Premises of Digital Transformation in Autopoietic Organizations

This chapter exposes how the realization of digital transformation (DT) derives from the decisional communication of rule-making “chosen” alternatives, which originate in the self-referenced informational space, according to the dual perspective of reality adopted in evolutionary economics. Based on a critical analysis of scholarly literature to identify key proposals that support the definition of DT strategies, this research establishes the relevance of the fundamental tenets of autopoiesis theory, such as operational closure, structural coupling, and languaging, in the context of digitalization, to harmonize such DT strategy proposals to the structure of the organization in terms of decision premises. The internal availability of these decision premises determines the type of digitalization potential that can be self-observed by the organization, reinterpreting the attributes of DT in a framework that recognizes the sets of DT alternatives as decision premise dichotomies, with implications for theory and practice.

Computational Foundations of the Anticipatory Artificial Autopoietic Cellular Automata

A survey of the Varela automata of autopoiesis is presented. The computation of the Varela program, with initial conditions given by a living cell, is not able to self-maintain the membrane of the living cell. In this chapter, the concept of anticipatory artificial autopoiesis (AAA) is introduced. In this chapter, the authors present a new algorithm of the anticipatory artificial autopoiesis, which extend the Varela automata. The main enhancement consists in defining an asymmetric membrane of the artificial lining cell. The simulations show the anticipatory generation of artificial living cells starting with any initial conditions. The new concept of anticipatory artificial autopoiesis is related to artificial life (Alife) and artificial intelligence (AI). This is a breakthrough in the computational foundation of autopoiesis.

Network Organizations

Contemporary organizations must not only react, but also adapt to the never-ending changes. These are both technological and social in nature and occur in all areas of life and the economy. As a result, the current forms of management and traditional business models do not always turn out to be effective. One of the solutions for the new market requirements is cooperation in network structures. Modern technology makes it possible. The chapter presents the genesis and definition of organizations with a network and hybrid structure, and the factors determining the undertaking of cooperation activities between different organizations. The consequences of such cooperation between organizations were also indicated. The chapter presents the outcomes of the survey conducted in 2020, under exceptional conditions, due to the epidemiological situation in the world. In this unusual situation, network cooperation acquires a new, special meaning for the future economy.

Social Network Analysis

Social network analysis (SNA) is an attractive problem for a long time when social communities were popular since 2010. Scholars like to explore the meaning behind the numerous interactions generated at these social media sites. The primary and essential issue of SNA is to monitor, estimate, and engage the potential influencers who are most relevant and active to network. If we can analyze the social network this way, business enterprises could use minimal efforts to sustain the activity of influential users, improve sales, and enhance their reputations. In this chapter, a research framework based on multiple-criteria decision making (MCDM) is proposed. The authors will show how scholars could use dynamic self-organizing map (SOM) based on multiple-objective evolving algorithm (MOEA) and static weighted influence non-linear gauge system (WINGS) to analyze a social network. Finally, comparisons are made between the innovative approaches and the methods in tradition.

Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks

Organizations worldwide are supporting their processes and decisions with enterprise systems (ES). Large amounts of data are produced and reproduced in these increasingly complex sociotechnical systems, opening new opportunities for the adoption of self-supervised learning techniques. Complex networks are viable solutions to create models that learn from data. This chapter presents (1) a review on the possibilities of networks for self-supervised learning, (2) three cases illustrating the potential of complex networks to address the autopoietic nature of ES (adoption of enterprise resource planning, web portal development, and healthcare data analytics), and (3) a framework to mine sociotechnical patters uncovering the entanglement of human practice and information technologies. For theory, this chapter explains the potential of complex networks to assess enterprise systems dynamics. For practice, the proposed framework can assist managers in establishing a strategy to continuously learn from their data to support decision-making in self-adapting scenarios.

Self-Organizing Manufacturing Systems in Industry 4.0

This chapter presents the evolution of simulation modelling methodology in the context of the Industry 4.0 paradigm and the development of autonomous, self-organizing manufacturing systems. Such a system is managed by a decision-making system that uses a detailed model of the factory, known as the “digital twin” to monitor and control the manufacturing process and test possible process reorganization scenarios. To allow self-organization within the physical world, the “digital twin” model must itself be self-organizing. That means that the structure of the simulation model can be constructed from process data, which is a novel concept, called data-driven modelling. As self-organization leads to the reorganization of existing elements and their relationships within a system, we can treat such manufacturing systems as autopoietic. The chapter introduces the Industry 4.0 paradigm and its background and presents the main self-organizing manufacturing concepts, and the state of technology supporting these concepts.

Social Active Inference

The free energy principle and its corollary, active inference, were introduced by Karl Friston as an explanation embodied perception and action in neuroscience, and since, it has been used to address many other issues in different fields mainly related to cognitive science like learning, optimal decision, or interpersonal inference. Negotiation is a process where each negotiator has conflicting motivation is aiming to maximize his utility and where agreement is reached when the opposing interests are balanced. The purpose of this chapter is to illustrate how the free energy principle might be used through active inference in modeling a negotiation process based on an example of real life. The work is an attempt to bring together a dynamic logic framework with appropriate operators to consider motivation among agents on one hand and the active inference framework on the other hand.

Rationalising Technological Temporality

Literature on the implementation of digital technologies in formal educational settings reveals that schools ‘do' respond to technology induced change yet not as fast as desired. In context, a dynamic autopoietic scenario is exemplified through the recursive dialogues taking place between implemented ICT related policies in education and teachers' actions to the adopted digital technologies. The chapter therefore proposes and describes an analytical lens which, based on outcomes of the author's personally conducted research, is directed towards recognising teachers' response to these structural modifications. Subsequently, to substantiate the model in this grey area of technology accommodation and adaptation in formal educational contexts, a strategy-in-practice approach is employed. By grounding information systems research into self-performed practice, case studies and discussions on the validity of the proposed analytical framework are made.

Autopoietic Knowledge Management Systems

Knowledge management in an organisation is a key activity that aims to improve the organisation's competitiveness through gathering, processing, storing, and sharing of organisational knowledge. Socio-technical solutions designed to support knowledge management are systems for managing knowledge in an organisation. IT systems can support employees in their knowledge processes as well as independently generate, process, and codify knowledge, thus supporting the processes of organisational learning and development of knowledge bases. The cyclical and recurrent character of activities, both in terms of the interactions between process participants in organisations and actions of IT systems themselves, can be perceived in terms of autopoiesis, which explains the significance of knowledge management systems in organisational knowledge processing. The aim of this chapter is to analyse a knowledge management system through the lens of autopoietic perception of the activities taking place in an organisation, which are performed in poietic space.

Cognitive Agent for the Quality Management in Flexographic Printing on Packages

The aim of the chapter is to develop an approach for improving quality management in flexographic printing on packages using cognitive agent. A hybrid agents' architecture based on the learning intelligent distribution agent architecture (LIDA) and hierarchical temporal memory has been developed. Such approach has not been developed before; therefore, it is the main contribution of this chapter. The first part of the chapter presents the introduction to the research problem and background. Next, research methodology and the LIDA cognitive agents have been described. The main part of chapter presents the cognitive agent's architecture and functionality related to quality management in flexographic printing. The last part presents discussion, future works, and major conclusions.