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Aadityan Ganesh

Publications and source records attributed to Aadityan Ganesh.

9 recordsLinked to original sources

Welfare Approximation in Multilateral Trade

We introduce the study of \emph{multilateral trade}: a mechanism-design problem in which a single potential trade involves $k$ agents and can be executed only if all $k$ agents agree to participate. The classical case $k=2$ is the well-studied bilateral trade problem, where a seller and a buyer with private values must decide whether to trade an item initially held by the seller. Existing extensions of bilateral trade have largely focused on markets with many buyers and many sellers, but where each realized transaction is still bilateral, requiring agreement only between the matched buyer and seller. Our formulation captures settings in which the trade itself requires joint participation, coupling the agents' incentives and creating new challenges. We study welfare approximation in this setting under incentive compatibility, individual rationality, and budget balance. We give a DSIC mechanism with approximation ratio $O(k^2)$, and a BIC mechanism with approximation ratio $\widetilde O(k^{3/2})$. We prove matching lower bounds up to polylogarithmic factors. Finally, we extend the model to an $\ell$-out-of-$k$ partial-agreement setting, where the trade may occur once at least $\ell$ agents participate. In this relaxed model, the welfare guarantees improve smoothly as $k-\ell$, the number of agents whose participation is not required, grows, and we obtain matching upper and lower bounds up to polylogarithmic factors.

cs.GT

Characterizing Off-Chain Influence Proof Transaction Fee Mechanisms

Roughgarden (2020) initiates the study of Transaction Fee Mechanisms (TFMs), and posits that the on-chain game of a ``good'' TFM should be on-chain simple (OnCS), i.e., incentive compatible for users and the miner. Recent work of Ganesh, Thomas and Weinberg (2024) posits that they should additionally be Off-Chain Influence Proof (OffCIP), which means that the miner cannot achieve any additional revenue by separately conducting an off-chain auction to determine on-chain inclusion. They observe that a cryptographic second-price auction satisfies both properties, but leave open the question of whether other mechanisms (e.g, non-cryptographic) satisfy these properties. In this paper, we characterize OffCIP TFMs: They are those satisfying a burn identity relating the burn rule to the allocation rule. In particular, we show that auction is OffCIP if and only if its (induced direct-revelation) allocation rule $\bar{X}(\cdot)$ and burn rule $\bar{B}(\cdot)$ (both of which take as input users' values $v_1, \dots, v_n$) are truthful when viewing $\big(\bar{X}(\cdot), \bar{B}(\cdot)\big)$ as the allocation and pricing rule of a multi-item auction for a single additive buyer with values $\big(φ(v_1),\ldots, φ(v_n)\big)$ equal to the users' virtual values. Building on this burn identity, we characterize deterministic OffCIP and OnCS TFMs that do not use cryptography: They are posted-price mechanisms with specially-tuned burns. As a corollary, we show that such TFMs can only exist with infinite supply and prior-dependence. However, we show that for randomized TFMs, there are additional OnCS and OffCIP auctions that do not use cryptography (even when there is finite supply, under prior-dependence with a bounded prior distribution). Holistically, our results show that while OffCIP is a fairly stringent requirement, families of OffCIP mechanisms can be found for a variety of settings.

cs.GT

Truthful, Credible, and Optimal Auctions for Matroids via Blockchains and Commitments

