Search result: Catalogue data in Spring Semester 2023

Management, Technology and Economics Master Information
Elective Courses
Systems Design and Risks
NumberTitleTypeECTSHoursLecturers
363-0543-00LAgent-Based Modelling of Social SystemsW3 credits2V + 1UG. Vaccario
AbstractAgent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems. The course is based on formal models of agents and their interactions. Computer simulations using Python allow the quantitative analysis of a wide range of social phenomena, e.g. cooperation and competition, opinion dynamics, spatial interactions and behaviour in social networks.
ObjectiveA successful participant of this course is able to
- understand the rationale of agent-based models of social systems
- understand the relation between rules implemented at the individual level and the emerging behavior at the global level
- learn to choose appropriate model classes to characterize different social systems
- grasp the influence of agent heterogeneity on the model output
- efficiently implement agent-based models using Python and visualize the output
ContentThis full-featured course on agent-based modeling (ABM) allows participants with no prior expertise to understand concepts, methods and tools of ABM, to apply them in their master or doctoral thesis. We focus on a formal description of agents and their interactions, to allow for a suitable implementation in computer simulations. Given certain rules for the agents, we are interested to model their collective dynamics on the systemic level.

Agent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems.
Agents represent the basic constituents of such systems. The are described by internal states or degrees of freedom (opinions, strategies, etc.), the ability to perceive and change their environment, and the ability to interact with other agents. Their individual (microscopic) actions and interactions with other agents, result in macroscopic (collective, system) dynamics with emergent properties, which we want to understand and to analyze.

The course is structured in three main parts. The first two parts introduce two main agent concepts - Boolean agents and Brownian agents, which differ in how the internal dynamics of agents is represented. Boolean agents are characterized by binary internal states, e.g. yes/no opinion, while Brownian agents can have a continuous spectrum of internal states, e.g. preferences and attitudes. The last part introduces models in which agents interact in physical space, e.g. migrate or move collectively.

Throughout the course, we will discuss a wide variety of application areas, such as:
- opinion dynamics and social influence,
- cooperation and competition,
- online social networks,
- systemic risk
- emotional influence and communication
- swarming behavior
- spatial competition

While the lectures focus on the theoretical foundations of agent-based modeling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agent-based models in guided and in self-chosen projects, which they present and jointly discuss.
Lecture notesThe lecture slides will be available on the Moodle platform, for registered students only.
LiteratureSee handouts. Specific literature is provided for download, for registered students only.
Prerequisites / NoticeParticipants of the course should have some background in mathematics and an interest in formal modeling and in computer simulations, and should be motivated to learn about social systems from a quantitative perspective.

Prior knowledge of Python is not necessary.

Self-study tasks are provided as home work for small teams (2-4 members).
Weekly exercises (45 min) are used to discuss the solutions and guide the students.

The examination will account for 70% of the grade and will be conducted electronically. The "closed book" rule applies: no books, no summaries, no lecture materials. The exam questions and answers will be only in English. The use of a paper-based dictionary is permitted.
The group project to be handed in at the beginning of July will count 30% to the final grade.
363-0588-00LComplex Networks Information W4 credits2V + 1UG. Casiraghi
AbstractThe course provides an overview of the methods and abstractions used in (i) the quantitative study of complex networks, (ii) empirical network analysis, (iii) the study of dynamical processes in networked systems, (iv) the analysis of robustness of networked systems, (v) the study of network evolution, and (vi) data mining techniques for networked data sets.
Objective* the network approach to complex systems, where actors are represented as nodes and interactions are represented as links
* learn about structural properties of classes of networks
* learn about feedback mechanism in the formation of networks
* learn about statistical inference and data mining techniques for data on networked systems
* learn methods and abstractions used in the growing literature on complex networks
ContentNetworks matter! This holds for social and economic systems, for technical infrastructures as well as for information systems. Increasingly, these networked systems are outside the control of a centralized authority but rather evolve in a distributed and self-organized way. How can we understand their evolution and what are the local processes that shape their global features? How does their topology influence dynamical processes like diffusion? And how can we characterize the importance of specific nodes?

This course provides a systematic answer to such questions, by developing methods and tools which can be applied to networks in diverse areas like infrastructure, communication, information systems, biology or (online) social networks. In a network approach, agents in such systems (like e.g. humans, computers, documents, power plants, biological or financial entities) are represented as nodes, whereas their interactions are represented as links.

The first part of the course, "Introduction to networks: basic and advanced metrics", describes how networks can be represented mathematically and how the properties of their link structures can be quantified empirically.

In a second part "Stochastic Models of Complex Networks" we address how analytical statements about crucial properties like connectedness or robustness can be made based on simple macroscopic stochastic models without knowing the details of a topology.

In the third part we address "Dynamical processes on complex networks". We show how a simple model for a random walk in networks can give insights into the authority of nodes, the efficiency of diffusion processes as well as the existence of community structures.

