learning {manynet}

Making learning models on networks

Description

These functions allow learning games to be played upon networks.

• play_learning() plays a DeGroot learning model upon a network.

• play_segregation() plays a Schelling segregation model upon a network.

Usage

play_learning(.data, beliefs, steps, epsilon = 5e-04)

play_segregation(
  .data,
  attribute,
  heterophily = 0,
  who_moves = c("ordered", "random", "most_dissatisfied"),
  choice_function = c("satisficing", "optimising", "minimising"),
  steps
)

Arguments

.data

An object of a manynet-consistent class:

• matrix (adjacency or incidence) from {base} R

• edgelist, a data frame from {base} R or tibble from {tibble}

• igraph, from the {igraph} package

• network, from the {network} package

• tbl_graph, from the {tidygraph} package

beliefs

A vector indicating the probabilities nodes put on some outcome being 'true'.

steps

The number of steps forward in learning. By default the number of nodes in the network.

epsilon

The maximum difference in beliefs accepted for convergence to a consensus.

attribute

A string naming some nodal attribute in the network. Currently only tested for binary attributes.

heterophily

A score ranging between -1 and 1 as a threshold for how heterophilous nodes will accept their neighbours to be. A single proportion means this threshold is shared by all nodes, but it can also be a vector the same length of the nodes in the network for issuing different thresholds to different nodes. By default this is 0, meaning nodes will be dissatisfied if more than half of their neighbours differ on the given attribute.

who_moves

One of the following options: "ordered" (the default) checks each node in turn for whether they are dissatisfied and there is an available space that they can move to, "random" will check a node at random, and "most_dissatisfied" will check (one of) the most dissatisfied nodes first.

choice_function

One of the following options: "satisficing" (the default) will move the node to any coordinates that satisfy their heterophily threshold, "optimising" will move the node to coordinates that are most homophilous, and "minimising" distance will move the node to the next nearest unoccupied coordinates.

See Also

Other makes: create, generate, play, read, write()

Other models: play

Examples

  play_learning(ison_networkers, 
      rbinom(net_nodes(ison_networkers),1,prob = 0.25))
  startValues <- rbinom(100,1,prob = 0.5)
  startValues[sample(seq_len(100), round(100*0.2))] <- NA
  latticeEg <- create_lattice(100)
  latticeEg <- add_node_attribute(latticeEg, "startValues", startValues)
  latticeEg
  play_segregation(latticeEg, "startValues", 0.5)
  # graphr(latticeEg, node_color = "startValues", node_size = 5) + 
  # graphr(play_segregation(latticeEg, "startValues", 0.2), 
  #            node_color = "startValues", node_size = 5)