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

Causation is the main foundation upon which the possibility of science rests. Without causation, there would be no scientific understanding, explanation, prediction, nor application in new technologies. How we discover causal connections is no easy matter, however. Causation often lies hiddenfrom view and it is vital that we adopt the right methods for uncovering it. The choice of methods will inevitably reflect what one takes causation to be, making an accurate account of causation an even more pressing matter. This (...) enquiry informs the correct norms for an empirical study of the world. In Causation in Science and the Methods of Scientific Discovery, Rani Lill Anjum and Stephen Mumford propose nine new norms of scientific discovery. A number of existing methodological and philosophical orthodoxies are challenged as they argue that progress in science is being held back by an overlysimplistic philosophy of causation. (shrink)

‘Serendipity’ is a category used to describe discoveries in science that occur at the intersection of chance and wisdom. In this paper, I argue for understanding serendipity in science as an emergent property of scientific discovery, describing an oblique relationship between the outcome of a discovery process and the intentions that drove it forward. The recognition of serendipity is correlated with an acknowledgment of the limits of expectations about potential sources of knowledge. I provide an analysis of (...) serendipity in science as a defense of this definition and its implications, drawing from theoretical and empirical research on experiences of serendipity as they occur in science and elsewhere. I focus on three interrelated features of serendipity in science. First, there are variations of serendipity. The process of serendipitous discovery can be complex. Second, a valuable outcome must be obtained before reflection upon the significance of the unexpected observation or event in respect to that outcome can take place. Therefore, serendipity is retrospectively categorized. Third, the primacy of epistemic expectations is elucidated. Finally, I place this analysis within discussions in philosophy of science regarding the impact of interpersonal competition upon the number and significance of scientific discoveries. Thus, the analysis of serendipity offered in this paper contributes to discussions about the social-epistemological aspects of scientific discovery and has normative implications for the structure of epistemically effective scientific communities. (shrink)

What is the role of chance in scientific discovery? And, more to the point, if chance plays a key role in scientific discovery, what room is left for reason? These are grounding questions in the debates, for instance, over whether there is a distinction to be made between discovery and justification in science, and whether innate genius must play a role in discovery or if there exists some method that can be taught to anyone. While the role of chance (...) has been discussed throughout the history of science, it has resurfaced in recent debates over how science should be funded, and particularly in the field of biomedical research. The word serendipity, for example, has become increasingly... (shrink)

The idea of elegance in science is not necessarily a familiar one, but it is an important one. The use of the term is perhaps most clear-cut in mathematics - the elegant proof - and this is where Ian Glynn begins his exploration. Scientists often share a sense of admiration and excitement on hearing of an elegant solution to a problem, an elegant theory, or an elegant experiment. The idea of elegance may seem strange in a field of endeavour (...) that prides itself in its objectivity, but only if science is regarded as a dull, dry activity of counting and measuring. It is, of course, far more than that, and elegance is a fundamental aspect of the beauty and imagination involved in scientific activity. Ian Glynn, a distinguished scientist, selects historical examples from a range of sciences to draw out the principles of science, including Kepler's Laws, the experiments that demonstrated the nature of heat, and the action of nerves, and of course the several extraordinary episodes that led to Watson and Crick's discovery of the structure of DNA. With a highly readable selection of inspiring episodes highlighting the role of beauty and simplicity in the sciences, the book also relates to important philosophical issues of inference, and Glynn ends by warning us not to rely on beauty and simplicity alone - even the most elegant explanation can be wrong. (shrink)

