Abstract:Recent epistemology of modality has seen a growing trend towards metaphysics-first approaches. Contrastingly, this paper offers a more philosophically modest account of justifying modal claims, focusing on the practices of scientific modal inferences. Two ways of making such inferences are identified and analyzed: actualist-manipulationist modality (AM) and relative modality (RM). In AM, what is observed to be or not to be the case in actuality or under manipulations, allows us to make modal inferences. AM-bas… Show more
“…Instead, these models represent actual targets-say, samples of krypton gas-as having certain modal or counterfactual properties: e.g., "if this actual gas sample's pressure were to be increased, then ceteris paribus its temperature would increase." As Hirvonen et al (2021) have noted, this kind of modal reasoning about actual targets is thoroughly epistemologically unproblematic. This general observation about the modal content of models directed at actual systems applies even in those cases where intervention is impossible (cf.…”
Section: An Epistemological Gap: Surrogative Functionmentioning
confidence: 99%
“…It is precisely because the alleged targets of hypothetical models are, as it were, purely hypothetical, that there is a genuine challenge with respect to evaluating these models' surrogate function. Again, even in cases of modeling actual targets where our models might provide modal information, and where we cannot directly intervene on the target of the model, the models' surrogative function can still in principle be evaluated by observation (Hirvonen et al, 2021) or computer simulation (cf. Parker, 2017).…”
Section: An Epistemological Gap: Surrogative Functionmentioning
Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this "hypothetical modeling". This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account for the epistemology of hypothetical models seems to cohere with the most characteristic function of scientific modeling in general, i.e., surrogative representation. The second epistemological challenge is a version of "reliability challenges" familiar from other areas. There is a challenge to explain how hypothetical models could be a reliable guide to what is possible, given that they are not and cannot be compared against their nonactual targets and updated accordingly. I close with some brief remarks on possible solutions to these challenges.
“…Instead, these models represent actual targets-say, samples of krypton gas-as having certain modal or counterfactual properties: e.g., "if this actual gas sample's pressure were to be increased, then ceteris paribus its temperature would increase." As Hirvonen et al (2021) have noted, this kind of modal reasoning about actual targets is thoroughly epistemologically unproblematic. This general observation about the modal content of models directed at actual systems applies even in those cases where intervention is impossible (cf.…”
Section: An Epistemological Gap: Surrogative Functionmentioning
confidence: 99%
“…It is precisely because the alleged targets of hypothetical models are, as it were, purely hypothetical, that there is a genuine challenge with respect to evaluating these models' surrogate function. Again, even in cases of modeling actual targets where our models might provide modal information, and where we cannot directly intervene on the target of the model, the models' surrogative function can still in principle be evaluated by observation (Hirvonen et al, 2021) or computer simulation (cf. Parker, 2017).…”
Section: An Epistemological Gap: Surrogative Functionmentioning
Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this "hypothetical modeling". This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account for the epistemology of hypothetical models seems to cohere with the most characteristic function of scientific modeling in general, i.e., surrogative representation. The second epistemological challenge is a version of "reliability challenges" familiar from other areas. There is a challenge to explain how hypothetical models could be a reliable guide to what is possible, given that they are not and cannot be compared against their nonactual targets and updated accordingly. I close with some brief remarks on possible solutions to these challenges.
Several recent accounts of modeling have focused on the modal dimension of scientific inquiry. More precisely, it has been suggested that there are specific models and modeling practices that are best understood as being geared towards possibilities, a view recently dubbed modal modeling. But modalities encompass much more than mere possibility claims. Besides possibilities, modal modeling can also be used to investigate contingencies, necessities or impossibilities. Although these modal concepts are logically connected to the notion of possibility, not all models are equal in their affordances for these richer modal inferences. This paper investigates the modal extent of selected models and argues that analyzing singular model-target pairings by themselves is typically not enough to explain their modal aptness or to identify the kinds of modalities they can be used to reason about. Furthermore, it is argued that some important concepts that are not explicitly modal - like biological robustness - can be understood modally through their relational nature to a background space of possibilities. In conclusion, it is suggested that the strategy of modal modeling is contrastive, situating particular possibilities in larger modal spaces and studying the structural relations within them.
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