I highlight a metaphysical concern that stands in the way of more widespread adoption of causal modeling techniques such as causal Bayes nets. Researchers in some fields may resist adoption due to concerns that they don't 'really' understand what they are saying about a system when they apply such techniques. Students in these fields are repeated exhorted to be cautious about application of statistical techniques to their data without a clear understanding of the conditions required for those techniques to yield (...) genuine insight into the data. They are acutely aware that anyone can chuck some data into a software package and get what looks like an answer, even though these tests may not be well-defined for the data on which they can apparently be run. This is thus a healthy skepticism for uptake of causal modeling methods, which points directly to the need for a metaphysical understanding of causation in order to successfully use the modeling methods. Without a clear understanding of what the methods are committing to, including what it means to say that there exists a causal relationship, researchers have limited ability to identify potentially bad output, or to independently verify results. (shrink)

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic (...) of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)

Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate (...) in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion. (shrink)

In classifications of vocabulary knowledge, vocabulary size and depth have often been separately conceptualized (Schmitt, 2014). Although size and depth are known to be substantially correlated, it is not clear whether they are a single construct or two separate components of vocabulary knowledge (Yanagisawa & Webb, 2020). This issue has not been addressed extensively in the literature and can be better examined using structural equation modeling (SEM), with measurement error modeled separately from the construct of interest. The current study reports (...) on conventional and Bayesian SEM approaches (e.g., Muthén & Asparouhov, 2012) to examine the factor structure of the size and depth of second language vocabulary knowledge of Japanese adult learners of English. A total of 255 participants took five vocabulary tests. One test was designed to measure vocabulary size in terms of the number of words known, while the remaining four were designed to measure vocabulary depth in terms of word association, polysemy, and collocation. All tests used a multiple-choice format. The size test was divided into three subtests according to word frequency. Results from conventional and Bayesian SEM show that a correlated two-factor model of size and depth with three and four indicators, respectively, fit better than a single-factor model of size and depth. In the two-factor model, vocabulary size and depth were strongly correlated (r =.945 for conventional SEM and.943 for Bayesian SEM with cross loadings), but they were distinct. The implications of these findings are discussed. (shrink)

The Great Psychotherapy Debate: Models, Methods, and Findings comprehensively reviews the research on psychotherapy to dispute the commonly held view that the benefits of psychotherapy are derived from the specific ingredients contained in a given treatment (medical model). The author reviews the literature related to the absolute efficacy of psychotherapy, the relative efficacy of various treatments, the specificity of ingredients contained in established therapies, effects due to common factors, such as the working alliance, adherence and allegiance to the therapeutic (...) protocol, and effects that are produced by different therapists. In each case, the evidence convincingly corroborates the contextual model and disconfirms the prevailing medical model. (shrink)

Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.

This is a conversation about the application of modeling methods in philosophy and how modeling helps to address philosophical issues that are otherwise difficult to solve. We also talk about the role of mathematics and language in modeling. As an illustration, we analyze the No Alternatives Argument.

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic (...) of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)

In this paper, I draw a parallel between the stability of physical systems and that of economic ones, such as the US financial system. I argue that the use of equilibrium assumptions is central to the analysis of dynamic behavior for both kinds of systems, and that we ought to interpret such idealizing strategies as footholds for causal exploration and explanation. Our considerations suggest multi-scale modeling as a natural home for such reasoning strategies, which can provide a backdrop for the (...) assessment and prevention of financial crises. Equilibrium assumptions are critical elements of the epistemic scaffolding that make up multi-scale models, which we should understand as a means of constructing explanations of dynamic behavior. (shrink)

Edited by Daniel Rothbart of George Mason University in Virginia, this book is a collection of Rom Harré's work on modeling in science (particularly physics and psychology). In over 28 authored books and 240 articles and book chapters, Rom Harré of Georgetown University in Washington, DC is a towering figure in philosophy, linguistics, and social psychology. He has inspired a generation of scholars, both for the ways in which his research is carried out and his profound insights. For Harré, the (...) stunning discoveries of research demand a kind of thinking that is found in the construction and control of models. Iconic modeling is pivotal for representing real-world structures, explaining phenomena, manipulating instruments, constructing theories, and acquiring data. This volume in the new Elsevier book series Studies in Multidisciplinarity includes major topics on the structure and function of models, the debates over scientific realism, explanation through analogical modeling, a metaphysics for physics, the rationale for experimentation, and modeling in social encounters. * A multidisciplinary work of sweeping scope about the nature of science * Revolutionary interpretation that challenges conventional wisdom about the character of scientific thinking * Profound insights about fundamental challenges to contemporary physics * Brilliant discoveries into the nature of social interaction and human identity * Presents a rational conception of methods for acquiring knowledge of remote regions of the world * Written by one of the great thinkers of our time. (shrink)

