Steel and bone: Mesoscale modeling and middle-out strategies in physics and biology

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. Mesoscale models capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science (steel) and biology (bone), where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate (mesoscopic) scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale systems. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems

Playing with networks : How economists explain

Peer reviewe

Factive Scientific Understanding Without Accurate Representation

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the (difference-making) features of their real-world target system(s). My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves

The strong emergence of molecular structure

One of the most plausible and widely discussed examples of strong emergence is molecular structure. The only detailed account of it, which has been very influential, is due to Robin Hendry and is formulated in terms of downward causation. This paper explains Hendry’s account of the strong emergence of molecular structure and argues that it is coherent only if one assumes a diachronic reflexive notion of downward causation. However, in the context of this notion of downward causation, the strong emergence of molecular structure faces three challenges that have not been met and which have so far remained unnoticed. First, the putative empirical evidence presented for the strong emergence of molecular structure equally undermines supervenience, which is one of the main tenets of strong emergence. Secondly, it is ambiguous how the assumption of determinate nuclear positions is invoked for the support of strong emergence, as the role of this assumption in Hendry’s argument can be interpreted in more than one way. Lastly, there are understandings of causation which render the postulation of a downward causal relation between a molecule’s structure and its quantum mechanical entities, untenable

What is theoretical progress of science?

The epistemic conception of scientific progress equates progress with accumulation of scientific knowledge. I argue that the epistemic conception fails to fully capture scientific progress: theoretical progress, in particular, can transcend scientific knowledge in important ways. Sometimes theoretical progress can be a matter of new theories ‘latching better onto unobservable reality’ in a way that need not be a matter of new knowledge. Recognising this further dimension of theoretical progress is particularly significant for understanding scientific realism, since realism is naturally construed as the claim that science makes theoretical progress. Some prominent realist positions (regarding fundamental physics, in particular) are best understood in terms of commitment to theoretical progress that cannot be equated with accumulation of scientific knowledge

A single evolutionary innovation drives the deep evolution of symbiotic N₂-fixation in angiosperms

Symbiotic associations occur in every habitat on earth, but we know very little about their evolutionary histories. Current models of trait evolution cannot adequately reconstruct the deep history of symbiotic innovation, because they assume homogenous evolutionary processes across millions of years. Here we use a recently developed, heterogeneous and quantitative phylogenetic framework to study the origin of the symbiosis between angiosperms and nitrogen-fixing (N2) bacterial symbionts housed in nodules. We compile the largest database of global nodulating plant species and reconstruct the symbiosis’ evolution. We identify a single, cryptic evolutionary innovation driving symbiotic N2-fixation evolution, followed by multiple gains and losses of the symbiosis, and the subsequent emergence of ‘stable fixers’ (clades extremely unlikely to lose the symbiosis). Originating over 100 MYA, this innovation suggests deep homology in symbiotic N2-fixation. Identifying cryptic innovations on the tree of life is key to understanding the evolution of complex traits, including symbiotic partnerships

Compare and Contrast: How to Assess the Completeness of Mechanistic Explanation

Opponents of the new mechanistic account of scientific explanation argue that the new mechanists are committed to a ‘More Details Are Better’ claim: adding details about the mechanism always improves an explanation. Due to this commitment, the mechanistic account cannot be descriptively adequate as actual scientific explanations usually leave out details about the mechanism. In reply to this objection, defenders of the new mechanistic account have highlighted that only adding relevant mechanistic details improves an explanation and that relevance is to be determined relative to the phenomenon-to-be-explained. Craver and Kaplan (B J Philos Sci 71:287–319, 2020) provide a thorough reply along these lines specifying that the phenomena at issue are contrasts. In this paper, we will discuss Craver and Kaplan’s reply. We will argue that it needs to be modified in order to avoid three problems, i.e., what we will call the Odd Ontology Problem, the Multiplication of Mechanisms Problem, and the Ontic Completeness Problem. However, even this modification is confronted with two challenges: First, it remains unclear how explanatory relevance is to be determined for contrastive explananda within the mechanistic framework. Second, it remains to be shown as to how the new mechanistic account can avoid what we will call the ‘Vertical More Details are Better’ objection. We will provide answers to both challenges