This paper describes a class of idealized models that illuminate minimal conditions for inequity. Some such models will track the actual causal factors that generate real world inequity. Others may not. Whether or not these models do track these real-world factors is irrelevant to the epistemic role they play in showing that minimal commonplace factors are enough to generate inequity. In such cases, it is the fact that the model does not fit the world that makes it a particularly powerful (...) argumentative tool. As I will argue, this epistemic role is a particularly important one when it comes to modeling inequity, because such models are often also aimed at interventions to stop it. Given this, it is crucial to know if we intervene on the current causes of inequity, what other, common social factors might continue to contribute to it. (shrink)

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, (...) as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure. (shrink)

Computer simulation modeling is an important part of contemporary scientific practice but has not yet received much attention from philosophers. The present project helps to fill this lacuna in the philosophical literature by addressing three questions that arise in the context of computer simulation of Earth's climate. Computer simulation experimentation commonly is viewed as a suspect methodology, in contrast to the trusted mainstay of material experimentation. Are the results of computer simulation experiments somehow deeply problematic in ways that the results (...) of material experiments are not? I argue against categorical skepticism toward the results of computer simulation experiments by revealing important parallels in the epistemologies of material and computer simulation experimentation. It has often been remarked that simple computer simulation models---but not complex ones---contribute substantially to our understanding of the atmosphere and climate system. Is this view of the relative contributions of simple and complex models tenable? I show that both simple and complex climate models can promote scientific understanding and argue that the apparent contribution of simple models depends upon whether a causal or deductive account of scientific understanding is adopted. When two incompatible scientific theories are under consideration, they typically are viewed as competitors, and we seek evidence that refutes at least one of the theories. In the study of climate change, however, logically incompatible computer simulation models are accepted as complementary resources for investigating future climate. How can we make sense of this use of incompatible models? I show that a collection of incompatible climate models persists in part because of difficulties faced in evaluating and comparing climate models. I then discuss the rationale for using these incompatible models together and argue that this climate model pluralism has both competitive and integrative components. (shrink)

The Modeling of Nature provides an excellent introduction to the fundamentals of natural philosophy, psychology, logic, and epistemology.

Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...) fide object, specified by the modeler and used to represent and reason about some portion of the concrete empirical world. On some indirect views, model systems are abstract entities, such as mathematical structures, while on other views they are concrete hypothetical things. Here I assess these views and offer a novel account of models. I argue that regarding models as abstracta results in some significant tensions with the practice of modeling, especially in areas where non-mathematical models are common. Furthermore, viewing models as concrete hypotheticals raises difficult questions about model-world relations. The view I argue for treats models as direct, albeit simplified, representations of targets in the world. I close by suggesting a treatment of model-world relations that draws on a recent work by Stephen Yablo concerning the notion of partial truth. (shrink)

We review the most prominent modeling approaches in social epistemology aimed at understand- ing the functioning of epistemic communities and provide a philosophy of science perspective on the use and interpretation of such simple toy models, thereby suggesting how they could be integrated with conceptual and empirical work. We highlight the need for better integration of such models with relevant findings from disciplines such as social psychology and organization studies.

In the last decades of the XX century the world community precisely realized the huge danger of the ecological situation which had been developed on our planet under influence of negative technogenic and other anthropogenous factors. Very complex there were ecological conditions in the territory of the former USSR, including Central Asian region, in particular Uzbekistan, which had experienced all the toughness of the former colonial regime. Understanding the consequences of the ecological catastrophe in the region helps to model sociopolitical (...) and economic activities in the field of preservation of the environment, to realize the fact, that in the system of interstate and interethnic relations the ecological policy should have a priority direction, and its integration, in general,is a modern globalization of international sociopolitical, economic and other relations. Thus, necessity of all-round development of coordination and integration of the ecological policies of the all States of our region, and also near and far abroad. Main principles of interstate ecological relations have been fixed in the declaration of Rio de Janeiro (1992) where it is underlined that “close cooperation and mutual aid of the countries in economic, political, ecological andcultural fields” is necessary, demanding modeling of nature protection actions on a global scale. Principles of integration of the ecological policy which are carried out by the States of the Central Asia, correspond to the regulations of Conference of Rio de Janeiro and equitable to the interests of all people living both inside and outside the region. (shrink)

It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...) conventions with both naturalistic empirical data and computational modeling. We first show conventionalization of lexicons in two different classes of naturally emerging signed systems: (a) protolinguistic “homesigns” invented by linguistically isolated Deaf individuals, and (b) a natural sign language emerging in a recently formed rich Deaf community. We find that the latter conventionalized faster than the former. Second, we model conventionalization as a population of interacting individuals who adjust their probability of sign use in response to other individuals' actual sign use, following an independently motivated model of language learning (Yang, , ). Simulations suggest that a richer social network, like that of natural (signed) languages, conventionalizes faster than a sparser social network, like that of homesign systems. We discuss our behavioral and computational results in light of other work on language emergence, and other work of behavior on complex networks. (shrink)

