About State Key Laboratory of Management and Control for Complex Systems

State Key Laboratory of Management and Control for Complex Systems

The State Key Laboratory of Management and Control for Complex Systems, shortened in its own English materials to SKL-MCCS, is a national research laboratory hosted by the Institute of Automation of the Chinese Academy of Sciences in Beijing. Its address is given in the source profile as 95 Zhongguancun East Road, the Institute of Automation's address in the Zhongguancun district of Haidian. The laboratory studies how complex systems, in engineering, society and the economy, can be modelled, analysed, managed and controlled, drawing on intelligent science, social computing and control theory.

The phrase complex systems has a precise meaning in this context. A complex system is one whose behaviour emerges from many interacting components and cannot be predicted reliably from any single component alone: a city's traffic, a power network, a financial market, an online social platform or a large industrial plant. Managing such systems requires methods that combine mathematical modelling with computation and with knowledge of human behaviour. SKL-MCCS was created to develop those methods in one place, and it remains closely identified with the concepts of parallel intelligence, parallel management and social computing developed by its founding director, Wang Feiyue, whose name is also rendered Fei-Yue Wang in English publications.

Origins from 1991 to state key laboratory status in 2011

The laboratory's own English history, published by the Institute of Automation, traces a twenty-year path to state key laboratory status. It started as the Key Laboratory for Control of Complex Systems in 1991. In 1994 it was formally approved to open to the outside as the Key Laboratory of Complex Systems Engineering of the Chinese Academy of Sciences. In 2011, the Ministry of Science and Technology of the People's Republic of China, known as MOST, officially approved the plan of construction of the State Key Laboratory of Management and Control for Complex Systems.

That staged progression is typical of China's laboratory system, in which a research group first proves itself as an institute or academy key laboratory before being elevated, after national review, to a state key laboratory with a construction plan, an academic committee and periodic national assessment. The 1991 starting date is significant because it places the laboratory's origins at the beginning of the modern study of complex systems in China, before the internet made social and economic systems available for computational study at scale. The group that became SKL-MCCS therefore spent two decades building theory for systems that, by the time of its elevation in 2011, could suddenly be observed and modelled with large volumes of real data.

An intermediate stage in that development is recorded in Wang Feiyue's biography. In 1999 he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, supported by the Outstanding Overseas Chinese Talents Program and the CAS Hundred Talents Program, and in 2002 he was appointed director of the Key Lab of Complex Systems and Intelligence Science of the Chinese Academy of Sciences. Those units belonged to the same institutional lineage of complex systems research at the institute from which the state key laboratory was eventually formed.

Host institution: the Institute of Automation

The Institute of Automation is one of the Chinese Academy of Sciences' principal institutes for automation, pattern recognition and artificial intelligence. Its English materials describe research departments covering pattern recognition theory and application, complex system management and parallel control, application-specific integrated circuit design, brain-inspired computation, precise perception and intelligent control, intelligent medicine and digital content understanding. SKL-MCCS is one of its two national-level key laboratories and supplies the institute's strength in the management and control side of that portfolio, complementing its better-known work in pattern recognition and intelligent systems.

The Zhongguancun location places the laboratory among Beijing's densest concentration of universities and academy institutes. For a laboratory whose subject is the modelling of social, economic and engineering systems, that setting provides both collaborators and, in the life of the capital itself, an immediate example of the complex systems under study, from metropolitan transport to large online platforms.

Leadership: Wang Feiyue and Dai Ruwei

The laboratory's own profile names two figures at its head. Professor Wang Feiyue is its director, and academician Dai Ruwei is the director, or chair, of its academic committee. In the Chinese state key laboratory system, that division is standard: the director is responsible for the laboratory's research and operations, while the academic committee, usually chaired by a senior scientist, advises on direction and reviews progress.

