This is an important book precisely because there is none other quite like it.

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

Helen Longino seeks to break the current deadlock in the ongoing wars between philosophers of science and sociologists of science--academic battles founded on disagreement about the role of social forces in constructing scientific knowledge. While many philosophers of science downplay social forces, claiming that scientific knowledge is best considered as a product of cognitive processes, sociologists tend to argue that numerous noncognitive factors influence what scientists learn, how they package it, and how readily it is accepted. Underlying this disagreement, however, (...) is a common assumption that social forces are a source of bias and irrationality. Longino challenges this assumption, arguing that social interaction actually assists us in securing firm, rationally based knowledge. This important insight allows her to develop a durable and novel account of scientific knowledge that integrates the social and cognitive. Longino begins with a detailed discussion of a wide range of contemporary thinkers who write on scientific knowledge, clarifying the philosophical points at issue. She then critically analyzes the dichotomous understanding of the rational and the social that characterizes both sides of the science studies stalemate and the social account that she sees as necessary for an epistemology of science that includes the full spectrum of cognitive processes. Throughout, her account is responsive both to the normative uses of the term knowledge and to the social conditions in which scientific knowledge is produced. Building on ideas first advanced in her influential book Science as Social Knowledge, Longino brings her account into dialogue with current work in social epistemology and science studies and shows how her critical social approach can help solve a variety of stubborn problems. While the book focuses on epistemological concerns related to the sociality of inquiry, Longino also takes up its implications for scientific pluralism. The social approach, she concludes, best allows us to retain a meaningful concept of knowledge in the face of theoretical plurality and uncertainty. (shrink)

Discusses the nature of thought and volition, examines past philosophical theories, and clarifies the relation between will and freedom.

The role of values in scientific research has become an important topic of discussion in both scholarly and popular debates. Pundits across the political spectrum worry that research on topics like climate change, evolutionary theory, vaccine safety, and genetically modified foods has become overly politicized. At the same time, it is clear that values play an important role in science by limiting unethical forms of research and by deciding what areas of research have the greatest relevance for society. Deciding how (...) to distinguish legitimate and illegitimate influences of values in scientific research is a matter of vital importance.Recently, philosophers of science have written a great deal on this topic, but most of their work has been directed toward a scholarly audience. This book makes the contemporary philosophical literature on science and values accessible to a wide readership. It examines case studies from a variety of research areas, including climate science, anthropology, chemical risk assessment, ecology, neurobiology, biomedical research, and agriculture. These cases show that values have necessary roles to play in identifying research topics, choosing research questions, determining the aims of inquiry, responding to uncertainty, and deciding how to communicate information. Kevin Elliott focuses not just on describing roles for values but also on determining when their influences are actually appropriate. He emphasizes several conditions for incorporating values in a legitimate fashion, and highlights multiple strategies for fostering engagement between stakeholders so that value influences can be subjected to careful and critical scrutiny. (shrink)

Thomas Nagel addresses the conflict between the claims of the group and those of the individual. Nagel attempts to clarify the nature of the conflict – one of the most fundamental problems in moral and political theory – and argues that its reconciliation is the essential task of any legitimate political system.

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

This Article outlines a vision of responsible innovation and outlines a public policy and implementation strategy for it.

For the last forty years, two claims have been at the core of disputes about scientific change: that scientists reason rationally and that science is progressive. For most of this time discussions were polarized between philosophers, who defended traditional Enlightenment ideas about rationality and progress, and sociologists, who espoused relativism and constructivism. Recently, creative new ideas going beyond the polarized positions have come from the history of science, feminist criticism of science, psychology of science, and anthropology of science. Addressing the (...) traditional arguments as well as building on these new ideas, Miriam Solomon constructs a new epistemology of science. After discussions of the nature of empirical success and its relation to truth, Solomon offers a new, social account of scientific rationality. She shows that the pursuit of empirical success and truth can be consistent with both dissent and consensus, and that the distinction between dissent and consensus is of little epistemic significance. In building this social epistemology of science, she shows that scientific communities are not merely the locus of distributed expert knowledge and a resource for criticism but also the site of distributed decision making. Throughout, she illustrates her ideas with case studies from late-nineteenth- and twentieth-century physical and life sciences. Replacing the traditional focus on methods and heuristics to be applied by individual scientists, Solomon emphasizes science funding, administration, and policy. One of her goals is to have a positive influence on scientific decision making through practical social recommendations. (shrink)

