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--- proyectos:tfg:bibliografia:flack2017 [2017/11/08 02:20]
Joaquín Herrero Pintado
+++ proyectos:tfg:bibliografia:flack2017 [2018/05/25 16:44] (actual)
Joaquín Herrero Pintado
@@ Línea 1: / Línea 1: @@
-====== Flack, J., Life’s Information Theory (2017) ======
+====== Flack, Jessica, Life’s Information Theory (2017) ======
-en mari Walker, S. (ed), 2017, From Matter to Life – Information and Causality
+en Smari Walker, S. (ed), 2017, From Matter to Life – Information and Causality
+I propose that biological systems are information hierarchies organized into
+multiple functional space and time scales. This multi-scale structure results
+from the collective effects of components estimating, in evolutionary or
+ecological time, regularities in their environments by coarse-graining or
+compressing time-series data and using these perceived regularities to tune
+strategies. As coarse-grained (slow) variables become for components better
+predictors than microscopic behavior (which fluctuates), and component
+estimates of these variables converge, new levels of organization consolidate.
+This process gives the appearance of downward causation – as components
+tune to the consolidating level, variance at the component level decreases.
+Because the formation of new levels results from an interaction between
+component capacity for regularity extraction, consensus formation, and how
+structured the environment is, the new levels, and the macroscopic, slow
+variables describing them, are characterized by intrinsic subjectivity. Hence
+the process producing these variables is perhaps best viewed as a locally
+optimized collective computation performed by system components in their
+search for configurations that reduce environmental uncertainty. If this view
+is correct, identifying important, functional macroscopic variables in
+biological systems will require an understanding of biological computation. I
+will discuss how we can move toward identifying laws in biology by
+studying the computation inductively. This includes strategy extraction from
+data, construction of stochastic circuits that map micro to macro, dimensionreduction
+techniques to move toward an algorithmic theory for the
+macroscopic output, methods for quantifying circuit collectivity, and
+macroscopic tuning and control.

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