SearcharxivSearch

arXiv · 2608.01527

What Multichoice Values Cannot See: The Information Content of Anonymous Values for Games with Graded Participation

Abstract

In a cooperative game with graded participation, each of $n$ players acts at one of $m$ ordered levels; multichoice games, voting with abstention, and graded feature attribution all take this form. We determine exactly what the entire family of linear, player-symmetric values, power indices, and importance measures proposed for this setting, present and future, can and cannot see, for all $m$ at once. The mechanism is short: the functional computing any one player's payoff under an anonymous value is invariant under permutations of the other $n-1$ players, and by the branching rule such invariants exist only in the Specht constituents $(n)$ and $(n-1,1)$ of $(\mathbb{C}^m)^{\otimes n}$. Consequences: the joint information of all anonymous values is a component of dimension polynomial in $n$ against an $m^n$-dimensional game space, with the classical binary theory as the $m=2$ shadow. Three players can hide: for $m\ge3$ the blind space is nonzero already at $n=3$, and the first invisible constituent is not a correlation but a chirality, realized by two distinct monotone abstention-voting rules on three voters that every anonymous power index scores identically. For $n\ge4$ the blind space is spanned by $\pm1$ games on four profiles each. Order-$d$ interaction probes see exactly the partitions with at most $d$ cells outside the first row, with full recovery only at $d=n-\lceil n/m\rceil$. Audit evasion gets easier than in the binary theory: a coalition of $c$ players evades every order-$d$ audit iff $c-\lceil c/m\rceil\ge d+1$, so with three or more levels, trios can restructure invisibly to every value-based payment scheme. Algorithmically, the visible part of a game is a polynomial-size, cheaply estimable sketch, while exact computation of any full-support value requires all $m^n-1$ nonzero queries. All dimension and rank claims are verified computationally.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matthew Fried. 2026-08-02. What Multichoice Values Cannot See: The Information Content of Anonymous Values for Games with Graded Participation. https://arxiv.org/abs/2608.01527

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

KEEP EXPLORING

Related papers

An Algorithm-to-Contract Framework without Demand Queries

Consider costly and time-consuming tasks that add up to the success of a project, and must be fitted into a given time-frame. This is an instance of the classic budgeted maximization (knapsack) problem, which admits an FPTAS. Now assume an agent is performing these tasks on behalf of a principal, who is the one to reap the rewards if the project succeeds. The principal must design a contract to incentivize the agent. Is there still an approximation scheme? In this work we lay the foundations for an algorithm-to-contract framework, which transforms algorithms for combinatorial problems to handle contract design problems subject to the same combinatorial constraints. Our approach diverges from previous works in avoiding the assumption of demand oracle access. As an example, for budgeted maximization, we show how to "lift" the classic FPTAS to the best-possible (approximately-IC) FPTAS for the contract problem. We establish this through our local-to-global framework, in which the local step is to approximately solve a two-sided strengthened variant of the demand problem. The global step then utilizes the local one to find the approximately optimal contract. We apply our framework to a host of combinatorial constraints: multi-dimensional budgets, budgeted matroid, and budgeted matching constraints. In all cases we essentially match the best purely algorithmic approximation. Separately, we also develop a method for multi-agent contract settings. Our method yields the first approximation schemes for multi-agent contract settings that go beyond additive reward functions.

cs.GT

Information Requirements for Service Allocation and Aggregate Verification

A service system may use the same categories to assign standard allocations and to check whether each service is fulfilled. Finer categories can match individual needs more closely, but they divide the observations available for monitoring. We study this conflict for a fixed menu from which participants select by declaring a category, with fulfilment assessed from each category's aggregate outcomes during a fixed period. We relate allocation loss to variation in preferred allocations within categories and identify conditions under which aggregate observations preserve the verification performance of individual records. For nested refinements under stated utility and observation assumptions, an allocation-loss tolerance and a per-category detection target define a feasibility band. Categories must be fine enough to provide suitable allocations but sufficiently populated to support verification. A source-dependent lower bound on declaration entropy and a minimum contributor requirement give necessary information and population constraints. For an explicit finite population with quadratic utility and binary service outcomes, we prove the exact feasible range across all categorical designs and exhibit designs attaining the information lower bound at specified tolerances. The results provide conditions for choosing categories jointly for allocation and verification.

cs.GT

Simultaneous Envy and Equitability Guarantees

Recent work in fair division has focused on either simultaneously satisfying closely related fairness notions or achieving a single notion across the ex-ante and ex-post worlds. We study the compatibility of two fundamentally different fairness notions: envy-freeness and equitability. For indivisible goods-only and chores-only settings, we study the existence and complexity of simultaneously satisfying their relaxations, revealing sharp contrasts between the two settings. We show that EF1+EQ1 may fail to exist even for normalized binary goods: we construct an instance with 113 agents and 341 goods in which every agent approves exactly 165 goods, but no complete allocation satisfies both notions. Our main algorithmic result computes an EF1+EQ1 allocation for every normalized binary goods instance with at most seven agents. Thus, the smallest number of agents admitting a counterexample lies between 8 and 113, leaving the cases from 8 through 112 unresolved. In sharp contrast, binary chores admit the stronger EFX+EQX guarantee for any number of agents, even without normalization. We further initiate the study of cross-notion ex-ante and ex-post guarantees, asking whether randomized allocations can provide ex-ante guarantees for one notion while preserving ex-post guarantees for another.

cs.GT