Course description
Course Description
The MSc Agent-Based Computational Economics and E-Markets provides students with a computational or algorithmic approach to the micro economics of the new IT-based economy. Students also get intensive instruction in JAVA programming.
Modules and Options
The lists of modules below represent the range of options available for each year of study. This may not be a complete list of the options you will study, and may be subject to change, so please contact the department for further details.
Stage 1
AGENT TECHNOLOGY FOR E-COMMERCE
COMBINATORIAL OPTIMISATION
Compulsory: INTRODUCATION TO JAVA AND AGENT-BASED ECONOMIC PLATFORMS
Compulsory: MATHEMATICAL RESEARCH TECHNIQUES USING MATLAB
Compulsory: TOPICS ON FINANCIAL MATHEMATICS AND MARKET ANALYSIS
Core: AGENT-BASED COMPUTATIONAL ECONOMICS AND E-MARKETS
Core: DISSERTATION
DIGITAL SIGNAL PROCESSING
EMPIRICAL METHODS OF ECONOMICS AND FINANCE
FINANCIAL ENGINEERING AND RISK MANAGEMENT
FIXED INCOME SECURITIES, CREDIT RISK AND CREDIT RISK RATINGS
GAME THEORY AND APPLICATIONS
HEURISTIC AND EVOLUTIONARY COMPUTATION
HIGH FREQUENCY FINANCE AND COMPUATIONAL MARKET MICRO-STRUCTURE
INTRODUCTION TO COMPUATIONAL FINANCE AND MARKET ANALYSIS
INTRODUCTION TO E-COMMERCE LAW
LINEAR MODELS
MACHINE LEARNING AND DATA MINING
MATHEMATICS OF PORTFOLIOS
MICROECONOMICS
NONLINEAR PROGRAMMING
ORDINARY DIFFERENTIAL EQUATIONS
PERVASIVE COMPUTING AND AMBIENT INTELLIGENCE
STOCHASTIC PROCESSES
THEORY OF INDUSTRIAL ORGANISATION
Teaching and Assessment Methods
A: Knowledge and Understanding
Learning Outcomes
A1 : Knowledge of the core concepts in the micro economics of market structure, industrial organisation, game theory and economic networks
A2 : Knowledge of the significance of the new IT based network economy and E-markets
A3 : Knowledge of a computational approach of economic networks, automation in E-Markets and of advanced optimisation and estimation techniques
A4 : Knowledge of the significance of evolutionary computation for economics and design issues of market micro structure using artificial agent environments
A5 : Knowledge of mathematical, statistical and numerical methods to understand complex dynamics in markets
Teaching Methods
Outcomes A1-A3 are acquired through lectures, classes and related course work.
Outcomes A3 and A4 are achieved by specially devised laboratory based courses where students will be assisted in developing and running their own Matlab programs for financial analysis.
The development of the dissertation in consultation with a supervisor provides an additional opportunity for the acquisition of outcomes A1-A4.
Lectures are used to present materials - ideas, data and analytical tools - in a clear and structured manner. Lectures are also used to stimulate students' interest in learning financial research and operational methods. Classes and preparation for lectures and classes, provide an opportunity for students to develop their knowledge and understanding of the content of the courses.
The dissertation provides an opportunity for students to develop their knowledge and understanding further through undertaking a piece of independent, though supervised, advanced research.
Students are expected to extend and enhance the knowledge and understanding they acquire from lectures and classes by regularly consulting library materials relating to the course.
Assessment Methods
All courses taken from the different departments will be assessed by the rules of assessment applicable in the department responsible for the course. Learning outcomes A1-A5 will be assessed by compulsory end of year examinations, optional term papers, class tests and the MSc Dissertation.