The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful motivation (...) for choosing the strategy of modeling. Second, we show that the development of the model follows a trajectory from a “how possibly” to a “how actually” model. We discuss how and to what extent Volterra and D’Ancona were able to advance their model along that trajectory. It turns out they were unable to establish that their model was fully applicable to any system. Third, we consider another instance of model-based science: Darwin’s model of the origin and distribution of coral atolls in the Pacific Ocean. Darwin argued more successfully that his model faithfully represents the causal structure of the target system, and hence that it is a “how actually” model. (shrink)

This collaborative publication offers salient instructional models, methods, and modalities centered on the whole person.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S (...) requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. (shrink)

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall (...) dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning, which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning, incremental learning, and cyclic incremental learning. We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible. (shrink)

In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of (...) science, namely within the philosophical debate on modeling. Specifically, I review my empirical investigations into the issues of model de-idealization, model justification and performativity. (shrink)

One approach to applied AI is to automate business processes and remove people from the system. Another approach is to use AI methods to model how work actually gets done, so we can understand the essential role of knowledge people have about each other ("social knowledge") in allocating resources, assigning jobs, and forming teams.

In the first part of the book Ewa Bińczyk discusses postulates that have been formulated in response to the problem of the unwanted consequences of the practical success of technoscience (deriving mainly from science and technology studies). In the second part Tomasz Stępień analyses nanotechnology as example of technoscience development and presents the nano-assessment framework.

This chapter examines modal modelling in climate science. It considers two related topics. The first is the use of climate models to attribute extreme weather events to climate change. The second is the interpretation and use of collections of climate models. Each topic is the subject of a current debate within climate science and philosophy of science, and each has an important modal component. The debates are similar in that each involves a contrast between probabilistic and non-probabilistic methods. (...) In each case, the chapter examines the kind of modal claims scientists make and how they are justified. As climate change is an important practical problem, each debate has a link to policy decisions, and so the chapter ends with a discussion of how modal modelling, and the possibilities it identifies, can support decision-making. (shrink)

Model organisms are central to contemporary biology and studies of embryogenesis in particular. Biologists utilize only a small number of species to experimentally elucidate the phenomena and mechanisms of development. Critics have questioned whether these experimental models are good representatives of their targets because of the inherent biases involved in their selection (e.g., rapid development and short generation time). A standard response is that the manipulative molecular techniques available for experimental analysis mitigate, if not counterbalance, this concern. But the most (...) powerful investigative techniques and molecular methods are applicable to single-celled organisms (‘microbes’). Why not use unicellular rather than multicellular model organisms, which are the standard for developmental biology? To claim that microbes are not good representatives takes us back to the original criticism leveled against model organisms. Using empirical case studies of microbes modeling ontogeny, we break out of this circle of reasoning by showing: (a) that the criterion of representation is more complex than earlier discussions have emphasized; and, (b) that different aspects of manipulability are comparable in importance to representation when deciding if a model organism is a good model. These aspects of manipulability harbor the prospect of enhancing representation. The result is a better understanding of how developmental biologists conceptualize research using experimental models and suggestions for underappreciated avenues of inquiry using microbes. More generally, it demonstrates how the practical aspects of experimental biology must be scrutinized in order to understand the associated scientific reasoning. (shrink)

This essay provides an introduction to the terminology, concepts, methods, and challenges of image‐based modeling in biology. Image‐based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework of image‐based (...) systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum. (shrink)

