Коллектив авторов - Рефлексивные процессы и управление. Сборник материалов XI Международного симпозиума 16-17 октября 2017 г., Москва

• Theamount of data analysed per issue is increased, compared to standard statistical analysis, even when experts suggest that the variety of data they analyse in a situation, cannot be analysed with an algorithm.

• Theissue analysis can clearly explain the reasoning behind the outcome prediction, the risk of ignoring important attributes is reduced.

• The prescriptive analytics can support or even replace, within limits, the expert decision taking. In the decision situations, where the Big Data based reasoning capacity matches the complexity of the environmental situations. A clear example of such development are the traffic/ routing/ logistics management systems, where drivers are relying on the navigation systems.

3.3 The generation and use of theoretic knowledge is redefined:

• The theoretic knowledge generation can become data driven. Instead of confronting tacit knowledge of multiple experts, the models of behaviour can be extrapolated directly from the data of the issues themselves. Standardisation and the relevance of the issues would no longer be matter of expert perception, but data based. An example: in financial institutions operational risk management directives are based on the risk probability and consequences perception of the risk management experts. Based on the experiences in the financial sector, it is easy to conclude, that they often put emphasis on the wrong risks.

• Storing the theoretic knowledge: the big data theoretic knowledge is stored in the form of predictive or prescriptive models or is based upon reports, produced by the models. It upgrades and complements the existing theoretic body of knowledge.

• The theoretic knowledge use is instance based. The experience, stored in models is applied upon the instance data. The elaborations, predictions and prescriptions are used to support decision taking, communication and automation. The main advantage of using big data based models is, that users do not need to analyse the complete theoretic backgrounds, but can focus on instances.

The backdraft of implementing BDA is, that it cannot imply all of the knowledge on complex issues, especially if the data quality or the number of cases recorded is not sufficient to provide reliable models. Therefore Big Data stored knowledge should be focussed on supporting processes, related to relatively simple instances, with multiple repetitions.

3.4 The mechanisms to manage the representation of the complexity are redefined. The main goal of an organisation is to coordinate individual capabilities to achieve itsindividually defined goals. The knowledge of an organisation is a complex combination of structured rules and shared tacit knowledge on multiple levels, gained from previous experiences – gained through previous activities or acquired from the environment.

Figure 3. Learning by observation

BDA tools use a different approach than Business intelligence. Instead of reducing variety and focusing solely on financial business outcomes, they provide the option to understand, predict or even propose activities on the detailed data, provided by the organisation.

3.5 The inter and trans team learning and experience sharing is redefined.

Figure 4. Sharing experience across teams

The experiences of multiple teams in similar situations can be successfully identified, understood, and learned upon with the help of BDA. For example, the effects of using multiple communication marketing campaigns in multiple markets can be compared.

There is though a limitation of using BDA to support learning between the teams. It works well in a highly repetitive processes, where data on similar situations are easily obtainable, as for instance sales, or mass production. If there are not enough similar cases, or if the data variety to explain a cases is too high, BDA cannot adequately provide insight.

3.6 BDA support the interhierarchical learning processes and reduce the number of the hierarchical recursion levels.

Figure 5. Understanding the drivers

The BDA is used by the higher levels in two ways: First, by elaborating the feedbacks of the lower structural recursion levels, it can fine-tune the activities, guiding to the desired results. Secondly, it can use BDA to better understand the needs, processes and relations at lower levels to propose solutions that provide value added for all the subjects, affected by the organizations. The higher capacity to manage variety also reduces the need for hierarchy and allows structural recursion. In some cases, the automated guiding systems can entirely eliminate the need for intermediaries between the consumer and provider on a global scale.

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Abstract. As a way of conceptualizing and of pursuing epistemological practices, such as learning, designing and researching, cybernetics should enjoy a front row position within academic settings today. However, being aligned orthogonally to – and occasionally challenging – the utilitarianism, revenue models, reward-orientation, and control structures of many academic and research organizations, cybernetics as an academic discipline is in serious crisis. In many parts of the world, it no longer enjoys the levels of funding support, student numbers, academic workforce and opportunities to offer study programs it enjoyed a few decades ago. While many cybernetic ideas and theories have been absorbed by other fields, where they are pursued in specialist engineering approaches, the study of cybernetics as a generalist philosophy has all but disappeared from formal curricula in many parts of the world. Furthermore, in many parts of the word the more generalist subject of cybernetics and its more specialist sister subject of computing have drifted apart, resulting in a disciplinary as well as philosophical fragmentation of the field.

Keywords: Cybernetics, reflexive pedagogy, second-order cybernetic concepts

In some ways, cybernetics is back where it was shortly after World War II, having to rely on its appeal to bright and enthusiastic minds to approach and pursue the subject, to build new communities, and to develop the future of cybernetics practically from scratch. Given the success of cybernetics in the middle of the last century, it is prudent to take a look back, and to examine how early cybernetic thinkers may have connected with control and communication early in their lives.

It may be no coincidence that cybernetics owes its greatest advances to a generation, which, during its childhood years, commonly played with toy steam engines. This toy may very well have played a key role in acquainting this generation early on with principles of control and communication, of measurement, signaling and adaptation via feedback, to instill an interest in self-regulating systems. Being mass manufactured, the toy steam engine was itself a product of the industrial paradigm initiated by its full-scale predecessor, and as a miniaturized reincarnation, it helped a new generation to pave the path from the industrial era to the information era.