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).Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991), 8 page