An ontological approach as a tool for managing the processes of constructing mathematical models based on interval data and further use of these models for solving applied problems is proposed in this article. Mathematical models built using interval data analysis are quite effective in many applications, as they have “guaranteed” predictive properties, which are determined by the accuracy of experimental data. However, the application of mathematical modeling methods is complicated by the lack of software tools for the implementation of (...) procedures for constructing this type of mathematical models, creating an ontological model that operates by the categories of the subject area of mathematical modeling, regardless of the modeling object proposed in this article. This approach has made it possible to generate tools for mathematical modeling of various objects based on the interval data analysis for any software development environment selected by the user. The technology of creating the software on the basis of the developed ontological superstructure for mathematical modeling using the interval data for different objects, as well as various forms of user interface implementation, is presented in this article. A number of schemes, which illustrate the technology of using the ontological approach of mathematical modeling based on interval data, are presented, and the features of its interpretation when solving environmental monitoring problems are described. (shrink)

The energy conversion units and energy storage equipment connected to the multi-energy system are becoming diversified, and the uncertain factors brought by distributed wind power and photovoltaic power generation make the system energy flow structure more complex, which brings great difficulties to the modeling and application of traditional energy hub modeling methods. This study deeply analyzes the multi-energy flow coupling structure and operation mechanism of multi-energy systems, and carries out the power flow calculation and analysis of multi-energy systems based (...) on an energy hub, so as to ensure the safe and stable operation of regional energy. Based on the physical characteristics of energy systems such as power systems, thermal systems, and gas systems, this article studies the comprehensive power flow model including the electric-gas-thermal multi-energy coupling network and proposes the power flow decomposition of the energy supply subsystem and its applicable equation based on Newton–Raphson method. The effectiveness of the proposed method under different operation modes is verified by case studies. The calculation results show that under constant load, the energy hub running in fixing thermal by electricity and fixing electricity by thermal mode has little influence on the voltage of each node in the power sub-network. Within the constraint range, the natural gas flow obtained from the natural gas subsystem is coupled with the power subsystem to meet the load demand. The influence on the power flow at each node of the heat network is not obvious. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with (...) their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Two families of mathematical methods lie at the heart of investigating the hierarchical structure of genetic variation in Homo sapiens: /diversity partitioning/, which assesses genetic variation within and among pre-determined groups, and /clustering analysis/, which simultaneously produces clusters and assigns individuals to these “unsupervised” cluster classifications. While mathematically consistent, these two methodologies are understood by many to ground diametrically opposed claims about the reality of human races. Moreover, modeling results are sensitive to assumptions such as preexisting theoretical commitments to (...) certain linguistic, anthropological, and geographic human groups. Thus, models can be perniciously reified. That is, they can be conflated and confused with the world. This fact belies standard realist and antirealist interpretations of “race,” and supports a pluralist conventionalist interpretation. (shrink)

The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of a phenomenon under investigation into quantitative facts about that phenomenon. These facts (...) are evaluated by their accuracy and precision. Precision is generally achieved by least squares methods and accuracy by calibration. For calibration standards are needed. Then two polar strategies can be distinguished: white-box modeling and black-box modeling. The first strategy of modeling aims at estimating the invariant equations of the phenomenon, thereby fulfilling Hertz’s correctness requirement. The latter strategy of modeling is to use known stable facts about the phenomenon to adjust the model parameters, thereby fulfilling Hertz’s appropriateness requirement. For this latter strategy, the requirement of models as homomorphic mappings has been dropped. Where one will find the axiomatic approach more often used for measurement in the laboratory, the empirical approach is more appropriate for measurement outside the laboratory. The reason for this is that for measurement of phenomena outside the laboratory, one also needs to take account of the environment to achieve accurate results. Environments are generally too relation-rich for an axiomatic approach, which are only applicable for relation-poor systems. The white-box modeling strategy, reflecting the complexity of the environment due to its correctness requirement, will, however, lead to immensely large models. To avoid this problem, modular design is an appropriate strategy to reduce this complexity. Modular design is a grey-box modeling strategy. Grey-box models are assemblies of modules; these are black boxes with standard interface. It should be noted that the structure of the assemblage need not be homomorphic to the relations describing the interaction between phenomenon and environment. These three modeling strategies map out the possible designs for computer simulations as measuring instruments. Whether a simulation is based on a white-box, grey-box or black-box model is only determined by the relationship between the phenomenon and its environment and not by e.g. its materiality or physicality. (shrink)

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt (...) to derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used (...) neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans’ belief on AI's operations and provide human-centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, (...) size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

