Discourse about smart algorithms and digital social agents still refers primarily to the construction of artificial intelligence that reproduces the faculties of individuals. Recent developments, however, show that algorithms are more efficient when they abandon this goal and try instead to reproduce the ability to communicate. Algorithms that do not “think” like people can affect the ability to obtain and process information in society. Referring to the concept of communication in Niklas Luhmann’s theory of social systems, this paper critically reconstructs the debate on the computational turn of big data as the artificial reproduction not of intelligence but of communication. Self-learning algorithms parasitically take advantage – be it consciously or unaware – of the contribution of web users to a “virtual double contingency.” This provides society with information that is not part of the thoughts of anyone, but, nevertheless, enters the communication circuit and raises its complexity. The concept of communication should be reconsidered to take account of these developments, including (or not) the possibility of communicating with algorithms.

Key words: social algorithms, big data, artificial intelligence, neural networks, deep learning, double contingency, systems theory.

Purpose: This study aims to address the possibility of integrating some elements of the “radical constructivist” approach to management accounting teaching. It answers the following two questions: to what extent should management accounting educators construct a “radical constructivist” foundation to guide active learning? Then, in which ways can management accounting educators use qualitative methods to facilitate “radical constructivist” education? Design/methodology/approach – The study uses a teaching cycle that implements innovative learning elements, e.g. learning from ordinary people, designed following the principles of “radical constructivism”, to engage students with “externalities” at the centre of their knowledge construction. It adopts an ethnographic approach comprising interviews and participant observation for the data collection, followed by the application of qualitative content and narrative analysis of the data. Findings: The study findings and reflections illustrate that the majority of students respond positively to radical constructivist learning if the educators can develop an innovative problem-solving and authentic environment that is close to their real lives. The radical constructivist teaching cycle discussed in this study has challenged the mindsets of the management accounting students as it altered the traditional objectivist academic learning approaches that students were familiar with. Its use of qualitative methods facilitated active learning. Student feedback was sought as part of the qualitative design, which provided a constructive mechanism for the students and educators to learn and unlearn from their mistakes. This process enriched the understanding of learners (students) and educators of successful engagement in radical constructivist management accounting education and provides a base upon which to design future teaching cycles. Originality/value – The paper provides proof of the ability of accounting educators, as change agents, to apply radical constructivist epistemology combined with multiple qualitative research methods by creating new constructive learning structures and cultures associated with innovative deep-learning tasks in management accounting education.

Key words: radical constructivism, deep learning, , qualitative methodology, teaching cycle, management accounting education.

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short of replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According to Charles Darwin, the difference in mind between humans and higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfillment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper, we show how to use the structural machine to build a cognitive reasoning system that integrates the knowledge from various digital symbolic and sub-symbolic computations. This approach is analogous to how the neocortex repurposed the reptilian brain and paves the path for digital machines to mimic living organisms using an integrated knowledge representation from different sources. View Full-Text

Key words: cognition, computing models, deep learning, autopoiesis, knowledge structures structural machines, autopoietic machines.