Rational data selection from heterogeneous information space: problem statement

. The paper considers the problem of providing rational data selection from heterogeneous information space for adaptive-heuristic intelligence embedded in the mechanism of robust management of socio-economic systems. The relevance of solving the presented problem is determined by the need to adapt the work of the socio-economic system to the paradigm of modernity - sustainable functioning and development in conditions of accelerated changes. Systems move from complex to complex-adaptive, diffuse forms with the development of the ability to self-regulate like living, biological systems. To survive and develop, they try to make flexible, anticipatory adaptations within the limits of acceptable deviations of physiological parameters from some parameters - constants of health, which represent the conditions of maintaining homeostasis. The prototype is the automatic robust control of closed systems, in which the leading parameters are given a certain degree of freedom and the limit of their permissible changes is set. The effectiveness of such control strongly depends on the reliability of the data - facts entered the information granules of adaptive and robust homeostasis. We propose a technology for obtaining reliable information about the state of the studied objects for robust management based on the use of rational choice methods.


Introduction
The survival and development of socio-economic systems today takes place in conditions where chaos and uncertainty are a reality, and the need for constant adaptation to a complex reality is a way of life.Thus, modern publications such as E. Toffler's "Future Shock" [1], N. Taleb's "Risking Your Own Skin" [2] reveal an empirically supported warning against adaptation failures due to the acceleration of change.Organisational systems in such an environment operate in a mode of active adaptation.They are classified as Complex Adaptive Systems (CAS) by the consulting firm BCG [3].
In order to achieve sustainable development of socio-economic systems through adaptation, leading consulting groups (BCG, McKinsey, Battelle, Futures Group, Deloitte & Touche) and major companies in the world (Rand Corporation, Shell, General Motors, Dell, Intel, Unite Parcel Service) are studying the properties and behaviour of biological systems that allow them to exist and evolve in complex environmental conditions [4][5][6][7][8][9][10][11][12].The search has been for analogies between the properties and behaviour of organisational systems and the properties and behaviour of living systems.Identifying properties of organisational systems with those of living systems manifests itself by establishing analogy: "it is absolutely impossible to separate management from the organism" (St.Bier) [13], "the system is an organism" (N.Moiseev) [14,15], "self-regulation as an adaptive response of the system" (V.S. Styepin) [16].Such statements pose a challenge to the development of organismic methodology in the study of systems in connection with general research methodology.
The ideas and methods of organismal methodology are used by the authors of the article to build a theoretical framework for robust control of open systems [17].It includes: the principles of constructing a robust control model; consideration of the concept of "adaptation" as a necessary, directed change that ensures the stability, growth and development of the system within the evolutionarily achieved norm of reaction and due to the compensatory effect; the mechanism of robust control.The constructive elements of the mechanism of robust control are adaptive homeostasis, robust homeostasis, robust limit for the assembly of information granules of adaptive and robust homeostasis, "floating equilibrium" in the digitised homeostatic space and autonomous adaptive-empirical intelligence.The "robust limit" is a reasonable "norm" of economic and organisational changes in the order parameters (leading variables) of the activity of the economic system from the point of view of achievement of the set goals [4,13,18].
In the field of research on adaptation of complex systems, most of the works, the results of which are presented in publications, are devoted to the ways of achieving the equilibrium state of the system at the expense of its internal resources and the creation of their reserves.Among the published ideas that are closest to the topics studied by the authors of the article are: -adaptation as a basis for the evolution of economic systems, scenario of qualitative change in the management of socio-economic systems: "the concept of equilibrium and diminishing returns have to be abandoned and a new theory related to non-equilibrium states and innovation processes in the economy has to be considered" [19]; -the concept of developing the mechanism of adaptive management, which "lies on the way of synthesis of automated and management technologies of economic objects [20].
The recognised movement in systems research is supported by the evolution of scientific rationality: from classical to non-classical and post-non-classical [16,21].
An important aspect of the effectiveness of adaptation technology is the problem of real-time extraction of sufficiently reliable information from the chaos of the external environment and the stochastic order of the internal environment of the system, which is received by the operational centre of the system.The artificial intelligence embedded in the robust control mechanism acts as this centre.
The research is aimed at the solution of this scientific problem, in particular, at the development of theoretical bases of rational data selection from the heterogeneous information space of the external reflexive-active and internal environment, which allows to achieve the correspondence of data for the development of "correct" variants of scenarios of proactive adaptation of complexity socio-economic systems, satisfying the environmental conditions and developed by the mechanism of robust control.

