We show that previous results from epistemic network models by Kevin J. S. Zollman and Erich Kummerfeld showing the benefits of decreased connectivity in epistemic networks are not robust across ...changes in parameter values. Our findings motivate discussion about whether and how such models can inform real world epistemic communities.
I compare two different arguments for the importance of bringing new voices into science: arguments for increasing the representation of women, and arguments for the inclusion of the public, or for ...“citizen science”. I suggest that in each case, diversifying science can improve the quality of scientific results in three distinct ways: epistemically, ethically, and politically. In the first two respects, the mechanisms are essentially the same. In the third respect, the mechanisms are importantly different. Though this might appear to suggest a broad similarity between the cases, I show that the analysis reveals an important respect in which efforts to include the public are more complex. With citizen science programs, unlike with efforts to bring more women into science, the three types of improvement are often in conflict with one another: improvements along one dimension may come at a cost on another dimension, suggesting difficult trade-offs may need to be made.
This paper describes the main features and goals of the speculative work in modern sciences that has greatly accelerated since World War II due to the exponential increase in computing power and ...newly available theoretical and conceptual tools. It points to the long historical strand of speculative philosophical work in symbiosis with the sciences, suggests the reasons for its unexpected neglect in contemporary professional philosophy of science, why it should be a major approach, and why such pursuit is not inevitable. Finally, the paper outlines potential topics, fields, and tools for such collaborative work and argues it is likelier to be more fruitful today than at any point in the past hundred years.
Certain scientific explanations of physical facts have recently been characterized as distinctively mathematical—that is, as mathematical in a different way from ordinary explanations that employ ...mathematics. This article identifies what it is that makes some scientific explanations distinctively mathematical and how such explanations work. These explanations are non-causal, but this does not mean that they fail to cite the explanandum's causes, that they abstract away from detailed causal histories, or that they cite no natural laws. Rather, in these explanations, the facts doing the explaining are modally stronger than ordinary causal laws (since they concern the framework of any possible causal relation) or are understood in the why question's context to be constitutive of the physical arrangement at issue. A distinctively mathematical explanation works by showing the explanandum to be more necessary (given the physical arrangement in question) than ordinary causal laws could render it. Distinctively mathematical explanations thus supply a kind of understanding that causal explanations cannot.
We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the
pragmatic-representational (PR) view of data
. On the PR view, data are ...representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data.
There is considerable current debate about the need for replication in the science of social psychology. Most of the current discussion and approbation is centered on direct or exact replications, ...the attempt to conduct a study in a manner as close to the original as possible. We focus on the value of conceptual replications, the attempt to test the same theoretical process as an existing study, but that uses methods that vary in some way from the previous study. The tension between the two kinds of replication is a tension of values—exact replications value confidence in operationalizations; their requirement tends to favor the status quo. Conceptual replications value confidence in theory; their use tends to favor rapid progress over ferreting out error. We describe the many ways in which conceptual replications can be superior to direct replications. We further argue that the social system of science is quite robust to these threats and is self-correcting.
Meehl argued in 1978 that theories in psychology come and go, with little cumulative progress. We believe that this assessment still holds, as also evidenced by increasingly common claims that ...psychology is facing a “theory crisis” and that psychologists should invest more in theory building. In this article, we argue that the root cause of the theory crisis is that developing good psychological theories is extremely difficult and that understanding the reasons why it is so difficult is crucial for moving forward in the theory crisis. We discuss three key reasons based on philosophy of science for why developing good psychological theories is so hard: the relative lack of robust phenomena that impose constraints on possible theories, problems of validity of psychological constructs, and obstacles to discovering causal relationships between psychological variables. We conclude with recommendations on how to move past the theory crisis.
In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing ...the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal-environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper.
The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, ...the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a
postulate
, an
unfalsifiable principle
, a
natural law
, and an
imperative
. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences.
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