This book deals with the theoretical, methodological, and empirical implications of bounded rationality in the operation of institutions. It focuses on decisions made under uncertainty, and presents a reliable strategy of knowledge acquisition for the design and implementation of decision-support systems. Based on the distinction between the inner and outer environment of decisions, the book explores both the cognitive mechanisms at work when actors decide, and the institutional mechanisms existing among and within organizations that make decisions fairly predictable. While a (...) great deal of work has been done on how organizations act as patterns of events for (boundedly) rational decisions, less effort has been devoted to study under which circumstances organizations cease to act as such reliable mechanisms. Through an empirical strategy on open-ended response data from a survey among junior judges, the work pursues two main goals. The first one is to explore the limits of "institutional rationality" of the Spanish lower courts on-call service, an optimal scenario to observe decision-making under uncertainty. The second aim is to achieve a better understanding of the kind of uncertainty under which inexperienced decision-makers work. This entails exploring the demands imposed by problems and the knowledge needed to deal with them, making this book also a study on expertise achievement in institutional environments. This book combines standard multivariate statistical methods with machine learning techniques such as multidimensional scaling and topic models, treating text as data. Doing so, the book contributes to the collaboration between empirical social scientific approaches and the community of scientists that provide the set of tools and methods to make sense of the fastest growing resource of our time: data. (shrink)

RESUMEN Introducción: La dimensión ambiental constituye un proceso esencial en la formación de profesionales, de modo que en el desempeño de su profesión puedan educar hacia el desarrollo sostenible a las futuras generaciones. Objetivo: Representar en un modelo pedagógico la lógica de la dimensión ambiental en la formación laboral investigativa de los profesionales en la universidad. Materiales y métodos: Se utilizaron métodos teóricos, empíricos y matemáticos-estadísticos. Resultados: Elaboración del modelo pedagógico de dimensión ambiental en la formación laboral investigativa de los (...) profesionales en formación. El modelo está estructurado en tres subsistemas: uno teórico, se centra en la integración de saberes pedagógico ambientales a partir de las relaciones que se establecen entre sus componentes, otro metodológico, el cual resume la proyección ambiental de la formación laboral investigativa y un subsistema procedimental, que manifiesta la instrumentación transformadora ambiental que debe ocurrir en los estudiantes. El modelo aporta una cualidad sinérgica que le imprime identidad, totalidad y contribuye a su autodesarrollo. Conclusiones: Con la elaboración del modelo se revelan nuevas interpretaciones que se manifiestan como relaciones teóricas funcionales surgidas entre los componentes de los subsistemas que lo integran y a la vez, entre los subsistemas como macro componentes. La validación del modelo resultó estadísticamente significativa, mediante la instrumentación de una estrategia pedagógica para la dimensión ambiental en la formación laboral investigativa. ABSTRACT Introduction: The environmental component is an essential process in the training of professionals, that enables them to educate future generations towards sustainable development. Objective: To represent in a pedagogical model the logic of the environmental dimension in the research training of professionals. Materials and methods: Theoretical, empirical and mathematical-statistical methods were used. Findings: The development of the pedagogical model of environmental dimension in research and labor training of professionals. The model is structured in three subsystems: the first focuses on the integration of environmental pedagogical knowledge from the relationships established between its components; the second is of a methodological character and summarizes the environmental projection of research training; whereas the third one is procedural, expressing the environmental transformative instrumentation that must occur in students. The model provides a synergistic quality that consolidates identity, totality and contributes to its self-development. Conclusions: With the elaboration of the model new interpretations are revealed that are manifested as functional theoretical relations arising between the components of the subsystems that integrate it and at the same time, between the subsystems as macro components. The evaluation of the model was statistically significant, through the instrumentation of a pedagogical strategy for the environmental dimension in the research and labor training. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction (...) between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

At present, high-tech enterprises are mainly organizations engaged in the production, research, and development and service of high-tech products. The current development of high-tech industries in various countries in the world is of great significance to improving social productivity and overall national strength. This article mainly introduces the modeling and analysis of the complex system of human capital accumulation in high-tech enterprises based on big data. This paper proposes a theoretical analysis of corporate human capital data and proposes regression analysis (...) and analytic hierarchy process to calculate and analyze the process of corporate human capital accumulation and collect and analyze the data using questionnaire surveys and other methods. In addition, complex systems were modeled based on big data, and a cloud computing-based human capital big data analysis platform was established. The experimental results show that complex system modeling based on big data has a positive impact on the human capital accumulation of high-tech enterprises. For high-tech companies at different stages of development, the relationship between various types of human capital and corporate performance is inconsistent with the market economy. In contrast, the current salary system of some high-tech companies still has many drawbacks. The cost of using human resources controlled by the wage system accounts for more than 24%. (shrink)

