We consider a package assignment problem with money, in which a set M of objects is allocated to agents. Each agent has preferences that are not necessarily quasi-linear. The admissible set of object allocations is chosen by the planner to pursue specific objectives in conjunction with the rule....

We consider a package assignment problem with money, in which a set M of objects is allocated to agents. Each agent has preferences that are not necessarily quasi-linear. The admissible set of object allocations is chosen by the planner to pursue specific objectives in conjunction with the rule....

We consider the problem of allocating heterogeneous objects to agents with money, where the number of agents exceeds that of objects. Each agent can receive at most one object, and some objects may remain unallocated. A bundle is a pair consisting of an object and a payment. An agent's...

We consider the problem of allocating heterogeneous objects to agents with money, where the number of agents exceeds that of objects. Each agent can receive at most one object, and some objects may remain unallocated. A bundle is a pair consisting of an object and a payment. An agent's...

We consider the problem of allocating multiple units of an indivisible object among agents and collecting payments. Each agent can receive multiple units of the object, and his (consumption) bundle is a pair of the units he receives and his payment. An agent's preference over bundles may be...

We consider the problem of allocating multiple units of an indivisible object among agents and collecting payments. Each agent can receive multiple units of the object, and his (consumption) bundle is a pair of the units he receives and his payment. An agent's preference over bundles may be...

This paper presents a combinatorial auction which is of particular interest when short completion times are of importance. It is based on a method for approximating the bidders' preferences over two types of items when complementarity between the two may exist. The resulting approximated...