The first part of this paper finds Craver’s (2007) mutual manipulability theory (MM) of constitution inadequate, as it definitionally ties constitution to the feasibility of idealized experiments, which, however, are unrealizable in principle. As an alternative, the second part develops an abductive theory of constitution (NDC), which exploits the fact that phenomena and their constituents are unbreakably coupled via common causes. The best explanation for this common-cause coupling is the existence of an additional dependence relation, viz. constitution. Apart from adequately (...) capturing the essential characteristics of constitution missed by MM, NDC has important ramifications for constitutional discovery—most notably, that there is no experimentum crucis for constitution, not even under ideal discovery circumstances. (shrink)

While ideal interventions are acknowledged by many as valuable tools for the analysis of causation, recent discussions have shown that, since there are no ideal interventions on upper-level phenomena that non-reductively supervene on their underlying mechanisms, interventions cannot—contrary to a popular opinion—ground an informative analysis of constitution. This has led some to abandon the project of analyzing constitution in interventionist terms. By contrast, this paper defines the notion of a horizontally surgical intervention, and argues that, when combined with some innocuous (...) metaphysical principles about the relation between upper and lower levels of mechanisms, that notion delivers a sufficient condition for constitution. This, in turn, strengthens the case for an interventionist analysis of constitution. (shrink)

Gebharter has proposed using one of the best known Bayesian network causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic constitution behaves like deterministic direct causation, such that PC is directly applicable to mixed variable sets featuring both causal and constitutive dependencies. Gebharter claims that such mixed sets, under certain restrictions, comply with PC’s background assumptions. The aim of this article is to show that Gebharter’s proposal incurs severe problems, ultimately rooted in (...) the widespread non-compliance of mechanistic systems with PC’s assumptions. This casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC’s assumptions. (shrink)

Mechanisms are usually viewed as inherently hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behaviour. In order to adequately draw quantitative predictions from a model of a mechanism, the model needs to capture this hierarchical aspect. The recursive Bayesian network formalism was put forward as a means to model mechanistic hierarchies by decomposing variables. The proposal was recently criticized by Gebharter and Gebharter and Kaiser, who instead propose to decompose arrows. In this paper, I defend the (...) RBN account from the criticism and argue that it offers a better representation of mechanistic hierarchies than the rival account. (shrink)

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal (...) models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. (shrink)

Glennan appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality . I argue that Glennan’s manoeuvre fails. The appeal to interventions is not able to address the ontological regress, and it blocks the explanatory regress only at the cost of making the account inapplicable to non-modular mechanisms. I offer a solution to the explanatory regress that makes use of dynamic Bayesian networks. My argument is illustrated by a case study from systems biology, (...) namely, the mechanism for the irreversibility of apoptosis. I conclude by pointing out the implications of my argument for Glennan’s mechanistic account of causality and, more generally, for accounts of mechanistic explanation based on interventions. 1 Introduction2 Glennan’s Account of Causality3 Objections to Glennan’s Account4 Glennan’s Replies5 Ontological Symmetry?6 Explanatory Symmetry?7 A Solution to the Explanatory Regress8 The Prospects of Glennan’s Account. (shrink)

What can we learn from “minimal” economic models? I argue that learning from such models is not limited to conceptual explorations—which show how something could be the case—but may extend to explanations of real economic phenomena—which show how something is the case. A model may be minimal qua certain world-linking properties, and yet “not-so-minimal” qua learning, provided it is externally valid. This, in turn, depends on using the right principles for model building and not necessarily “isolating” principles. My argument is (...) buttressed by a case study from computational economics, namely, two agent-based models of asset pricing. (shrink)

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.

In philosophy of science, constitutive explanations have attracted much attention since Craver’s influential book Explaining the Brain (2007). His Mutual Manipulability (MM) theory of constitution aimed to explicate constitution as a non-causal explanatory relation and to demarcate between constituents and non-constituents. But MM received decisive criticism. In response, Craver et al. (2021) have recently proposed a new theory, called Matched Interlevel Experiments (MIE), which is currently gaining traction in various fields. The authors claim that MIE retains “the spirit of MM (...) without conceptual confusion.” Our paper argues that this claim is not borne out: neither does MIE meet MM’s objectives nor is it free of conceptual confusion. At the same time, we show that it is possible to meet MM’s objectives in a conceptually sound manner—by adopting the so-called No De-Coupling theory of constitution. (shrink)

According to Woodward’s influential account of explanation, explanations have a counterfactual structure, and explanatory counterfactuals are analysed in terms of causal relations and interventions. In this paper, we provide a formal semantics of explanatory counterfactuals based on a Ramsey Test semantics of conditionals. Like Woodward’s account, our account is guided by causal considerations. Unlike Woodward’s account, it makes no reference to causal graphs and it also covers cases of explanation where interventions are impossible.