This study compares Pairs of subjects with Single subjects in a task of discovering scientific laws with the aid of experiments. Subjects solved a molecular genetics task in a computer micro‐world (Dunbar, 1993). Pairs were more successful in discovery than Singles and participated more actively in explanatory activities (i.e., entertaining hypotheses and considering alternative ideas and justifications). Explanatory activities were effective for discovery only when the subjects also conducted crucial experiments. Explanatory activities were facilitated when paired subjects made requests of (...) each other for explanation and focused on them. The study extends from individual to collaborative discovery activities the importance to the discovery process of setting goals to find hypotheses and evidence (Dunbar, 1993) and to construct explanations of phenomena and processes encountered in examples (Chi, Bassok, Lewis, & Glaser, 1989). (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Jewish Thought and Scientific Discovery in Early Modern EuropeNoah J. EfronAlmost a quarter-century ago Benjamin Nelson published his famous plea for what he called a “differential” and “comparative historical sociology of ‘science’ in civilizational perspective.” 1 Like Max Weber, Robert Merton, and Joseph Needham, Nelson believed that the growth of western science could be better understood when compared to the ways “science” fared in other cultures (...) with other intellectual and institutional and economic sensibilities and structures. A particularly propitious case study for such a differential and comparative historical study of science, as Hillel Levine observed in his 1983 essay about Jewish reactions to heliocentrism, are the Jews of early modern Europe, who constituted “a society geographically and culturally contiguous with those who framed and advanced occidental ‘science.’” 2Since about the time of Nelson’s plea, David B. Ruderman has been writing the history of Jewish attitudes towards, and engagements with, science during the period in which modern western science was constituting itself. It is no exaggeration to say that he established this as a field of study, producing a steady stream of historical studies, initially of Italian Jews who read and wrote about nature and its study, but continually expanding his compass. 3 The field [End Page 719] Ruderman established and the studies he produced have been “repercussive” (to borrow his own adjective) in many directions—shedding light on Jewish-Christian relations, on early-modern Jewish intellectual history, on the deployments of Kabbalah and magic in early modern times, and so on. One of Ruderman’s achievements—perhaps not the most important in his view (for he considers himself a historian of Jewish thought and culture and not a historian of science) but of great importance nonetheless—has been to begin to lay the groundwork for a comparative history of Jewish and Christian attitudes towards and participation in the “new sciences.”Ruderman’s newest book is a big step towards Nelson’s desideratum, using Jewish attitudes as the comparative case. It is a collection of twelve linked essays (five of which have appeared elsewhere in one form or another) framed by a synthetic introduction and epilogue. The essays are individual or group portraits of Jews who evinced interest in natural philosophy or medicine, in a variety of settings and for a variety of reasons, between the sixteenth and eighteenth centuries. Ruderman describes his subject as “three distinct but interrelated groups among early modern Jews”: (1) “converso physicians and other university-trained intellectuals who fled Spain and Portugal in the seventeenth century and settled in Holland, Italy, Germany, England, and even eastern Europe, serving as doctors and purveyors of scientific learning throughout the Jewish communities of Europe, while yielding considerable political and economic power,” (2) “certain circles of Jewish scholars in central and eastern Europe [who] pursued scientific learning, especially astronomy, in more informal settings as a desirable supplement to rabbinic study,” and (3) “the hundreds of Jews who attended Italian medical schools, primarily the University of Padua, from the late sixteenth through the eighteenth centuries.” 4In fact Ruderman covers even more ground than this. As background, he includes a brief essay on the attitudes of tenth- to fifteenth-century Jews towards nature and its study, surely the best survey of the sort available. Of the thirty or so early modern Jews that he describes in the remainder of the book, about twenty fit in at least one of the three camps upon which he focuses. 5 Nine [End Page 720] emerged from different circumstances (though these too were often highly influenced by one or more of the focus populations). This fact does nothing to impugn Ruderman’s typology—the groups he described were, in fact, of particular importance, and his identification of them as the principle populations contemplating and promulgating natural philosophy among Jews is in itself an advance of some moment. But the fact that a third of the cases Ruderman described do not fit into his own typology is testimony both to the complexity of his subject, and to his agility in addressing it.By anthologizing a dozen depictions of different thinkers in different milieus, Ruderman affords what might be called a “hawk... (shrink)

Multiple simultaneous independent discoveries, so well enunciated by Robert K. Merton in the early 1960s but already discussed for several hundreds of years, is a classic concept in the sociology of science. In this paper, the concept of multiple simultaneous independent errors is proposed, analyzed, and discussed. The concept of Selective Pessimistic Induction is proposed and used to connect MIDs with MIEs. Five types of MIEs are discussed: multiple errors in the interpretation of experimental data or computational results; (...) multiple misjudgments of the value of another’s research results or conclusions; multiple cases of false anticipation of achieving a certain experimental result; multiples of ignoring or omitting relevant precedents; and multiple instances of failure due to a not-yet-conceived scientific concept or principle. Causal MIDs and MIEs are those that can be traced directly to antecedent knowledge. Acausal MIDs and MIEs are those involving a consequential and identifiable leap from antecedent knowledge. Examples of causal and acausal MIEs are provided, mostly but not exclusively from the discipline of chemistry. Comparisons are made between MIDs and MIEs. Topics for future research are discussed and implications of these concepts are proposed. (shrink)