Methods
The scientific significance of the problem consists in the development of an approach to structuring the chaos of the external environment through the formation of factor structures and data clouds overlaying them by periodically conducting "time slices" of data.The proposed approach is based on the rational selection of real-time data for the development of "correct scenarios of anticipatory adaptation" of socio-economic systems under conditions of expanded uncertainty and turbulence [22][23][24].
The execution of the derived function is assigned to an autonomous adaptive-heuristic intelligence embedded in the robust control mechanism.The mechanism of robust control comes into operation as a configurator that synthesises various ideas about strategic, tactical, and operational actions of the control and selectively selects them to build and maintain the "floating equilibrium" of the system (temporary attractor) in the digitised homeostatic space due to anticipatory adaptation.
The urgency of solving this problem is determined by the need to match the work of the socio-economic system to the paradigm of modernity -sustainable functioning and development in the conditions of accelerating changes, which are often either spontaneous and/or fundamental in nature.In addition, the fragility of management activity is enhanced by chaos, uncertainty, and turbulent outbursts.Systems are moving from complex to complexity, adaptive, diffuse forms.To survive and evolve, they seek to implement flexible anticipatory adaptation to overcome strategic uncertainty using explicit information operated on by mathematical methods and computational algorithms.
The external environment is traditionally represented by a set of factor structures with fuzzy information about the influence of factors on the state of socio-economic system.For example, these are strong, moderate, and weak manifestation of factors.For transition from fuzzy to clear information, it is proposed to cover each factor structure with information cloud containing data-facts, numerical characteristics of the role of factors in the sequence of situations with a certain measure of accuracy.Operational sources of data-facts on adaptation management are considered as "time sections" of the information cloud of factor structures of the external environment and the results of the system's activity in one time rhythm.
The process of obtaining reliable information about the state of the studied objects (external and internal environment) for its consumer -robust management is based on the use of rational choice methods.Rationality in the studied context means the correspondence of data in terms of the possibility of implementing the "right" options of scenarios of advanced adaptation to the existing conditions.Rationality is the correspondence of data in terms of the possibility of implementing the "right" options of scenarios of anticipatory adaptation to the existing conditions.The dynamic nature of the rationality of data selection is determined by the frequency and scale of "time sections" within the current situation, the definition of which we refer to a new task in the theory of management of organisational systems.
The developed technology of data preparation for decision-making on the anticipatory adaptation of open systems is considered as an integral functional structure of robust control of open complex systems referred to adaptive diffuse self-regulating and selforganising systems with the development of their ability to self-regulation.Self-regulation is a property of living systems, which is carried out within the limit of acceptable deviation of physiological parameters from some health constants, representing the conditions of homeostasis preservation.
The task of creating conditions for self-regulation of open systems of the inorganic world naturally arises.The prototype is the automatic robust control of closed systems, in which the leading parameters are given a certain degree of freedom and the limit of their permissible changes is set.Adaptation actions are performed by artificial intelligence of the mechanism of robust control of a closed system.The mechanism of robust control is a configurator that synthesises various ideas about strategic, tactical, and operational actions and makes selective selection to build and maintain a "floating equilibrium" (temporary attractor) of the system in homeostatic space.
To ensure the stability of open systems, a model of robust control with special information structures consisting of granules of leading variables about the state of the external environment (at the input of the system) and at the output of the system of leading variables about the results of its activities, with which it goes to the external environment.The input of special information structures, called as adaptive and robust homeostasis, is the input of regulators, by analogy with the functions in a living system that allow the system (organism) to expand the boundaries of possibilities of its existence, growth, and development.Homeostasis (managerial) is a special information structure consisting of information granules of order parameters (leading variables), acting as a regulator, and determining the permissible balancing of the system's equilibrium state without violating the stability of functioning under the conditions of the external environment.The functional dependence between the leading variables of the external and internal environment defines the homeostatic space -the form of the "body" of the system designed for dynamic formation (balancing) by periods (t belonging to T) of time attractors, i.e., "floating equilibrium" of the system.Homeostatic space of a system is a closed multi-parametric limiting space of a virtual construction consisting of a set of lines -functional links between the parameters of adaptive (at the input of the system) and robust (at the output of the system) homeostasis.
The effectiveness of robust control strongly depends on the reliability of the data-facts entered the information granules of adaptive and robust homeostasis.Work with dynamically changing information is assigned to the mechanism of robust control with built-in adaptive-heuristic intelligence, acting in interactive mode with consumers of input data for decision-making and scenarios of anticipatory adaptation.Adaptive-heuristic intelligence is a certain integrity of interconnectedness of "thinking" artificial entities, configured to correctly interpret external data, to learn from such data and to use the obtained knowledge to achieve specific goals and objectives with the help of flexible anticipatory adaptation of the system.The mechanism of robust control comes into operation as a configurator that synthesises various ideas about strategic, tactical, and operational actions of the control system and selectively selects them (compensatory actions) to build and maintain the "floating equilibrium" of the system (temporary attractor) in the digitised homeostatic space.

