🤖 AI Summary
This study investigates the computational complexity of achieving envy-free allocations by adding items when item supplies are limited and agents face no individual budget constraints. Focusing on binary additive valuations, the work establishes a sharp dichotomy based on the number of added item types: a polynomial-time algorithm exists for a single item type, whereas the problem becomes NP-complete with just two item types—even weakly NP-complete for only two agents. The approach models envy relations via a system of difference constraints and employs the Bellman-Ford algorithm to compute a minimal feasible extension. Complexity reductions further delineate the hardness across multiple scenarios. This work fully resolves the open two-type case posed by Bentert et al. and establishes the first precise computational complexity boundary for this setting.
📝 Abstract
We study envy elimination by adding goods (EEAG) when the additional pool has bounded supply and no separate budget bound. We establish a sharp type-count dichotomy for binary additive valuations. With one additional item type, EEAG is polynomial-time solvable for any number of agents. More generally, our algorithm permits arbitrary nonnegative integer per-copy values. The envy constraints form a system of difference constraints, and Bellman--Ford returns the componentwise least feasible extension. In contrast, with exactly two additional item types, EEAG is \textsf{NP}-complete even when both types have positive finite supply and the approvers of one type form a subset of the approvers of the other. This closes the two-type case left open by Bentert et al. Separately, we prove weak \textsf{NP}-completeness even for two agents with identical additive valuations, one initially endowed good, and a growing number of unit-supply item types. Thus, bounded-supply hardness appears both with two item-types and many agents and with two agents and many item-types.