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«Masterstudium Business Informatics an der Technischen Universität Wien Studienplan 2011 in der Fassung vom Juni 2013 Gültig ab 1. Oktober 2013 ...»

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• Information economics: Introduction to information economics; the interdependence between information theory and economics, Henri Theil's contributions; the price system as information structure; critical mass models; simulation of information processes in social systems; information in articial life models; early roots of current research topics by Hurwicz, Simon, and Newell; the concept of information in the macroeconomic policy debate; some economic models induced by new information technologies; information structures in production units; economics of language; social network theory

• Computational social simulation: Basic concepts of agent based simulation; comparison of agent based simulation with other simulation methods; some seminal examples of agent based simulations; introduction to simulation software; developing an agent based simulation; generating dynamic social networks; experimenting with agent based simulations; analyzing simulation results

Expected Prerequisites:

The contents of the module BIN/MOS - Modeling and Simulation and the contents of the bachelor module WIW/GOE - Grundlagen der Ökonomie.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures, a student project building the major part of the computational social simulation lecture, experiments with partly or fully pre-implemented models in groups to develop a simple agent based model.

Courses of Module:

3.0/2.0 VO Information Economics 3.0/2.0 VU Computational Social Simulation BAE/MGT - Management ECTS-Credits: 9.0 Summary: In this module, students are provided with fundamental knowledge and skills necessary to manage complex socio-technical systems. This comprises planning and control of generic management processes based on IT support, human resource management and leadership as well as the management of business relations in form of negotiations.

Besides lectures and nal exams, the courses include case studies, role plays, teamwork, and take-home exercises.

Learning Outcomes:


• Knowledge necessary to manage complex socio-economic systems

• Applying the management process approach in dierent domains to establish open and closed loop management systems

• Tools and instrument necessary to manage human performance during the entire employee lifecycle

• Decision and negotiation analysis theories


• Further development of analytical and synthetical skills in the evaluation of complex socio-economical problems

• Critical discussion and evaluation of alternative or conicting theories and concepts

• Negotiation skills

• Skills in using management information systems including decision and negotiation support


• Interactive parts of the courses deepen teamwork and conict management competences.


• IT-based management: In this course Kenneth Arrow's organizational control theory is used to distinguish control in the large and control in the small. Control in the large deals with the design and implementation of operating rules and control in the small deals with enforcement of these rules. It applies a process perspective that has the advantage that it can be used in dierent contexts and implemented in IT solutions. The following topics are taught: Generic management process model;

cost management; risk management; sales management; production management;

nancial management; integrated ERP system.

• Human resource management and leadership: The main goal is to provide students with the theoretical foundations and basic instruments of Human Resource (HR) management and leadership. The following topics are taught: Introduction and theoretical foundations; organization of HR management; HR planning, recruitment, and selection; performance and reward management; training and development; leadership and management; HR controlling and specic topics of HR management.

• International negotiation: This course prepares students for business negotiations in dierent settings and contexts. The following topics are taught: Theories of the negotiation process and the application of these theories to a variety of settings;

rational models of bargaining behavior that have been developed in economics and decision sciences; cognitive and behavioral theories that investigate how bargaining behavior may diverge from the predictions of rational models; developing bargaining skills by applying the theoretical concepts in a variety of negotiating exercises and cases; exposure to new communication and computer technologies that are used in negotiation analysis and support, and in the conducting of negotiations in e-business and beyond.

Expected Prerequisites: Basic knowledge in business administration and management (organization, innovation and marketing, nance and controlling, and production and logistics).

Those topics are taught in the bachelor modules WIW/GBW - Grundlagen der Betriebswirtschaft and WIW/MGT - Managementwissenschaften.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures, reading assignments, experimental learning techniques including case studies, role plays, teamwork, and take-home exercises, discussions of the applied theories and concepts from a meta perspective.

Courses of Module:

3.0/2.0 VU IT-based Management 3.0/2.0 VO Human Resource Management and Leadership 3.0/2.0 VU International Negotiations BIN/BEN - Business Engineering ECTS-Credits: 15.0 Summary: This module deals with designing an enterprise in an e-business and ecommerce context. This covers the area of analyzing its performance (business intelligence), understanding the electronic markets and networks as well as planning and designing activities or the respective systems (e-commerce), modeling the internal processes and workows (workow modeling), designing new products and services and innovating products and services. Thus, the module covers all business engineering aspects from analysis, abstraction, modeling, design, planning up to implementation. The module is organized along lectures, class room tasks, assignments, and hands-on exercises (alone or in groups). When appropriate, tools are provided.

