Incentive-Compatible Experimental Design

TL;DR

This paper develops a framework for designing experiments involving self-interested agents, ensuring incentive compatibility so that agents act naturally, and analyzes the power of such designs under various conditions.

Contribution

It introduces conditions for incentive-compatible experimental designs that account for strategic behavior and interference among agents, using statistical methods and asymptotic analysis.

Findings

Conditions for incentive compatibility using performance statistics.

Strategies to improve the power of incentive-compatible experiments.

Analysis of the power in non-interfering settings.

Abstract

We consider the design of experiments to evaluate treatments that are administered by self-interested agents, each seeking to achieve the highest evaluation and win the experiment. For example, in an advertising experiment, a company wishes to evaluate two marketing agents in terms of their efficacy in viral marketing, and assign a contract to the winner agent. Contrary to traditional experimental design, this problem has two new implications. First, the experiment induces a game among agents, where each agent can select from multiple versions of the treatment it administers. Second, the action of one agent -- selection of treatment version -- may affect the actions of another agent, with the resulting strategic interference complicating the evaluation of agents. An incentive-compatible experiment design is one with an equilibrium where each agent selects its natural action, which is the action that would maximize the performance of the agent if there was no competition (e.g., expected number of conversions if agent was assigned the contract). Under a general formulation of experimental design, we identify sufficient conditions that guarantee incentive-compatible experiments. These conditions rely on the existence of statistics that can estimate how agents would perform without competition, and their use in constructing score functions to evaluate the agents. In the setting with no strategic interference, we also study the power of the design, i.e., the probability that the best agent wins, and show how to improve the power of incentive-compatible designs. From the technical side, our theory uses a range of statistical methods such as hypothesis testing, variance-stabilizing transformations and the Delta method, all of which rely on asymptotics.