Recession of the galaxies indicates a repulsive force and suggests that Newton's formulation of gravitation is not complete. A possible modification is proposed, and this Neo-Newtonian equation allows a quantitative treatment of Mach's principle. It also limits the velocity of matter to c and gives a correct prediction for the perihelion of Mercury.

This paper investigates the adequacy of evidential relevance relations proposed by Glymour and others. These accounts incorporate, as a necessary condition, what I call the Positive Instance Condition (PIC): the evidence statement and auxiliary assumptions entail a “positive instance” of the hypothesis. I argue that any account which incorporates PIC as a necessary condition while allowing “bootstrap testing” is doomed to fail. A nonbootstrapping evidential relevance relation of similar form is proposed, and it is argued that, in addition to avoiding published counter examples, this new relation meets two general requirements which, if not met, would undermine the ability of any account that incorporates PIC to accord with our intuitions of evidential relevance.

Geometry was a main source of inspiration for Carnap's conventionalism. Taking Poincaré as his witness, Carnap asserted in his dissertation Der Raum ([1922] 1978) that the metrical structure of space is conventional while the underlying topological structure describes ‘objective’ facts. With only minor modifications he stuck to this account throughout his life. The aim of this paper is to disprove Carnap's contention by invoking some classical theorems of differential topology. It is shown that his metrical conventionalism is indefensible for mathematical reasons. This implies that the relation between topology and geometry cannot be conceptualized as analogous to the relation between the meaning of a proposition and its expression in some language as the logical empiricists used to say.

A pluralist about explanation posits many explanatory relevance relations, while an invariantist denies any substantial role for context in fixing genuine explanation. This article summarizes one approach to combining pluralism and invariantism that emphasizes the contrastive nature of explanation. If explanations always take contrasts as their objects and contrasts come in types, then the role for the context in which an explanation is given can be minimized. This approach is illustrated using a classic debate between natural theology and natural selection about the structure of bees’ honeycombs.

Leibnizian metaphysics underpins the universally held view that spacetime must be inextendible—that it must be “as large as it can be” in a sense. But here we demonstrate a surprising fact within the context of general relativity: the property of inextendibility turns out to be unstable when attention is restricted to certain collections of “physically reasonable” spacetimes.

Recent accounts of multiscale modeling investigate ontic and epistemic constraints imposed by relations between component models at varying relative scales (macro, meso, micro). These accounts often focus especially on the role of the meso, or intermediate, relative scale in a multiscale model. We aid this effort by highlighting a novel role for mesoscale models: functioning as a focal point, and explanation, for disagreement between researchers who otherwise share theoretical commitments. We present a case study in multiscale modeling of insect behavior to illustrate, arguing that the cognitive map debate in neuroethology research is best understood as a mesoscale disagreement.

A common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone.

Both Henri Poincaré and (more recently) Roger Shepard have argued that the geometry and topology of physical space are internalized by the mind in the form, not (or not only) of a Euclidean manifold, but in terms of the group of rigid Euclidean transformations. Since this issue can have bearing on various metaphysical and epistemological questions, we explore the different reasons they offer for holding this view. In this context, we show how most commentators misunderstand Poincaré's ‘heated sphere/plate’ model and introduce Shepard's ideas to the philosophy of science community.

Any given ecosystem includes macro- and micro-organismal ecology. I examine the concept of stability and its meaning in these two size scales. Historically, the stability concept follows species population dynamics and equilibrium. In micro-ecology, however, classification is rare and, in the majority of cases, not possible. Therefore, the microbial taxonomic composition is regarded in a nonspecific way, as opposed to animals and plant communities. Such different consideration leads to the emphasis on functional stability in micro-ecology versus the structural in macro-ecology. I discuss the implication of these differences in meanings and the possibilities of integrating them.

I defend a novel account of scientific progress centered around justification. Science progresses, on this account, where there is a change in justification. I consider three options for explicating this notion of change-in-justification. This account of scientific progress dispels with a condition for scientific progress that requires accumulation of truth or truth-likeness, and it emphasizes the social nature of scientific justification.

In recent debates about the replication crisis, two positions have been dominant: one that focuses on methodological reforms and one that focuses on theory building. This paper takes up the suggestion that there might be a deeper difference in play, concerning the ways the very subject matter of psychology is construed by opposing camps, i.e., in terms of stable effects versus in terms of complexity. I argue that each gets something right, but neither is sufficient. My analysis suggests that the context sensitivity of the psychological subject matter needs to be front and center of methodological and theoretical efforts.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.

The demise of the value-free ideal constitutes a threat to public trust in science. One proposal is that, whenever making value judgments, scientists rely only on democratic values. Because the influence of democratic values on scientific claims and recommendations is legitimate, public trust in science is warranted. I challenge this proposal. Appealing to democratic values will not suffice to secure trust because of at least two obstacles: polarization and marginalization.

Timothy Williamson claimed to prove with a coin-tossing example that hyperreal probabilities cannot save the principle of regularity. A premise of his argument is that two specified infinitary events must be assigned the same probability because, he claims, they are isomorphic. But as has been pointed out, they are not isomorphic. A way of framing Williamson’s argument that does not make it depend on the isomorphism claim is in terms of shifts in Bernoulli processes, the usual mathematical model of sequential coin tossing. But even so framed, the argument still fails.