1 mei 2015

Animation: why infrastructures are complex systems

This web lecture explains: • The growing complexity of infrastructures; • The challenges with respect to infrastructures; • Why it is such a daunting task to design and govern infrastructures and to take decisions about new and huge investments.

1 februari 2021

Course Agent Based Modeling

Understanding Complex Adaptive Systems requires tools that themselves are complex to create and understand. Shalizi defines Agent Based Modelling as “An agent is a persistent thing which has some state we find worth representing, and which interacts with other agents, mutually modifying each other’s states. The components of an agent-based model are a collection of agents and their states, the rules governing the interactions of the agents and the environment within which they live.” This course contains 12 chapters. Firstly, an introduction to Complexity and to Agent based modelling is given (chapter 1 and 2). In the next chapters, complex system properties are explained in more detail (chapter 3-7 :Emergence, Path dependence, Adaptiveness, Chaos and instability, Diversity and Self-similarity). Subsequently, in chapter 8, an introduction is given of Robustness and Resilience. Lastly, in chapter 9-12, four other concepts are explained to understand the agent based modelling of complex adaptive systems: Evolution, Network theory and observer dependence. The course can be found on the following link: https://ocw.tudelft.nl/courses/agent-based-modeling-of-complex-adaptive-systems-basic/ This publication also includes various examples (selected research articles) of the use of ABM when it comes to understanding and analyzing resilience. A course summary is also part of the resources. This course relates to the collection "Measuring and Analyzing Resilience of STE Systems."

19 oktober 2021

Introduction to Systems Thinking

19 december 2024

Facing Floods: A stakeholder river management game

In river management, decisions are influenced by multiple stakeholders, each with different priorities, needs, and levels of influence. While collaboration is essential to solve river-related challenges, these differences create a complex dynamic environment in decision-making processes. This game simulates the challenges of river management by placing players in the roles of various stakeholders, each with their own goals, needs, and budgets. Players must work toward achieving their individual objectives while negotiating and cooperating with others, balancing personal interests with the shared responsibility of managing the river. The "Facilitator Guide" (see downloads) is designed to assist you in organizing and facilitating a game with 7 or more players, as well as preparing for the session. Additionally, you will find the various game materials required for gameplay. These materials need to be printed on A4 or A3 paper, with detailed printing instructions provided in the "Print Instructions" file. Learning goals • Experience the decision-making process related to complex systems as the development and management of river engineering infrastructure. • Get acquainted with the interests and goals of the involved stakeholders, realize that they may be conflicting or equivalent, and possibly bond with each other. • Debate the effectiveness and impacts of river interventions on the river system, environment and society, from the perspective of a stakeholder. • Appreciate the challenges of proposing a solution that integrates different river interventions and satisfies the goals of multiple stakeholders. • Reflect on the challenges of decision-making processes in complex systems. This game was developed for the River Engineering module, more specifically the unit River Functions and Management, of the MSc Civil Engineering program of the Technical University of Delft (TU Delft). However, the game is suitable for anyone interested in learning about stakeholder involvement and interactions in river management. More information about the use of the game can be found in the facilitator manual. Credits: this game was developed by the TU Delft Gamelab (website:https://copy.gamelab.tbm.tudelft.nl/).

29 augustus 2023

Modern Distributed Systems

Distributed systems are the backbone of modern society but entail challenges in areas such as complexity and energy-use. Discover distributed systems from first principles, understand the architectures and techniques derived from them and explore examples of current practical use. This course will provide learners with a fundamental understanding (theoretical and practical foundations) of how cloud, edge, and big data processing systems work and how they address common challenges for distributed systems such as performance, resilience, and scalability. Modern IT infrastructure is built as distributed systems, an exciting concept that started with the first computers and evolved rapidly into its present form. From online video meetings to internet services, from social media platforms to online games, we all use and interact with distributed systems on a daily basis and increasingly depend on them. Designing and operating such large-scale distributed systems, however, is complex and typically involves making reasonable compromises. There are fundamental technical barriers as well as economic arguments why we cannot make these systems behave as if they were running on a single, perfectly reliable machine. In this course, learners will be introduced to the essential functional and non-functional concerns of distributed systems and the common problems encountered while designing them, such as consistency, availability, elasticity, and scalability. A variety of practical solutions that have been established in the leading tech industry in recent years will be reviewed. These provide re-usable building blocks to create new large-scale applications. These recent developments, especially around cloud computing, large-scale data processing, distributed machine learning, and other fields are often not reflected in textbooks and are absent from many traditional curricula but are at the heart of this course. The learning progress is assessed through a variety of different activities including quizzes, design exercises, experiments, and open questions, with peer review of other students’ solutions. In the final project, learners will design a distributed system based on the learners’ own experience and interests and describe the functional and non-functional properties of the system.