B: Intellectual/Cognitive Skills
Learning Outcomes
B1 : Theoretical appraisal of the new IT based network economy and E-Markets
B2 : Construct models of market micro structure under different assumptions for empirical and numerical testing
B3 : Acquire critical frame of reference regarding the automation in markets
B4 : Acquire theoretical knowledge on the significance of the use of artificial intelligence and agent technologies in modelling and understanding market environments
B5 : Carry out independent research
Teaching Methods
Skills B1-B4 are acquired and enhanced primarily through the work that students do for their courses, although lectures and lab demonstrations provide a means for teachers to demonstrate these skills through example.
Student preparation involves the reading, interpretation and evaluation of the finance literature, including texts and research papers, and the analysis of empirical evidence. Teachers provide feedback on student work through comment and discussion. In addition, teachers engage students outside the classroom through office hours, appointments and email.
Skill B2 is honed in the lab based classes.
The dissertation is additionally used to develop a student's mastery of the combined application of financial principles and empirical methods, as well as their analytical ability and understanding of the complete research process.
Assessment Methods
Skills B1-B5 are assessed throughout the courses comprising the degree by means of written examinations with optional term papers.
Skills B1-B4 are also assessed in certain courses through written tests.
The MSc dissertation provides a further opportunity to assess skills B1-B5.
Skill B5 is assessed through the dissertation and optional term papers.
C: Practical Skills
Learning Outcomes
C1 : Identify, select and gather information using relevant sources, including the library and online searches
C2 : Organise ideas in a systematic and critical fashion
C3 : Present and critically assess advanced theories on market micro structure coherently in writing
C4 : Acquire statistical and mathematical tools for analysis of networks in markets
C5 : Design computational tests for competing hypothesis regarding markets
C6 : Implement evolutionary computational methods AI learning in market environments
C7 : Acquire experience to formulate market related problems and then use object oriented programs for simulation
Teaching Methods
Skills C1-C5 are acquired and enhanced primarily through the work that students do for their courses. Lectures also provide a means of teachers demonstrating these skills through example. Skills C4-C6 are acquired to a greater degree in courses that focus on agent modelling, evolutionary computation and the lab based compulsory courses especially designed for this degree scheme. These skills are reinforced or supplemented depending on the optional courses taken.
The dissertation is additionally used to provide an opportunity for students to acquire skills C1-C5. The bridge between micro economics of the networked economy and computational models will be built by the new CCFEA-lab based module.
Assessment Methods
Skills C1-C6 are assessed throughout the courses comprising the degree by means of written examinations with optional term papers. The dissertation also provides a further opportunity to assess skills C1-C7.
Skills C4-C7 are also informally assessed by student's class presentations in the lab based courses.
D: Key Skills
Learning Outcomes
D1 : Communication in writing, using appropriate terminology and technical language
D2 : Production of a word-processed research dissertation. Student will have knowledge of an advanced programming language (JAVA, Matlab) to help program agent models and other computer simulations
D3 : Use of mathematical techniques to construct market models and the use of econometric/statistical and other computational methods to analyse market data
D4 : Application of analytical and computational simulation techniques to address market phenomena
D5 : Joint problem solving in lab oriented courses and classes and group project
D6 : Capacity to (a) organise and implement a plan of independent study (b) reflect on his or her own learning experience and adapt in response to feedback and (c) recognise when he or she needs to learn more and appreciate the role of additional research
Teaching Methods
Students are guided in acquiring skills D1-D5 through lectures, classes and individual advice from teachers. These skills are further developed as students pursue the learning activities associated with their courses.
The dissertation enables students to acquire skill D2 and also assists them in acquiring skills D1, D4 and D5.
Students also have the opportunity to develop skills in working in groups through their participation in classes for courses, especially the applied ones.
Assessment Methods
Skills D1, D3, D4 and D5 are assessed throughout the courses comprising the degree by means of examination with optional term papers or written tests. Both the group projects and the dissertation provide further means for an overall assessment of communication (D1), using IT (D2), problem-solving skills (D4), and self-learning (D6).
