Recital 33 GDPR has often been interpreted as referring to ‘broad consent’. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with ‘recognised ethical standards’. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and (...) class='Hi'>social science. However, the lack of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research. (shrink)

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and (...) case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. (shrink)

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that (...) neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life. (shrink)

This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data (...) in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities. (shrink)

This book argues that this powerful technique permits the social sciences to meet an explanation, in which one 'grows' the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors.

The social sciences and humanities are fragmented into specialized areas, each with their own parlance and procedures. This hinders information sharing and the growth of a coherent body of knowledge. Modeling and simulation can be the scientific lingua franca, or shared technical language, that can unite, integrate, and relate relevant parts of these diverse disciplines.Models are well established in the scientific community as mediators, contributors, and enablers of scientific knowledge. We propose a potentially revolutionary linkage between social sciences, (...) humanities and computer simulation, forging what we call “human simulation.” We explore three facets of human simulation, namely: the simulation of humans, the design of simulations for human use, and simulations that include humans as well as simulated agents among the actors. We describe the potential of human simulation using several illuminating examples. We also discuss computational, epistemological, and hermeneutical challenges constraining the use of human simulation. (shrink)

The tremendous recent growth of the women's movement as a political force has been accompanied by an event of equal import to the academic world--the development of the discipline of women's studies. Colleges across the nation are establishing programs in this area. Women's Studies is a classroom anthology designed for use in these newly-introduced courses.

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis (...) from a bibliometric perspective, aiming to provide a reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

This paper argues that analyses of the ways in which Big Data has been enacted in other academic disciplines can provide us with concepts that will help understand the application of Big Data to social questions. We use examples drawn from our Science and Technology Studies analyses of -omic biology and high energy physics to demonstrate the utility of three theoretical concepts: primary and secondary inscriptions, crafted and found data, and the locus of legitimate interpretation. These help us (...) to show how the histories, organisational forms, and power dynamics of a field lead to different enactments of big data. The paper suggests that these concepts can be used to help us to understand the ways in which Big Data is being enacted in the domain of the social sciences, and to outline in general terms the ways in which this enactment might be different to that which we have observed in the ‘hard’ sciences. We contend that the locus of legitimate interpretation of Big Data biology and physics is tightly delineated, found within the disciplinary institutions and cultures of these disciplines. We suggest that when using Big Data to make knowledge claims about ‘the social’ the locus of legitimate interpretation is more diffuse, with knowledge claims that are treated as being credible made from other disciplines, or even by those outside academia entirely. (shrink)

The rapidly changing field of genetics affects society through advances in health-care and through implications of genetic research. This study addresses the impacts of new genetic discoveries and technologies on different segments of today's society. The book begins with a chapter on genetic complexity, and subsequent chapters discuss moral and ethical questions arising from today's genetics from the perspectives of health care professionals, the media, the general public, special interest groups and commercial interests.

Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the (...) social sciences. The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)

Social scientists and computer scientist are divided by small differences in perspective and not by any significant disciplinary divide. In the field of text analysis, several such differences are noted: social scientists often use unsupervised models to explore corpora, whereas many computer scientists employ supervised models to train data; social scientists hold to more conventional causal notions than do most computer scientists, and often favor intense exploitation of existing algorithms, whereas computer scientists focus more on developing new (...) models; and computer scientists tend to trust human judgment more than social scientists do. These differences have implications that potentially can improve the practice of social science. (shrink)

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer application to the (...) humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. (shrink)

The middle chapters of Soames’s The World Philosophy Made are briefly summarized and examined. There are some local slips, but globally the work displays an impressive knowledge of and a distinctive viewpoint on a wide range of important intellectual disciplines and their original roots in and continuing connections with philosophy.

