Information Theoretic cutting of a cake

TL;DR

This paper applies information theory to analyze fair division algorithms like 'divide and choose' and 'adjusted winner', exploring the role of partial information, implicit communication, and welfare maximization in resource allocation.

Contribution

It introduces an information-theoretic perspective to fair division, analyzing negotiation benefits, implicit information transmission, and welfare optimization in resource allocation algorithms.

Findings

Quantifies negotiation benefits in divide and choose.

Analyzes implicit information transmission via actions.

Provides an upper bound on welfare increase in clustering.

Abstract

Cutting a cake is a metaphor for the problem of dividing a resource (cake) among several agents. The problem becomes non-trivial when the agents have different valuations for different parts of the cake (i.e. one agent may like chocolate while the other may like cream). A fair division of the cake is one that takes into account the individual valuations of agents and partitions the cake based on some fairness criterion. Fair division may be accomplished in a distributed or centralized way. Due to its natural and practical appeal, it has been a subject of study in economics. To best of our knowledge the role of partial information in fair division has not been studied so far from an information theoretic perspective. In this paper we study two important algorithms in fair division, namely "divide and choose" and "adjusted winner" for the case of two agents. We quantify the benefit of negotiation in the divide and choose algorithm, and its use in tricking the adjusted winner algorithm. Also we analyze the role of implicit information transmission through actions for the repeated divide and choose problem by finding a trembling hand perfect equilibrium for an specific setup. Lastly we consider a centralized algorithm for maximizing the overall welfare of the agents under the Nash collective utility function (CUF). This corresponds to a clustering problem of the type traditionally studied in data mining and machine learning. Drawing a conceptual link between this problem and the portfolio selection problem in stock markets, we prove an upper bound on the increase of the Nash CUF for a clustering refinement.