16 februari 2020

3D Anatomy Lyon: The articular complex of the wrist - video of 3D model

In this video the anatomy of the articular complex of the wrist is shown and explained. Two joints form an articulation complex called the "wrist": the radiocarpal and the midcarpal. Functionally, the join complex of the wrist has a third joint level: the inferior radioulnar, which enables pronation and supination. This video is in the "3D Anatomy Lyon" series from the Université Claude Bernard in Lyon, France. NOTE: THIS VIDEO IS UNDER A NON-DERIVATIVE LICENSE. THIS MEANS THAT IF YOU REMIX, TRANSFORM, OR BUILD UPON THE MATERIAL, YOU MAY NOT DISTRIBUTE THE MODIFIED MATERIAL.

17 juni 2025

The Complex World of AI

Join Luiz Canalle as he explores the history of Artificial Intelligence (AI), from early game-playing programs in the 1950s to today's advanced systems. Learn about the rise of recommendation algorithms, and the advent of Generative AI. Understand the difference between Narrow AI and General AI, and delve into the principles of machine learning and deep learning. Stay informed and critical about AI's impact on our lives. #humaninthelead

9 november 2020

Complexity Theory (web lectures)

A complex system is defined as one in which many independent agents interact with each other in multiple (sometimes infinite) ways. This variety of actors also allows for the “spontaneous self- organization”; that sometimes takes place in a system. This self-organization occurs without anyone being in charge or planning the organization. Rather, it is more a result of organisms/agents constantly adapting to each other. The complex systems are also adaptive (i.e., they always adapt in a way that benefits them). Another important concept in complexity theory is that there is no master controller of any system. In short, we have to deal with a lot of uncertainty and many actors with different interests. Counterintuitive: • Minor events can have enormous consequences because of the chain of reactions they might incite. • Major changes may have an almost insignificant effect on the system as a whole. The 3 web lectures which are part of this resource will explain the complexity theory, its importance for analyzing systems and the implications for decision making . After these 3 web lectures students will understand the characteristics of complex systems like positive and negative feedback loops, deep uncertainty, tipping points and the key implications for decision making (adaptive planning, close monitoring). This web lectures belong to a module of "The Next Generation Infrastructures Course", which can be found here: https://ocw.tudelft.nl/courses/ngi-2/.

15 oktober 2024

The Delft Method for System Dynamics - Book

The Delft Method for System Dynamics is a proven method of learning basic System Dynamics (SD). The method focusses on learning by doing: first you try to make an exercise, and if you do not understand something or do not know something, then you can look it up in the theory part of the book. The book contains theory exercises on topics like causal loop diagramming, delays, and how to motivate why SD is appropriate. The book also contains modelling exercises that show students how to build low to medium complexity models, and how to use these quantitative models for scenario or policy analysis. The theory chapters cover all phases of the modelling cycle: problem articulation, conceptualisation, formulation, evaluation (including validation and scenario analysis), and policy analysis. This book is intended for students and educators in large or small System Dynamics courses, and for motivated students that want to learn SD in their own pace.

15 oktober 2024

The Delft Method for System Dynamics - Modelling Exercises

These modelling exercises are part of the TU Delft Open textbook "The Delft Method for System Dynamics". The Delft Method for System Dynamics (SD) is a proven method of learning basic System Dynamics. The method focusses on learning by doing: first you try to make an exercise, and if you do not understand something or do not know something, then you can look it up in the theory part of the book. The book contains theory exercises on topics like causal loop diagramming, delays, and how to motivate why SD is appropriate. The book also contains modelling exercises that show students how to build low to medium complexity models, and how to use these quantitative models for scenario or policy analysis. The theory chapters cover all phases of the modelling cycle: problem articulation, conceptualisation, formulation, evaluation (including validation and scenario analysis), and policy analysis. This book is intended for students and educators in large or small System Dynamics courses, and for motivated students that want to learn SD in their own pace.