Determines the (normative) value of the nodes in a map, by running through the entire map a number of times

This function is used to determine to what extent the nodes (concepts) in a CM are considered to be positive (are supported), negative (not supported) or ambiguous (has positive and negative consequences) as derived from the argumentation in the map. It determines the evaluation of a node (cause-concept) by analysing its outgoing relations (consequent paths) taking into account the initial value (positive, negative, ambiguous) of the nodes in the consequent path (effect-concepts) and the sign (positive, negative, non-existent) of the relation between the node (cause-concept) and the nodes in it's consequent paths (effect-concepts). If a node (cause-concept) is positively linked to a consequent node (effect-concept) which is valued positively (a contributes positively to b and b is seen as a positive thing); then logically the node (cause-concept) is also regarded as positive. A negative relation to a positive consequent node (effect-concept) (a diminishes b, while b is seen as a positive thing) logically leads to the conclusion that the node (cause-concept) is valued negatively. A negative relation to a negatively valued node (effect-concept) suggest that the cause-concept positive. The function takes the dyads of nodes (cause and effect-concept) and determines the value of all cause-concepts. As nodes may have multiple consequent paths, that may lead to different conclusions as to the value of the cause-concept, the function needs to be iterated a number of times to reach a balance and derive an accurate evaluation of the nodes that takes into account all relations in the map. As for cyclical maps, it is possible that no balance may be reached we propose setting the diameter of the map as the maximum number of iterations

Usage

evaluate_concepts(edgelist, nodelist)

Arguments

edgelist: an edgelist
nodelist: a nodelist, if you want to add the evaluation to the dataframe with the basic CM measures as calculated above, be sure to use the 'node_measures' list that was returned when running the calculate_degrees function.

Value

Returns a list with the resulting edgelist and nodelist

Examples

# INCOMPLETE
# Load the data
data("edgelist")
#> Warning: data set ‘edgelist’ not found
data("nodelist")
#> Warning: data set ‘nodelist’ not found

# Run the evaluation analysis