Less-excludable Mechanism for DAOs in Public Good Auctions

Jing Chen; Wentao Zhou

Home/Archive/arXiv

arXiv · 2504.11854

Less-excludable Mechanism for DAOs in Public Good Auctions

Jing Chen·Wentao Zhou

View original source ↗Read source document ↗Export metadata ↓

Abstract

With the rise of smart contracts, decentralized autonomous organizations (DAOs) have emerged in public good auctions, allowing "small" bidders to gather together and enlarge their influence in high-valued auctions. However, models and mechanisms in the existing research literature do not guarantee non-excludability, which is a main property of public goods. As such, some members of the winning DAO may be explicitly prevented from accessing the public good. This side effect leads to regrouping of small bidders within the DAO to have a larger say in the final outcome. In particular, we provide a polynomial-time algorithm to compute the best regrouping of bidders that maximizes the total bidding power of a DAO. We also prove that such a regrouping is less-excludable, better aligning the needs of the entire DAO and the nature of public goods. Next, notice that members of a DAO in public good auctions often have a positive externality among themselves. Thus we introduce a collective factor into the members' utility functions. We further extend the mechanism's allocation for each member to allow for partial access to the public good. Under the new model, we propose a mechanism that is incentive compatible in generic games and achieves higher social welfare as well as less-excludable allocations.

Explore related subjects

cs.GT

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Jing Chen, Wentao Zhou. 2025-06-18. Less-excludable Mechanism for DAOs in Public Good Auctions. https://arxiv.org/abs/2504.11854

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

KEEP EXPLORING

Related papers

arXiv2026

Ascending Auctions for Combinatorial Markets with Frictions: A Unified Framework via Discrete Convex Analysis

We develop a unified ascending-auction framework for computing Walrasian equilibria in combinatorial markets with strong substitutes valuations and piecewise-linear payment functions. Our auction extends the celebrated ascending auctions of Gul and Stacchetti (2000) and Ausubel (2006) to accommodate payment frictions (e.g., transaction taxes or commission fees). This is achieved by incorporating directional price updates that reflect heterogeneous payment structures. Our framework also generalizes the unit-demand imperfectly transferable utility models of Alkan (1989, 1992) to a fully combinatorial setting, thereby unifying these paradigms. Furthermore, this is the first study to compute the minimum -- also known as the buyer-optimal -- equilibrium in combinatorial markets with such frictions. Our analysis builds upon discrete convex analysis. Our main technical contribution is a characterization of valid price-update directions, together with a strongly polynomial-time algorithm for computing them. Notably, the algorithm uses only demand- and exchange-oracle queries and never requires handling information of exponential size. To compute such a direction, we formulate a lexicographic extension of the polymatroid sum problem and characterize its dual solution via a reduction to a convex flow problem. Exploiting the $\text{L}^\natural$-convexity of the dual objective, we show that the desired direction can be constructed from the minimal dual solution. This convexity also yields transparent economic and potential-based interpretations of the auction dynamics, strengthening the connection between ascending auctions and discrete optimization.

cs.GT↗

arXiv2026

Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI

Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI safety. While mechanism design, as the theory of designing rules to align individual and collective objectives, can incentivize cooperative behavior, it is still an open question whether it alone is sufficient to maximize LLM agents' social welfare. This work proves that the answer is negative: drawing from incomplete contract theory, we formally show that when contracts cannot distinguish all relevant future contingencies, there is a strictly positive welfare loss that no realistic mechanism can eliminate. We show that prosocial agents, who weigh others' welfare alongside their own, can close this gap and achieve outcomes that are socially superior and individually beneficial. Experimentally, we show that in multi-agent resource-allocation environments and canonical social dilemmas where agents are powered by large language models, prosociality is beneficial. The implication for AI safety is clear: to enable cooperative interactions at scale, designing adequate mechanisms is not sufficient; agents must be built to be intrinsically prosocial.

cs.GT↗

arXiv2026

Efficient Nash Equilibrium Computation for Cybersecurity Games

Game-theoretic analyses of cyber defence often compute equilibria of games whose payoffs exist only as the output of a simulator. Iterative equilibrium-finding methods grow a set of attacker and defender policies and need the payoff of every attacker--defender pair, so they are bottlenecked by payoff estimation: each payoff costs many simulator runs. We introduce Regret-Weighted Payoff Sampling (RWPS), which spends a fixed simulation budget on the payoffs the equilibrium actually depends on and predicts the rest with a model trained on every payoff measured so far. Standard error bounds for estimated games are driven by the worst-estimated payoff, so they cannot credit an estimator that is inaccurate only where accuracy does not matter. We prove a bound that weights payoff errors by the opponent's equilibrium strategy, a certificate that can be computed from simulated payoffs alone, and a condition under which errors in the predicted payoffs cannot change either player's regret. On three synthetic general-sum games, one of them a Colonel Blotto game of military resource allocation, the new bounds are four to six times tighter than the standard one, and RWPS finds less exploitable equilibria than minimum-regret-first search, information-gain search and progressive sampling at the same budget. On two cyber-defence simulators, CyGym and a new game whose hosts are LLM agents exposed to prompt injection, it gives the least exploitable equilibria at the smallest budgets.

cs.GT↗