disagreement-cartography

Research

Map the structure of disagreement across perspectives using Policy Delphi, Argument Delphi, or SAST methods.

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Disagreement Cartography

Purpose: Rather than forcing convergence, map the topology of disagreement — identify clusters of opinion, the arguments supporting each cluster, and the fault lines between them. Useful for wicked problems where premature consensus would be harmful.

When to use:

Wicked problems with legitimate value conflicts
Policy questions where understanding disagreement matters more than resolving it
Situations where forced consensus would mask important minority views
Interdisciplinary disputes with incommensurable frameworks

Budget

Parameter	Constraint
Rounds	1–2 (mapping, not converging)
Perspectives	≥4 independent
Cluster minimum	≥2 distinct clusters to be useful

State Ledger

Key	Type	Description
question	string	The focal question or issue
perspectives	array	List of perspective descriptions
judgments	array	Raw collected judgments
clusters	array	Identified opinion clusters
arguments	array	Extracted arguments per cluster
disagreement_map	object	Final visualization structure

Available Tactics

disagreement-mapping — Core flow: collect → cluster → extract arguments → visualize

Available SOPs

judgment-collection
cluster-analysis
argument-extraction
disagreement-visualization

Execution Guidance

Frame the question to elicit diverse positions (not just ratings)
Run disagreement-mapping tactic
Ensure each cluster has its strongest arguments represented
Produce disagreement map showing fault lines and bridging possibilities

Output Format

clusters:
  - id: <cluster_id>
    position: <summary>
    size: <number of perspectives>
    core_arguments: [...]
fault_lines:
  - between: [cluster_a, cluster_b]
    nature: <empirical/value/definitional>
bridging_possibilities: [...]

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
disagreement-mapping	Map disagreement structure by collecting judgments, clustering opinions, extracting arguments per cluster, and visualizing fault lines.