Lecture

Systems Thinking: Introduction

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In course

HUM-226: Wellbeing and Planetary Boundaries

Ce cours approfondit les acquis d'ENV-101 pour mieux comprendre et mettre en œuvre la durabilité : comment transformer la société vers le bien-être pour tous dans les limites écologiques, avec des approches et solutions sectorielles (énergie, bâtiments, mo ...

Description

This lecture introduces the concept of systems thinking, emphasizing how systems interact to create emergent properties that are not present in individual elements. The instructor explains the importance of understanding systems to make sense of the world, using examples like the BBC news website and historical trends such as the Industrial Revolution and the Anthropocene. The lecture covers the levels of understanding a system, from events and trends to structure, highlighting the significance of feedback loops and the complexity of wicked problems.

Instructors (2)

Sascha Nick

Philippe Thalmann

Philippe Thalmann was born in Lausanne in 1963. He graduated in Economics from the University of Lausanne in 1984, where he earned a postgraduate diploma in Economics in 1986. Mr. Thalmann entered the doctoral program in Economics of Harvard University (Cambridge, U.S.A.) in 1986, which he completed with a Ph.D. in 1990. His dissertation is entitled: "Essays in the Economics of Government Revenues and Spending". Returning to Switzerland, he was hired as an assistant professor first at the University of Geneva (teachings in Public Economics), then at the University of Lausanne (teachings in Econometrics and Introductory Economics). Since 1994, Mr. Thalmann is associate professor of Economics as the Swiss Federal Institute of Technology at Lausanne.

Official source

Related concepts (22)

Feedback

Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop. The system can then be said to feed back into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems: Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument.

Complexity class

In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

Negative feedback

Negative feedback (or balancing feedback) occurs when some function of the output of a system, process, or mechanism is fed back in a manner that tends to reduce the fluctuations in the output, whether caused by changes in the input or by other disturbances. Whereas positive feedback tends to lead to instability via exponential growth, oscillation or chaotic behavior, negative feedback generally promotes stability. Negative feedback tends to promote a settling to equilibrium, and reduces the effects of perturbations.

Complexity

Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, leading to non-linearity, randomness, collective dynamics, hierarchy, and emergence. The term is generally used to characterize something with many parts where those parts interact with each other in multiple ways, culminating in a higher order of emergence greater than the sum of its parts. The study of these complex linkages at various scales is the main goal of complex systems theory.

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