Computational modelling and decision theory

Group	Teacher	Department	Language
Year 2	Albert Armisen Morell	Operaciones, Innovación y Data Sciences	ENG

Course Learning Objectives

The overall objective of the course is to equip students with a robust understanding of decision theories and computational modelling, and how these can be practically applied to solve complex problems in business. Additionally, students will gain valuable programming skills in Python, enhancing their capability in business analytics, data science, AI, and machine learning.

- Understanding Decision Theory: Gain a foundational understanding of decision theory, including the use of a decision matrix and the concepts of decision-making under ignorance and risk.
- Learning Utility Theory: Develop an understanding of utility theory and its application in decision-making under uncertainty.
- Exploring Different Decision Theories: Understand the differences and applications of Causal and Evidential Decision Theory.
- Mastering Bayesian Decision Theory: Acquire knowledge of Bayesian Decision Theory and its use in probabilistic decision-making.
- Applying Game Theory in Business: Understand the principles of game theory and its role in strategic business decision-making.
- Understanding Social Choice Theory: Learn about the principles of social choice theory and how individual preferences can translate into collective decisions.
- Grasping the Basics of Computational Modelling: Learn the fundamental principles of computational modelling and its applications in solving complex business problems.
- Understanding Monte Carlo Simulation: Gain an understanding of Monte Carlo simulation and its role in risk assessment, forecasting, and optimizing complex systems.
- Learning Agent-Based Modelling: Understand the concepts of agent-based modelling and its implications in various business contexts.
- Applying Python and Computational Thinking: Gain practical skills in Python programming and computational thinking and understand their relevance in modern business contexts.
- Solving Real-World Business Problems: Apply computational thinking and Python programming skills to address real-world business problems.
- Tackling Advanced Business Problems: Develop the ability to handle complex business problems that require advanced computational thinking and programming skills.

CONTENT

1. Introduction to Decision Theory & Decision Matrix This session introduces the students to the basics of decision theory, explaining how rational choices are made under certain conditions. Students learn how to structure complex problems using a decision matrix and how it can be used to guide decision-making processes in business.
2. Decision-Making Under Ignorance & Risk Students learn how to handle decision-making when outcomes are uncertain (ignorance) or when probabilities of different outcomes are known (risk). This provides valuable tools for risk management and strategic planning in business.
3. Utility Theory This session introduces students to the concept of utility, the measure of relative satisfaction, or value, from a certain decision. Understanding utility theory helps in making decisions under uncertainty and is fundamental in economics and game theory.
4. Causal vs Evidential Decision Theory This session compares and contrasts two major decision theories - Causal and Evidential. Understanding these theories equips students with different perspectives on decision-making, which can be useful in strategic planning and policy formulation.
5. Introduction to Bayesian Decision Theory This introduces students to a probabilistic approach to decision-making, which incorporates new evidence as it becomes available. This can enhance business forecasting, decision analysis, and risk management.
6. Game Theory in Business Decision Making This session gives students insights into strategic decision-making in scenarios where outcomes depend on the actions of others. Game theory is a powerful tool for business strategy, negotiations, pricing strategies, and more.
7. Social Choice Theory This session explores how individual preferences can be aggregated into collective decision. This can aid in understanding voting systems, fairness, and collective welfare, all of which have implications for organizational decision-making and policy design.
8. Introduction to Computational Modelling This session introduces students to the concept of using computational models to solve complex problems. These skills are invaluable in today's data-driven business environment, in everything from operations to marketing.
9. Monte Carlo Simulation Learn a computational algorithm that relies on repeated random sampling to compute results. It's useful in risk assessment, forecasting, and optimizing complex systems in business.
10. Agent-Based Modelling This session introduces a type of computational model that simulates the actions and interactions of autonomous "agents" to assess their effects on the system as a whole. Useful in various business contexts, like supply chain, customer behavior analysis, and more.
11. Applying Python and Computational Thinking This session blends practical coding skills with computational problem-solving approaches. Python is widely used in business analytics, data science, AI, machine learning, and more, making this skillset highly valuable.
12. Applied Computational Thinking Problems This session applies the computational thinking and Python programming skills to solve real-world business problems. This hands-on approach helps to reinforce the concepts and techniques learned in the course.
13. Advanced Applied Computational Thinking Problems The session challenges students with complex business problems that require advanced computational thinking and programming skills. This solidifies the learning from the course, leaving students with strong, practical skills in both decision theory and computational modelling.

1. Introduction to Decision Theory & Decision Matrix

This session introduces the students to the basics of decision theory, explaining how rational choices are made under certain conditions. Students learn how to structure complex problems using a decision matrix and how it can be used to guide decision-making processes in business.