Learning Outcomes:


• Methodologies to analyze and model an enterprises' processes and data to prepare it for analysis

• Techniques to analyze the data to create hypotheses and bases for business decisions


• Skills needed to understand complex business processes, interdependencies in data, and ways to interpret structures detected in data


• Work in the multidisciplinary settings required to collect, transform, and interprete data

• Creating hypotheses and verify these based on the information acquired from different data sources, employing a range of dierent techniques


• Business intelligence: Data warehousing; data mining and knowledge discovery;

OLAP; reference architecture of business intelligence; fast analysis of shared multidimensional information (FASMI); semantic modeling of OLAP solutions and logic modeling (STAR, SNOWFLAKE); ETL process; predictive and descriptive rules (classication, regression, association, clustering)

• E-commerce: Basics of e-commerce and e-business and diusion aspects; business models; IT-Governance; e-strategy and e-marketing; electronic markets and networks; interorganisational systems; recommender systems; auctions; planing and implementation of e-commerce systems

• Workow modeling and implementation: Process modeling; workow patterns;


• Innovation: Adoption and diusion; invention, innovation and imitation; innovation examples; innovation process and management; objects and types of innovation;

business ideas, planning and implementation Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures, class room tasks, assignments, and hands-on exercises (alone or in groups). When appropriate, tools are provided.

Courses of Module:

6.0/4.0 VU Business Intelligence 3.0/2.0 VU E-Commerce 3.0/2.0 VU Workow Modeling and Process Management 3.0/2.0 VU Innovation BIN/MOS - Modeling and Simulation ECTS-Credits: 6.0 Summary: This module oers a compact introduction into the art of modeling and into the technique of simulation. Students learn how to perform a simulation study correctly following all necessary steps from data acquisition, (mathematical) modeling, implementation, validation, identication, and result analysis. Dierent modeling techniques (from dierential and dierence equations via cellular automata to soft computing-based modeling techniques and discrete event modeling) are compared and appraised with respect to data availability. Using standard and experimental simulation systems students work on case studies in various application areas (from economics via production to physics and health care).

Learning Outcomes:


• Dierent methods for modeling dynamical processes

• Choose of an appropriate model with respect to the available data

• Technical knowledge to compare dierent possible modeling approaches and to decide for the most appropriate approach with respect to data and modeling goal


• Skills trained cognitive and practically to perform a simulation study correctly, from denition and modeling via software choice and implementation and via model identication and model validation up to experiment design, experiment execution, and result analysis


• Communication with experts in various application areas in order to gather information for appropriate modeling and for appropriate simulation documentation

• Innovation in formulating dependencies in the model and for the design of communication of appropriate and creative results Syllabus: The module consists of partly independent lecture parts, which introduce into the technique of a simulations study, which impart knowledge for dierent modeling approaches and for appropriate and practical implementations (supported by an e-learning system), and which emphasize on model validation and identication.

• Introduction into the technique of a simulation study sketching all steps from mathematical modeling via implementation and validation until experiment design and experiment execution

• Input/Output modeling (Black Box modeling)

• Discrete transfer functions and Markov models versus general nonlinear discrete models and classications of the models with respect to nonlinear properties and data availability

• System dynamics, Forrester's modeling approach (industrial dynamics, system dynamics) sketched as qualitative and quantitative white-box modeling approach

• Soft computing techniques

• Cellular automata modeling: Modeling with spatial aspects and multidomain modeling by coupling cellular automata models with system dynamics models

• Discrete event modeling: Statistical features and asynchronous time base modeling (time event oriented modeling) into modeling of discrete dynamical systems

• Validation and identication: Procedures for model validation and algorithms for identication

• Advanced modelled and simulation tasks comparing dierent modeling techniques

• Complimentary modules as crash courses for modeling and simulation with MATLAB, Simulink, SimEvents, AnyLogic, and others (e.g., DSOL) and a review of mathematical algorithms (basics ODE solver, solutions of dierence equations) Expected Prerequisites: Medium knowledge in mathematical analysis and programming, basic knowledge in statistics and numerical algorithms.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures on modeling and simulation concepts, exercises on modeling and simulation examples with MATLAB, Simulink, Java, and AnyLogic using the webbased MMT E-Learning system to experiment with partly pre-implemented models to become familiar with model features and concepts by using personal notebooks, case studies, students' projects in groups on modeling and simulation.

Courses of Module:

6.0/4.0 VU Modeling and Simulation FMF/KBS - Knowledge-based Systems ECTS-Credits: 6.0 Summary: This module oers an introduction into important concepts of knowledgebased systems like problem solving techniques, formalisms to represent knowledge, and corresponding deduction concepts. Students acquire systematic knowledge about the fundamental principles underlying knowledge-based systems, both from a theoretical and from a practical implementation side. Students continue to train their capabilities to analyze problems, their abilities to argue and reason correctly in proofs, which result in solutions, and their abilities to implement solutions. Topics are presented in lectures, exercises are done and presented by students, larger problems are solved and implemented by students in the lab.

Learning Outcomes:


• Fundamental knowledge about the theoretical basic principles of knowledge-based systems

• Fundamental concepts, which are necessary (i) to understand the working principles and (ii) to implement knowledge-based systems

• Basic knowledge about the realization of knowledge-based systems

• Handling of formal descriptions and processing algorithms attached


• Ability to formally analyze the discussed techniques and methods

• Ability to choose methods and techniques for a given task

• Ability to analyze simple task, to develop a solution and to implement the solution into a system

• Critical evaluation and reection of solutions

• Presentation of solutions


• Self-organization and personal responsibility

• Personal initiative and curiosity

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