralized exchanges (DEXs) such as Uniswap V2, is centered on maximizing the monetized arbitrage profit across all possible loop starting tokens (Zhang et al., 2024). Let $T = \{1,2,\ldots,n\}$ denote $n$ distinct tokens arranged in a cyclic arbitrage loop, and $F_k$ the Constant Product Market Maker (CPMM) swap function for each pool $k$. Given reserves $(r^{(k)}_{\text{in}}, r^{(k)}_{\text{out}})$ and swap fee fraction $\lambda$, define:

$$F_k(u) = r^{(k)}_{\text{out}} - \frac{r^{(k)}_{\text{in}}\,r^{(k)}_{\text{out}}}{r^{(k)}_{\text{in}} + (1-\lambda)u}$$

For each token $i\in T$, let $\Delta x_i$ be the input amount of token $i$, passing through the loop and yielding output $\Delta x^{\text{out}}_i$. The raw and monetized profits are

$$\pi_i(\Delta x_i) = \Delta x^{\text{out}}_i - \Delta x_i \,,\qquad \Pi_i(\Delta x_i) = P_i[\Delta x^{\text{out}}_i - \Delta x_i]$$

where $P_i$ is the fiat/centralized exchange (CEX) price of token $i$.

The MaxMax strategy computes, for each token $i$, the optimal swap size

$x^*_i = \arg\max_{x\ge 0}\, \Pi_i(x)$

obtains the corresponding maximal profit $\Pi^*_i = \Pi_i(x^*_i)$, and selects the token $i^*$ maximizing $\Pi^*_i$:

$$i^* = \arg\max_{i\in T} \Pi^*_i\,,\qquad \text{MaxMax profit} = \Pi^*_{i^*}$$

The optimal $x^*_i$ is usually computed via root-finding (bisection or Newton–Raphson) on the first-order condition

$$\frac{d}{dx}\left[F_n \circ \cdots \circ F_1(x)\right] \Big|_{x=x^*_i} = 1$$

2. MaxMax in Distributed Voting: The Max–Max Objective

In distributed voting, the max–max objective considers $n$ voters partitioned into $k$ groups, with each group $V_i$ selecting a representative, and a mechanism picking the final winner $w\in C$. Let $d: V\times C \to \mathbb{R}_+$ be the underlying cost metric. The max–max cost is

$$\operatorname{maxmax}(w) := \max_{i\in [k]}\, \max_{v\in V_i} d(v, w)$$

i.e., the cost for the single worst-off voter in the worst-off group (Abam et al., 21 Sep 2025).

The optimal deterministic mechanism (“Arbitrary Dictator,” DET–MAXMAX) achieves distortion exactly 3 by fixing a representative voter in each group and always electing the top choice of a distinguished group. Randomized variants (RAND–DET–MAXMAX, RAND–RAND–MAXMAX) select group representatives and/or the final group at random, but provably cannot surpass distortion 3, matching the deterministic lower bound.

3. Maximal Strategy in λ-Calculus and the Maximal MAM

In the λ-calculus, the “maximal strategy” (referred to as “MaxMax” in some expository settings, though the paper’s terminology is maximal) always selects reduction steps so as to maximize the length of the evaluation sequence (Accattoli, 2017). This is achieved by postponing erasing redexes (where the bound variable does not occur in the body) until their arguments are fully normalized. The maximal context formalism precisely defines which reduction is chosen at each stage, yielding unique reduction sequences and permitting efficient simulation via the Maximal MAM (abstract machine).

The Maximal MAM ensures faithful, polynomial-overhead implementation of this longest-path evaluation, labeling environment entries to avoid unnecessary computation. However, the maximal strategy itself is not a “reasonable cost model”—the number of maximal steps may be super-polynomial in the size of the term.

4. Algorithmic Procedures and Practical Examples

MaxMax Arbitrage Algorithm (DEXs)

for i in T: def G_i(x): u = x for k in 1...n: u = F_k(u | reserves of pool k) return P_i * (u - x) # Solve d/dx u(x) = 1 for x_i^* via bisection/Newton Π_i^* = G_i(x_i^*) i^* = argmax_i Π_i^* return i^*, Π_{i^*}^*

Illustrative Arbitrage Example

A 3-token loop $(X\to Y\to Z\to X)$ with reserves, fee, and prices yields per-token monetized profits:

