Search results

This paper aims to contribute to recent debates about financial knowledge by opening the black box of its algorithmization to understand how information systems can address the major challenges related to interactions between algorithmic trading and financial markets.

The paper analyses financial algorithms in three steps. First, the authors introduce the phenomenon of flash crash; second, the authors conduct an epistemological analysis of algorithmization and identify three epistemological regimes – epistemic, operational and authority – which differ in terms of how they deal with financial information. Third, the authors demonstrate that a flash crash emerges when there is a disconnection between these three regimes.

The authors open the black box of financial algorithms to understand why flash crashes occur and how information technology research can address the problem. A flash crash is a very rapid and deep fall in security prices in a very short time due to an algorithmic misunderstanding of the market. Thus, the authors investigate the problem and propose an interdisciplinary approach to clarify the scope of algorithmization of financial markets.

To manage the misalignment of information and potential disconnection between the three regimes, the authors suggest that information technology can embrace the complexity of the algorithmization of financial knowledge by diversifying its implementation through the development of a multi-sensorial platform. The authors propose sonification as a new mechanism for capturing and understanding financial information. This approach is then presented as a new research area that can contribute to the way financial innovations interact with information technology.

The shift from data-informed to data-driven educational policymaking is conceptually framed by institutional and transhumanist perspectives. Examples of the shift to large-scale quantitative data driving educational decision-making suggest that data-driven educational policy will not adjust for context to the degree as done by the data-informed or data-based policymaking. Instead, the algorithmization of educational decision-making is both increasingly realizable and necessary in light of the overwhelmingly big data on education produced annually around the world. Evidence suggests that the isomorphic shift from localized data and individual decision-making about education to large-scale assessment data has changed the nature of educational decision-making and national educational policy. Big data are increasingly legitimized in educational policy communities at national and international levels, which means that algorithms are assumed to be the best way to analyze and make decisions about large volumes of complex data. There is a conceptual concern, however, that decontextualized or de-humanized educational policies may have the effect of increasing student achievement, but not necessarily the translation of knowledge into economically, socially, or politically productive behavior.

The purpose of the chapter is to distinguish the process of formation of algorithm of making of managerial decisions in the age of constant changes. The methodology of the chapter includes the method of theoretical foundations, the method of theoretical differentiations of categorical tools, the method of algorithmization, and the method adaptive perception. The research results include the following theses: the problems of the research are current and important; categorical structure of definition “making of managerial decisions” is reflected within functions of management, the process of managerial activities, and decision making on the basis of the risk; actions on solving problems; the aspect of making of managerial decision is limited; authors' algorithm of making of managerial decision reflects rationality and irrationality of the formed situation. This article could be supplemented with practical recommendations in the sphere of making of managerial decisions.

With the advent of the fourth industrial revolution and the intelligent economy, this conceptual chapter explores the evolution of educational governance from one based on governing by numbers and evidence-based governance to one constituted around governance by data or data-based educational governance. With the rise of markets and networks in education, Big Data, machine data, high-dimension data, open data, and dark data have consequences for the governance of national educational systems. In doing so, it draws attention to the rise of the algorithmization and computerization of educational policy-making. The author uses the concept of “blitzscaling”, aided by the conceptual framing of assemblage theory, to suggest that we are witnessing the rise of a fragmented model of educational governance. I call this governance with a “big G” and governance with a “small g.” In short, I suggest that while globalization has led to the deterritorializing of the national state, data educational governance, an assemblage, is bringing about the reterritorialization of things as new material projects are being reconstituted.

“Landamatics” is a label given by American scholars to designate this algorithmic‐heuristic theory and method of performance, learning and instruction. Landamatics analyses and explains the mental processes which underlie expert performance, learning and decision making. It also defines specific ways of purposeful and accelerated development of such processes in novices and non‐experts through a special course of instruction.

This paper aims to discuss issues related to science, technology, engineering and mathematics (STEM) education. It is emphasized that the need to prepare students with twenty-first-century skills through STEM-related teaching is strong, especially at the elementary level. The authors present selected previous experiences, publications, research into the study topic, and review examples of primary school robotics and programming curricula, as well as associated educational activities. The authors stress that workshops, using kits to build and programme robots, are a modern form of interdisciplinary education of children and youth.

The rationale for conducting such activities in schools is found in the European reference framework in the context of training of key competences. Classes in robotics – if properly taught – will have an impact on the development of mathematical literacy and scientific–technical information and social competences. At the same time, competence is understood to mean a combination of knowledge, skills and attitudes appropriate to the situation. Besides, an analysis is presented of basic legal regulations in this matter as well as results of a survey, conducted in Poland and Ukraine among in-service teachers and prospective teachers.

The study was carried out with the participation of 91 primary school teachers and future teachers in the province of Silesia and at the University of Silesia in Poland, and in Ukraine, at Borys Grinchenko Kyiv University. The survey contained 15 questions about the pedagogical research “Robotics and children”. The study was carried out to determine the needs of modern education to introduce the basics of robotics in the educational process of primary school.

It can be expected that the findings from the prior Polish and Ukrainian surveys can be extrapolated into curricula for secondary schools and vocational education and training (VET) in the coming years.

This paper aims to analyse three decision-making approaches that involve humans and artificial autonomous agents, namely, human “in the loop”, “on the loop” and “out of the loop” and identifies the decision characteristics that determine the choice of a decision-making approach.

This is a conceptual paper that analyses the relationships between the human and the artificial autonomous agents in the decision-making process from the perspectives of the agency theory, sustainability, legislation, economics and operations management.

The paper concludes that the human “out of the loop” approach is most suitable for quick, standardised, frequent decisions with low negative consequences of a wrong decision by the artificial intelligence taken within a well-defined context. Complex decisions with high outcome uncertainty that involve significant ethical issues require human participation in the form of a human “in the loop” or “on the loop” approach. Decisions that require high transparency need to be left to humans.

The paper evaluates the decision-making approaches from the perspectives of the agency theory, sustainability, legislation, economics and operations management and identifies the decision characteristics that determine the choice of a decision-making approach.