Episode 2: Michel De Montaigne — Les Essais

Episode 2: Michel De Montaigne —Les Essais

In this second installment, we delve into a profound examination of yet another passage from Montaigne's work, 'Essais', the 'Liars' (Book I).

This is a formidable challenge for Hellixia, given that it relies on a translation from Old French.

When they disguise and change, when they are often put back on the same story, it is difficult for them not to make mistakes, because the thing as it is, having lodged itself first in memory and having been imprinted there by way of knowledge and science, it is difficult for it not to be represented in the imagination by dislodging the falsehood, which cannot have as firm and steady a foothold, and for the circumstances of the first learning not to cause the memory of the added, false or bastardized pieces to be lost.

In what they invent completely, because there is no contrary impression that contradicts their falsehood, they seem to have all the less to fear to make mistakes. However, this fiction, because it is a vain and ungraspable body, readily escapes memory if it is not well secured.

If, like truth, lies had only one face, we would be in a better position, for we would take the opposite of what the liar said as certain. But the reverse of truth has a hundred thousand faces and an indefinite field. The Pythagoreans posit that good is certain and finite, evil infinite and uncertain. A thousand roads deviate from the goal, only one leads to it.

This post is also linked to a discussion we had at Marcello Di Bello's presentation, "Cross-Examination with Bayesian Networks" (BayesiaLab Conference, 2022).

Workflow for Creating a Network of Keywords

  • Create a new node: Start by creating a new node and label it as "Montaigne". This node will serve as a container for the text you want to analyze.
  • Enter the excerpt: Input the selected text into the "Montaigne" node as a comment.
  • Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
  • Review the dimensions: Examine the dimensions or keywords returned by Hellixia. Remove any dimensions that seem redundant or irrelevant to your analysis.
  • Use the Embedding Generator on all remaining nodes. This tool captures and quantifies the semantics associated with the names and comments of each node.
  • Set the target node: Set "Montaigne" as the Target Node. The subsequent analyses and operations will focus on this node.
  • Run the Naive Learning algorithm.
  • Change node styles: Alter the style of all nodes to "Badges". This style will display the comment within each node.
  • Switch to Validation Mode F5.
  • Run the Arc Force analysis.
  • Apply the Radial Layout: While still in the Arc Force analysis tool, run the **Radial Layout. This layout arranges the nodes in a clockwise manner according to the strength of their relationships with the target node.
  • Show the Arc Comments: These comments will provide information about the strength of the relationships between nodes.

Workflow for Creating the Semantic Network

  • Copy the "Montaigne" node: Begin by copying the node titled "Montaigne".
  • Paste the node into a new graph: Create a new graph and paste the copied "Montaigne" node into it.
  • Run the Dimension Elicitor using the following keywords to guide the analysis of the node: Contents, Ideas, Milestones, Rules, Themes, Theses, and the General Context set to "Philosophy".
  • Review the returned dimensions: Examine the dimensions provided by Hellixia. Remove any dimensions that appear redundant or irrelevant to your analysis.
  • Exclude the "Montaigne" node**. **
  • Use the Embedding Generator on all remaining nodes. This will help capture the semantic associations of their names and comments.
  • Create a semantic network: Use the Maximum Weight Spanning Tree algorithm to form a semantic network from the analyzed text.
  • Change node styles to "Badges". This style will allow the comment within each node to be shown.
  • Apply the Dynamic Grid Layout: Use this layout option to organize the nodes on your graph. Note that this layout algorithm is not deterministic, meaning it doesn't always produce the same results given the same input. It randomly favors vertical, horizontal, or mixed orientations. Run this layout multiple times until you find a layout that best suits your preferences.
  • Switch to Validation Mode F5.
  • Select Skeleton View: Since the network you're generating does not represent causal relationships, choose the Skeleton View. This will remove the arc orientations, leaving only connections between nodes without indicating a direction.

Workflow for Node Force Analysis

  • Switch back to Modeling Mode F4.
  • Change node styles to Discs.
  • Symmetric Layout.
  • Enter **Validation Mode. **
  • Analyze **Node Force. **

Workflow for Creating the Hierarchical Semantic Network

  • Run Variable Clustering: This will identify and group similar variables based on their semantics.
  • Open the **Class Editor. **
  • Within the Class Editor, activate the **Class Description Generator.
  • Use it to create meaningful names for the factors you're working with.
  • Save the descriptions you've just created using the Export Descriptions feature.
  • Switch back to Modeling Mode F4.
  • Execute Multiple Clustering to create latent variables.
  • Next, execute the structural learning algorithm Taboo. Make sure to enable the option "Delete Unfixed Arcs." This should result in the creation of a hierarchical network.
  • Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
  • Switch to Validation Mode F5.
  • Use Node Force.

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