Because of its complexity, contemporary scientific research is almost always tackled by groups of scientists, each of which works in a different part of a given research domain. We believe that understanding scientific progress thus requires understanding this division of cognitive labor. To this end, we present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to climb uphill in this landscape, where elevation represents the significance of the (...) results discovered by employing a research approach. We consider three different search strategies scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community as a whole influence their actions. This is compared with two social research strategies, which we call the follower and maverick strategies. Followers are biased towards what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform both of the other strategies. Finally, we show that in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains. (shrink)

THE VALUE OF SCIENCE INTRODUCTION The search for truth should be the goal of our activities; it is the sole end worthy of them. Doubtless we should first bend our efforts to assuage human suffering, but why ? Not to suffer is a negative ...

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of (...) resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society. (shrink)

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor”, that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led (...) to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments. (shrink)

Some scientists are happy to follow in the footsteps of others; some like to explore novel approaches. It is tempting to think that herein lies an epistemic division of labor conducive to overall scientific progress: the latter point the way to fruitful areas of research, and the former more fully explore those areas. Weisberg and Muldoon’s model, however, suggests that it would be best if all scientists explored novel approaches. I argue that this is due to implausible modeling choices, and (...) I present an alternative ‘epistemic landscape’ model that demonstrates the alleged benefits from division of labor, with one restriction. (shrink)

When should a scientific community be cognitively diverse? This article presents a model for studying how the heterogeneity of learning heuristics used by scientist agents affects the epistemic efficiency of a scientific community. By extending the epistemic landscapes modeling approach introduced by Weisberg and Muldoon, the article casts light on the micro-mechanisms mediating cognitive diversity, coordination, and problem-solving efficiency. The results suggest that social learning and cognitive diversity produce epistemic benefits only when the epistemic community is faced with problems of (...) sufficient difficulty. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I argue (...) that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

Current theories of the division of cognitive labor are confined to the “context of justification,” assuming exogenous theories. But new theories are made from the same labor that is used for developing existing theories, and if none of this labor is ever allocated to create new alternatives, then scientific progress is impossible. A unified model is proposed in which theories are no longer given but a function of the division of labor in the model itself. The interactions of individuals balancing (...) the exploitation of existing theories and the exploration of new theories results in a robust cyclical pattern. (shrink)

Computer simulation of an epistemic landscape model, modified to include explicit representation of a centralized funding body, show the method of funding allocation has significant effects on communal trade-off between exploration and exploitation, with consequences for the community’s ability to generate significant truths. The results show this effect is contextual, and depends on the size of the landscape being explored, with funding that includes explicit random allocation performing significantly better than peer review on large landscapes. The article proposes a way (...) of incorporating external institutional factors in formal social epistemology, and offers a way of bringing such investigations to bear on current research policy questions. 1Introduction2Theoretical Background3Model Description4Simulation Details 4.1Simulating the epistemic landscape4.2Simulating agents4.3Simulating communal knowledge4.4Simulating funding strategies4.5Simulating merit dynamics5Results and Discussion 5.1Experiment 1: The winner-takes-it-all mechanism only5.2Experiment 2: All dynamic mechanisms5.3Experiment 3: Adding a new funding mechanism 5.4Experiment 4: Varying the degree of myopia5.5Experiment 5: Variability of individual epistemic gain5.6Experiment 6: Likelihood of renewal6Discussion7Conclusion. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...) order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

Computer simulation of an epistemic landscape model, modified to include explicit representation of a centralized funding body, show the method of funding allocation has significant effects on communal trade-off between exploration and exploitation, with consequences for the community’s ability to generate significant truths. The results show this effect is contextual, and depends on the size of the landscape being explored, with funding that includes explicit random allocation performing significantly better than peer-review on large landscapes. The paper proposes a way of (...) incorporating external institutional factors in formal social epistemology, and offers a way of bringing such investigations to bear on current research policy questions. (shrink)