How many notions of cause are there? The causality literature is witnessing a flourishing of pluralist positions. Here I focus on a recent debate on whether interpreting causality in terms of inferential relations commits one to semantic pluralism (Reiss 2011) or not (Williamson 2006). I argue that inferentialism is compatible with a ‘weak’ form of monism, where causality is envisaged as one, vague cluster concept. I offer two arguments for this, one for vagueness, one for uniqueness. Finally, I qualify in (...) what sense the resulting form of monism is ‘weak’.¿Cuántas nociones de causa hay? Hay un resurgir de posiciones pluralistas en la bibliografía sobre causalidad. Me ocuparé aquí de un debate reciente sobre si la interpretación de la causalidad en términos de relaciones inferenciales nos compromete con el pluralismo semántico (Reiss 2011) o no (Williamson 2006). Defiendo que el inferencialismo es compatible con una forma ’debil’ de monismo, donde la causalidad se presenta como un único concepto vago y de agrupación (cluster). Ofrezco para ello dos argumentos, uno sobre vaguedad y otro sobre unicidad. Finalmente, matizaré en qué sentido esta forma demonismo es ’débil’. (shrink)

Agent-based models in philosophy of science.

The core idea of statistical accounts of biological functions is that to function normally is to provide a statistically typical contribution to some goal state of the organism. In this way, statistical accounts purport to naturalize the teleological notion of function in terms of statistical facts. Boorse’s, 542–573, 1977) original biostatistical account was criticized for failing to distinguish functions from malfunctions. Recently, many have attempted to circumvent the criticism, 519–541, 2012, Journal of Medicine and Philosophy, 39, 634–647, 2014). Here, I (...) review such attempts and find them inadequate. The reason, ultimately, is that functional attribution depends on how traits would behave in relevant situations, a condition that resists statistical characterizations in terms of how they typically behave. This, I conclude, undermines the attempt to naturalize functions in statistical terms. (shrink)

I argue for an inferentialist account of the meaning of causal claims, which draws on the writings of Sellars and Brandom. The account is meant to be widely applicable. In this work, it is motivated and defended with reference to complex systems sciences, i.e., sciences that study the behaviour of systems with many components interacting at various levels of organisation (e.g. cells, brain, social groups). Here are three, seemingly-uncontroversial platitudes about causality. (1) Causal relations are objective, mind-independent relations and, as (...) such, analysable in objective, mind-independent terms. (2) There is a tight connection between our practice of predicting, explaining and controlling phenomena, and the use of causal notions. (3) The second platitude should be explained in terms of the first. Contrary to this widely-held stance, I suggest that we reverse the order of analysis, by taking our activities of agents as the raw material in terms of which to account for the obtaining of causal relations. To this end, I propose and defend an inferentialist account of causality. Causality is a ‘category’ that the knowing subject employs to ‘mediate’ between himself and the world. In inferentialist terms, this mediation is the result of the concept of cause figuring in a network of inferences, used in our practice of gathering evidence and using it to explain, predict and intervene. Complexity only makes the mediation more difficult, thereby rendering the meaning of causality more evident. (shrink)

Alexander Gebharter has recently proposed to use Bayesian network causal discovery methods to identify the constitutive dependencies that underwrite mechanistic explanations. The proposal depends on using the assumptions of the causal Bayesian network framework to implicitly define mechanistic constitution as a kind of deterministic direct causal dependence. The aim of this paper is twofold. In the first half, we argue that Gebharter’s proposal incurs severe conceptual problems. In the second half, we present an alternative way to bring Bayesian network tools (...) to bear on the issue of understanding mechanistic constitution. More precisely, our proposal interprets constitution as the relation explaining why a target phenomenon has its characteristic causal role in terms of the causal roles of some of its spatiotemporal parts---where the notion of causal role is probabilistically understood. (shrink)

This interview discusses the use of agent-based modelling in philosophy of science with leading scholars in the field, Dunja Šešelja, Samuli Reijula, and Matteo Michelini.

The aim of the present paper is to approach Juan Luis Vives' conception of freedom of the will in light of scholastic discussions on will and free choice, and point to some interesting similarities with the analysis of free choice contained in Jean Buridan's Quaestiones super decem libros Ethicorum Aristotelis ad Nicomachum.