We consider a revenue-optimizing auctioneer in single-dimensional environments with matroid feasibility constraints. Akbarpour and Li (2020) argue that any revenue-optimal, truthful, and credible mechanism requires unbounded communication. Recent works (Ferreira and Weinberg, 2020; Essaidi et al., 2022; Chitra et al., 2024) circumvent their impossibility for the single-item setting through the use of cryptographic commitments and blockchains. We extend their results to matroid feasibility constraints. At a high level, the two-round Deferred-Revelation Auction (DRA) discussed by Ferreira and Weinberg (2020) and Chitra et al., (2024) requires each bidder to submit a deposit, which is slashed upon presenting verifiable evidence indicating a deviation from the behaviour prescribed by the mechanism. We prove that the DRA satisfies truthfulness, credibility and revenue-optimality for all matroid environments when bidders' values are drawn from $α$-strongly regular distributions for $α> 0$. Further, we argue that the DRA is not credible for any feasibility constraint beyond matroids and for any smaller deposits than suggested by previous literature even in single-item environments. Finally, we modify the Ascending Deferred-Revelation Auction (ADRA) for single-item settings proposed by Essaidi et al., (2022) for arbitrary bidder value distributions. We implement a deferred-revelation variant of the deferred-acceptance auction for matroids due to Bikhchandani et al., (2011), which requires the same bounded communication as the ADRA.

cs.GT

Combinatorial Pen Testing (or Consumer Surplus of Deferred-Acceptance Auctions)

Pen testing is the problem of selecting high-capacity resources when the only way to measure the capacity of a resource expends its capacity. We have a set of $n$ pens with unknown amounts of ink and our goal is to select a feasible subset of pens maximizing the total ink in them. We are allowed to learn about the ink levels by writing with them, but this uses up ink that was previously in the pens. We identify optimal and near optimal pen testing algorithms by drawing analogues to auction theoretic frameworks of deferred-acceptance auctions and virtual values. Our framework allows the conversion of any near optimal deferred-acceptance mechanism into a near optimal pen testing algorithm. Moreover, these algorithms guarantee an additional overhead of at most $(1+o(1)) \ln n$ in the approximation factor to the omniscient algorithm that has access to the ink levels in the pens. We use this framework to give pen testing algorithms for various combinatorial constraints like matroid, knapsack, and general downward-closed constraints, and also for online environments.

cs.GT

Revisiting the Primitives of Transaction Fee Mechanism Design

Transaction Fee Mechanism Design studies auctions run by untrusted miners for transaction inclusion in a blockchain. Under previously-considered desiderata, an auction is considered `good' if, informally-speaking, each party (i.e., the miner, the users, and coalitions of both miners and users) has no incentive to deviate from the fixed and pre-determined protocol. In this paper, we propose a novel desideratum for transaction fee mechanisms. We say that a TFM is off-chain influence proof when the miner cannot achieve additional revenue by running a separate auction off-chain. While the previously-highlighted EIP-1559 is the gold-standard according to prior desiderata, we show that it does not satisfy off-chain influence proofness. Intuitively, this holds because a Bayesian revenue-maximizing miner can strictly increase profits by persuasively threatening to censor any bids that do not transfer a tip directly to the miner off-chain. On the other hand, we reconsider the Cryptographic (multi-party computation assisted) Second Price Auction mechanism, which is technically not `simple for miners' according to previous desiderata (since miners may wish to set a reserve by fabricating bids). We show that, in a slightly different model where the miner is allowed to set the reserve directly, this auction satisfies simplicity for users and miners, and off-chain influence proofness. Finally, we prove a strong impossibility result: no mechanism satisfies all previously-considered properties along with off-chain influence proofness, even with unlimited supply, and even after soliciting input from the miner.

cs.GT

Fundamental Limits of Throughput and Availability: Applications to prophet inequalities & transaction fee mechanism design

This paper studies the fundamental limits of availability and throughput for independent and heterogeneous demands of a limited resource. Availability is the probability that the demands are below the capacity of the resource. Throughput is the expected fraction of the resource that is utilized by the demands. We offer a concentration inequality generator that gives lower bounds on feasible availability and throughput pairs with a given capacity and independent but not necessarily identical distributions of up-to-unit demands. We show that availability and throughput cannot both be poor. These bounds are analogous to tail inequalities on sums of independent random variables, but hold throughout the support of the demand distribution. This analysis gives analytically tractable bounds supporting the unit-demand characterization of Chawla, Devanur, and Lykouris (2023) and generalizes to up-to-unit demands. Our bounds also provide an approach towards improved multi-unit prophet inequalities (Hajiaghayi, Kleinberg, and Sandholm, 2007). They have applications to transaction fee mechanism design (for blockchains) where high availability limits the probability of profitable user-miner coalitions (Chung and Shi, 2023).

