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Unit outline_

ECON6703: Mathematical Methods of Econ Analysis A

Semester 1, 2023 [Normal day] - Remote

This unit is an introduction to mathematical economics. It has three purposes. First, to introduce students to the mathematical concepts and methods that are central to modern economics. Second, to give a set of economic applications of the mathematical methods. Third, to develop the students' ability to formulate logical arguments with the degree of precision and rigour demanded in modern economics. The mathematical topics covered include introductory analysis and topology, convex analysis, linear algebra, calculus of functions of several variables, optimisation, and introduction to dynamic programming and dynamical systems.

Unit details and rules

Academic unit Economics
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
ECON6003
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Murali Agastya, murali.agastya@sydney.edu.au
Type Description Weight Due Length
Monitored exam
? 
Final exam
Online exam
45% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
Online task Homework Assignments
Problem Sets
20% Multiple weeks No more than 2 business days per task
Outcomes assessed: LO1 LO3 LO2
Assignment Graded assignment
To be completed within class time
35% Week 07
Due date: 05 Apr 2023 at 18:00
2 hours
Outcomes assessed: LO1 LO2 LO3

Assessment summary

Detailed information for each assessment can be found on Canvas.

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

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

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.

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.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Basic logic, Sets, Types of Proofs Seminar (3 hr) LO1 LO2 LO3
Week 02 Sequences, Limits, Euclidean Spaces Seminar (3 hr) LO1 LO2 LO3
Week 03 Linear Algebra Seminar (3 hr) LO1 LO2 LO3
Week 04 Continuity, Differentiation, Mean Value Theorems, Implicit Function Theorem, Inverse Function theorem Seminar (3 hr) LO1 LO2 LO3
Week 05 Week 4 topics Continued Seminar (3 hr) LO1 LO2 LO3
Week 06 Convexity Seminar (3 hr) LO1 LO2 LO3
Week 07 Graded Assignment Practical (3 hr) LO1 LO2 LO3
Week 08 Unconstrained multivariate optimisation Seminar (3 hr)  
Week 09 Constrained multivariate optimisation Seminar (3 hr) LO1 LO2 LO3
Week 10 Constrained optimisation (cont.) Seminar (3 hr) LO1 LO2 LO3
Week 11 Dynamic programming Seminar (3 hr) LO1 LO2 LO3
Week 12 Topics in Mathematical Economics Seminar (3 hr) LO1 LO2 LO3
Week 13 Topics in Mathematical Economics Seminar (3 hr) LO1 LO2 LO3

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 the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
  • Preparation: Students should commit to spending approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.
  • Prior Knowledge: This course is an introduction to mathematical economics it is NOT an introduction to mathematics even though it is essentially a "math" course.  Knowledge of basic algebraic manipulations and a working knowledge of single variable Calculus is assumed.

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

Recommended textbook:

Carl Simon and Lawrence Blume, Mathematics for Economists, New York, NY: Norton, 2010 

Other readings will be available for download from the Canvas site regularly depending on the current topic being covered.

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.

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

  • LO1. apply economic theories and concepts to problems and practice
  • LO2. critically evaluate underlying theories, concepts, assumptions, limitations and arguments in economics and related fields of study in business
  • LO3. develop coherent arguments when recommending solutions and critically evaluate theories in major fields of study.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

The course pattern is roughly the same as the version delivered in Semester 1, 2022.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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