Emergence (coarse-graining and blackboxing)¶
>>> import pyphi >>> network = pyphi.examples.macro_network()
The connectivity matrix is all-to-all:
>>> network.cm array([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]])
We’ll set the state so that nodes are OFF.
>>> state = (0, 0, 0, 0)
At the “micro” spatial scale, we can compute the major complex, and determine the \(\Phi\) value:
>>> major_complex = pyphi.compute.major_complex(network, state) >>> major_complex.phi 0.113889
The question is whether there are other spatial scales which have greater values of \(\Phi\). This is accomplished by considering all possible coarse-graining of micro-elements to form macro-elements. A coarse-graining of nodes is any partition of the elements of the micro system. First we’ll get a list of all possible coarse-grainings:
>>> grains = list(pyphi.macro.all_coarse_grains(network.node_indices))
We start by considering the first coarse grain:
>>> coarse_grain = grains
CoarseGrain has two attributes: the
partition of states into macro
elements, and the
grouping of micro-states into macro-states. Let’s first
look at the partition:
>>> coarse_grain.partition ((0, 1, 2), (3,))
There are two macro-elements in this partition: one consists of micro-elements
(0, 1, 2) and the other is simply micro-element
We must then determine the relationship between micro-elements and macro-elements. When coarse-graining the system we assume that the resulting macro-elements do not differentiate the different micro-elements. Thus any correspondence between states must be stated solely in terms of the number of micro-elements which are ON, and not depend on which micro-elements are ON.
For example, consider the macro-element
(0, 1, 2). We may say that the
macro-element is ON if at least one micro-element is ON, or if all
micro-elements are ON; however, we may not say that the macro-element is ON if
1 is ON, because this relationship involves identifying
grouping attribute of the
CoarseGrain describes how the state of
micro-elements describes the state of macro-elements:
>>> grouping = coarse_grain.grouping >>> grouping (((0, 1, 2), (3,)), ((0,), (1,)))
The grouping consists of two lists, one for each macro-element:
>>> grouping ((0, 1, 2), (3,))
For the first macro-element, this grouping means that the element will be OFF if zero, one or two of its micro-elements are ON, and will be ON if all three micro-elements are ON.
>>> grouping ((0,), (1,))
For the second macro-element, the grouping means that the element will be OFF if its micro-element is OFF, and ON if its micro-element is ON.
One we have selected a partition and grouping for analysis, we can create a mapping between micro-states and macro-states:
>>> mapping = coarse_grain.make_mapping() >>> mapping array([0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 3])
The interpretation of the mapping uses the little-endian convention of indexing (see Little-endian convention).
>>> mapping 1
This says that micro-state 7 corresponds to macro-state 1:
>>> pyphi.convert.le_index2state(7, 4) (1, 1, 1, 0)
>>> pyphi.convert.le_index2state(1, 2) (1, 0)
In micro-state 7, all three elements corresponding to the first macro-element are ON, so that macro-element is ON. The micro-element corresponding to the second macro-element is OFF, so that macro-element is OFF.
CoarseGrain object uses the mapping internally to create a state-by-state
TPM for the macro-system corresponding to the selected partition and grouping
>>> coarse_grain.macro_tpm(network.tpm) Traceback (most recent call last): ... pyphi.exceptions.ConditionallyDependentError...
However, this macro-TPM does not satisfy the conditional independence
assumption, so this particular partition and grouping combination is not a
valid coarse-graining of the system. Constructing a
MacroSubsystem with this
coarse-graining will also raise a
Let’s consider a different coarse-graining instead.
>>> coarse_grain = grains >>> coarse_grain.partition ((0, 1), (2, 3)) >>> coarse_grain.grouping (((0, 1), (2,)), ((0, 1), (2,)))
>>> mapping = coarse_grain.make_mapping() >>> mapping array([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 2, 3])
>>> coarse_grain.macro_tpm(network.tpm) array([[[0.09, 0.09], [1. , 0.09]], [[0.09, 1. ], [1. , 1. ]]])
We can now construct a
MacroSubsystem using this coarse-graining:
>>> macro_subsystem = pyphi.macro.MacroSubsystem( ... network, state, network.node_indices, coarse_grain=coarse_grain) >>> macro_subsystem MacroSubsystem((m0, m1))
We can then consider the integrated information of this macro-network and compare it to the micro-network.
>>> macro_sia = pyphi.compute.sia(macro_subsystem) >>> macro_sia.phi 0.597212
The integrated information of the macro subsystem (\(\Phi = 0.597212\)) is greater than the integrated information of the micro system (\(\Phi = 0.113889\)). We can conclude that a macro-scale is appropriate for this system, but to determine which one, we must check all possible partitions and all possible groupings to find the maximum of integrated information across all scales.
>>> M = pyphi.macro.emergence(network, state) >>> M.emergence 0.483323 >>> M.system (0, 1, 2, 3) >>> M.coarse_grain.partition ((0, 1), (2, 3)) >>> M.coarse_grain.grouping (((0, 1), (2,)), ((0, 1), (2,)))
The analysis determines the partition and grouping which results in the maximum value of integrated information, as well as the emergence (increase in \(\Phi\)) from the micro-scale to the macro-scale.
macro module also provides tools for studying the emergence of systems
>>> import pyphi >>> network = pyphi.examples.blackbox_network()
We consider the state where all nodes are OFF:
>>> state = (0, 0, 0, 0, 0, 0) >>> all_nodes = (0, 1, 2, 3, 4, 5)
The system has minimal \(\Phi\) without blackboxing:
>>> subsys = pyphi.Subsystem(network, state, all_nodes) >>> pyphi.compute.phi(subsys) 0.215278
We will consider the blackbox system consisting of two blackbox elements, \(ABC\) and \(DEF\), where \(C\) and \(F\) are output elements and \(AB\) and \(DE\) are hidden within their respective blackboxes.
>>> partition = ((0, 1, 2), (3, 4, 5)) >>> output_indices = (2, 5) >>> blackbox = pyphi.macro.Blackbox(partition, output_indices)
Blackboxes have a few convenient attributes and methods. The
attribute returns the elements which are hidden within blackboxes:
>>> blackbox.hidden_indices (0, 1, 3, 4)
micro_indices attribute lists all the micro-elements in the box:
>>> blackbox.micro_indices (0, 1, 2, 3, 4, 5)
macro_indices attribute generates a set of indices which index the
blackbox macro-elements. Since there are two blackboxes in our example, and
each has one output element, there are two macro-indices:
>>> blackbox.macro_indices (0, 1)
macro_state method converts a state of the micro elements to the state
of the macro-elements. The macro-state of a blackbox system is simply the state
of the system’s output elements:
>>> micro_state = (0, 0, 0, 0, 0, 1) >>> blackbox.macro_state(micro_state) (0, 1)
Let us also define a time scale over which to perform our analysis:
>>> time_scale = 2
As in the coarse-graining example, the blackbox and time scale are passed to
>>> macro_subsystem = pyphi.macro.MacroSubsystem(network, state, all_nodes, ... blackbox=blackbox, ... time_scale=time_scale)
We can now compute \(\Phi\) for this macro system:
>>> pyphi.compute.phi(macro_subsystem) 0.638888
We find that the macro subsystem has greater integrated information (\(\Phi = 0.638888\)) than the micro system (\(\Phi = 0.215278\))—the system demonstrates emergence.