Background To produce and understand words, humans access the mental lexicon. From a functional perspective, the long-term memory component of the mental lexicon is comprised of three levels: the concept level, the lemma level, and the phonological level. At each level, different kinds of word information are stored. Semantic as well as phonological cues can help to facilitate word access during a naming task, especially when neural dysfunctions are present. The processing corresponding to word access occurs in specific parts of (...) working memory. Neural models for simulating speech processing help to uncover the complex relationships that exist between neural dysfunctions and corresponding behavioral patterns. Methods The Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA) are used to develop a quantitative neural model of the mental lexicon and its access during speech processing. By simulating a picture-naming task (WWT 6-10), the influence of cues is investigated by introducing neural dysfunctions within the neural model at different levels of the mental lexicon. Results First, the neural model is able to simulate the test behavior for normal children that exhibit no lexical dysfunction. Second, the model shows worse results in test performance as larger degrees of dysfunction are introduced. Third, if the severity of dysfunction is not too high, phonological and semantic cues are observed to lead to an increase in the number of correctly named words. Phonological cues are observed to be more effective than semantic cues. Conclusion Our simulation results are in line with human experimental data. Specifically, phonological cues seem not only to activate phonologically similar items within the phonological level. Moreover, phonological cues support higher-level processing during access of the mental lexicon. Thus, the neural model introduced in this paper offers a promising approach to modeling the mental lexicon, and to incorporating the mental lexicon into a complex model of language processing. (shrink)

In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them (...) is the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

This volume brings together leading scholars working on understanding and representation in philosophy of science. It features a critical conversation format between contributors that advances debates concerning scientific understanding, scientific representation, and their delicate interplay.

The book offers an interdisciplinary perspective on finance, with a special focus on stock markets. It presents new methodologies for analyzing stock markets’ behavior and discusses theories and methods of finance from different angles, such as the mathematical, physical and philosophical ones. The book, which aims at philosophers and economists alike, represents a rare yet important attempt to unify the externalist with the internalist conceptions of finance.

Modeling is a relatively new topic in biblical and related subjects—it was first introduced in the 1970s—and it is controversial because the application of social-scientific models raises the difficult question of the cultural gap between the present societies, where the models are usually developed, and the ancient cultural context to which the models are applied.Because biblical and related studies may not belong to the most familiar scholarly fields of the readers of this journal, I first sketch an overall picture of (...) the development of the discipline and its main methods (Section 2). The subsequent sections summarize the arguments presented for and against modeling in biblical studies (Section 3), and discuss .. (shrink)

This research aims at establishing the impact of information and communication technologies (ICT) on e-commerce development of industrial enterprises by means of economic and mathematical modelling. The goal was achieved using the following methods: theoretical generalization, analysis and synthesis (to critically analyse the scientific approaches of scientists regarding the expediency of using mathematical models in the context of enterprises’ e-commerce development), target, comparison and grouping (to reveal innovative methodological approach to assessing ICT impact on e-commerce development of industrial (...) enterprises), tabular, analytical and integral method (for summarizing the analysis results of enterprises readiness to implement ICT, ICT use in the activities of industrial enterprises of Ukraine and the analysis of e-commerce development), mathematical modelling (to build a regression model determining impact of changes in ICT use on the market share occupied by industrial enterprises), generalization (to determine promising directions of e-commerce developing of industrial enterprises). The implementation of a comprehensive approach to understanding the importance of ICT influence on e-commerce development of industrial enterprises will ensure acceleration of the digitalization of business processes, will contribute to the speed increase of enterprises response to customer requests, and increase the market share occupied by enterprises. A new vision of directions for developing e-commerce of industrial enterprises is suggested, which are determined by the need for enterprise rebranding, the development of e-commerce tools and technologies, the importance of outsourcing service automation and promotion of subscription trade. ICT is considered as integration factor that determines prospects for e-commerce development of industrial enterprises and contributes to increasing efficiency of online business management. Research results demonstrate that the use of economic and mathematical modelling is an important tool for assessing ICT impact, and its absence can negatively affect the accuracy and validity of online business management. (shrink)