• Start $X$: $\Pi^*_X = \$33.6$- Start$Y$:$\Pi^*_Y = \$201.1$</li> <li>Start$Z$:$\Pi^*_Z = \$206.0$MaxMax selects$Z$. Contrast: MaxPrice (naïve) would have chosen$Z$, but had$P_X$$been higher, would not always find the best.</li> </ul> <h3 class='paper-heading' id='distributed-voting-two-stage-max-max-mechanisms'>Distributed Voting: Two-Stage Max–Max Mechanisms</h3> <ul> <li><strong>DET–MAXMAX:</strong> For each group, select a fixed representative; overall winner is top choice of a fixed group’s representative.</li> <li><strong>RAND–DET–MAXMAX:</strong> Groups select deterministic reps; final winner is chosen uniformly from reps.</li> <li><strong>RAND–RAND–MAXMAX:</strong> Both the representative and final winner are chosen uniformly at random within their respective stages.</li> </ul> <p>All achieve distortion factor exactly 3 for worst-case max–max cost; this bound is proven tight (<a href="/papers/2509.17134" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Abam et al., 21 Sep 2025</a>).</p> <h3 class='paper-heading' id='maximal-mam-for-calculus'>Maximal MAM for λ-Calculus</h3> <p>The Maximal MAM state is a 5-tuple$$(F, t, \pi, E, \varphi)$$and simulates maximal β-reduction using multiplicative, exponential, and commutative transitions, maintaining maximal context invariants. For example, evaluation of$$t_0 = (\lambda x.x)((\lambda y.I)((\lambda z.z) c))$$proceeds by delaying erasing redexes, yielding the longest reduction path.</p> <h2 class='paper-heading' id='theoretical-guarantees-and-comparative-analysis'>5. Theoretical Guarantees and Comparative Analysis</h2><h3 class='paper-heading' id='arbitrage-maxmax-vs-alternatives'>Arbitrage: MaxMax vs. Alternatives</h3> <ul> <li><strong>MaxPrice:</strong> Always starting at the highest-priced token, may underperform by up to 40<li><strong><a href="https://www.emergentmind.com/topics/convex-optimization" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Convex Optimization</a>:</strong> Solves a multi-input convex program, theoretically dominates MaxMax, but empirical improvement is marginal (≤0.2<li><strong>Complexity:</strong> MaxMax requires$$n$$univariate convex optimizations (sub–block-time even for$$n=10$$); ConvexOpt requires solving a small convex nonlinear program ($$\approx$seconds for moderate$n$).

Voting: Tightness of Bound

No deterministic or randomized distributed ordinal mechanism can achieve distortion better than 3 for the max–max objective; worst-case examples demonstrate tightness (Abam et al., 21 Sep 2025).

λ-Calculus: Implementation Reasonableness

• Correctness: Maximal MAM provides sound and complete simulation (weak bisimulation) of maximal strategy.
• Complexity: Overhead is polynomial in both term size and reduction length. However, the total number of steps in the maximal derivation can be super-polynomial (Accattoli, 2017).

6. Practical Significance and Limitations

The MaxMax methodology is characterized by:

• Guaranteed optimality within its formal model (always matches or exceeds simpler heuristics).
• Efficient computability, enabling deployment in real-time or on-chain arbitrage (milliseconds per loop).
• Close empirical proximity to more sophisticated, but computationally costly, optimization methods.
• In voting, an intrinsic barrier at distortion 3 due to metric triangle inequality, insensitive to randomization or rule complexity.

A plausible implication is that in settings characterized by search among multiple candidate actions, the MaxMax principle provides a robust baseline: for each starting action, compute the corresponding outcome measure, and select the input yielding the highest attainable value. This is provably optimal or nearly so in several distinct application domains. However, for cost metrics that penalize aggregate or average suffering, or for evaluation strategies seeking minimal not maximal length, the MaxMax paradigm is not directly applicable.

7. Summary Table: MaxMax Across Domains

Domain	Objective Definition	Key Guarantee / Limitation
DEX Arbitrage	Maximize monetized profit over all input tokens	Always ≥ MaxPrice; nearly matches convex optimization (Zhang et al., 2024)
Distributed Voting	Minimize worst-off cost (max–max)	Distortion $\leq3$ (tight bound) (Abam et al., 21 Sep 2025)
λ-Calculus Eval.	Maximize reduction sequence length	Unique path, reasonable MAM simulation (Accattoli, 2017)

The MaxMax framework thus offers a unifying perspective for strategies that optimize for the best outcome across all input choices, yielding both strong theoretical guarantees and practical efficiency in diverse computational settings.