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While Turner Chapter 4 does not explicitly deal with the question of the scope of mechanistic explanations, his chapter may be taken to emphasize the importance of a different type of why-question articulated in the following way: Suppose we find that a trend in a given clade, over a given time interval is passive.
This tells us something about the process that generated the trend, but we might wish to know more.
Why is the trend passive? One part of the answer to this question will be a story about the fixed boundary in the state space.
Turner, Chapter 4, p. Passive trends need not result in random and diverse outcomes, because unbiased evolutionary sorting processes can be constrained by fixed boundary conditions. Such boundary conditions can be understood as physical and biological constraints that for instance set limits for the minimum size of mammals.
This perspective supports the claim that 2 One challenge for such an account may be to accommodate the reliance on stochastic models for some evolutionary explanations. I cannot go into detail with this discussion but just wish to note that stochasticity may be another interesting limit case for mechanistic accounts. See also Merlin Chapter 5 for the importance of stochasticity in the context of developmental biology for understanding evolvability. The interest in constraints on evolutionary trajectories and phylogenetic structures also suggests that we may have to revisit the assumption that biological explanations per definition are limited in generality and characterized by a lack of appeal to laws Green, b.
The interest in understanding the constraints on biological variation is also shared by some functional biologists. Wouters coined the term design explanations to accommodate the explanatory power of models describing the constraints imposed on different life forms, and Braillard has stressed the difference between mechanistic explanations and design explanations in the context of systems biology.
The salient feature of design explanations is a dependency relation between structure and function that can be formalized and generalized. This raises interesting questions about the scope of abstract generalizations in biology and challenges the assumption that explanatory models must always include mechanistic details. In fact, some models in systems biology may explain by showing why certain mechanistic details do not matter for the outcome.
Gross Chapter 8 points to similarities between Batterman s notion of Type-ii questions, and what he takes to be explanatory relations of non-dependence in systems biology. A Type-ii question asks why, in general, patterns of a given type can be expected to obtain.
One way of answering this type of question is to show why an outcome is explanatorily independent of particular causal details. Just like the end point for a marble rolling inside a bowl is independent of initial conditions pertaining to the specific placement of the ball, some dynamic states in biology are insensitive to specific molecular details.
Gross emphasizes that some explanations of biological robustness or dynamical switches are best explained by uncovering relations of non-dependence, an explanatory feature that is not accounted for by interventionist accounts. To support his argument, Gross examines a case of dynamic modeling of egg formation in the frog Xenopus laevis where many changes of component parts are not change- relating to the overall system behavior. Whereas such features are considered constitutively irrelevant from a manipulationist perspective, the explanatory job of the dynamic model is precisely that of accounting for this invariance through a description of dynamic trajectories and threshold effects.
Hence, Gross argues that systems biologists not only wish to understand the change-relating features of the system but also seek to explain why certain dynamic patterns obtain as a result of the architecture of the system, and how robustness can be understood through attention to relations of non-dependence.
Pluralism and explanatory integration Several authors of Explanation in Biology stress that some kind of pluralistic approach to scientific explanation is required.
In some contexts, different types of explanations are required to answer different types of questions Section 3. In other contexts, a more integrated kind of pluralism may be needed as a result of the complexity of practical problems that require multidisciplinary solutions Mekios, Chapter 3. Accordingly, philosophers may need to combine different explanatory accounts to capture the complexity of concrete episodes in biological research practice.
For instance, Fagan Chapter 17 shows how explaining stem cell reprogramming requires a combination of interventionist, gene-centric, and mechanistic approaches because none of these are fully satisfactory on their own. In her modified account of mechanistic explanation, Fagan combines aspects from each in a joint description of interdependent factors. Drawing on the example of research on gene regulation in immune responses in leukocytes, Baetu demonstrates that even if a complete mechanistic model is at hand, mathematical models are needed to clarify issues concerning the quantitative aspects such as the degree of sensitivity and robustness of the system.
In this way, quantitative modeling and mathematical derivation can complement mechanistic models by a more detailed description of the dynamics of the system. Moreover, Baetu argues that how-possibly models in some contexts can help to revise mechanistic models, or to show that no additional mechanistic components are likely to be needed to explain a given phenomenon see p.
Several contributions take up the question about the explanatory relevance of mechanistic details and the power of mathematical models in facilitating generalization. Baker Chapter 10 defends the importance of mathematical explanations in biology, and considers deduction from a general law or pattern important for explaining specific phenomena as special cases of a more general pattern. At first glance, this is exactly the explanatory pattern that mechanistic accounts were defined in opposition to.
Breidenmoser and Wolkenhauer s contribution Chapter 11 can be seen as a response to this worry. They argue that generalizations in terms of formalized organizing principles in systems biology are complementary to mechanistic explanations, rather than alternatives.
The complementarity lies in the different levels of abstraction at which we can conceptualize biological functions. Organizing principles in this context define abstract system-level properties that every model of a particular type of system share, given what follows with mathematical necessity from assumptions made about the system.
These principles cannot account for the causal workings of specific systems, as mechanistic explanations can.
Instead, the explanatory virtue of organizing principles lies in their ability to unify different models with shared structural features.
Thus, they do not replace mechanistic models but afford generalizations across systems exhibiting different mechanistic details. In this construal, providing a mechanistic explanation is a matter of narrowing the sets of possible initial conditions by examining the properties of the parts and organization of a system.
Press argues that this holds: whether or not the description of the mechanism mentions laws explicitly.
Biology By Discovery
And its explanatory force will derive from these constraints and their role in supporting a cursory covering law explanation p. Press stresses that his account vindicates both the explanatory importance of uncovering mechanisms, i.
Similarly, Franklin-Hall Chapter 18 argues for a unified perspective of explanation construction via the principle of Causal Economy, although in the context of causal selection. I have some concerns about causal economy as an ideal, as well as about the construal of mechanistic features as constraints on laws of nature.
I cannot go into details with these concerns here, but I wish to point to a general drawback of reinstalling the covering-law model as an overall model for biological explanations. Unificationist accounts miss out on important motivations and consequences of explanatory diversity in biology.
For instance, the covering-law model emphasizes the argumentative structure of explanations, rather than the purposes for which they were developed.
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