Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations toward in-groups.

Many epidemics consist in individuals spreading infection to others. From the population perspective, they also have population characteristics important in modeling, explaining, and intervening in epidemics. I analyze epidemiology’s contemporary population perspective through the example of epidemics by examining two central principles attributed to Geoffrey Rose: a distinction between the causes of cases and the causes of incidence, and between “high-risk” and “population” strategies of prevention. Both principles require revision or clarification to capture the sense in which they describe distinct perspectives on the same phenomenon (such as an epidemic), each perspective capturing a different level of contrastive analysis.

Several philosophers propose probabilistic measures of how well a potential scientific explanation would explain the given evidence. These measures could elaborate “best” in “inference to the best explanation”. This paper argues that none of these measures (and no other measure built exclusively from such probabilities) succeeds. The paper considers the various rival explanations that scientists proposed for the parallelogram of forces. Scientists regarded various features of these proposals as making them more or less “lovely” (in Lipton’s sense). None of these probabilistic measures of loveliness can reflect these features. The paper concludes by considering the kinds of probabilities that could reflect these features.

Some New Mechanists have proposed that claims of compositional relations are justified by combining the results of top-down and bottom-up interlevel interventions. But what do scientists do when they can perform, say, a cellular intervention, but not a subcellular detection? In such cases, paired interlevel interventions are unavailable. We propose that scientists use abduction and we illustrate its use through a case study of the ionic theory of resting and action potentials.

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Strong emergentism is the position that certain higher-level properties display a kind of metaphysical autonomy from the lower-level properties in which they are grounded. The prospect of collapse is a problem for strong emergentism. According to those who press the collapse problem any purportedly strongly emergent feature inheres in the emergence base and so is not genuinely autonomous from that base. Umut Baysan and Jessica Wilson (2017) argue that power emergentism avoids the collapse problem. In this paper, I challenge the claim that power emergentism avoids the collapse problem and argue for explanatory emergentism in its place.

In scientific modeling, continuum idealizations bridge scales but at the cost of fundamentally misrepresenting the microstructure of the system. This engenders a mystery. If continuum idealizations are dispensable in principle, this de-problematizes their representational inaccuracy, since continuum properties reduce to lower-scale properties, but the mystery of how this reduction could be carried out endures. Alternatively, if continuum idealizations are indispensable in principle, this is consistent with their explanatory and predictive success but renders their representational inaccuracy mysterious. I argue for a deflationary solution, enlisting the applied scientific method of upscaling as demonstrated in a case from soil hydrology.

Experimental archaeology is often understood both as testing hypotheses about processes shaping the archaeological record and as generating tacit knowledge. Considering lithic technologies, I examine the relationship between these conceptions. Experimental archaeology is usefully understood via “maker’s knowledge”: archaeological experiments generate embodied know-how enabling archaeological hypotheses to be grasped and challenged, and further, well-positioning archaeologists to generate integrated interpretations. Finally, experimental archaeology involves “material speculation”: the constraints and affordances of archaeologists and their materials shape productive exploration of the capacities of objects and human skill in ways relevant to archaeological questions.

Simple random-sampling resolutions of the raven paradox relevantly diverge from scientific practice. We develop a stratified random-sampling model, yielding a better fit and apparently rehabilitating simple random sampling as a legitimate idealization. However, neither accommodates a second concern, the objection from potential bias. We develop a third model that crucially invokes causal considerations, yielding a novel resolution that handles both concerns. This approach resembles Inference to the Best Explanation (IBE) and relates the generalization’s confirmation to confirmation of an associated law. We give it an objective Bayesian formalization and discuss the compatibility of Bayesianism and IBE.

For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size (n) to learn from data by securing (i)–(ii). It is argued that the criticism of the postdata severity evaluation of testing results based on a small n by Rochefort-Maranda (2020) is meritless because it conflates [a] misuses of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature.