Engineering Economics

Syllabus

• E-mail: colleen.obriant at ucr.edu
• Class: T/R 9:30-10:50 Dundee A1003
• Office hours via Zoom: 12pm Tuesdays and Thursdays

Course Overview

Every major system, whether human or machine, faces the same challenge: making decisions over time, under uncertainty. This course examines that problem from the shared perspectives of engineering and economics, using Reinforcement Learning (RL) as the unifying framework. Students will explore how engineers use RL to design autonomous systems and how economists apply it to model human behavior and evaluate trade-offs under risk. No prior programming experience is required.

Course Policies

This course will be challenging. You will learn to frame real-world economic decision problems as dynamic discrete choice models, estimate and interpret them using econometric and reinforcement learning methods, and apply those tools to analyze and communicate optimal behavior under uncertainty over time. To help you succeed, I’m determined to give you all the support you need to be successful:

• Classwork in Groups: Each class session will include an in-class project to be completed in small groups. This classwork must be finished and submitted before the end of the class period. You will submit one assignment per group. Groups will be randomly assigned at the start of each unit. For the final unit, you will be allowed to submit requests for preferred group members.
• Homework: After class, you will complete homework individually. These assignments are designed to help you clarify your thinking, reflect on concepts from class, and practice core math and programming skills. Homework is due before the next class meeting. If you get stuck, come to office hours (12pm Tuesdays and Thursdays over Zoom), or reach out for an appointment. I’m here to support you! Late work will not be accepted after the end of week 3 (1/23) under any circumstances because answer keys will be published immediately.
• Attendance: Attendance is crucial. Your first two absences are waived. After that, each additional absence, whether excused or unexcused, will reduce your final course grade by 5 percentage points.
• Unit Test Retakes: There will be four unit tests. Unit tests 1 through 3 each have a retake option, and the higher of the two scores will count toward your grade. Retakes will cover the same material but will not be identical to the original test. Each unit test is worth 20% of your final grade. No make-up exams will be offered because of the built-in retake opportunity. All unit tests will be taken during the first 30 minutes of lab, so arriving on time is required. The final 20 minutes of lab will be used to review the unit test.

ChatGPT Statement

You may not use artificial intelligence tools in this course. All work you submit must reflect your own understanding and effort. The purpose of this course is for you to develop the ability to reason through economic models, write and debug code, and explain your thinking clearly. Using AI tools would undermine that learning process.

Grading

Your final grade will be determined by:

Classwork	10%
Homework	10%
Unit Tests (4)	80%
Absences exceeding 2	-5

At the end of the quarter, I will take your final score and round it to the nearest whole number (an 89.50 becomes a 90; an 89.49 becomes an 89). Then I’ll apply this scale to determine your letter grade:

A	93 and above
A-	90-92
B+	88-89
B	83-87
B-	80-82
C+	78-79
C	73-77
C-	70-72
D+	68-69
D	63-67
D-	60-62
F	less than 60

Tentative Course Schedule

Unit 1: Modeling, Decision Making, and Economic Foundations (Weeks 1-4)

In this unit, you will learn to:

• Apply declarative programming tools to economic data tasks, including writing SQL-style queries, producing data visualizations, and estimating linear models.
• Describe and interpret the statistical components of model fitting, including objectives, assumptions, and estimation outputs.
• Compare and apply core econometric methods used in economics, including ordinary least squares, logistic regression, and maximum likelihood estimation.
• Analyze dynamic decision problems in which agents choose actions over time, including games against nature or strategic opponents, using value functions.
• Explain and evaluate economic concepts related to intertemporal choice and uncertainty, including time value of money, present value, and rates of return.

Unit 1 Test: Thursday 2/5 in Lab

Retake: Thursday 2/12 in Lab

Unit 2: Reinforcement Learning and Imperative Programming (Weeks 5-6)

In this unit, you will learn to:

• Implement and evaluate reinforcement learning algorithms to train an agent or robot to complete specified tasks.
• Apply imperative programming constructs such as loops and conditional logic to control program flow and learning behavior.

Unit 2 Test: Thursday 2/19 in Lab

Retake: Thursday 2/27 in Lab

Unit 3: Estimate an IRL/DDC Model (Weeks 7-8)

In this unit, you will replicate and analyze the Rust (1987) model by:

• Formulating a dynamic discrete choice problem in economic terms.
• Specifying the structural model, including states, actions, payoffs, and transition processes.
• Estimating the model parameters using observed data and appropriate computational methods.
• Interpreting and evaluating the results in light of the economic decision problem.

Unit 3 Test: Thursday 3/5 in Lab

Retake: Thursday 3/12 in Lab

Unit 4: Final Projects (Weeks 9-10)

In the final unit, you will synthesize and create a complete applied project by:

• Framing a new real-world problem as a dynamic discrete choice/inverse reinforcement learning model.
• Designing and specifying the economic and computational structure of the model.
• Estimating the model using data.
• Interpreting, evaluating, and communicating your findings.

Unit 4 Test: Final Exam Period Friday, March 20 11:30am

Materials

You will need a laptop computer in class every day. If you need a laptop, you can check one out at the laptop kiosks in the HUB or at either campus library. Other materials will be published on this course website.

Campus Closure

In case of campus closure due to bad weather or an unforeseen emergency, I’ll host our regular class session over Zoom.

Academic Integrity

Here at UCR, we are committed to upholding and promoting the values of the Tartan Soul: Integrity, Accountability, Excellence, and Respect. As a student in this class, it is your responsibility to act in accordance with these values by completing all assignments in the manner described, and by informing the instructor of suspected acts of academic misconduct by your peers. By so doing, you will not only affirm your own integrity, but also the integrity of the intellectual work of this University, and the degree which it represents. Should you choose to commit academic misconduct in this class, you will be held accountable according to the policies set forth by the University, and will incur appropriate consequences both in this class and from Student Conduct and Academic Integrity Programs. For more information regarding University policy and its enforcement, please visit: conduct.ucr.edu. My specific expectations for academic integrity in this class are as follows:

• Keep your eyes on your own exam.
• I encourage collaboration with peers during in-class assignments, but you may not consult the internet or any AI tools while completing these assignments, as the goal is to develop your own problem-solving skills and reinforce your understanding through discussion, not rely on external answers.

Accomodations

UC Riverside is committed to providing equal access to learning opportunities to students with documented disabilities. To ensure access to this class, and your program, please contact the Student Disability Resource Center (SDRC) to engage in a confidential conversation about the process for requesting accommodations in the classroom. More information can be found on sdrc.ucr.edu. If you are a student registered with the SDRC, please ensure you request your quarterly accommodations through rability.ucr.edu.