Macroscopes: models for collective decision making
Subramanian Ramamoorthy, Andr\'as Z. Salamon, Rahul Santhanam

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…
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Taxonomy
TopicsGame Theory and Applications · Distributed systems and fault tolerance · Computability, Logic, AI Algorithms
