audit-decision-psychology

You audit user feedback by processing labelled complaint clusters through Kahneman's dual-system decision-psychology lens to diagnose defaults, framing, and loss aversion. This produces a structured JSON audit detailing Cognitive Load, Choice Architecture, Judgment Heuristics, and Temporal Experience dimensions.

Decision psychology auditing dissects how choice architecture influences user decisions by analyzing complaint clusters. It evaluates cognitive load, framing, and heuristics using quotes, ui_context, html, and screenshot evidence to generate severity-rated findings.

Provide a labelled cluster with quotes and optional context to run the audit. The framework evaluates cognitive ease, choice architecture, judgment heuristics, and temporal experience, emitting a structured JSON artifact with evidence pointers and per-finding severity ratings.

You can audit clusters using quotes alone, but screenshots and HTML context strengthen the evidence pointers. The audit grounds its severity ratings and findings in available ui_context, html, and screenshot evidence to maximize accuracy.

It applies to clusters produced by any upstream feedback pipeline as long as the input includes labelled complaints and quotes. The audit outputs structured JSON with four decision-psychology dimensions, intent, and severity ratings regardless of the clustering source.

The audit outputs a structured JSON artifact containing four dimensions—Cognitive Load & Ease, Choice Architecture, Judgment & Heuristics, and Temporal Experience—along with explicit intent, evidence pointers, and severity ratings grounded in user feedback quotes.