CGSS: Introduction

Complexity and Global Systems Science (CGSS) will cover Game Theory and mechanism design, complex network, socio-physics, and critical thinking essays regarding the topic.

Complexity science is related to systems that are made up of thousands of units, whereas global systems describe large systems. Systemic instabilities are of a major interest and need to be understood.

Collateral costs are hard to track but can be associated with financial crises, conflicts, terrorism, crime and corruption, epidemics and cybercrime. Reducing collateral costs provides a new opportunity to tackle each problem. However, this required to understand complex systems.

In a complex system a large number of interacting system elements follow non-linear interdependencies. They are dynamic and probabilistic and therefore elude easy descriptions.

It is necessary to make a difference between a complicated and a complex systems. On the one hand, a car is a complicated system out of thousands of parts. However, each part constitutes a specific task and can be understood (mostly). On the other hand traffic of cars is complex and predicting traffic jams is fiendishly hard (e.g. the phenomena of phantom traffic jams).

Complex systems often exhibit self-organisation (e.g. pedestrian forming lanes to walk into different directions), however, self-organisation is not a guarantee to an efficient solution (e.g. the Love Parade disaster).

Predictability of complex systems is limited. Dynamics of such system usually are highly sensitive and therefore small differences in initial setup can cause largely different results (e.g. butterfly effect in weather forecasting).

Control over complex system is an illusion due to a irreducible randomness and delays in consequences together with regime shifts (i.e. only if a threshold is met a change becomes (catastrophically) visible). Goodhart’s law/Principle of Le Chatelier states that a system tends to counteract external control attempts.

The unstable supply chains and phantom traffic jams are caused by delays in the system that are then amplified and maintained throughout without possibility of stopping them. However, it can be modelled. Those models often show oscillations that propagate and make it difficult to obtain a specific state. Tragedies of the Commons are another classical case where oscillations eventually cause a break-down.

Strongly coupled system behave different: they have faster dynamics, extreme events, self-organisation, emergent system behaviour and low predictability.

Cascade effects in networks together with probabilistic events and delays make causal analysis difficult. A blackout is such an event where failure in a single node in the system can stop the whole system. Whereas more connectivity allows for quicker results in positive ways, it also allows for quicker spreading of negative results. In addition to an catastrophic event, secondary and tertiary disasters may follow up. A causality network can be modelled to identify n-ary disasters based on a specific disaster. If an effective decoupling strategy could be setup, the catastrophic spread could be interrupted.

Decentralisation seems to be a useful tool in reducing inherent risk.

Big Data is a double-sided sword. The more data you have, the more patterns you find. However, those patterns are mere correlation and do not represent causation. Therefore simply sifting through data does not allow to find causation. The idea was to create AI, that are able to detect patterns and find causation. However, AIs are themselves driven by data and can therefore be manipulated by data (chat bots learn racism from users in the internet, police machine learning discriminates against Black or Hispanic people in the US).