Decision Analysis with Artificial Intelligence Support (MOD500)

Content

- Introduction: Embarking on decision insights and the pivotal role of data and AI.

- Uncertainty Modeling: Utilizing data analytics and AI tools to encapsulate the nuances of uncertain outcomes.

- Decision Theory: Theoretical underpinnings of informed decision-making.

- Basic Decision Analysis: Integrate Probabilistic Modeling and AI in Sensitivity Analysis, Value of Information Analysis, Risk Profiles, and Stochastic Dominance Analysis.

- Decision Modeling using Influence Diagrams: Structuring decision processes visually with AI enhancements.

- Modeling Risk Preferences: Understanding Risk Aversion, Risk Attitudes, and Utility Functions.

- Assessment of Probability Distributions: Leveraging data analytics and AI for deeper insights into potential outcomes' probabilities.

- The Decision Analysis Cycle/Process: A systematic approach to structured decision-making.

- Multiple Objective Decision Making: How to juggle and prioritize contrasting objectives.

- Options Thinking & Valuation in Decision Analysis: Infusing AI capabilities for advanced strategic foresight and valuation.

- ChatGPT in Decision Insights: Hands-on exploration of how ChatGPT can augment decision modeling and analysis. Join us on this transformative journey where traditional decision modeling meets the revolutionizing power of AI, ensuring a future of unparalleled clarity and informed actions.

Learning outcome

Knowledge:

• An understanding of modeling's role in strategic decision-making and of what constitutes a good decision model
• A thorough understanding of essential elements of good modeling principles to strive for clarity in complex and uncertain decision-making situations.
• Be able to recognize and account for the human biases and errors that most often affect decision making.
• Develop models, tools, and mental frameworks that will allow you to deal effectively with uncertainty
• Revise your beliefs after gathering additional information using Bayesian methods
• Examine and quantify the value created by gathering additional information
• Quantify your appetite for risk and how to factor this into your decision making
• Use Bayesian Networks to help structure a decision model
• Design models with a parametric approach to maximize insights
• Anticipate decision-makers' questions and design in features to answer them
• Build a decision model for a typical engineering or corporate decision situation
• Discover how to create flexible models that allow you to analyze multiple strategic alternatives
• Understand sensitivity analysis and the information it provides
• Conduct probabilistic analysis to general additional insights and understand risk
• Identify how to effectively communicate the insights derived from your model

Skills:

• Skills needed to build a good basic decision model and to use it in generating powerful insights into the decision situation
• Be able to apply and construct decision models and to use the most important elements in decision analysis relevant to engineering type decision-making in the face of uncertainty.

General qualifications:

Students should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.

Required prerequisite knowledge

Recommended prerequisites

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