A fourth part "Network Optimisation and Inference" introduces models for the emergence of complex topological features which are due to stochastic optimization processes, as well as statistical methods to detect patterns in large data sets on networks.

In a fifth part, we address "Network Dynamics", introducing models for the emergence of complex features that are due to (i) feedback phenomena in simple network growth processes or (iii) order correlations in systems with highly dynamic links.

A final part "Research Trends" introduces recent research on the application of data mining and machine learning techniques to relational data.
Lecture notesThe lecture slides are provided as handouts - including notes and literature sources - to registered students only.
All material is to be found on Moodle.
LiteratureSee handouts. Specific literature is provided for download - for registered students, only.
Prerequisites / NoticeThere are no pre-requisites for this course. Self-study tasks (to be solved analytically and by means of computer simulations) are provided as home work. Weekly exercises (45 min) are used to discuss selected solutions. Active participation in the exercises is strongly suggested for a successful completion of the final exam.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
363-1070-00LCyber SecurityW3 credits2GS. Frei
AbstractThis course provides a solid understanding of the fundamental mechanics and limitations of cyber security to provide guidance for future leaders as well as individuals constituting our society.
Introduction to the concepts, developments, and the current state of affairs in the cyber security domain. We look at the topic from the attackers, defenders and societies perspective.
ObjectiveUpon completion of this course students understand the essential developments, principles, challenges as well as the the limitations and the state of practice in cyber security from the technological, economic, legal, and social perspective.
The course provides an interdisciplinary overview, guidance, and understanding of the dynamics in cyber security to guide decision making in business and society. Students understand the topics from the attackers, defenders, and societies perspective.
ContentIntroduction
- Brief history of the rise of the Internet from the attackers, defenders, commercial and society perspective
- Learning points from past and current assumptions, approaches, successes, failures, and surprises

Internet Infrastructure
- Establish a high level understanding of the fundamental design principals and functional blocks of the Internet infrastructure
- Understand strengths and weaknesses of present design choices from security perspective
- High level understanding of relevant networking concepts, protocols, software applications, policies, processes & organizations in order to assess these topics
- Establish a functional, high level understanding of relevant aspects of cryptography

Cyber Security & Risk
- Recognize cyber security as an interdisciplinary, highly dynamic, complex and adaptive system where increased interaction and dependencies between physical, communication, and social layers brings fundamentally different (and unpredictable) threats
- Core security assets such as: confidentiality, integrity, availability, authenticity, accountability, non repudiation, privacy
- Dominant players, protocols, and technologies
- Different threat actors along the dimensions attacker goals, resources, approach, and threat

Economics of Cyber Security
Understand security challenges and limitations from an economic, rather than technological perspective
- From security perspective: incentives of industry vs. users, security as a negative externality, zero marginal cost of software, network effect, time to market, lock-in, switching cost, economics of usability, security as a trade-off
- Social and psychological aspects of security

Attacker Capabilities
- Attacker capabilities and the offensive use from technical, economic, organizational, and operational perspective
- Understand common and novel attack and evasion techniques, proliferation of expertise and tools, optimal timing to use zero-day attacks
- Attack types and malware development lifecycle and detection evasion techniques
- Botnets, exploit markets, plausible deniability, distributed denial of service (DDoS)
- Processes and dynamics in the (in)security community, cyber-underground

Defense Options and Limitations
- Functional principles, capabilities, and limitations of diverse protection and detection technologies
- Security effectiveness and evaluation/testing of security technologies
- Trade-off between efficiency and resilience against structurally novel attacks
- Effectiveness baseline security measures
- Know cyber information sources and frameworks

Cyber Security Challenges
- Increasing software complexity and vulnerabilities, the illusion of secure software
- Full disclosure debate, economics of bug bounty programs
- Internet of things, Industry control systems (SCADA/ICS)
- Security and integrity of the supply chain (IoT, Smart-X)
- Social media and mass protests
- Erosion of privacy

Legal Aspects
- Legal aspects of cyber security, compliance, and policies
- Know the fundamental national and international legal and regulatory requirements in connection with cyber security on a cross-sector and sector-specific level
- Understanding of legal risks and measures for risk mitigation

Guest Talks:
- Pascal Gujer - Digital Forensics Expert Kapo Zurich (Cantonal Police Departement Zurich)
- Maxim Salomon - Previously at Roche now with Google as Technical Program Manager for Security of Mergers & Acquisitions "The safety vs. security of cyber physical systems"
- Marc Ruef - Security Expert, "Navigating the Cyber Underground"
- Roger Halbheer - Executive Security Advisor for Microsoft in EMEA
Lecture notesLecture slides will be made available online.
LiteraturePaper reading provided during the lectures
Prerequisites / Noticenone
CompetenciesCompetencies
Analytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Personal CompetenciesCritical Thinkingassessed
363-1114-00LIntroduction to Risk Modelling and ManagementW3 credits2VH. Schernberg, B. J. Bergmann, D. N. Bresch
AbstractThis course is a practical, hands-on introduction to various aspects of modelling, dealing with and managing risks across different industries, contexts and applications.
ObjectiveThe course illustrates what is required of the 21st century’s risk manager. It provides a qualitative and quantitative introduction to some of the various risks that societies and businesses face and to their management.

