Steve Yeh

A principal assigns workers to projects by capping the effort they may exert. Workers differ in their preferences for projects, enjoy collaborating, but find spreading effort across projects costly. Optimal project assignments constitute a nested hierarchy. Each worker’s assignment is derived from the same ranking of projects— the principal’s marginal value in the optimum— but with two idiosyncratic cutoffs: he is unconstrained at projects above the first, capped between the two, and unassigned below the second. The principal tends to cap a worker where he most prefers to work. Shocks propagate through the network induced by the project assignment. An improvement local to one project can degrade every other project in the organization.

In conventional network games, each player chooses one action that impacts all of her neighbors. We study players that select neighbor-specific efforts and face a convex total effort cost. Our main result highlights a striking reversal in comparative statics: When bilateral efforts are strategic complements (substitutes), shocks propagate through the network in a pattern befitting strategic substitutes (complements) in standard network games. This reversal, together with other fundamental changes in player behavior, suggests that retaining the single effort assumption when it is not appropriate may lead to poor policy recommendations.

I propose a stability concept for network formation under incomplete information. A network is plausibly pairwise stable if players’ beliefs are consistent with common knowledge of network stability. I provide a general condition under which plausibly pairwise stable networks exhibit clustering. In a simple model of vertical differentiation, I characterize minimal plausibly pairwise stable networks. I show that incomplete information can stabilize segregated and sparse networks that are unstable under complete information. I discuss how incomplete information resolves the tension between stability and efficiency in the presence of negative linking externalities.

A principal assigns agents to tasks in a multi-task project. Agents tend to shirk at their tasks and rely on others’ efforts because effort on one task substitutes for effort on similar tasks. Assigning the same agent to more tasks mitigates free-riding, but convex effort costs prevent him from completing all of his tasks. For a class of projects, an optimal task assignment assigns each agent to a module comprising a task central to the project and all locally related tasks. For general projects, modular task assignments are approximately optimal with a performance guarantee that depends on a project's task structure.