We investigate an assignment market in which multiple objects are assigned, together with associated payments, to a group of agents with unit demand preferences. Preferences over bundles, the pairs of (object, payment), accommodate income effects. Among all (Walrasian) equilibria in such a...

We study an assignment market where multiple heterogenous objects are sold to unit demand agents who have general preferences accommodating imperfect transferability of utility and income effects. In such a model, there is a minimum price equilibrium. We establish the structural...

In multi-object auction models with unitary demand agents, if agents’ utility functions satisfy quasi-linearity, three auction formats, sealed-bid auction, exact ascending auction, and approximate ascending auction, are known to identify the minimum price equilibrium (MPE), and exhibit...

We study the slot allocation problem where agents have quasi-linear single-peaked preferences over slots and identify the rules satisfying efficiency, strategy-proofness, and individual rationality. Since the quasi-linear single-peaked domain is not connected, the famous characterization of the...

We consider the allocation problem of assigning heterogenous objects to a group of agents and determining how much they should pay. Each agent receives at most one object. Agents have non-quasi-linear preferences over bundles, each consisting of an object and a payment. Especially, we focus on...

We investigate an assignment market where multiple objects are assigned, together with associated payments, to a group of agents with unit demand preferences. Preferences over bundles, the pairs of (object, payment), accommodate income effects. Among all (Walrasian) equilibria in such a market,...

We consider the allocation problem of assigning heterogeneous objects to a group of agents and determining how much they should pay. Each agent receives at most one object. Agents have non-quasi-linear preferences over bundles, each consisting of an object and a payment. Especially, we focus on...