The course encourages students to think critically about models and mathematical representations of risks. It identifies and explores the current challenges of managing today’s risks given available technologies.

After taking this course, students can formulate a risk analysis problem with quantitative methods in a particular field.
ContentThe course describes the building blocks of risk modelling as well as the process of risk-management. It examines at different approaches to modelling and dealing with as well as mitigating different kind of risks in different industries.

The lectures emphasise the decision-making processes in various businesses and how risk-management relates to a company's value chain. Applications range from enterprise risk management, natural catastrophes, climate risk, energy market risk, risk engineering, financial risks, operational risk, cyber risk and more.

Note that the programme varies every year. Therefore, all aforementioned topics are not necessarily explored every year.

The panel of lecturers comprises risk professionals from various industries and government as well as academics from different disciplines.

The course covers the following areas:

1. Fundamentals of Risk Modelling: Probability, Uncertainty, Vulnerability...
2. Fundamentals of Risk Management and Enterprise Risk Management
3. Risk Modelling and Management across Different Areas, with invited speakers
Lecture notesThe course materials are provided via Moodle. For each session, slides (and in most cases a video recording) are available.
LiteratureAdditional readings will be discussed during the lectures.
Prerequisites / NoticeThe course is opened to students from all backgrounds. Some experience with quantitative disciplines such as probability and statistics, however, is useful.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
363-1100-00LRisk Case Study Challenge Restricted registration - show details
Does not take place this semester.
W3 credits2S
AbstractThis Risk Case Study Challenge gives MSc students the challenging opportunity to work on a real risk-modelling and/or risk-management case in close collaboration with a Risk Center corporate partner. The Corporate Partner for the Spring 2022 Edition will be announced soon.
ObjectiveDuring the challenge students acquire a practical understanding of
o The business of the corporate partner (typically bank or re/insurance)
o Risk management and risk modelling in the context of the challenge
o The role of operational risk management.

Importantly, students learn to frame a real risk-related business case with the help of a case manager from the corporate partner. They also learn to coordinate as a group, to integrate and learn from business insights in order to elaborate a solution for their case.

Finally, students communicate their solution to an assembly of professionals from the Corporate Partner. This teaches them valuable communication and presentation skills for next stage of their career.
ContentStudents work on a real-world, risk-related case. The case is based on a business-relevant topic. Topics are provided by a the Risk Center corporate partner.

While gaining substantial insights into this particular industry's risk modelling and/or management practices, students explore the case or problem on their own. They work in teams and develop solutions.

The cases allow students to use logical problem-solving skills with an emphasis on evidence and application.Typically, the cases are complex, contain ambiguities, and may be addressed in more than one way.

During the seminar, students visit the corporate partner’s offices, conduct interviews with members of the management team as well as internal and external experts (such as ETH faculty), and finally present their results in a professional manner.
Lecture notesThere is no script.
LiteratureThe relevant literature will be provided by the Risk Center professors connected to the Challenges.
Prerequisites / NoticePlease apply for this course via the official website (Link).

Apply no later than February 15, 2022.
The number of participants is limited.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationassessed
Leadership and Responsibilityassessed
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection fostered
Self-direction and Self-management assessed
363-1091-00LSocial Data ScienceW2 credits3GD. Garcia Becerra
AbstractSocial Data Science is introduced as a set of techniques to analyze human behaviour and social interaction through digital traces.
The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective.
ObjectiveA successful participant of this course will be able to:
- understand a wide variety of techniques to retrieve digital trace data from online data sources
- store, process, and summarize online data for quantitative analysis
- perform statistical analyses to test hypotheses, derive insights, and formulate predictions
- interpret the results of data analysis with respect to theoretical and testable principles of human behavior
- understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity
ContentSocial Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data.
SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales.
The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data.
Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior.
Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression.
These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior.

The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects.

The course will cover various examples of the application of SDS:
- Search trends to measure information seeking
- Popularity and social impact
- Evaluation of sentiment analysis techniques
- Twitter social network analysis

The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective.
The block course contains lectures and exercise sessions during the morning and afternoon of five days.
Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures.
Lecture notesThe lecture slides will be available on the Moodle platform, for registered students only.
LiteratureSee handouts. Specific literature is provided for download, for registered students only.
Prerequisites / NoticeParticipants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective.

Prior knowledge of advanced R, information retrieval, or information systems is not necessary.

Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform guided exercises.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Media and Digital Technologiesassessed
Problem-solvingfostered
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityassessed
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
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