Dirk Helbing is a Professor of Sociology at ETH Zurich specializing in modeling complex systems, leveraging the availability of big data. He is best known for his “social force model” in which pedestrians are described and analyzed as “social forces” that self-organize themselves. In the latest issue of Nature, Prof Helbing expands his model to capture and explain the growing societal vulnerabilities due to our increased, yet poorly understood, interdependencies. Much of his assessment resonates with other publications such as the WEF’s Global Risks Reports, and the book Antifragility by Nassim Nicholas Taleb.
The paper “Globally networked risks and how to respond” starts from the premise that increased complexity, resulting from globalization and technological advances, can make man-made systems unstable, “creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best.” This is in part explained through the lens of so-called “cascade effects”:
“Our society is entering a new era—the era of a global information society, characterized by increasing interdependency, interconnectivity and complexity, and a life in which the real and digital world can no longer be separated. However, as interactions between components become ‘strong’, the behaviour of system components may seriously alter or impair the functionality or operation of other components. Typical properties of strongly coupled systems in the above-defined sense are: (1) Dynamical changes tend to be fast, potentially outstripping the rate at which one can learn about the characteristic system behaviour, or at which humans can react. (2) One event can trigger further events, thereby creating amplification and cascade effects, which implies a large vulnerability to perturbations, variations or random failures. Cascade effects come along with highly correlated transitions of many system components or variables from a stable to an unstable state, thereby driving the system out of equilibrium. (3) Extreme events tend to occur more often than expected for normally distributed event sizes”
Helberg describes the recent financial meltdown as a cascade disaster and points toward other threats including cyber warfare, economic crises and pandemic diseases as parts of a man-made “global time-bomb”. To manage this level of un-certainty and vulnerability, he suggests a “re-design of the system” with the following principles:
“Managing complexity using self-organization. When systems reach a certain size or level of complexity, algorithmic constraints often prohibit efficient top-down management by real-time optimization. However, “guided self-organisation” is a promising alternative way of managing complex dynamical systems, in a decentralized, bottom-up way. The underlying idea is to use, rather than fight, the system-immanent tendency of complex systems to self-organize and thereby create a stable, ordered state. For this, it is important to have the right kinds of interactions, adaptive feedback mechanisms, and institutional settings. By establishing proper ‘rules of the game’, within which the system components can self-organize, including mechanisms ensuring rule compliance, top-down and bottom-up principles can be combined and inefficient micro-management can be avoided….
Coping with networked risks. To cope with hyper-risks, it is necessary to develop risk competence and to prepare and exercise contingency plans for all sorts of possible failure cascades. The aim is to attain a resilient (‘forgiving’) system design and operation…An additional principle of reducing hyper-risks is the limitation of system size, to establish upper bounds to the possible scale of disaster…. Last but not least, reducing connectivity may serve to decrease the coupling strength in the system…”
Despite the emerging understanding of the challenge of un-controllable complex systems, we do not have the intellectual capabilities to capture the level of interdependencies. Toward that end, Prof. Helbing calls for the creation of a new science, which he call “Global Systems Science”:
“Given that many twenty-first-century problems involve socio-economic challenges, we need to develop a science of economic systems that is consistent with our knowledge of complex systems. A massive interdisciplinary research effort is indispensable to accelerate science and innovation so that our understanding and capabilities can keep up with the pace at which our world is changing (‘innovation accelerator’).
In the following, I use the term Global Systems Science to emphasize that integrating knowledge from the natural, engineering and social sciences and applying it to real-life systems is a major challenge that goes beyond any currently existing discipline. There are still many unsolved problems regarding the interplay between structure, dynamics and functional properties of complex systems. A good overview of global interdependencies between different kinds of networks is lacking as well. The establishment of a Global Systems Science should fill these knowledge gaps, particularly regarding the role of human and social factors.
Progress must be made in computational social science, for example by performing agent-based computer simulations of learning agents with cognitive abilities and evolving properties. We also require the close integration of theoretical and computational with empirical and experimental efforts, including interactive multi-player serious games, laboratory and web experiments, and the mining of large-scale activity data.”
Finally, to provide for the necessary data that can be used by scientists to study global network risks, he along with his colleagues of the FutureICT project calls for the creation of an information infrastructure, or a “Planetary Nervous System”, which would be widely available:
“The data generated by the Planetary Nervous System could be used to feed a “Living Earth Simulator”, which would simulate simplified, but sufficiently realistic models of relevant aspects of our world. Similar to weather forecasts, an increasingly accurate picture of our world and its possible evolutions would be obtained over time as we learn to model anthropogenic systems and human responses to information. …
Finally, a “Global Participatory Platform”would make these new instruments accessible to everybody and create an open ‘information ecosystem’, which would include an interactive platform for crowd sourcing and cooperative applications.”