cs.GT

Computing Optimal Manipulations in Cryptographic Self-Selection Proof-of-Stake Protocols

Cryptographic Self-Selection is a paradigm employed by modern Proof-of-Stake consensus protocols to select a block-proposing "leader." Algorand [Chen and Micali, 2019] proposes a canonical protocol, and Ferreira et al. [2022] establish bounds $f(α,β)$ on the maximum fraction of rounds a strategic player can lead as a function of their stake $α$ and a network connectivity parameter $β$. While both their lower and upper bounds are non-trivial, there is a substantial gap between them (for example, they establish $f(10\%,1) \in [10.08\%, 21.12\%]$), leaving open the question of how significant of a concern these manipulations are. We develop computational methods to provably nail $f(α,β)$ for any desired $(α,β)$ up to arbitrary precision, and implement our method on a wide range of parameters (for example, we confirm $f(10\%,1) \in [10.08\%, 10.15\%]$). Methodologically, estimating $f(α,β)$ can be phrased as estimating to high precision the value of a Markov Decision Process whose states are countably-long lists of real numbers. Our methodological contributions involve (a) reformulating the question instead as computing to high precision the expected value of a distribution that is a fixed-point of a non-linear sampling operator, and (b) provably bounding the error induced by various truncations and sampling estimations of this distribution (which appears intractable to solve in closed form). One technical challenge, for example, is that natural sampling-based estimates of the mean of our target distribution are \emph{not} unbiased estimators, and therefore our methods necessarily go beyond claiming sufficiently-many samples to be close to the mean.

cs.GT

Fair Healthcare Rationing to Maximize Dynamic Utilities

Allocation of scarce healthcare resources under limited logistic and infrastructural facilities is a major issue in the modern society. We consider the problem of allocation of healthcare resources like vaccines to people or hospital beds to patients in an online manner. Our model takes into account the arrival of resources on a day-to-day basis, different categories of agents, the possible unavailability of agents on certain days, and the utility associated with each allotment as well as its variation over time. We propose a model where priorities for various categories are modelled in terms of utilities of agents. We give online and offline algorithms to compute an allocation that respects eligibility of agents into different categories, and incentivizes agents not to hide their eligibility for some category. The offline algorithm gives an optimal allocation while the on-line algorithm gives an approximation to the optimal allocation in terms of total utility. Our algorithms are efficient, and maintain fairness among different categories of agents. Our models have applications in other areas like refugee settlement and visa allocation. We evaluate the performance of our algorithms on real-life and synthetic datasets. The experimental results show that the online algorithm is fast and performs better than the given theoretical bound in terms of total utility. Moreover, the experimental results confirm that our utility-based model correctly captures the priorities of categories

cs.MA

Disjoint Stable Matchings in Linear Time

We show that given a SM instance G as input we can find a largest collection of pairwise edge-disjoint stable matchings of G in time linear in the input size. This extends two classical results: 1. The Gale-Shapley algorithm, which can find at most two ("extreme") pairwise edge-disjoint stable matchings of G in linear time, and 2. The polynomial-time algorithm for finding a largest collection of pairwise edge-disjoint perfect matchings (without the stability requirement) in a bipartite graph, obtained by combining König's characterization with Tutte's f-factor algorithm. Moreover, we also give an algorithm to enumerate all maximum-length chains of disjoint stable matchings in the lattice of stable matchings of a given instance. This algorithm takes time polynomial in the input size for enumerating each chain. We also derive the expected number of such chains in a random instance of Stable Matching.

cs.DS