Wang Feiyue, born in November 1961, received his doctorate in computer and systems engineering from Rensselaer Polytechnic Institute in Troy, New York, in 1990. He joined the University of Arizona in the same year and became a professor there, directing its Robotics and Automation Laboratory and its Program in Advanced Research for Complex Systems. From 2006 to 2010 he served as vice president for research, education and academic exchanges at the Institute of Automation, and in 2011, supported as a state specially appointed expert, he became director of the new state key laboratory. Standard biographical sources describe him as the laboratory's chief scientist and founding director. He has served as editor-in-chief of IEEE Transactions on Computational Social Systems and of the IEEE and Chinese Association of Automation journal Journal of Automatica Sinica, and previously as editor-in-chief of IEEE Transactions on Intelligent Transportation Systems from 2009 to 2016 and of IEEE Intelligent Systems. He was elected a Fellow of the IEEE in 2004 and is also a Fellow of the American Association for the Advancement of Science and of the International Federation of Automatic Control, both recorded as 2007 elections.

Dai Ruwei, the academic committee chair named in the laboratory's profile, is a member of the Chinese Academy of Sciences associated with the earlier development of complex systems and intelligence science at the institute. His presence at the head of the committee links the state key laboratory to the generation that built the institute's complex systems programme in the 1990s, the lineage from which the 1991 predecessor laboratory came.

People and scale at the time of its profile

The Institute of Automation's English profile of the laboratory, which the clean-file description for this entry follows, gives a concrete picture of its scale at the time that profile was written. The laboratory then had 70 full-time faculty members, among them 16 professors and 5 winners of the National Science Fund for Distinguished Young Scholars. In addition, it had more than 200 graduate students and more than 300 floating researchers and engineers, and it hosted more than 100 visiting scholars from China and abroad.

Those categories are worth explaining for readers outside the Chinese system. Full-time faculty are the laboratory's permanent research staff. Floating researchers and engineers are a characteristic feature of open laboratories in China: scientists based elsewhere who work at the laboratory for periods of time on collaborative projects, supported in part by the laboratory's open research funds. Visiting scholars extend that openness internationally. A laboratory with several hundred people passing through it in these categories is functioning not only as a research group but as a national meeting point for its discipline, which is one of the stated purposes of the state key laboratory system.

These figures are reported here as the laboratory's own profile figures, not as a current census. State key laboratories change size as projects begin and end, and this profile makes no claim about present-day headcount beyond what the source states.

Research focus: complex systems and intelligent science

In its own summary, the laboratory focuses mainly on the theory and application of complex systems and intelligent science, using the network environment to give full play to multidisciplinary coordinated advantages. Based on systems engineering technology, it aims to solve problems of modelling, analysis, management and optimisation of complex systems in engineering, society, economy and related fields through the theories and methods of intelligent science. Its stated goal is to become an internationally well-known organisation for scientific research, technical innovation and talent cultivation.

Each element of that summary corresponds to a recognisable body of work. Modelling asks how a complex system can be represented computationally so that its behaviour can be explored without experimenting on the real system. Analysis asks what properties such a system has: whether it is stable, where it is fragile, and how small changes propagate. Management and optimisation ask how decisions should be made inside the system, by operators, regulators or automated controllers, when no decision-maker can see or control the whole. Intelligent science supplies the tools, including machine learning, agent-based modelling and knowledge automation, that make those tasks tractable at the scale of modern data.

Parallel systems, social computing and parallel intelligence

The laboratory is most widely cited internationally for a family of ideas developed by Wang Feiyue and his collaborators. The first is the parallel systems approach, often summarised by the letters ACP: Artificial societies, Computational experiments and Parallel execution. In this approach, researchers build an artificial version of a social or engineering system, run computational experiments on the artificial system to learn how the real one might behave, and then execute management and control in parallel between the artificial and real systems, each informing the other. Wang's keynote work on the road to computational and smart societies presented exactly this ACP-based parallel systems approach.