With In Search of Mechanisms, Carl F. Craver and Lindley Darden offer both a descriptive and an instructional account of how biologists discover mechanisms. Drawing on examples from across the life sciences and through the centuries, Craver and Darden compile an impressive toolbox of strategies that biologists have used and will use again to reveal the mechanisms that produce, underlie, or maintain the phenomena characteristic of living things. They discuss the questions that figure in the search for mechanisms, characterizing the (...) experimental, observational, and conceptual considerations used to answer them, all the while providing examples from the history of biology to highlight the kinds of evidence and reasoning strategies employed to assess mechanisms. At a deeper level, Craver and Darden pose a systematic view of what biology is, of how biology makes progress, of how biological discoveries are and might be made, and of why knowledge of biological mechanisms is important for the future of the human species. (shrink)

In this thesis I am going to explain the role of metaphor in articulation of new scientific theories, explicitly speaking, indeed, I have not a word about metaphorical thinking in theory invention, implicitly speaking. In fact, I talk about conceptual metaphor instead of linguistic metaphor. As another classification, this investigation belongs to “justification context”, rather than “discovery context”. Employing Boyd’ ideas on metaphor in science can lend a hand for acquiring this point. In Boyd’ set of beliefs; we need (...) another reference theory which has not the problems of kiripki-pautnam’s reference fixing theory, thus, Boyd proposes the “epistemic-access theory of reference”. With accepting “interacting view” of Block on metaphor, he demonstrates that in this theory of reference we are not encountering the problems such as referent shifting, lack of precise of metaphor which exist in kripki-pautnam’s reference fixing. On the other hand, by admitting “relativity”, Kuhn criticizes the Boyd’s attitude on metaphor in science. To conclude, I defend kripki-pautnam’s reference fixing theory; since, it seems to be implausible to accept Boyd’s criticism. (shrink)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been more (...) fact-oriented and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

An important fact about human labor is that it can result not just in reproduction of what it started with, but in something new, a surplus product. When the latter is a means of production, it makes possible a mechanism of change consisting of reproduction by means of the expanded means of production. Each iteration of the labor process can differ from the preceding one insofar as it incorporates the surplus generated previously. Over the long-term, this cyclical process can lead (...) to the self-transformation of labor and, through it, of human societies and cultures. In this paper, I argue that this mechanism of change is also at work in the history of science. I argue that the form this mechanism takes in science is that of a feedback loop between discovery and instrument construction. This process requires the integration, and transformation into material form, of different kinds of knowledge. Based on this mechanism, I defend a concept of scientific progress as transcendence of the limitations of native human epistemic abilities. I also criticize narrowly biologistic approaches to the history of science for ignoring the role of surplus generation in transforming the labor process, and discuss some problems associated with viewing science as labor. (shrink)

Human stories lie at the heart of professional practice in the human, social services, though these are often discounted when it comes to researching such services and sharing practice through publication. This article identifies and addresses certain methodological and epistemological biases and consequent challenges in human science research, and discusses the importance of story (autoethnography) and discovery (heuristics) in research which can inform practice, meaningfully and ethically. It considers this by addressing both research and publication, illustrating both the challenges (...) and the solutions from the authors’ own experiences. The article argues for reclaiming the subjective and the subjectivist dimension in human, social science, and, therefore, the experiential, in both research and publication; and addresses four problems with regard to publishing in the human sciences, namely: privilege, quality, anonymity in peer-review, and capital. (shrink)

