Information flow between components of a system takes many forms and is key
to understanding the organization and functioning of large-scale, complex
systems. We demonstrate three modalities of information flow from time series X
to time series Y. Intrinsic information flow exists when the past of X is
individually predictive of the present of Y, independent of Y's past; this is
most commonly considered information flow. Shared information flow exists when
X's past is predictive of Y's present in the same manner as Y's past; this
occurs due to synchronization or common driving, for example. Finally,
synergistic information flow occurs when neither X's nor Y's pasts are
predictive of Y's present on their own, but taken together they are. The two
most broadly-employed information-theoretic methods of quantifying information
flow---time-delayed mutual information and transfer entropy---are both
sensitive to a pair of these modalities: time-delayed mutual information to
both intrinsic and shared flow, and transfer entropy to both intrinsic and
synergistic flow. To quantify each mode individually we introduce our
cryptographic flow ansatz, positing that intrinsic flow is synonymous with
secret key agreement between X and Y. Based on this, we employ an
easily-computed secret-key-agreement bound---intrinsic mutual
information&mdashto quantify the three flow modalities in a variety of systems
including asymmetric flows and financial markets.