Outline

Overview

The next generation of AI is expected to transition from its primarily predictive capabilities to autonomous decision-making units. This unit explores the economic implications of this change by augmenting standard economic theory to include AI-driven agents. These include the impact on economic inequality, wealth distribution, and economic disparity; the influence/perpetuation of bias in pricing, risk assessment, and insurance; the economics of work and organisation; and issues in law and economics. Through a mix of theoretical exploration and occasional case studies, students will gain critical insights into the economic challenges and opportunities presented by the next generation of AI.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ECOS2001 or ECOS2901
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Game theory, particularly games of complete information
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Murali Agastya, murali.agastya@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Closed-book, paper-based exam	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Graded homework 1 Failure to submit on time shifts weight to the in-semester test	15%	Week -04 Due date: 28 Aug 2026 at 23:59	72 hours	AI allowed
Outcomes assessed:
Written test	In-semester test Coordinated by School of Economics during In-Semester exams time.	30%	Week 08	1 hour	AI prohibited
Outcomes assessed:
Out-of-class quiz	Graded homework 2 Failure to submit on time shifts weight to the final exam	15%	Week 11 Due date: 23 Oct 2026 at 23:59	72 hours	AI allowed
Outcomes assessed:

Assessment summary

Detailed information is supplied on Canvas, and leading up to the Assessments. In summary, there are two closed book tests/exams and two out of class quizzes.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a High Distinction indicates work of an exceptional standard, a Distinction a very high standard, a Credit a good standard, and a Pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Penalties for Insemester test and Final Exam are in line with School of Economics general policies. Graded homework 1 -- missing this assessment will simply transfer its weight to the Insemester Test. Graded homework 2 -- missing this assessment will simply transfer its weight to the Insemester Test. No further Special Considerations apply to Graded Homework 1 & 2.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	AI as Technological Revolution: History, Stylised Facts and Neoclassical Growth	Lecture (3 hr)
Week 02	Automation and its Implications in Neoclassical Growth Models	Lecture (3 hr)
Week 03	Tasks, Skills, and Labour Market Displacement	Lecture (3 hr)
Week 04	AI as a Prediction Machine: Decisions, Judgment, and Uncertainty	Lecture (3 hr)
Week 05	Game Theory tutorial, Games played by Finite state automata	Lecture (3 hr)
Week 06	Automation, Delegation, and Organisational Design	Lecture (3 hr)
Week 07	Social Choice, RLHF, and the Limits of Alignment	Lecture (3 hr)
Week 08	IN SEMESTER TEST	Lecture (3 hr)
Week 09	Strategic Communication: Cheap Talk, Prompts, and Misinformation	Lecture (3 hr)
Week 10	Authority, Initiative, and the Control of AI - a Theory of the firm perspective	Lecture (3 hr)
Week 11	AI Liability: A Law and Economics Perspective	Lecture (3 hr)
Week 12	AI Governance and Regulation	Lecture (3 hr)
Week 13	Misc. Topics	Lecture (3 hr)

Attendance and class requirements

Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on Canvas. However, you should not rely on lecture recording to substitute your classroom learning experience.
Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

There is no textbook for this unit. Course material is drawn from multiple sources (published journal articles, unpublished working papers and manuscripts, and various textbooks) to create a unified presentation. Students will have to rely primarily on the lectures (which are recorded) and a selection of readings prescribed each week, and occasional lecture notes. Nearly all of these can be obtained online, either through the unit's Canvas site or the University of Sydney Library online portal.

All lectures rely on formal models of economic theory. As such, students should be comfortable with following a sequence of mathematical/logical arguments. Basic mathematics preparation at the level of ECON1003 is assumed.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Demonstrate familiarity on how economic modelling changes with the inclusion of AI agents.
LO2. Identify and articulate the likely outcomes form the inclusion of AI in Economic models
LO3. Recognise real-world economic situations that can be studied by developed models and predict the likely outcomes
LO4. Demonstrate understanding of the limitations of the various models, distinguish between competing explanations, and critically evaluate competing theories
LO5. Participate in public policy discussions on the role of AI in economics
LO6. Evaluate applied economic research.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

First time being offered this year.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ECOS3038: AI and Economics

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links