One of the major issues for user assistance systems consists of “providing help at an appropriate level”. In this paper we analyze the problem of modeling task experience — a prerequisite for provisioning adequate help. In contrast to level-based approaches we propose an ontology-based model, which allows fine-grained modeling of task experience using the concepts of the task domain as granules. The model is semantic in the sense that it allows to take advantage of the relations between concepts to provide (...) novel semantic services and interactions. We present the SACHS (Semantic Annotations for a Controlling Help System, a semantic help system for a spreadsheet-based financial controlling system) software as an exemplary application of the proposed task experience model. (shrink)

Our aim in this article is to show how the theory of conceptual spaces can be useful in describing diachronic changes to conceptual frameworks, and thus useful in understanding conceptual change in the empirical sciences. We also compare the conceptual space approach to Moulines’s typology of intertheoretical relations in the structuralist tradition. Unlike structuralist reconstructions, those based on conceptual spaces yield a natural way of modeling the changes of a conceptual framework, including noncumulative changes, by tracing the changes to the (...) dimensions that reconstitute a conceptual framework. As a consequence, the incommensurability of empirical theories need not be viewed as a matter of conceptual representation. (shrink)

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. Mesoscale models (...) capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science and biology, where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale system. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems. (shrink)

Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...) clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (shrink)

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on (...) the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

The bookModeling Reality covers a wide range of fascinating subjects, accessible to anyone who wants to learn about the use of computer modeling to solve a diverse range of problems, but who does not possess a specialized training in mathematics or computer science. The material presented is pitched at the level of high-school graduates, even though it covers some advanced topics (cellular automata, Shannon's measure of information, deterministic chaos, fractals, game theory, neural networks, genetic algorithms, and Turing machines). These advanced (...) topics are explained in terms of well known simple concepts: Cellular automata - Game of Life, Shannon's formula - Game of twenty questions, Game theory - Television quiz, etc. The book is unique in explaining in a straightforward, yet complete, fashion many important ideas, related to various models of reality and their applications. Twenty-five programs, written especially for this book, are provided on an accompanying CD. They greatly enhance its pedagogical value and make learning of even the more complex topics an enjoyable pleasure. (shrink)

After 100+ years of studying syllogistic reasoning, what have we learned? Well, Riesterer and colleagues suggest that we have learned to throw away most of the data! If that seems like a bad idea to you then, be assured, that the authors agree with you. The sad fact is that the conclusion of “No Valid Conclusion” (NVC) is one of the most frequently selected responses in syllogistic reasoning but these “majority data” have been ignored by most researchers. Riesterer and colleagues (...) introduce heuristic rules for predicting NVC and provide directions to modelers on how to up their game. (shrink)

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.

This article aims to answer what I call the “constitution question of engineering modeling”: in virtue of what does an engineering model model its target system? To do so, I will offer a category-theoretic, structuralist account of design, using the olog framework. Drawing on this account, I will conclude that engineering and scientific models are not only cognitively but also representationally indistinguishable. I will finally propose an axiological criterion for distinguishing scientific from engineering modeling.

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high (...) level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

Predictive modeling in education draws on data from past courses to forecast the effectiveness of future courses. The present effort sought to identify such a model of instructional effectiveness in scientific ethics. Drawing on data from 235 courses in the responsible conduct of research, structural equation modeling techniques were used to test a predictive model of RCR course effectiveness. Fit statistics indicated the model fit the data well, with the instructional characteristics included in the model explaining approximately 85% of the (...) variance in RCR instructional effectiveness. Implications for using the model to develop and improve future RCR courses are discussed. (shrink)

One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted the crowdsourced acceptability (...) judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that "morphous" computational models with morpheme units outperformed "amorphous" computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora. (shrink)

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

Based on the Competition Model, the current study investigated how cue availability and cue reliability as two important input factors influenced second language (L2) learners' cue learning of the English article construction. Written corpus data of university-level Chinese-L1 learners of English were sampled for a comparison of English majors and non-English majors who demonstrated two levels of L2 competence in English article usage. The path model analysis in structural equation modeling was utilized to investigate the relationship between the input factors (...) and L2 usage (frequency and accuracy of article cue production). The findings contribute novel and scarce empirical evidence that confirms a central claim of the Competition Model, i.e., the changing importance of cue availability and cue reliability in the frequency and accuracy of production. Cue availability was found to determine L2 production frequency regardless of level of L2 competence. Cue reliability was the input factor that differentiated competence levels. When learners stayed at a relatively lower L2 proficiency, cue reliability played an important role in influencing L2 frequency of usage rather than accuracy of usage. When learners developed increased exposure to and stronger competence in the target language, cue reliability played a significant role in determining learners' success of cue learning. The study is methodologically innovative and expands the empirical applicability of the Competition Model to the domain of second language production and construction learning. (shrink)

Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of any (...) model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained. (shrink)

Many in philosophy understand truth in terms of precise semantic values, true propositions. Following Braun and Sider, I say that in this sense almost nothing we say is, literally, true. I take the stand that this account of truth nonetheless constitutes a vitally useful idealization in understanding many features of the structure of language. The Fregean problem discussed by Braun and Sider concerns issues about application of language to the world. In understanding these issues I propose an alternative modeling tool (...) summarized in the idea that inaccuracy of statements can be accommodated by their imprecision. This yields a pragmatist account of truth, but one not subject to the usual counterexamples. The account can also be viewed as an elaborated error theory. The paper addresses some prima facie objections and concludes with implications for how we address certain problems in philosophy. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...) different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

