43 Visions For Complexity

Stefan Thurner

• Verlag: WSPC
• Jahr: 2016
• ISBN:
  9789813206861
• Thema: Physical Sciences
• Thema: Chaotic Behavior in Systems

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Coping with the complexities of the social world in the 21st century requires deeper quantitative and predictive understanding. Forty-three internationally acclaimed scientists and thinkers share their vision for complexity science in the next decade in this invaluable book. Topics cover how complexity and big data science could help society to tackle the great challenges ahead, and how the newly established Complexity Science Hub Vienna might be a facilitator on this path.

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0 Readership: Researchers working on interdisciplinary topics and complexity. -->
Complexity Science, Neural Networks, Modeling, Interdisciplinary Approaches0

the Legendre transformations, as well as large deviation theory arguments, imply that the entropy S of any thermodynamically describable system should be extensive, i.e., S(N) ∝ N (N→∞), N being the number of elements of the system. To achieve such extensivity, the entropic functional needs to be adapted to the class of correlations of the system. More specifically, let us focus on a possible generalization of BG statistics based on the nonadditive entropy

, with

p_i ln p_i. If we have a large system with W (N) ∝ μ^N (μ>1) equiprobable states, then S_BG(N) = k ln W (N) ∞ N, hence extensive. But if W (N) ∝ N^p (p>0), the BG entropy violates thermodynamics, whereas S_q=1-1/p (N) ∝ N satisfies it! This is the heart of the idea: change the entropic functional in order to preserve thermodynamics! This simple standpoint has astonishing consequences in natural, artificial and social complex systems (bibliography at http://tsallis.cat.cbpf.br/biblio.htm). As recent applications we mention the experimental validation of a 20-year-old prediction, the emergence of q-statistical behavior in high-energy collisions at LHC/CERN along 14 experimental decades, a notable numerical discovery in the celebrated standard map. Partial financial support by the John Templeton Foundation is acknowledged.

 

Constantino Tsallis / Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rio de Janeiro; Santa Fe Institute
is emeritus researcher at the Centro Brasileiro de Pesquisas Fisicas, head of the National Institute of Science and Technology for Complex Systems, and external professor at the Santa Fe Institute. He holds a doctorat d’État ès Sciences Physiques from the University of Paris-Orsay. He is doctor honoris causa from various universities in Latin America and Europe, member of the Brazilian Academy of Sciences, Mexico Prize laureate, and holds the Aristion from the Academy of Athens. He has supervised over forty PhD and master theses, and delivered over one thousand of invited lectures around the world.

Causality is the agency or efficacy that connects one process (the cause) with another (the effect), where the first is understood to be partly responsible for the second.

Reality is the state of things as they actually exist, rather than as they may appear or might be imagined.
(Wikipedia)

The reality of complexity is that causality is very difficult to establish, if at all. Yet we live in a complex world that we seek to manage by establishing causalities. Reality is also that establishing causality is one of the most difficult problems for science, especially for the sciences that deal with the real world. How to untangle or better understand the relationship between causality and reality will be a key in finding ways to sustainably manage our lives, our health care, our laws, and our cities in an ever more complex world.

In My life in Science Sydney Brenner points out that one of the most common ways to explain the concept of complexity, namely: The whole is greater than the sum of its parts, should actually read: A system, the whole, is greater than the sum of its parts studied in isolation. Or, even better: A system, the whole, can never be more than the sum of its parts and their interactions. In other words, emergence must be explained from the interactions between the parts in a system.

David Pines wrote in an article celebrating the 30 years existence of the Santa Fe Institute: The central task of theoretical physics in our time is no longer to write down the ultimate equations, but rather to catalogue and understand emergent behavior in its many guises, including potentially life itself. […] For better or worse, we are now witnessing a transition from the science of the past, so intimately linked to reductionism, to the study of complex adaptive matter, firmly based in experiment, with its hope for providing a jumping-off point for new discoveries, new concepts, and new wisdom.

The ultimate equations in this quote constitute causality. Cataloguing and understanding emergent behavior constitutes the relation to reality. To transit from the one to the other requires a fundamental change in the perspectives of exploring scientists and thus presents one of the greatest challenges to complexity science.

The relevance of this challenge for the Complexity Science Hub lies in Vienna’s unique position as the meeting point between Eastern and Western Europe. In the relationship between the two, causality and reality have always been at odds. During the first 45 years after World War II, the West (USA) and the East (Soviet Union) kept each other in balance with a simple causal relationship:

If one started to bomb the other, humanity as a whole would be extinguished.

