Information Models of Man in Contexts of Information Society: Theoretical and Strategic Perspectives

Author(s):  
Jela Steinerová

Over the last decades information and knowledge have become global and traditional disciplines of humanities and social sciences have searched for new methodological models related to knowledge and information. Based on information science as social science I would like to investigate the role of human beings in creation, communication and use of information. Information problems of people are complex real-life problems which need new methods...

2019 ◽  
Vol 1 (1) ◽  
pp. 177-183
Author(s):  
Jan Guncaga ◽  
Lilla Korenova ◽  
Jozef Hvorecky

AbstractLearning is a complex phenomenon. Contemporary theories of education underline active participation of learners in their learning processes. One of the key arguments supporting this approach is the learner’s simultaneous and unconscious development of their ability of “learning to learn”. This ability belongs to the soft skills highly valued by employers today.For Mathematics Education, it means that teachers have to go beyond making calculations and memorizing formulas. We have to teach the subject in its social context. When the students start understanding the relationship between real-life problems and the role of numbers and formulas for their solutions, their learning becomes a part of their tacit knowledge. Below we explain the theoretical background of our approach and provide examples of such activities.


2021 ◽  
Vol 51 (2) ◽  
pp. 176-192
Author(s):  
Nadia Ruiz

Brian Epstein has recently argued that a thoroughly microfoundationalist approach towards economics is unconvincing for metaphysical reasons. Generally, Epstein argues that for an improvement in the methodology of social science we must adopt social ontology as the foundation of social sciences; that is, the standing microfoundationalist debate could be solved by fixing economics’ ontology. However, as I show in this paper, fixing the social ontology prior to the process of model construction is optional instead of necessary and that metaphysical-ontological commitments are often the outcome of model construction, not its starting point. By focusing on the practice of modeling in economics the paper provides a useful inroad into the debate about the role of metaphysics in the natural and social sciences more generally.


Author(s):  
Alex Rosenberg

Each of the sciences, the physical, biological, social and behavioural, have emerged from philosophy in a process that began in the time of Euclid and Plato. These sciences have left a legacy to philosophy of problems that they have been unable to deal with, either as nascent or as mature disciplines. Some of these problems are common to all sciences, some restricted to one of the four general divisions mentioned above, and some of these philosophical problems bear on only one or another of the special sciences. If the natural sciences have been of concern to philosophers longer than the social sciences, this is simply because the former are older disciplines. It is only in the last century that the social sciences have emerged as distinct subjects in their currently recognizable state. Some of the problems in the philosophy of social science are older than these disciplines, in part because these problems have their origins in nineteenth-century philosophy of history. Of course the full flowering of the philosophy of science dates from the emergence of the logical positivists in the 1920s. Although the logical positivists’ philosophy of science has often been accused of being satisfied with a one-sided diet of physics, in fact their interest in the social sciences was at least as great as their interest in physical science. Indeed, as the pre-eminent arena for the application of prescriptions drawn from the study of physics, social science always held a place of special importance for philosophers of science. Even those who reject the role of prescription from the philosophy of physics, cannot deny the relevance of epistemology and metaphysics for the social sciences. Scientific change may be the result of many factors, only some of them cognitive. However, scientific advance is driven by the interaction of data and theory. Data controls the theories we adopt and the direction in which we refine them. Theory directs and constrains both the sort of experiments that are done to collect data and the apparatus with which they are undertaken: research design is driven by theory, and so is methodological prescription. But what drives research design in disciplines that are only in their infancy, or in which for some other reason, there is a theoretical vacuum? In the absence of theory how does the scientist decide on what the discipline is trying to explain, what its standards of explanatory adequacy are, and what counts as the data that will help decide between theories? In such cases there are only two things scientists have to go on: successful theories and methods in other disciplines which are thought to be relevant to the nascent discipline, and the epistemology and metaphysics which underwrites the relevance of these theories and methods. This makes philosophy of special importance to the social sciences. The role of philosophy in guiding research in a theoretical vacuum makes the most fundamental question of the philosophy of science whether the social sciences can, do, or should employ to a greater or lesser degree the same methods as those of the natural sciences? Note that this question presupposes that we have already accurately identified the methods of natural science. If we have not yet done so, the question becomes largely academic. For many philosophers of social science the question of what the methods of natural science are was long answered by the logical positivist philosophy of physical science. And the increasing adoption of such methods by empirical, mathematical, and experimental social scientists raised a second central question for philosophers: why had these methods so apparently successful in natural science been apparently far less successful when self-consciously adapted to the research agendas of the several social sciences? One traditional answer begins with the assumption that human behaviour or action and its consequences are simply not amenable to scientific study, because they are the results of free will, or less radically, because the significant kinds or categories into which social events must be classed are unique in a way that makes non-trivial general theories about them impossible. These answers immediately raise some of the most difficult problems of metaphysics and epistemology: the nature of the mind, the thesis of determinism, and the analysis of causation. Even less radical explanations for the differences between social and natural sciences raise these fundamental questions of philosophy. Once the consensus on the adequacy of a positivist philosophy of natural science gave way in the late 1960s, these central questions of the philosophy of social science became far more difficult ones to answer. Not only was the benchmark of what counts as science lost, but the measure of progress became so obscure that it was no longer uncontroversial to claim that the social sciences’ rate of progress was any different from that of natural science.


