Macroscopes: models for collective decision making

TL;DR

This paper introduces the macroscope model for collective decision making where parties with partial, overlapping information make global decisions without first creating a complete view, capturing real-world complexities.

Contribution

It formalizes the macroscope model using communication complexity and provides general and application-specific results for efficient collective decision making.

Findings

Efficient solutions depend on the allotment structure and meta-information.

Results apply to functions like change detection and statistical property computation.

The model is relevant to financial markets and sensor networks.

Abstract

We introduce a new model of collective decision making, when a global decision needs to be made but the parties only possess partial information, and are unwilling (or unable) to first create a globalcomposite of their local views. Our macroscope model captures two key features of many real-world problems: allotment structure (how access to local information is apportioned between parties, including overlaps between the parties) and the possible presence of meta-information (what each party knows about the allotment structure of the overall problem). Using the framework of communication complexity, we formalize the efficient solution of a macroscope. We present general results about the macroscope model, and also results that abstract the essential computational operations underpinning practical applications, including in financial markets and decentralized sensor networks. We illustrate the computational problem inherent in real-world collective decision making processes using results for specific functions, involving detecting a change in state (constant and step functions), and computing statistical properties (the mean).