In those 45 years a more realistic relationship took shape in Vienna: It became an important hub in the UN network of institutes that aimed to bridge the political and cultural divides between east and west.

Following the end of communism in Eastern Europe in 1989, Vienna used its position between east and west to recreate itself as a hub in central Europe. It is now in a unique position to add further reality to the still simple causal relationship between east and west. A complexity hub that focuses on better understanding the relationship between causality and reality will be a tremendous asset on expanding that position.

 

Jan W. Vasbinder / Nanyang Technological University, Singapore
studied physics at the Technical University of Delft (1972). He started his professional career as a researcher in a nuclear laboratory. Until 1981 he worked in the nuclear industry in Israel and the Netherlands. In 1981 he was appointed Attaché for Science and Technology in Washington and Ottawa. In 2003 he initiated the institute Para Limes in Europe, and in July 2011 he moved to the Nanyang Technological University (NTU) in Singapore to become the director of the Complexity Program aimed at developing a Complexity Institute at NTU. The Complexity Program is now renamed Para Limes. His motto is: The value of knowledge is in its use.

From schools of fish and flocks of birds, to digital networks and self-organizing biopolymers, our understanding of spontaneously emergent phenomena, self-organization, and critical behavior is in large part due to complex systems science. The complex systems approach is indeed a very powerful conceptual framework to shed light on the link between the microscopic dynamical evolution of the basic elements of the system and the emergence of macroscopic phenomena; often providing evidence for mathematical principles that go beyond the particulars of the individual system, thus hinting to general modeling principles. By killing the myth of the ant queen and shifting the focus on the dynamical interaction across the elements of the systems, complex systems science has ushered our way into the conceptual understanding of many phenomena at the core of major scientific and social challenges such as the emergence of consensus, social opinion dynamics, conflicts and cooperation, contagion phenomena. For many years though, these complex systems approaches to real-world problems were often suffering from being oversimplified and not grounded on actual data.

In the last decade however the research landscape has been redefined by the big data revolution. Not only was an increasing number of socio-economic data made readily available by the progressive digitalization of our world. The advent of mobile and pervasive technologies, the web and the myriad of digital social networks have triggered an unprecedented avalanche of social behavioral data ranging from human mobility and social interaction to the very real time monitoring of conversation topics, memes and information consumption. Nowadays complex systems

science has definitely moved from stylized models to data driven approaches that can be validated quantitatively. From the spreading of emerging infectious diseases and crime rate, to road traffic and crowd movement, microsimulation models are increasingly used for scenario analysis and in real-time forecast. Size does matter, and having high quality datasets for thousand or millions of individuals has triggered the search for statistical patterns, ordering principles, and generative mechanisms that could be used to achieve greater realism in the modeling of complex systems.

Complex systems science is probably entering the most exciting stage of its life. While complex systems science appears to be the key to scientific answers to major real-world problems, the field has still formidably hard problems to be solved. In some instances the field has developed in an uncoordinated way by ideas, methods, and models, and contributed in different domains, from physics and biology to mathematics and social and economic sciences. However, the more complex systems science is becoming the conceptual and methodological key to understand and deal with important real world problems, the more it needs to be put on unified and rigorous foundations. By combining applied and theoretical work, and using data as the necessary anchor to real world systems, the Complexity Science Hub Vienna is in the position to spearhead the much needed interdisciplinary research model for the next decade of complex systems science.

Alessandro Vespignani / Northeastern University, Boston
is currently Sternberg Family Distinguished University Professor at Northeastern University, where he is the founding director of the Northeastern Network Science Institute. Vespignani is elected fellow of the American Physical Society, member of the Academy of Europe, and fellow of the Institute for Quantitative Social Sciences at Harvard University. Recently Vespignani’s research activity focuses on the data-driven computational modeling of epidemic and spreading phenomena and the study of biological, social and technological networks.

Over the past quarter of a century, terms like complex adaptive system, the science of complexity, emergent behavior, self-organization, and adaptive dynamics have entered the literature, reflecting the rapid growth in collaborative, trans-disciplinary research on fundamental problems in complex systems ranging across the entire spectrum of science from the origin and dynamics of organisms and ecosystems to financial markets, corporate dynamics, urbanization and the h...