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Decision Intelligence and Generative AI: A Practical Workshop on Systematic Decision-Making Frameworks

An interactive workshop combining classical decision theory with generative AI, offering a multi-path learning experience via Jupyter Notebooks to help learners from diverse backgrounds master systematic decision-making methods.

决策智能生成式AI决策理论Jupyter NotebookSemantic KernelOpenAI系统化决策认知科学PythonC#

Published 2026-05-13 21:37Recent activity 2026-05-13 22:00Estimated read 6 min

Decision Intelligence and Generative AI: A Practical Workshop on Systematic Decision-Making Frameworks

Section 01

Introduction to the Decision Intelligence and Generative AI Practical Workshop

This workshop is an open-source project that at its core combines classical decision theory with generative AI technologies, providing an immersive learning experience through interactive Jupyter Notebooks. It aims to help learners from diverse backgrounds master systematic decision-making methods, featuring multi-path learning design, application of decision intelligence frameworks, and technical practices such as Semantic Kernel/OpenAI.

Section 02

The Importance of Decision-Making Ability and Challenges of Traditional Decision-Making

Decision-making ability is an underrated core competency. Whether you are a corporate executive, engineer, or individual, you need to make numerous decisions daily. Efficient decision-making is a key differentiator for success, but human decisions are prone to cognitive biases, information overload, and emotional interference. Traditional decision theory frameworks are abstract and difficult to implement, leading to the emergence of the decision intelligence discipline, which combines decision science, data science, and AI to provide systematic intelligent support tools for decision-makers.

Section 03

Project Design and Multi-Path Learning Methods

The project revolves around three core objectives: explaining decision theory concepts, introducing decision intelligence frameworks, and demonstrating AI application practices. Three paths are designed for learners from different backgrounds: the Reader Path (zero threshold, directly access pre-run content via browser), the Technical Practice Path (run modules after simple configuration, modify parameters to explore scenarios), and the AI Engineer Path (fork the project, deeply customize models or code).

Section 04

Analysis of Decision Intelligence Framework Modules

The framework includes multiple modules: 1a introduces the overall concept and demonstrates the application of the Eisenhower Priority Method; 1b focuses on decision framework construction skills, with AI assisting in breaking out of fixed thinking patterns; 1c discusses intelligence collection methodologies, combining the Battle of Eddington case and AI enhancement technologies; 1d and 1e respectively involve decision execution forms and the role of intuition, and also explore the cutting-edge question of whether AI can replace CEOs.

Section 05

Technical Implementation Details

The project uses Microsoft Semantic Kernel as the AI orchestration framework to maintain code portability and testability. The main programming language is C#/.NET, suitable for developers in the .NET ecosystem. All examples are encapsulated in Jupyter Notebooks, allowing learners to execute code cell by cell and modify parameters to observe AI outputs.

Section 06

Supporting Resources and Community Participation

The project has a supporting book Decision Intelligence with Generative AI (expected to be published by the end of 2025), which complements the workshop (the book focuses on theoretical cases, while the workshop focuses on code practice). The project is under development, with content updated frequently, and some modules are already available. Decision science experts, AI engineers, and educational creators are welcome to collaborate and contribute via GitHub.

Section 07

Project Value and Conclusion

This project represents a new learning paradigm: combining classical theory with cutting-edge technology, the interactive experience lowers the threshold while meeting the in-depth needs of advanced learners. In today's era of rapid AI development, understanding how to integrate AI into decision-making processes is crucial, and this project provides an excellent starting point for improving decision quality.

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