Search arXiv⌕ Search

arXiv · 2505.00783

Promises Made, Promises Kept: Safe Pareto Improvements via Ex Post Verifiable Commitments

Abstract

A safe Pareto improvement (SPI) [41] is a modification of a game that leaves all players better off with certainty. SPIs are typically proven under qualitative assumptions about the way different games are played. For example, we assume that strictly dominated strategies can be iteratively removed and that isomorphic games are played isomorphically. In this work, we study SPIs achieved through three types of ex post verifiable commitments -- promises about player behavior from which deviations can be detected by observing the game. First, we consider disarmament -- commitments not to play certain actions. Next, we consider SPIs based on token games. A token game is a game played by simply announcing an action (via cheap talk). As such, its outcome is intrinsically meaningless. However, we assume the players commit in advance to play specific (pure or correlated) strategy profiles in the original game as a function of the token game outcome. Under such commitments, the token game becomes a new, meaningful normal-form game. Finally, we consider default-conditional commitment: SPIs in settings where the players' default ways of playing the original game can be credibly revealed and hence the players can commit to act as a function of this default. We characterize the complexity of deciding whether SPIs exist in all three settings, giving a mixture of characterizations and efficient algorithms and NP- and Graph Isomorphism-hardness results.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nathaniel Sauerberg, Caspar Oesterheld. 2025-11-20. Promises Made, Promises Kept: Safe Pareto Improvements via Ex Post Verifiable Commitments. https://arxiv.org/abs/2505.00783

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Money Burning Mechanism Design: From Welfare to Surplus

We settle the worst-case approximability of consumer-surplus maximization in general multidimensional mechanism-design environments. We do so through two black-box reductions from welfare maximization to the agents' total utility. Our first reduction turns exact welfare maximization into a prior-free, universally truthful and ex-post individually rational mechanism that preserves at least a $1/H_n$ fraction of optimal welfare as expected consumer surplus. The guarantee holds for $n$ agents with arbitrary nonnegative valuations over a finite outcome space, where $H_n$ is the $n$-th harmonic number. The factor $H_n$ is worst-case optimal, including its constant, even for a single-item auction with a known i.i.d. prior and Bayesian incentive compatibility. Our second reduction allows existing truthful welfare approximation mechanisms to be reused for surplus maximization. For valuation classes closed under scaling, it converts any ex-post individually rational, truthful $α$-approximation for welfare with nonnegative payments into an $O(α\log(n))$-approximation for surplus. Our sharp guarantee resolves the welfare-approximation aspect of the open question of Hartline and Roughgarden [2008] on the power of money burning beyond $k$-unit auctions, and the question of Ezra et al. [2025] concerning optimal surplus guarantees for broader valuation classes. It also replaces the outcome-dependent $O(\log|\mathcal{O}|)$ guarantee of Fotakis et al. [2015] with the tight agent-dependent factor $H_n$. These results yield polynomial-time mechanisms with the exact $H_n$ guarantee for gross-substitutes. They also give prior-free, universally truthful approximations of $O(H_n\log^2\log m)$ for XOS valuations and $O(H_n\log^3\log m)$ for subadditive valuations using demand and value queries, where $m$ is the number of items.

cs.GT↗

Self-Bounding Regret Matching+ in Potential Games and Product-Simplex Optimization

Regret matching+ (RM+) is parameter free, scale invariant, and central to large game solving, but its only general individual-regret guarantee grows as $\sqrt{T}$. A recent ICLR result used this envelope to prove that RM+ reaches an $ε$-stationary point of a smooth objective over a product of simplices in $O(ε^{-4})$ iterations, or $O(ε^{-8})$ from the standard zero initialization. We give an exact one-step conservation law for RM+. It states that forward utility gain pays for both squared state motion and growth of the regret-state norm. Norm growth is at most $\sqrt{m-1}$ times forward gain for $m$ actions, and the coefficient is sharp. This yields four results for unmodified RM+. Its regret on any utility path is controlled by centered temporal variation. Its regret is uniformly bounded under alternating play in every finite exact potential game, resolving an open question and making squared activation gaps summable. Both certified lazy and ordinary cyclic play attain an $ε^{-2}$ exponent. On any smooth, possibly nonconcave simplex objective, RM+ finds an $ε$-KKT point in $O(ε^{-2})$ iterations. Most broadly, for a smooth objective over an arbitrary product of simplices, cyclic block RM+ attains the same $O(ε^{-2})$ exponent from arbitrary initialization, with an explicit trajectory-dependent constant. The proof controls the finite objective loss caused by low-state blocks and then self-bounds every block state and the total squared path length. Complete proofs cover zero states, sharpness, common-profile stationarity, and robust gain dominance. Oracle-normalized diagnostics compare RM+ with predictive and smooth extra-gradient variants on graphical potential games and dense nonconvex objectives.

cs.GT↗

Randomized Online Fair Division: High-Probability and Expected Realized Fairness

We study randomized algorithms for the fully online allocation of indivisible goods among $n\ge2$ agents with nonnegative additive valuations. Goods arrive sequentially and must be allocated immediately and irrevocably, with only $n$ known in advance. Since exact ex-ante envy freeness and proportionality are readily achievable, while no positive ex-post approximation is possible for the fairness notions considered here, we study the intermediate notions of high-probability fairness and expected realized fairness. Against a non-adaptive adversary, we give a randomized algorithm for proportionality up to one good (PROP1) whose parameter depends only on $n$ and that preserves exact ex-ante envy-freeness and proportionality. At confidence $1-δ$, its PROP1 guarantee improves on independent uniform allocation (Rand) by a factor of $Ω(\log n)$, uniformly over $δ\in(0,1/2]$. As $n\to\infty$, its expected realized PROP1 factor is at least $\frac{3-\sqrt5}{2}-o(1)$. We also show that the expected realized PROP1 factor of Rand is $(1+o(1))/\log n$, yielding an improvement of at least $\bigl(\frac{3-\sqrt5}{2}-o(1)\bigr)\log n$ for our algorithm. For every randomized online algorithm and every positive approximation factor, the success probability can be made arbitrarily small for envy freeness up to any good (EFX) and at most $\frac{n+1}{2n}$ for envy freeness up to one good (EF1). Consequently, every randomized fully online algorithm has an expected realized EFX guarantee of zero and an expected realized EF1 guarantee of at most $\frac{n+1}{2n}$.

cs.GT↗