Abstract

Many grant applications have a preliminary stage where only a select group are invited to submit a full application. Similarly, procurement contracts by governments are often awarded through a two-stage procedure. We model and analyze such environments where the designer cares about the style of the application as well as its quality. The designer has the option of choosing an initial stage, where contestants can enter and learn about their desirability while the designer learns about their style. We determine closed form solutions for equilibrium outcomes and designer payoffs and use this to analyze design questions regarding whether or not a second stage is desirable, different rules for deciding who will advance, as with whether or not to communicate the number of contestants that qualify for the second stage.

Highlights

  • There are many examples of contests run in two stages

  • The designer can choose between two qualifying rules: (1) all those that are discovered to have a desired style advance, or (2) of the contestants eligible to move to the second stage, two randomly advance

  • For consistency when comparing to when there is a unique equilibrium, we focus on the symmetric equilibrium.) each contestant bids according to the distribution function: Fi (x) =

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Summary

Introduction

There are many examples of contests run in two stages. Often grant applications have a preliminary stage where only some of the applications advance to the second stage. We assume for simplicity that the first stage is limited in scope such that a contestant can either put in the effort required for the designer to determine his or her type or not. This matches many real world contests where the first stage is meant to weed out those with an inappropriate style. The contribution of our paper is adding the possibility of a two-stage design to a contest where the designer cares about style as well as quality and can reveal information about the number of contestants.

The contest environment
Benchmark case: one stage
Two stages: all pass
All pass
Two stages: random two pass
Random two
Ranking the designs
Random two pass versus all pass
Minimum effort m versus 2m
Informing or not informing
One stage versus two stages
Robustness
Findings
Discussion and conclusion
Full Text
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