The question of the division of cognitive labor has given rise to various models characterizing the way scientists should distribute their efforts. These models often consider the scientific community as a self-governed sphere constituted by rational agents making choices on the basis of fixed rules. Such models have recently been criticized for not taking into account the real mechanisms of science funding. Hence, the question of the utility of the DCL models in guiding science policy remains an open one. In (...) this paper, we show that two unconsidered dimensions would have to be taken into account. First, DCL studies miss the existence of distinct levels of epistemic objectives organizing the research process. Indeed, the scientific field is structured as a system of hierarchical, interconnected practices which are defined both by their inherent purposes and by various superposed external functions. Second, I criticize the absence of ontological considerations, since the epistemological significance of pluralism is highly dependent on the nature of the object under study. Because of these missing dimensions, current DCL models might have a limited usefulness to identify good practices of research governance. (shrink)

Handling the impacts and consequences of technology has become a problem of political, social and scientific relevance since the Sixties. The earlier assumption that technological evolution would automatically lead to social and human progress in an emphatic sense can no longer be sustained. The ambivalence of technology has become a standing topic in the public, philosophical and scientific debate .In this situation new challenges to technology policy are emerging. Functions of an `early warning' with respect to the risks or potentials (...) of new technologies or to ways of avoiding or, at least, resolving technology conflicts by compromise have been postulated . Technology policy pursued by parliament or government, therefore, is in need of scientific consultation . Since the Seventies a number of institutions in this field have been founded , either primarily as parliamentary bureaus of knowledge-transfer to the decision-makers , as networks with a low degree of institutionalization or as independent scientific institutes .The scientific discussion on how to acquire and establish orientational knowledge for decision-makers facing the ambivalence of technology is sectoralized into two branches: the ethics of technology and technology assessment . These two branches are based on fundamentally different assumptions concerning how to give orientation to technology policy: the philosophical ethics, of course, emphasizes the normative implications of decisions on technology and the importance of moral conflicts , while technology assessment relies mainly on sociological or economic research .Both approaches have largely been developed and practised without regard to one another. In Germany, at the beginning of the Nineties the conceptual discussion around these questions was initiated. At first, a lot of misunderstandings appeared, both parties fundamentally criticizing each other, often one denying the competence of the other to deal appropriately with technology problems . In this paper the conflict is analyzed by looking at the situation in Germany, summarizing and weighing the main arguments which have been brought up by TA and the ethical approach to technology.As a result it will be shown that both are partly right, whereby the relative weight of both approaches in their capacity to give orientation is dependent on contextual circumstances. A non-reductionistic overall approach must take into account the normative aspects of the ethics of technology when handling moral conflicts as well as the need for operationalization, concreteness and established knowledge about the processes of “managing technology in society” postulated by TA. (shrink)

We give reasons for the importance of old narratives, including myths, in ethical thinking about science and technology. On the example of a legend about creating artificial men we explore the side effects of having too much success and the problem of intermediate social status of bioengineered artefacts.

Technological advance affects almost all areas of human life. Rapid digitization, increased mobility, new biotechnologies, and nanotechnology deeply influence, amongst others, industrial production, entertainment, work, military affairs, and individual life. Besides overwhelmingly positive effects on wealth, comfort, innovation, and development, this also raises questions of unintended effects, of tensions with democracy, of the role of citizens, and of its sustainability facing environmental issues. Tools and procedures are needed to cope with this challenging situation. Technology assessment has been developed more than (...) fifty years ago to enable science, the economy, and society to harvest the potential of new technology to the maximum extent possible and to deal responsibly with possible adverse effects. It was developed more than 50 years ago in the U.S. Congress and has diversified considerably in the meantime. Parliamentary TA in many European states and at the international level, participatory TA at the local and regional levels worldwide, and TA as part of engineering processes are the most relevant fields today. Technology assessment is a growing field of interdisciplinary research and scientific policy advice. This volume gives an overview of motivations of TA, its history and its current practices, develops a fresh theoretical perspective on TA rooted in social theory and philosophy, and draws conclusions from the theoretical perspective for the further development of TA's practices. It provides the first comprehensive view on the growing field of TA at the international level. (shrink)