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2016 | OriginalPaper | Buchkapitel

5. Some Trends and Forecasts

verfasst von : D. A. Novikov

Erschienen in: Cybernetics

Verlag: Springer International Publishing

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Abstract

Any mature science necessarily predicts its own development and the development of adjacent sciences. As Cybernetics represents a metascience (see Chap. 2) with respect to its components–control theory and others, its functions should include analyzing their trends, seeking for generalizations and forecasting. Ideally, the matter concerns normative forecasting, i.e., constructing a multi-alternative scenario forecast with separation of desired trajectories and an action plan for their implementation.

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Vorheriges Kapitel Systems Theory and Systems Analysis. Systems Engineering

The existing grant-based funding of research facilitates differentiation of sciences and partially stimulates the existence of scientific self-reproducing “sects” in all fields of investigations.

Actually, these conferences gather researchers from many other countries.

All figures show the relative shares of papers having a corresponding topic.

In AMCP-2014, about 25–33 % of the papers were dedicated to control problems in interdisciplinary systems (socioeconomic, organizational and technical, etc.). They have been eliminated from our analysis.

Notwithstanding its “classical” character, ACT has an intensive development, including the appearance of new problems in well-known fields (e.g., in linear control systems) and new controlled objects (e.g., the rapid growth of publications on quantum systems control).

According to Merriam-Webster dictionary, science is knowledge about or study of the natural world based on facts learned through experiments and observation; a particular area of scientific study (such as biology, physics, or chemistry) or a particular branch of science; a subject that is formally studied in a college, university, etc.

In the case of technical systems, initial information “suppliers” are mechanics, aerodynamics, and so on.

Interestingly, a broader retrospective review indicates that social systems cyclically interchange with technical ones in the focus of control theory, getting “back” at a new turn of the dialectical spiral. Indeed, perhaps the first object of control (in the prehistoric society) was a group of people, later on—transport and elementary mechanisms, again followed by groups of people (Plato-N. Machiavelli-F. Bacon-T. Gobbs-…-A. Ampere-B. Trentowski). Starting from the middle of the 19th century, control theory switched to technical (mechanical) systems. Today, control of human beings, their groups and/or collectives is again on the agenda.

Systems, where a specially organized activity of human beings is a determining factor for the development of large-scale (global) ecological systems.

In systems whose elements have strategic behavior, discrimination between control subjects and controlled ones can be ambiguous; e.g., in some situations a subordinate manipulates its superior.

For the sake of justice, note that at all times living systems encouraged scientists and engineers to apply analogies, i.e., to “repeat” certain properties of living nature objects in artificial systems.

Control problems of quantum systems are mostly treated in theory, but micro-level controlled objects (“microsystems”) have become almost common.

Not to mention the penetration of ICT into engineering and everyday life, the associated educatory and social capabilities and threats.

Network-centrism operates its own abbreviations differing from control theory (see above): C³I—Command, Control, Communications and Intelligence, C⁴I—Command, Control, Communications, Computers and Intelligence, and others.

The classical CBG has the following statement. Two commanders (colonels Blotto and Lotto) distribute their forces among a finite number of springboards. The winner at each springboard is the player having more forces. Each commander strives for winning at as many springboards as possible.

This statement is true for separate informational systems and for integrated informational systems of product life cycle management (PLM) including computer-aided design systems, which realize the complex of the listed functions.

Each of these classes possesses certain advantages and shortcomings, especially, in the sense of real-time requirements. Today, the choice of concrete tools is defined by the skill of a researcher or engineer, as well as by accumulated experience and traditions of corresponding scientific schools. Global challenges concern maximum suppression of existing shortcomings of separate tools and design of general methods for their integration subject to posed problems.

Alternatives are, e.g., consideration of evolutionary games [220] or learning effects in games [141].

This class also includes the problematique of artificial neural and immune networks, probabilistic automata, genetic algorithms, and so on.

Some authors insist on the birth of a new science called complexity science.

A cybernetical system always has the behavior defined by its embedded algorithms (“stochastic,” “nondeterministic,” and others), despite the seeming generation of new knowledge or demonstration of qualitatively new (“unexpected”) behavior. This is especially the case under interaction of very many elements (a simple-structure system shows a complex behavior).

For instance, a microrobot cannot move a heavy load, in contrast to many microrobots applying their joint efforts.

The complete model of a system is so complicated that the appearance of new properties represents a “miracle” for an external observer (at the same time, scientists intensively exploit it and start believing that an artificial system can demonstrate an “independent” behavior).

An uncertainty is always induced by some other uncertainty potentially comprising lack of knowledge (insufficient information) and/or the action of random factors (an uncertainty never arises from an abstract “complexity” and similar conceptual factors). Facing an “uncertainty,” one should analyze cause-and-effect relations and seek for its source (“initial uncertainty”). Of course, different complexity factors merely get the things into muddle.

In some classifications, big data handling is associated with 4D (data discovery, discrimination, distillation and delivery/dissemination).

As we have mentioned above, in the recent 15 years experts in control theory have tended to consider the problems of control, computations and communication jointly (the so-called C³ problem (Control, Computation, Communication)). According to this viewpoint, control actions are synthesized in real time taking into account the existing delays in communication channels and information processing time (including computations). There is another generally accepted term (large-scale systems control), but big data can be generated by “small” systems.

An alternative interpretation of “big control” concerns control of big data handling processes. Actually, this represents an independent and nontrivial problem.

The principal idea of using big data is revealing “implicit regularities,” i.e., answering nontrivial questions: epidemic prediction based on information from social networks and sales in drugstores; medical and technical diagnostics; retention of clients by analyzing sellers’ behavior in stores (the spatial movements of RFID-tags of products); and others.

Data unstructuredness can be the result of their omissions and/or different scales of studied phenomena and processes (in space and time, see the so-called multi-scale systems).

In the first place, these technologies must perform data aggregation (e.g., detecting changes in technological data or storing aggregated indices). Really, one does not need all data (especially, “homogeneous” data).

Mathematics rather easily operates structured data; and so, data structuring makes an important problem.

An additional encumbrance is the accumulated experience of a researcher/developer and the traditions of his scientific school. Successful solution of a certain problem leads to the conviction that same methods (only!) are applicable to the rest open problems.

In some cases, additional information can be obtained by increasing the volume of data (under correct processing).

We recognize the importance of model’s adequacy and stability of modeling results, but omit these problems.

Not to mention situations, when existing scientific paradigms make it impossible in principle to model system behavior on a large time horizon (e.g., accurate weather forecasting).

Though, it is possible to store data de bene esse (e.g., to verify a certain hypothesis in future based on them).

At the very least, a fragment of the “objective” picture (hushing up the whole truth); at the most, an arbitrary inconsistent system of beliefs about the reality.

Another “educational” question ensuing from the generality of control laws and principles can be stated as follows: “Is it better to organize a department for control problems in each “sectoral” university or a university dedicated to control problems with “sectoral” departments?”. The book will touch this question.

Speaking about “control theory,” we mean exactly mathematical control theory (and not a corresponding branch of management science discussed in numerous bélles-léttres textbooks available today at stores).

Titel: Some Trends and Forecasts
verfasst von: D. A. Novikov
Verlag: Springer International Publishing
Buch: Cybernetics
Print ISBN: 978-3-319-27396-9

Electronic ISBN: 978-3-319-27397-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-27397-6_5

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