Context: The integration of data measured in first- and third-person frameworks is a challenge that becomes more prominent as we attempt to refine the ties between the dimensions we assume to be objective and our experience itself. As a result, cognitive science has been a target for criticism from the epistemological and methodological point of view, which has resulted in the emergence of new approaches. Neurophenomenology has been proposed as a means to address these limitations. The methodological application of this (...) discipline, even in its mildest form, enriches the methodology typically used in cognitive sciences. Problem: Nowadays psychological studies are difficult to replicate. As a way to achieve replication of results published in a previous study in order to develop a methodological adaptation suitable for electroencephalographic measurements in a subsequent experiment, first-person accounts from the participants in our pilot study were included in the experiment construction. This study’s objective is to show the benefit of including a mild-neurophenomenology-inspired approach in the adaptation from an original paradigm, which requires, foremost, the ability to replicate the original results. Method: Interviews with open and semi-structured questions were carried out at the end of an Approach-Avoidance Task (AAT. The first-person reports, together with the behavioral outcomes of each pilot, were taken into account for the development of the next piloting phase until replication of the original results was achieved, and the final experimental design was elaborated. Results: A sequence of four pilots, where the integration of third- and first-person information derived from subjects’ behavior and reported experiences while carrying them out rendered the behavioral replication we sought to achieve, providing support for a first-person enriched cognitive science paradigm. Implications: Including first-person accounts systematically during the development and performance of classic cognitive paradigms ensures that those paradigms are measuring what they claim to measure. This is the next logical step to improve replication rates, to refine the explanation of the results and avoid confounding third-person data interpretation. Constructivist content: Including first-person experiences and acknowledging the active role that participants’ experiences regarding the paradigm had in the modeling of its final version is in concordance with a constructivist standing. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...) handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...) the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterance-final bias in learning and the distributional characteristics of child-directed speech in the 2 languages. They also show how computational modeling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of universal grammar. (shrink)

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we (...) argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond. (shrink)

Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user (...) as well as the system developer. This is particularly desirable in connection with certain intimate relationships, such as being the same as or being a part of. (shrink)

" In Imagination and the Meaningful Brain, psychoanalyst Arnold Modell claims that subjective human experience must be included in any scientific...

Computational models of infant word‐finding typically operate over transcriptions of infant‐directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP‐Parser (...) model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP‐Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP‐Parse mimics American English learners' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance. (shrink)

Recently, Doolittle and Inkpen formulated a thought provoking theory, asserting that evolution by natural selection was responsible for the sideways evolution of two radically different kinds of selective units (also called Domains). The former entities, termed singers, correspond to the usual objects studied by evolutionary biologists (gene, genomes, individuals, species, etc.), whereas the later, termed songs, correspond to re‐produced biological and ecosystemic functions, processes, information, and memes. Singers perform songs through selected patterns of interactions, meaning that a wealth of critical (...) phenomena might receive novel evolutionary explanations. However, this theory did not provide an empirical approach to study evolution in such a broadened context. Here, we show that analyzing songs and singers, using patterns of interaction networks as a common ontology for both, offers a novel, actionable, inclusive and mathematical way to analyze not only the re‐production but also the evolution and fitness of biological and ecosystemic interconnected processes. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is (...) the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

This article briefly review the fundamentals of structural equation modeling for readers unfamiliar with the technique then goes on to offer a review of the Martin and Cullen paper. In summary, a number of fit indices reported by the authors reveal that the data do not fit their theoretical model and thus the conclusion of the authors that the model was “promising” are unwarranted.

Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to is a (...) collection of propositions. (shrink)

Mathematical modeling is a convenient way for characterization of complex ecosystems. This approach was applied to study the dynamics of zooplankton in Lake Sevan (Armenia) at different stages of anthropogenic eutrophication with the use of a novel method called discrete modeling of dynamical systems with feedback (DMDS). Simulation demonstrated that the application of this method helps in characterization of inter- and intra-component relationships in a natural ecosystem. This method describes all possible pairwise inter-component relationships like “plus–plus,” “minus–minus,” “plus–minus,” “plus–zero,” “minus–zero,” (...) and “zero–zero” that occur in most ecosystems. Based on the results, a working hypothesis was formulated. It was found that the sensitivity to weak external influence in zooplanktons was the greatest during the mid period of eutrophication in Lake Sevan, whereas in the final stages of eutrophication, an outbreak in the biomass production of cyanobacteria was evident. To support this approach, a weak external disturbance in the form of magnetic storm was used to see its effect on species Daphnia longispina sevanica. A statistically significant correlation between the frequency of magnetic storms and the number of this species was revealed and an increase in the number of toxic cyanobacteria species as a consequence of eutrophication. This paper, for the first time, suggests a DMDS method, to diagnose impact of anthropogenic eutrophication on environment. (shrink)