Author(s):  
Shuker Khalil

The basic notions of soft sets theory are introduced by Molodtsov to deal with uncertainties when solving problems in practice as in engineering, social science, environment, and economics. This notion is convenient and easy to apply as it is free from the difficulties that appear when using other mathematical tools as theory of theory of fuzzy sets, rough sets, and theory of vague sets. The soft set theory has recently gaining significance for finding rational and logical solutions to various real-life problems, which involve uncertainty, impreciseness, and vagueness. The concepts of intuitionistic fuzzy soft left almost semigroups and the intuitionistic fuzzy soft ideal are introduced in this chapter, and some of their basic properties are studied.


2018 ◽  
Vol 55 (1) ◽  
pp. 27-49 ◽  
Author(s):  
Anna Yström ◽  
Susanne Ollila ◽  
Marine Agogué ◽  
David Coghlan

Collaboration has become a common way for organizational actors to engage in problem solving and innovation. Yet shifting from strategic interactions (driven by reduction of transaction costs) to transformational interaction (driven by collaborative transorganizational development) appears to be difficult to achieve in practice in a network setting. This article argues that such a shift can be enhanced by adopting an action learning approach, which entails working on real-life problems without clear solutions and collectively working to resolve them. Based on an action learning research process, this article therefore explores ways to support collective knowledge creation within an interorganizational network setting. It provides rich illustrations of how the interactions in the network changed through the process, and the participants moved from a space of territorial protection to a space for collaborative exploration. From this case, the article outlines a model for learning in interorganizational networks and discusses related challenges.


The Condor ◽  
2020 ◽  
Author(s):  
Ashley A Dayer ◽  
Jessica C Barnes ◽  
Alia M Dietsch ◽  
Jacqueline M Keating ◽  
Liliana C Naves

Abstract Conservation efforts are shaped by individual and collective human behaviors, cultural norms and values, economic pressures, and political and organizational structures. As such, the conservation social sciences—disciplines that draw on social science theories and approaches to improve conservation efforts—can play a vital role in advancing the science and practice of bird conservation. We connect the rich, ongoing discussion about the vital role of the conservation social sciences to the specific context of bird conservation and make an argument for the importance of proactive inclusion of these sciences in ornithological societies. First, we introduce the conservation social sciences and illustrate how they can improve the design and implementation of conservation programs and policies for birds. Drawing on discussions from a symposium we organized at the 2019 American Ornithological Society (AOS) annual meeting, we encourage the AOS to make institutional changes that could further support the inclusion of conservation social sciences. These changes ideally would include a working group, conference plenaries and themes, and high-quality social science publications, along with support and encouragement for ornithologists and bird conservationists to partake in trainings and collaborate with social scientists. Strategies for how to do so effectively can be adapted from other conservation societies that have paved the way for disciplinary inclusivity.


Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 246 ◽  
Author(s):  
Nizam Ghawadri ◽  
Norazak Senu ◽  
Firas Fawzi ◽  
Fudziah Ismail ◽  
Zarina Ibrahim

The primary contribution of this work is to develop direct processes of explicit Runge-Kutta type (RKT) as solutions for any fourth-order ordinary differential equation (ODEs) of the structure u ( 4 ) = f ( x , u , u ′ , u ′ ′ ) and denoted as RKTF method. We presented the associated B-series and quad-colored tree theory with the aim of deriving the prerequisites of the said order. Depending on the order conditions, the method with algebraic order four with a three-stage and order five with a four-stage denoted as RKTF4 and RKTF5 are discussed, respectively. Numerical outcomes are offered to interpret the accuracy and efficacy of the new techniques via comparisons with various currently available RK techniques after converting the problems into a system of first-order ODE systems. Application of the new methods in real-life problems in ship dynamics is discussed.


2017 ◽  
Vol 14 (02) ◽  
pp. 1702001 ◽  
Author(s):  
Young-Jae Ryoo ◽  
Takahiro Yamanoi

The special issue topics focus on the computational intelligence and its application for robotics. Its areas reach out comprehensive ranges; context-awareness software, omnidirectional walking and fuzzy controller of dynamic walking for humanoid robots, pet robots for treatment of ASD children, fuzzy logic control, enhanced simultaneous localization and mapping, fuzzy line tracking for mobile robots, and so on. Computational intelligence (CI) is a method of performing like humans. Generally computational intelligence means the ability of a computer to learn a specific task from data or experimental results. Meanwhile robotic system has many limits to behave like human beings. The robotic system might be too complex for mathematical reasoning, it might contain some uncertainties during the process, or the process might simply be stochastic in real life. Real-life problems cannot be translated into binary code for computers to process it. Computational intelligence might solve such problems.


2020 ◽  
pp. 58-78
Author(s):  
Beth M. Sheppard

During a bibliometric analysis of the scholarship of ninety-five social science faculty members at the University of West Georgia (UWG), observations were made concerning potential differences between how scholarly communication is practiced by the disciplines of the social sciences and biblical studies. The fields appear to diverge on the role of book reviews, prevalence of co-authored materials, use of ORCIDs, and adoption of DOIs. In addition to highlighting these points, the data set used for the project is described. Finally, a few theological reflections are offered.


2021 ◽  
Vol 12 (3) ◽  
pp. 384-402 ◽  
Author(s):  
Yanbing Wang ◽  
Joyce B. Main

Purpose While postdoctoral research (postdoc) training is a common step toward academic careers in science, technology, engineering and mathematics (STEM) fields, the role of postdoc training in social sciences is less clear. An increasing number of social science PhDs are pursuing postdocs. This paper aims to identify factors associated with participation in postdoc training and examines the relationship between postdoc training and subsequent career outcomes, including attainment of tenure-track faculty positions and early career salaries. Design/methodology/approach Using data from the National Science Foundation Survey of Earned Doctorates and Survey of Doctorate Recipients, this study applies propensity score matching, regression and decomposition analyses to identify the role of postdoc training on the employment outcomes of PhDs in the social science and STEM fields. Findings Results from the regression analyses indicate that participation in postdoc training is associated with greater PhD research experience, higher departmental research ranking and departmental job placement norms. When the postdocs and non-postdocs groups are balanced on observable characteristics, postdoc training is associated with a higher likelihood of attaining tenure-track faculty positions 7 to 9 years after PhD completion. The salaries of social science tenure-track faculty with postdoc experience eventually surpass the salaries of non-postdoc PhDs, primarily via placement at institutions that offer relatively higher salaries. This pattern, however, does not apply to STEM PhDs. Originality/value This study leverages comprehensive, nationally representative data to investigate the role of postdoc training in the career outcomes of social sciences PhDs, in comparison to STEM PhDs. Research findings suggest that for social sciences PhDs interested in academic careers, postdoc training can contribute to the attainment of tenure-track faculty positions and toward earning relatively higher salaries over time. Research findings provide prospective and current PhDs with information helpful in career planning and decision-making. Academic institutions, administrators, faculty and stakeholders can apply these research findings toward developing programs and interventions to provide doctoral students with career guidance and greater career transparency.


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