Reading
DT-Reflection-Tree achieves deterministic modeling of human reflection behaviors through JSON decision trees and rule engines. It generates structured psychological reflection paths without large models, providing a traceable decision support tool for mental health and self-awareness.
Section 01
DT-Reflection-Tree is a psychological decision modeling system that does not require large language models (LLM). It achieves deterministic modeling of human reflection behaviors through JSON decision trees and rule engines, generating structured reflection paths and providing a traceable and interpretable decision support tool for mental health and self-awareness.
Section 02
Most current intelligent systems rely on LLM, but the unpredictability and black-box nature of LLM are disadvantages in fields requiring interpretable, repeatable, and deterministic outputs. DT-Reflection-Tree proposes using purely rule-driven decision trees to simulate reflection, abandoning randomness to achieve fully predictable and traceable psychological decision modeling. This counter-trend design has unique value in specific scenarios.
Section 03
The model is built based on three psychological dimensions: Locus (sense of control: Victim/Victor), Orientation (contribution orientation: Contribution/Entitlement), and Radius (scope of attention: Self/Others). These three combine to form a 3×3 decision space. Technically, a JSON decision tree (reflection-tree.json) is used to store predefined questions, options, and branches, and a rule engine calculates the path to ensure the output is completely deterministic and the process is transparent and interpretable.
Section 04
Differences: 1. Determinism: Consistent output for the same input vs. randomness in LLM; 2. Privacy: Local processing without external APIs vs. potential data transmission to external parties; 3. Cost: No API fees vs. call costs. Limitations: Rule engines have low flexibility, cannot handle open-ended inputs, and their intelligence is limited by manual rules. However, they ensure output quality and consistency in structured scenarios.
Section 05
Applicable to daily reflection journaling, decision support, team building (understanding members' thinking), and psychological counseling assistance (structured assessment framework). The deterministic feature is suitable for research data collection, ensuring consistent decision-making processes among participants and facilitating comparison and tracking.
Section 06
The JSON configuration design simplifies customization. Modifying reflection-tree.json allows adjusting questions, options, and branches without changing code. Non-technical personnel (e.g., psychology experts) can directly edit the JSON to create new dimensions or paths. A tree-diagram.png is provided to visualize the decision tree, and write-up.md records design details to assist in understanding and expansion.
Section 07
Technology selection should be based on scenario needs rather than trends. In scenarios requiring determinism, interpretability, and privacy protection, rule engines are more suitable than LLM. Simplicity does not equal backwardness; determinism is more valuable than intelligence in specific scenarios. For developers, this is a case of weighing technical solutions, demonstrating how to implement valuable functions with a simple technology stack.
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