einet (version 0.1.0)

causal_emergence: Causal Emergence

Description

Given a microscale network, G, this function iteratively checks different coarse-grainings to see if it finds one with higher effective information.

Usage

causal_emergence(x, ...)

Arguments

x

igraph or matrix object.

...

Span, and threshold parameters

Value

A list with letters and numbers.

• g_micro - Graph of original micro-scale network.

• g_macro - Graph of macro-scale network.

• mapping - list mapping from micro to macro scales giving the largest increase in effective information.

• ei_macro - Effective information of macro scale network.

• ei_micro - Effective information of micro scale network.

• ce - Numerical value for causal emergence.

Examples

# NOT RUN {
graph <- matrix(
  cbind(
    c(0.0, 1.0, 0.0, 0.0),
    c(0.0, 0.0, 1.0, 0.0),
    c(0.0, 0.0, 0.0, 1.0),
    c(0.0, 0.0, 0.0, 0.0)
  ),
 nrow = 4
) %>%
  igraph::graph.adjacency(mode = "directed")

causal_emergence(graph)

# }