In this section
@inproceedings{Rosas:2020:10.1007/978-3-030-64919-7_19,
author = {Rosas, De Andraca FE and Mediano, P and Biehl, M and Chandaria, S and Polani, D},
doi = {10.1007/978-3-030-64919-7_19},
pages = {187--198},
publisher = {Springer},
title = {Causal blankets: theory and algorithmic framework},
url = {http://dx.doi.org/10.1007/978-3-030-64919-7_19},
year = {2020}
}
TY - CPAPER
AB - We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics—i.e. as the “differences that make a difference.” Furthermore, our theory provides a broadly applicable procedure to construct PALOs that requires neither a steady-state nor Markovian dynamics. Using our theory, we show that every bipartite stochastic process has a causal blanket, but the extent to which this leads to an effective PALO formulation varies depending on the integrated information of the bipartition.
AU - Rosas,De Andraca FE
AU - Mediano,P
AU - Biehl,M
AU - Chandaria,S
AU - Polani,D
DO - 10.1007/978-3-030-64919-7_19
EP - 198
PB - Springer
PY - 2020///
SN - 1865-0929
SP - 187
TI - Causal blankets: theory and algorithmic framework
UR - http://dx.doi.org/10.1007/978-3-030-64919-7_19
UR - http://hdl.handle.net/10044/1/115694
ER -