Management decision-making is a daily task that managers of various levels solve in every organization. Degree of difficulty of this process depends on the scope of authority, responsibility level, manager’s position in organizational hierarchy; on the changes in the environment, unpredictability of which causes emergence of significant amounts of alternatives. For this reason, managers do not rely only on intuition or personal experience (which limited with selective perception, cognitive ability, ability to withstand stress and/or the presence of bias), but use (...) tools (methods) that have stood the test of time and practice; are based at the analysis of a significant amount of primary and secondary information; involve team-building to find the optimal option or to generate ideas. A significant amount of research papers are devoted to such methods, however, the author forms a synergetic approach which contains a detailed analysis of their correlation with the key factors of the competitiveness of enterprise, such as organizational culture and leadership. Existing decision-making practices, problems and style of management at the tourism enterprises are revealed from conducted survey (the first half of 2014) in tourism firms of Dnepropetrovsk and Zaporizhzhia regions of Ukraine, in which 729 tourism managers took part. The most popular decision-making practices in tourism branch were specified, among them are modeling and monitoring of previous results. The study proved the hypothesis of the author concerning strong connection of decision-making management and type of organizational culture. Moreover, 63% of managers were dissatisfied with low attention to organizational culture at their companies. The basis for future research and the synergetic matrix for decision-making management were worked out in the article which give preconditions for further studies and improvements in tourism management of Ukrainian enterprises. (shrink)

This volume provides analyses of the logic-reality relationship from different approaches and perspectives. The point of convergence lies in the exploration of the connections between reality – social, natural or ideal – and logical structures employed in describing or discovering it. Moreover, the book connects logical theory with more concrete issues of rationality, normativity and understanding, thus pointing to a wide range of potential applications. -/- -/- The papers collected in this volume address cutting-edge topics in contemporary discussions amongst specialists. (...) Some essays focus on the role of indispensability considerations in the justification of logical competence, and the wide range of challenges within the philosophy of mathematics. Others present advances in dynamic logical analysis such as extension of game semantics to non-logical part of vocabulary and development of models of contractive speech act. -/- Table of Contents: Introduction: Majda Trobok, Nenad Miščević and Berislav Žarnić.- I. Logical and Mathematical Structures.- Life on the Ship of Neurath: Mathematics in the Philosophy of Mathematics: Stewart Shapiro.- Applied Mathemathics in the Sciences: Dale Jacquette.- The Philosophical Impact of the Löwenheim-Skolem Theorem: Miloš Arsenijević.- Debating (Neo)logicism: Frege and the neo-Fregeans: Majda Trobok.- II. Epistemology and Logic.- Informal Logic and Informal Consequence: Danilo Šuster.- Logical Consequence and Rationality: Nenad Smokrović.- Logic, Indispensability and Aposteriority: Nenad Miščević.- III . Dynamic Logical Models of Meaning.- Extended Game-Theoretical Semantics: Manuel Rebuschi.- Dynamic Logic of Propositional Commitments: Tomoyuki Yamada.- Is Unsaying Polite?: Berislav Žarnić.- IV Logical Methods in Ontological and Linguistic Analyses.- Towards a Formal Account of Identity Criteria: Massimiliano Carrara and Silvia Gaio.- A Mereology for the Change of Parts: Pierdaniele Giaretta and Giuseppe Spolaore.- Russell versus Frege: Imre Rusza.- Goodman’s OnlyWorld: Vladan Djordjević.-. (shrink)

Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about (...) the world are similar to scientific methods used to make claims about the world, but that the subjects of metaphysics are not the subjects of science. Those who argue that metaphysics uses a problematic methodology to make claims about subjects better covered by natural science get the situation exactly the wrong way around: metaphysics has a distinctive subject matter, not a distinctive methodology. The questions metaphysicians address are different from those of scientists, but the methods employed to develop and select theories are similar. In the first section of the paper, I will describe the sort of subject matter that metaphysics tends to engage with. In the second section of the paper, I will show how metaphysical theories are classes of models and discuss the roles of experience, common sense and thought experiments in the construction and evaluation of such models. Finally, in the last section I will discuss the way these methodological points help us to understand the metaphysical project. Getting the right account of the metaphysical method allows us to better understand the relationship between science and metaphysics, to explain why doing metaphysics successfully involves having a range of different theories, to understand the role of thought experiments involving fictional worlds, and to situate metaphysical realism in a scientifically realist context. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help (...) of an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)