The second idea is social computing, the study of social behaviour and social systems by computational means, to which Wang devoted the IEEE Transactions on Computational Social Systems as founding editor-in-chief. The third is parallel intelligence and its relatives, parallel management and parallel control, which extend the ACP idea from description to action: if an artificial system runs alongside a real one and can be experimented on safely, then management decisions can be tested in the artificial world before being applied in the real one. Later formulations by Wang and colleagues describe a progression from cyber-physical systems to cyber-physical-social systems, recognising that modern technical systems are inseparable from the people and institutions inside them, and a programme of knowledge automation, applying automation not only to physical processes but to the production and use of knowledge itself.

These ideas can sound abstract, but their intended applications are concrete: transportation systems in which traffic is managed with the help of continuously updated artificial counterparts, enterprises in which management is supported by parallel models, and social systems in which the effects of policies can be explored computationally before they are imposed on the public.

Applications in transport, enterprise and society

Intelligent transportation is the application area in which the laboratory's leadership has the longest public record. Wang Feiyue served as president of the IEEE Intelligent Transportation Systems Society and edited its transactions for seven years, and parallel approaches to traffic management and control are among the most developed examples of the laboratory's methods, because urban traffic is a classic complex system: large, data-rich, human-driven and impossible to experiment on directly. An artificial traffic system can be calibrated with real data, used to test signal timing or routing strategies, and then coupled back to the real network, which is the ACP method in operation.

Enterprise and service management form a second area. Published keynote material associated with the laboratory's circle includes work on parallel service management, and Wang's widely cited writing on the emergence of intelligent enterprises traces a path from cyber-physical systems to cyber-physical-social systems, arguing that firms themselves should be understood and managed as complex systems with artificial counterparts. A third area is the computational study of society more broadly, including the modelling of social dynamics and the governance of online communities, where the laboratory's combination of control theory and data science is directly relevant.

This profile does not attribute specific deployed systems or commercial products to the laboratory beyond what its own published summaries support. The laboratory presents itself, and is best described, as a methods laboratory: its product is theory, algorithms, software platforms and trained people, which other organisations then apply in particular cities, companies and agencies.

Publishing, journals and academic community

The laboratory's influence runs partly through journals. The Journal of Automatica Sinica, published jointly by the IEEE and the Chinese Association of Automation, and IEEE Transactions on Computational Social Systems both counted Wang Feiyue as editor-in-chief in the biographical record, and both journals are natural outlets for the laboratory's fields: control and automation in the first case, computational social science in the second. Wang has also served as vice president and secretary general of the Chinese Association of Automation, the national professional society in the laboratory's discipline.

Conferences play a similar role. The laboratory's researchers are regular keynote speakers at meetings on cybernetics, social computing and intelligent systems, where the ACP method and parallel intelligence are presented to international audiences. For a directory reader, the practical consequence is that the laboratory's name appears less often on buildings or products than on the title pages of the journals and conferences through which an entire research community organises itself. That is a different kind of institutional presence from a manufacturer or a broadcaster, but in basic research it is often the more durable one.

Assessment and significance

SKL-MCCS occupies a distinctive niche among China's state key laboratories. Most such laboratories are devoted to a physical science, a life science or an engineering technology. This one is devoted to a method: the management and control of systems too large, too human and too interconnected for classical control theory alone. Its creation in 2011, after twenty years as academy-level laboratories dating back to 1991, marked national recognition that social, economic and technical systems had merged into a single research problem.

Its significance can be summarised in three points. First, it gave the parallel systems and social computing programme a permanent institutional home at the Institute of Automation, with the staff, students and visiting researchers recorded in its profile. Second, through its director's editorships and association leadership, it helped define the vocabulary, including parallel intelligence, parallel management and knowledge automation, in which those ideas are now discussed internationally. Third, it trains researchers in a genuinely multidisciplinary craft that combines control engineering, computer science and social science, a combination that few single university departments provide. For those reasons the laboratory is profiled in this directory as one of Beijing's significant national research institutions, and as the clearest Chinese example of a state key laboratory built around the science of complexity itself.