2. Decision-Making Under Ignorance & Risk

Students learn how to handle decision-making when outcomes are uncertain (ignorance) or when probabilities of different outcomes are known (risk). This provides valuable tools for risk management and strategic planning in business.

3. Utility Theory

This session introduces students to the concept of utility, the measure of relative satisfaction, or value, from a certain decision. Understanding utility theory helps in making decisions under uncertainty and is fundamental in economics and game theory.

4. Causal vs Evidential Decision Theory

This session compares and contrasts two major decision theories - Causal and Evidential. Understanding these theories equips students with different perspectives on decision-making, which can be useful in strategic planning and policy formulation.

5. Introduction to Bayesian Decision Theory

This introduces students to a probabilistic approach to decision-making, which incorporates new evidence as it becomes available. This can enhance business forecasting, decision analysis, and risk management.

6. Game Theory in Business Decision Making

This session gives students insights into strategic decision-making in scenarios where outcomes depend on the actions of others. Game theory is a powerful tool for business strategy, negotiations, pricing strategies, and more.

7. Social Choice Theory

This session explores how individual preferences can be aggregated into collective decision. This can aid in understanding voting systems, fairness, and collective welfare, all of which have implications for organizational decision-making and policy design.

8. Introduction to Computational Modelling

This session introduces students to the concept of using computational models to solve complex problems. These skills are invaluable in today's data-driven business environment, in everything from operations to marketing.

9. Monte Carlo Simulation

Learn a computational algorithm that relies on repeated random sampling to compute results. It's useful in risk assessment, forecasting, and optimizing complex systems in business.

10. Agent-Based Modelling

This session introduces a type of computational model that simulates the actions and interactions of autonomous "agents" to assess their effects on the system as a whole. Useful in various business contexts, like supply chain, customer behavior analysis, and more.

11. Applying Python and Computational Thinking

This session blends practical coding skills with computational problem-solving approaches. Python is widely used in business analytics, data science, AI, machine learning, and more, making this skillset highly valuable.

12. Applied Computational Thinking Problems

This session applies the computational thinking and Python programming skills to solve real-world business problems. This hands-on approach helps to reinforce the concepts and techniques learned in the course.

13. Advanced Applied Computational Thinking Problems

The session challenges students with complex business problems that require advanced computational thinking and programming skills. This solidifies the learning from the course, leaving students with strong, practical skills in both decision theory and computational modelling.

Methodology

- Face to Face Lectures

- Guided learning: a mechanism that includes activities that are generally carried out outside the classroom, asynchronously wit specific instructions or guidelines indicated by the faculty. In this course, the online learning platform modules are foundational to your learning. They include various learning materials: required readings, assignments..., that you will be required to prepare and/or submit within specified deadlines.

- Autonomous learning: in activities or online platform activities, where the student must carry out readings, studies, visualizations, or other activities autonomously without specific instructions of guidelines. In this course, the online learning platform modules are foundational to your learning. They include various learning materials: required readings, assignments..., that you will be required to prepare and/or submit within specified deadlines.

Assessment criteria

-Professional maturity and active engagement: 20%
Students will be required to show professional maturity and active engagement in the pedagogical activities of the course. Maintaining such an attitude throughout the course presupposes a number of things. First, it assumes that you attend a majority of the sessions. Second, it assumes that you demonstrate interest. Third, it supposes that you contribute to maintaining a positive class atmosphere. Fourth, it presumes that you are prepared, such that when you offer comments, they evidence a previous analysis of the issue being discussed, showing a deep understanding of the corresponding class. Fifth, is supposes that you are a good listener, showing it through comments that are relevant to the discussion and/or linked to the comments of others. Finally, it assumes that you an effective communicator, presenting your arguments concisely and convincingly.

- Assignments: 40%
As part of their autonomous work, students will be required to complete a series of assignments during their independent study hours. In these assignment, students will be asked to deepen their understanding of the topics and apply their learnings to solve real-world problems. Assignments may include individual tasks, as well as group projects and oral presentations.

- Exams: 40%

Peer Evaluation
To encourage fruitful collaboration among team members and discourage free-riding behavior, a peer evaluation system will be in place, through which each student's Team Project grade will be weighted.

Discretional Bonus
In addition, at the discretion of the team of professors and based on their assessment of a student's global contribution and professional maturity, a bonus of up to 15% of the team project grade may be granted to an individual student.

Type:	BAS	Curs:	2	Period:	S semester
ECTS Credits:	4 ECTS

Computational modelling and decision theory (2235.YR.014948.2)

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