Results
The final message for solving the problem is the statement: adaptation management, introduced changes with the achievement of a compensatory effect, creates prerequisites for ensuring the sustainability of functioning and development of socio-economic systems.The use of artificial intelligence to automate decision-making will help managers to free themselves from labour-intensive and inert computational procedures and focus on the development of reflexive processes in the interaction between the manager and artificial intelligence.
The proposed approach provides an opportunity to solve the following important problems: E3S Web of Conferences 458, 09007 (2023) EMMFT-2023 https://doi.org/10.1051/e3sconf/202345809007 1.To understand and build the methodological basis and tools of the process of rational data selection from heterogeneous information space with its translation from fuzzy set of information to its clear evaluation.2. To allocate factor structures and fill them with a list of factors -sources of generating information about the state of the external environment, affecting the state of the organisational system and to develop a typological information model of the external environment, revealing the nature of the information cloud data of each factor structure.3. To reveal the interdependence between the leading variables of the external environment and the organisational system by building a system convergence of two different classes of systems.4. To consider as a source of data on the state of interacting systems the "temporary sections" of the information cloud of factor structures within the framework of successively changing situations in real time and to make a characteristic of the situation, to connect it with the data obtained by the "temporary sections" of the information cloud, to organise their storage for the history of the picture of changes in the characteristics of the situation.5. To derive a method of composing a granule from a set of sufficiently reliable data on the leading variables of the external environment and to construct from the granules of data a special information structure, which fulfils the role of a regulator of balancing the system under conditions of uncertainty and turbulence of the external environment and is called "adaptive homeostasis".6.To derive a method of composing an information granule from a set of sufficiently reliable data on the leading variables of organisational system activity and to construct a special information structure (robust homeostasis) from information granules.7. To form computational algorithms for establishing functional relationships between the leading variables introduced in adaptive and robust homeostasis and to establish the possibilities and limitations of balancing the "floating equilibrium" of the organisational system within the digitised homeostatic space.8. To generalise the models of data organisation in artificial intelligence and justify the priority model of data organisation for autonomous adaptive-heuristic intelligence of robust control mechanism.

Conclusion
The history of the origin of the theory of robust control is associated with the emergence of closed, mainly technical systems operating under conditions of uncertainty of changes and turbulence of the external environment.The introduction of robust control made it possible to prevent systems from such phenomena as "going out of control".The real revolution in the development of automatic control theory occurred in the 80s with the creation of the socalled H-infinity theory [25].This theory provided the impetus for the development of automatic robust control by opening up the possibility of considering problems with uncertainty.Robustness is a property that ensures preserving the quality of functioning within the limits of the requirements imposed on it when changing its parameters or structure due to anticipatory adaptation.Literally, the term "robust control" means control with a certain stability margin.
The main resource for launching robust control of open systems is sufficiently accurate real-time information about the state of the external and internal environment to develop "correct scenarios for anticipatory adaptation and, thus, to ensure the sustainability of functioning and development of socio-economic systems.The complexity of real-time datafacts collection is determined by its generation by different sources -factor structures of the external environment.Traditionally, information acquisition does not take place in real time and is based solely on "historical" data, which leads, due to the acceleration of situation change to the delay of rational decision-making on system adaptation.Switching decisionmaking on "historical" data to operational, collected and processed in real time data by methods of rational choice when embedded in an autonomous artificial intelligence served as the impetus for the supply to the study of the formulated scientific problem.