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...) to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address such epochal questions by way of a experiment: at the Danish Technical University in Copenhagen, an interdisciplinary group of computer scientists, physicists, economists, sociologists, and anthropologists is setting up a large-scale (...) data infrastructure, meant to continually record the digital traces of social relations among an entire freshman class of students. At the same time, fieldwork is carried out on friendship relations amongst the same group of students. On this basis, the question we pose is the following: what kind of knowledge is obtained on this social micro-cosmos via the Big and Small Data, respectively? How do the two relate? Invoking Bohr’s principle of complementarity as analogy, we hypothesize that social relations, as objects of knowledge, depend crucially on the type of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods but also for the type of societal knowledge that may be expected from new large-scale social databases. (shrink)

Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. (...) We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible. (shrink)

This book constitutes the revised versions of the invited and selected papers from the Second Epistemological Perspectives on Simulation Workshop, EPOS 2006, which was held in Brescia, Italy, during October 5-6, 2006. The 11 papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The topics addressed were epistemological and methodological contents, such as the relevance of empirical foundations for agent-based simulations, the role of theory, the concepts and meaning of emergence, the trade-off between simplification (...) and complexification of models. (shrink)

This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy. The first of the three parts examines methods of social scientific discovery. Chapters investigate the nature of causal analysis, philosophical issues around scale development in behavioural science research, imagination in social scientific practice, and relationships between paradigms of inquiry and scientific fraud. (...) The next part considers the practice of social science discovery. Chapters discuss the lack of genuine scientific discovery in finance where hypotheses concern the cheapness of securities, the logic of scientific discovery in macroeconomics, and the nature of that what discovery with the Solidarity movement as a case study. The final part covers formalising theories in social science. Chapters analyse the abstract model theory of institutions as a way of representing the structure of scientific theories, the semi-automatic generation of cognitive science theories, and computational process models in the social sciences. The volume offers a unique perspective on scientific discovery in the social sciences. It will engage scholars and students with a multidisciplinary interest in the philosophy of science and social science. (shrink)

The humanities and social sciences (HSS) are being turned upside down by advances in artificial intelligence (AI), and their very existence could be threatened. These sciences are being profoundly destabilised by a dual process of naturalisation of social phenomena and fetishisation of numbers, accentuated by the development of AI (part 1). Both STM (science, technology, medicine) and HSS are facing major epistemological challenges, but for the latter they carry the risk of marginalisation (part 2). The humanities and (...) social sciences remain the best equipped to question the social construct represented by the development of AI. However, this essential approach is not enough. We need to ask ourselves: how can the HSS reintroduce interpretation when they have less and less control over how data is put together? Only a balanced partnership between STM and HSS is likely to meet all these challenges (part 3). Using the case of education, which has long been at the forefront of developments in other sectors of social life, we would like to show how and on what priority issues such a partnership can be built (part 4). (shrink)

This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order (...) to illustrate concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles. (shrink)

By reconciling the linguistic device and the linguistic person, his book argues for a Vygotskyan cognitive science.

Readers of Philosophical Psychology may be most familiar with Ron Sun by way of an article recently appearing in this journal on creative composition expressed within his own hybrid computational intelligence model, CLARION (Sun, 2013). That article represents nearly two decades’ work in situated agency stressing the importance of psychologically realistic architectures and processes in the articulation of both functional, and reflectively informative, AI and agent- level social-cultural simulations. Readers may be less familiar with Sun’s 2001 “prolegomena” to (...) related multi-agent (proto-social) research also from this journal. That article argues that “a proper balance between “objective” social reality and individual cognitive processes” is necessary in order to understand “how individual belief systems... and the social/cultural belief system ... interact” (Sun, 2001, pages 10 and 23). This issue remains central in Sun’s 2012 edited volume, Grounding Social Sciences in the Cognitive Sciences, here addressed from within the expanding field of pioneering researchers bent on orchestrating that proper balance, the “cognitive social sciences.” Its fifteen chapters are sectioned according to culture, politics, religion, and economics, and closes with an especially rewarding pair of contributions from Gintis, and McCubbins and Turner, under the heading of “unifying perspectives.” Most entries – but for Sun’s own - are serviceably summarized in the introductory overview. So, rather than follow suit, this review will focus on setting out Sun’s vision, noting how this text helps us to realize it more clearly, with a positive focus on a few entries in particular. (shrink)

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: (...) natural big data, artificial big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)

This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative (...) and nationalistic pressures. (shrink)

The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use of (...) formal conceptions of computation for describing simulation is refuted; the roles of programming languages according to intentional accounts of computation are identified; and the roles of iconographic programming languages and aesthetic machines in simulation are characterized. The roles that simulation and intentional decision making may be able to play in a participative information society are also discussed. (shrink)

This chapter contains sections titled: Why Use Mathematical Models? Theory‐based Models Data‐based Modeling Computational Approaches Conclusions Notes.