This article discusses three recent philosophers who speak in different ways about the retroactive construction of reality by human knowledge. Bruno Latour unapologetically claims that the Egyptian Pharaoh Ramses II could not have died of tuberculosis, as determined by a team of French doctors in 1976, since this disease was not discovered until three thousand years after his death. Slavoj Žižek often makes comparable arguments, though his version of retroactivity draws on both psychoanalysis and dialectics in a way that is (...) never the case for Latour. Yet in his 2012 book Less Than Nothing, Žižek takes the more prudent, realist-sounding line that changes in scientific theories do not change nature itself. The real novelty in retroactive theories is provided by Thomas Kuhn, who draws our attention to a grey temporal zone in scientific discovery in which it is impossible to pinpoint the exact date of any breakthrough. For Kuhn, this stems from a dualism found in reality itself: one between the fact “that” a thing exists and the determination of “what” its properties are. The tension between these two moments is responsible for much of the vagueness and drama surrounding any paradigm shift in the sciences and is closely related to at least one plausible theory of metaphor. In closing, three Hollywood films are cited as helpful examples for understanding the different ways in which a paradigm shift works its retroactive magic. (shrink)

This paper analyzes features of the emergence of new fields in science by examining the cases of cytology and biochemistry. The first step in the emergence of these new fields was the discovery of a new entity. A subsequent claim was made that entities of this kind are found more generally; making this generalization constituted the construction of a new theory. As a line of research to test the theory began, a new domain was formed and the new field (...) emerged. In each case the theory proposed a new solution to an old problem in science. Some implications of this analysis for understanding the emergence of other fields are indicated. (shrink)

All types of logic started with Aristotle and have been corrected as a traditional, formal, conditional, classical logic and even modern logic carry the main problems of Aristotelian logic. Despite their important differences, because of these core commonalities they are all called Classical Logic. The fundamental limitations of classical logic make it impossible to advance the knowledge necessary to solve growing human problems. All human knowledge, especially scientific knowledge is based on the logical principles that seem to hinder the knowledge (...) of reality rather than facilitate it; although in the beginning, this logic has helped mankind in acquiring knowledge. Fuzzy logic has been successful as an effective solution in science and in practice, but it lacks the necessary philosophical foundations so it has faced with several major problems and on important paradox in theorizing. First, the main challenges in classical logic are diseased in this article, then it is explained how to solve the problems and most important paradox to apply the fuzzy logic for scientific discoveries in a new approach. Finally, the new paradigm of Fuzziological Epistemology (fuzzy based epistemology) has been proposed in term of Dynamic Fuzzy Puzzle Theory. (shrink)

The scientific reasoning strategies used to discover a new concept in a scientific domain were investigated in two studies. An innovative task in which subjects discover new concepts in molecular biology was used. This task was based upon one set of experiments that Jacob and Monod used to discover how genes are controlled, and for which they were awarded the Nobel prize. In the two studies reported in this article, subjects were taught some basic facts and experimental techniques in molecular (...) biology, using a simulated molecular genetics laboratory on a computer. Following their initial training, they were then asked to discover how genes are controlled by other genes. In Study 1, subjects found no evidence that was consistent with their initial hypothesis. Subjects then set one of two goals for conducting experiments and evaluating data. One goal was to search for evidence consistent with the current hypothesis (and they did not attend to the features of discrepant findings); none of the subjects who only had this goal succeeded at discovering how the genes were controlled. Other subjects in Study 1 used a different goal: Upon noticing evidence inconsistent with their current hypothesis, these subjects set a new goal of attempting to explain the cause of the discrepant findings. Using this goal, a subset of these subjects discovered the correct solution to the problem. Study 2 was conducted to test the hypothesis that subjects' goals of finding evidence consistent with their current hypothesis blocks consideration of alternate hypotheses and generation of new goals, it was predicted that if subjects could achieve their initial goal of discovering evidence consistent with their current hypothesis, they would then attend to particular features of discrepant evidence and solve the problem. To test this prediction, an additional mechanism of genetic control that was consistent with subjects' initial goal was added to the genes. Here, subjects had to discover two mechanisms of control: one mechanism consistent with their current hypothesis, and one inconsistent with their hypothesis. Twice as many subjects reached the correct solution in Study 2 than in Study 1. The findings of the two studies indicate that goals provide a powerful constraint on the cognitive processes underlying scientific reasoning and that the types of goals that are represented determine many of the reasoning errors that subjects make. (shrink)

