Michael Weisberg

Gravitational interactions allowed astronomers to conclude that dark matter rings all luminous galaxies in gigantic halos, but this only accounts for a fraction of the total mass of dark matter believed to exist. Where is the rest? We hypothesize that some of it resides in dark galaxies, pure dark matter halos that either never possessed or have totally lost their baryonic matter. This article explores methodological challenges that arise because of the nature of observation in astrophysics and examines how the blend of observation, simulation, and theory we call the Observing the Invisible approach might make detecting such dark objects possible.

Prior work has found that Americans’ views on evolution are significantly and positively related to their understanding of this theory. However, whether this relationship is cross-culturally robust is unknown. This article extends earlier work by measuring and comparing the acceptance and understanding of evolution among highly educated individuals in China and the United States. We find a significantly higher evolution acceptance level in the Chinese sample than in the US sample, but no significant difference in their average levels of evolution knowledge. Our analysis also shows that accepting evolutionary theory is related to understanding in both the US and the Chinese samples. These results provide evidence for the robustness of the relationship between understanding and acceptance of evolution across different cultural contexts. To our knowledge, this is the first attempt to comprehensively test understanding of evolutionary theory within a Chinese sample and to compare these results with the US sample.

Many people feel the pull of both creationism and evolution as explanations for the origin of species, despite the direct contradiction. Some respond by endorsing theistic evolution, integrating the scientific and religious explanations by positing that God initiated or guided the process of evolution. Others, however, simultaneously endorse both evolution and creationism despite the contradiction. Here, we illustrate this puzzling phenomenon with interviews with a diverse sample. This qualitative data reveals several approaches to coping with simultaneous inconsistent explanations. For example, some people seem to manage this contradiction by separating out ideological claims, which prioritize identity expression, from fact claims, which prioritize truth. Fitting with this interpretation, ambivalent individuals tended to call explanations “beliefs”, avoid mention of truth or falsity, and ground one or both beliefs in identity and personal history. We conclude with a brief discussion of the affordances of this distinction.

A substantial proportion of Chinese nationals seem to accept evolution, and the country is sometimes held up to show that the sorry state of evolution acceptance in the United States is not inevitable. Attempts to improve evolution acceptance generally focus on improving communication, curricular reform, and even identifying cognitive mechanisms that bias people against evolution. What is it that the Chinese scientific community did so well, and can it be generalized? This paper argues that evolution acceptance in China has a very specific history, one that other countries are very unlikely to emulate. We show that the interactions among science, education, mass media, social and political movements, and ideological arguments about evolution greatly influenced the Chinese public's understanding and acceptance of evolution. We find that it was not just formal education, but many more ideologically motivated methods of evolution exposure that contributed to the high rate of acceptance. But since the purpose of evolution dissemination has moved beyond merely teaching biology, the Chinese public persists with substantial misunderstandings of the theory. Thus, bottom line percentage of acceptance figures can be misleading; the details and the history really matter.

This article examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demonstrate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this and conclude that the result hinges on the similarity of informational states among agents of the same type. This is quite different from Schelling-like behavior and suggests that segregation is an instance of macrobehavior that can arise from a wide variety of micromotives.

Scientific models almost always contain idealizations, and this fact suggests methodological questions about how model building should proceed. Biologist Richard Levins addressed such questions by arguing that highly idealized models have a special role in helping to explain the behavior of populations. In When Less is More: Tradeoffs and Idealization in Model Building, I assess and partially endorse Levins' views first on their own terms and then through a novel analysis of idealization in modelling. This analysis begins with an articulation of theoretical desiderata including simplicity, precision, generality, representational capacity, and accuracy. After defining these desiderata, I evaluate them to determine any constraints that they put on the achievement of other desiderata. For example, I show that precision and generality "trade off" against one another. A full analysis of idealization requires coupling an examination of the constraints faced by modelers with an analysis of the scientific value of each desideratum. In this dissertation, I focus on the desideratum of generality and assess its importance for scientific explanation and some kinds of confirmation. I conclude When Less is More by discussing how an analysis of idealization, such as the one offered in this dissertation, can form the basis for determining a new set of methodological norms for model building

Aristotle’s On generation and corruption raises a vital question: how is mixture, or what we would now call chemical combination, possible? It also offers an outline of a solution to the problem and a set of criteria that a successful solution must meet. Understanding Aristotle’s solution and developing a viable peripatetic theory of chemical combination has been a source of controversy over the last two millennia. We describe seven criteria a peripatetic theory of mixture must satisfy: uniformity, recoverability, potentiality, equilibrium, alteration, incompleteness, and the ability to distinguish mixture from generation, corruption, juxtaposition, augmentation, and alteration. After surveying the theories of Philoponus (d. 574), Avicenna(d. 1037), Averroes (d. 1198), and John M. Cooper (fl. circa2000), we argue for the merits of Richard Rufus of Cornwall’s theory. Rufus (fl. 1231–1256) was a little known scholastic philosopher who became a Franciscan theologian in 1238, after teaching Aristotelian natural philosophy as a secular master in Paris. Lecturing on Aristotle’s De generatione et corruptione, around the year 1235, he offered his students a solution to the problem of mixture that we believe satisfies Aristotle’s seven criteria. # 2004 Elsevier Ltd. All rights reserved.