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a (...) simulation? These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

The social sciences are at a remarkable confluence of events. Advances in computing have made it feasible to analyze data at the scale of the population of the world. How can we combine the depth of inquiry in the social sciences with the scale and robustness of statistics and computer science? Can we decompose complex questions in the social sciences into simpler, more robustly testable hypotheses? We discuss these questions and the role of machine learning in (...) the social sciences. (shrink)

In contrast to dominant approaches to human reason involving essentially a logical and instrumental conception of rationality easily modeled by artificial intelligence mechanisms, I argue that the specific capacities of the human mind are meta-analytic in nature, understood as irreducible to the analytic or the logical, or else the computational. Firstly, the assumption of a meta-analytical level of rationality is derived from key insights developed in various branches of the social sciences. This meta-analytical level is then inferred from (...) Gödel’s incompleteness theorem. On these bases, and with the help of psychological approaches to consciousness, I argue that human rationality may be characterized as the capacity to make meaningful use of signs. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, (...) section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility. (shrink)

The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence. Some suggestions are offered about techniques for building agent-based models and for debugging them. (...) A scheme for structuring a simulation program into agents, the environment and other parts for modifying and observing the agents is described. The article concludes with some references to modelling tools helpful for building computer simulations. (shrink)

This open access book examines the methodological complications of using complexity science concepts within the social science domain. The opening chapters take the reader on a tour through the development of simulation methodologies in the fields of artificial life and population biology, then demonstrates the growing popularity and relevance of these methods in the social sciences. Following an in-depth analysis of the potential impact of these methods on social science and social theory, the (...) text provides substantive examples of the application of agent-based models in the field of demography. This work offers a unique combination of applied simulation work and substantive, in-depth philosophical analysis, and as such has potential appeal for specialist social scientists, complex systems scientists, and philosophers of science interested in the methodology of simulation and the practice of interdisciplinary computing research.​. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section 2 (...) gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

What place should formal or computational methods occupy in social psychology? We consider this question in historical perspective, survey the current state of the field, introduce the several new contributions to this special issue, and reflect on the future.

The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projected (...) that, in the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena. (shrink)

For centuries, philosophers studying the great mysteries of human subjectivity have focused on the mind/body problem and the difference between human beings and animals. Now a new ontological question takes center stage: to what extent can a manufactured object (a computer) exhibit qualities of mind? There have been passionate exchanges between those who believe that a "manufactured mind" is possible and those who believe that mind cannot exist except as a living, socially situated, embodied person. As with earlier arguments, this (...) one shows no sign of being resolved. But the fight over computationalism (the belief that all mental processes can be generated by computer programs) has immediate, "hard" consequences for technological research and development, social- and cognitive-science methodology, and for our everyday experience of the world and ourselves. This special issue consists of papers presented at a conference of the same title held at the Center for Cognitive Science at the University at Buffalo, 22-23 May 1990. The authors come from a variety of disciplinary backgrounds (Computer Science, Linguistics, Philosophy, Psychology, Sociology, etc.), and bring a wide variety of perspectives to the topic. (shrink)

Recent developments in social science methods have made most of the objectivism-subjectivism arguments in the philosophy of social science obsolete. Developments in experimental methods have made possible a behavioristic treatment of everything cherished as important in human action by the subjectivists; developments in computer and mathematical models have made possible a type of theory which carries out the program of the subjectivists but is not vulnerable to the arguments of the objectivists. What remains of the philosophical (...) argument are two types of theory (for example, game theory and learning theory) which are both useful, both scientific, and frequently equivalent. Choice between them by scientists can be made on empirical grounds rather than on the grounds developed in the philosophical controversy. (shrink)

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases (...) in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM. (shrink)