In 1869, Johann Friedrich Miescher discovered a new substance in the nucleus of living cells. The substance, which he called nuclein, is now known as DNA, yet both Miescher’s name and his theoretical ideas about nuclein are all but forgotten. This paper traces the trajectory of Miescher’s reception in the historiography of genetics. To his critics, Miescher was a “contaminator,” whose preparations were impure. Modern historians portrayed him as a “confuser,” whose misunderstandings delayed the development of molecular biology. Each of (...) these portrayals reflects the disciplinary context in which Miescher’s work was evaluated. Using archival sources to unearth Miescher’s unpublished speculations—including an analogy between the hereditary material and language, and a speculation that a series of asymmetric carbon atoms could account for hereditary variation—this paper clarifies the ways in which the past was judged through the lens of contemporary concerns. It also shows how organization, structure, function, and information were already being considered when nuclein was first discovered nearly 150 years ago. (shrink)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been more (...) fact-oriented and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

It is widely acknowledged that metaphors are used in science. Great scientists, such as Darwin and Einstein, believed that the use of metaphors is vital to the development of scientific ideas. The history of science is full of examples of scientific metaphors as tools at the forefront of discoveries of new facts and new concepts.

The ArgumentScientific tools—measurement and calculation instruments, techniques of inference—straddle the line between the context of discovery and the context of justification. In discovery, new scientific tools suggest new theoretical metaphors and concepts; and in justification, these tool-derived theoretical metaphors and concepts are morelikely to be accepted by the scientific community if the tools are already entrenched in scientific practice.Techniques of statistical inference and hypothesis testing entered American psychology first as tools in the 1940s and 1950s and then as cognitive theories (...) in the 1960s and 1970s. Not only did psychologists resists statistical metaphors of mind prior to the institutionalization of inference techniques in their own practice; the cognitive theories they ultimately developed about “the mind as intuitive statistician” still bear the telltale marks of the practical laboratory context in which the tool was used. (shrink)

Critical realism claims to bring a significant improvement to social science, especially in comparison with empiricist and interpretive approaches. So far, however, it has fallen short of the high expectations it raises. Critical realist arguments are convincing on the philosophical or meta-theoretical level but the contributions of critical realism to social science in terms of research activities at the field level are less clear. Nonetheless, there is no way back. Moving forward requires that the practice of doing social (...) scientific research be no longer perceived as limited to the justification of specific knowledge claims but must be seen as an integral part of the cycle of scientific discovery. (shrink)

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form.

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. (...) Because theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

The great biologist Louis Pasteur suppressed 'awkward' data because it didn't support the case he was making. John Snow, the 'first epidemiologist' was doing nothing others had not done before. Gregor Mendel, the supposed 'founder of genetics' never grasped the fundamental principles of 'Mendelian' genetics. Joseph Lister's famously clean hospital wards were actually notorious dirty. And Einstein's general relativity was only 'confirmed' in 1919 because an eminent British scientist cooked his figures. These are just some of the revelations explored in (...) this book. Drawing on current history of science scholarship, Fabulous Science shows that many of our greatest heroes of science were less than honest about their experimental data and not above using friends in high places to help get their ideas accepted. It also reveals that the alleged revolutionaries of the history of science were often nothing of the sort. Prodigiously able they may have been, but the epithet of the 'man before his time' usually obscures vital contributions made their unsung contemporaries and the intrinsic merits of ideas they overturned. These distortions of the historical record mostly arise from our tendency to read the present back into the past. But in many cases, scientists owe their immortality to a combination of astonishing effrontery and their skills as self-promoters. (shrink)

There is substantial evidence that traditional instructional methods have not been successful in helping students to restructure their commonsense conceptions and learn the conceptual structures of scientific theories. This paper argues that the nature of the changes and the kinds of reasoning required in a major conceptual restructuring of a representation of a domain are fundamentally the same in the discovery and in the learning processes. Understanding conceptual change as it occurs in science and in learning science will (...) require the development of a common cognitive model of conceptual change. The historical construction of an inertial representation of motion is examined and the potential instructional implications of the case are explored. (shrink)