# models.actual_causation¶

Objects that represent structures used in actual causation.

class pyphi.models.actual_causation.AcMip

A minimum information partition for ac_coef calculation.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\alpha$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared.

alpha

float – This is the difference between the mechanism’s unpartitioned and partitioned actual probability.

state

tuple[int] – state of system in specified direction (past, future)

direction

str – The temporal direction specifiying whether this AcMIP should be calculated with cause or effect repertoires.

mechanism

tuple[int] – The mechanism over which to evaluate the AcMIP.

purview

tuple[int] – The purview over which the unpartitioned actual probability differs the least from the actual probability of the partition.

partition

tuple[Part, Part] – The partition that makes the least difference to the mechanism’s repertoire.

probability

float – The probability of the state in the past/future.

partitioned_probability

float – The probability of the state in the partitioned repertoire.

Create new instance of AcMip(alpha, state, direction, mechanism, purview, partition, probability, partitioned_probability)

unorderable_unless_eq = ['direction']
order_by()
__bool__()

An AcMip is True if it has $$\alpha > 0$$.

phi
to_json()
class pyphi.models.actual_causation.Occurence(mip)

A maximally irreducible actual cause or effect.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\alpha$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared.

alpha

float – The difference between the mechanism’s unpartitioned and partitioned actual probabilities.

phi
direction

Direction – Either PAST or FUTURE.

mechanism

list[int] – The mechanism for which the action is evaluated.

purview

list[int] – The purview over which this mechanism’s $$\alpha$$ is maximal.

mip

AcMip – The minimum information partition for this mechanism.

unorderable_unless_eq = ['direction']
order_by()
__bool__()

An Occurence is True if $$\alpha > 0$$.

to_json()
class pyphi.models.actual_causation.Event

A mechanism which has both an actual cause and an actual effect.

actual_cause

Occurence – The actual cause of the mechanism.

actual_effect

Occurence – The actual effect of the mechanism.

Create new instance of Event(actual_cause, actual_effect)

mechanism
class pyphi.models.actual_causation.Account

The set of occurences with $$\alpha > 0$$ for both PAST and FUTURE.

class pyphi.models.actual_causation.DirectedAccount

The set of occurences with $$\alpha > 0$$ for one direction of a context.

class pyphi.models.actual_causation.AcBigMip(alpha=None, direction=None, unpartitioned_account=None, partitioned_account=None, context=None, cut=None)

A minimum information partition for $$A$$ calculation.

These can be compared with the built-in Python comparison operators (<, >, etc.). First, $$\alpha$$ values are compared. Then, if these are equal up to PRECISION, the size of the mechanism is compared.

alpha

float – The $$A$$ value for the subsystem when taken against this MIP, i.e. the difference between the unpartitioned constellation and this MIP’s partitioned constellation.

unpartitioned_constellation

tuple[Concept] – The constellation of the whole subsystem.

partitioned_constellation

tuple[Concept] – The constellation when the subsystem is cut.

subsystem

Subsystem – The subsystem this MIP was calculated for.

cut

The minimal cut.

before_state

Return the actual past state of the Context.

after_state

Return the actual current state of the Context.

unorderable_unless_eq = ['direction']
order_by()
__bool__()

An AcBigMip is True if it has $$A > 0$$.