Exact cuts needed for fair cake shares with equal lengths
Exact Cut Complexity of Equal-Length Proportional Cake Cutting
Multiagent SystemsArtificial IntelligenceData Structures and AlgorithmsComputer Science and Game Theory
Summary
This paper looks at how to fairly cut a cake into equal-length pieces so each person values their piece as fair. The authors found the exact minimum number of straight cuts needed to guarantee this, improving previous overestimates. They also show that it’s impossible to always find such a perfect division quickly, but approximate fair divisions can be made efficiently. Extensions include cases where no one envies another’s piece and new methods when using two 'knife' queries.
What this means in practice
- •For fair division software developers: Implement precise or approximate algorithms to fairly divide resources of fixed size among multiple users with minimal cuts.
- •For resource allocation system designers: Use complexity bounds to estimate the minimum operations needed to fairly allocate continuous divisible resources under strict size constraints.
A theory result. No direct application yet.
Authors
Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui
Abstract
We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a bundle of length exactly $1/n$ that is worth at least $1/n$ to that agent. Our main result determines the exact worst-case number of cuts needed to guarantee such an allocation, namely $2n-2$ for every $n$. This improves on the previously known quadratic upper bound. The proof combines the constrained necklace-splitting theorem with a recursive assignment argument based on Hall's theorem. The same bound extends to the stronger requirement of envy-freeness when $n$ is a prime power, and an $O(n^{1.525})$ upper bound holds for arbitrary $n$. We then ask whether such allocations can be computed. We first show that no deterministic protocol in the Robertson--Webb model terminates after finitely many queries on every instance and always returns an exact equal-length proportional allocation, even for two agents with identical valuations. For every $\varepsilon>0$, however, $O(n^2/\varepsilon)$ queries suffice to produce an allocation in which every agent receives a bundle of length exactly $1/n$ and value at least $1/n-\varepsilon$, again using at most $2n-2$ cuts. Finally, we augment the Robertson--Webb model with two-knife queries. We show that recursive balanced halving attains the optimal bound of $2n-2$ cuts when $n$ is a power of two. For arbitrary $n$, we give a procedure using $O(n\log n)$ cuts.