Why is evolutionary theory controversial among members of the American public? We propose a novel explanation: allegiance to different criteria for belief. In one interview study, two online surveys, and one nationally representative phone poll, we found that evolutionists and creationists take different justifications for belief as legitimate. Those who accept evolution emphasize empirical evidence and scientific consensus. Creationists emphasize not only the Bible and religious authority, but also knowledge of the heart. These criteria for belief remain predictive of views about evolution even when taking into account other related factors like religion, political affiliation, and education. Each view is supported by its own internally specified criteria for what constitutes a justified belief. Changing minds may thus require changing epistemic norms.

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss what consequences those tradeoffs have for theoretical practice

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth

Aristotle’s On generation and corruption raises a vital question: how is mixture, or what we would now call chemical combination, possible? It also offers an outline of a solution to the problem and a set of criteria that a successful solution must meet. Understanding Aristotle’s solution and developing a viable peripatetic theory of chemical combination has been a source of controversy over the last two millennia. We describe seven criteria a peripatetic theory of mixture must satisfy: uniformity, recoverability, potentiality, equilibrium, alteration, incompleteness, and the ability to distinguish mixture from generation, corruption, juxtaposition, augmentation, and alteration. After surveying the theories of Philoponus , Avicenna , Averroes , and John M. Cooper , we argue for the merits of Richard Rufus of Cornwall’s theory. Rufus was a little known scholastic philosopher who became a Franciscan theologian in 1238, after teaching Aristotelian natural philosophy as a secular master in Paris. Lecturing on Aristotle’s De generatione et corruptione, around the year 1235, he offered his students a solution to the problem of mixture that we believe satisfies Aristotle’s seven criteria.Author Keywords: Mixture; Mixt; Chemical combination; Accidental potential; Potential; Elements; Medieval chemistry; Peripatetic chemistry; Aristotelian science; Richardus Rufus; Averroes ; Avicenna ; Philoponus ; John M. Cooper

Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a new account of both concrete and mathematical models, with special emphasis on the intentions of theorists, which are necessary for evaluating the model-world relationship during the practice of modeling. Although mathematical models form the basis of most of contemporary modeling, my discussion begins with more traditional, concrete models such as the San Francisco Bay model.

Simulation and Similarity: Using Models to Understand the World is an account of modeling in contemporary science. Modeling is a form of surrogate reasoning where target systems in the natural world are studied using models, which are similar to these targets. My book develops an account of the nature of models, the practice of modeling, and the similarity relation that holds between models and their targets. I also analyze the conceptual tools that allow theorists to identify the trustworthy aspects of models. Taken as a whole, I try to account for the ways that modeling is actually practiced by theorists, while abstracting sufficiently to understand the similarities and differences among examples of concrete, mathematical, and computational modeling.I am grateful to Wendy Parker, Jay Odenbaugh, and Bill Wimsatt for their careful and interesting reading of my book, as well as their constructive criticisms. Although I naturally disagree with some of their critiques, I have learned much ..

Chemistry is the study of the structure and transformation of matter. When Aristotle founded the field in the 4th century BCE, his conceptual grasp of the nature of matter was tailored to accommodate a relatively simple range of observable phenomena. In the 21st century, chemistry has become the largest scientific discipline, producing over half a million publications a year ranging from direct empirical investigations to substantial theoretical work. However, the specialized interest in the conceptual issues arising in chemistry, hereafter Philosophy of Chemistry, is a relatively recent addition to philosophy of science. Philosophy of chemistry has two major parts. In the first, conceptual issues arising within chemistry are carefully articulated and analyzed. Such questions which are internal to chemistry include the nature of substance, atomism, the chemical bond, and synthesis. In the second, traditional topics in philosophy of science such as realism, reduction, explanation, confirmation, and modeling are taken up within the context of chemistry.

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.

This paper examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demon- strate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this, and conclude that the result hinges on the similarity of informational states amongst agents of the same type. This is quite dierent from Schelling-like behavior, and sug- gests (in his terms) that segregation is an instance of macro behavior which can arise from a wide variety of micro motives.