@article {2768,
	title = {Improved Measures of Integrated Information},
	journal = {PLOS Computational Biology},
	year = {2016},
	month = {11/2016},
	abstract = {<p>Although\&nbsp; there\&nbsp; is\&nbsp; growing\&nbsp; interest\&nbsp; in\&nbsp; measuring\&nbsp; integrated\&nbsp; information\&nbsp; in\&nbsp; computational\&nbsp; and cognitive systems, current methods for doing so in practice are computationally unfeasible.\&nbsp; Existing and\&nbsp; novel\&nbsp; integration\&nbsp; measures\&nbsp; are\&nbsp; investigated\&nbsp; and\&nbsp; classified\&nbsp; by\&nbsp; various\&nbsp; desirable\&nbsp; properties.\&nbsp;\&nbsp; A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization\&nbsp; method\&nbsp; (5\&nbsp; options),\&nbsp; choice\&nbsp; of\&nbsp; probability\&nbsp; distributions\&nbsp; to\&nbsp; compare\&nbsp; (3 {\texttimes} 4\&nbsp; options) and choice of measure for comparing probability distributions (7 options).\&nbsp; When requiring the Φ- measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical.\&nbsp; Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands.</p>
},
	doi = {10.1371/journal.pcbi.100512310.1371},
	url = {http://dx.plos.org/10.1371/journal.pcbi.1005123},
	author = {Max Tegmark},
	editor = {Seth, Anil}
}
