Lorenzo Casini

An Abductive Theory of Constitution

The first part of this article finds Craver’s mutual manipulability theory (MM) of constitution inadequate, as it definitionally ties constitution to the feasibility of ideal 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 fact is the existence of an additional dependence relation, namely, constitution. NDC has important ramifications for constitutional discovery—most notably, that there is no experimentum crucis for constitution, not even under ideal discovery circumstances.

How to Model Mechanistic Hierarchies

Mechanisms are usually viewed as hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behavior. To draw quantitative predictions from a model of a mechanism, the model must capture this hierarchical aspect. Recursive Bayesian networks (RBNs) were put forward by Lorenzo Casini et al. as a means to model mechanistic hierarchies by decomposing variables into their constituting causal networks. The proposal was criticized by Alexander Gebharter. He proposes an alternative formalism, which instead decomposes arrows. Here, I defend RBNs from the criticism and argue that they offer a better representation of mechanistic hierarchies than the rival account.

Not-So-Minimal Models Between Isolation and Imagination

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.

Can Interventions Rescue Glennan’s Mechanistic Account of Causality?

Glennan ([2011]) appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality (Glennan [1996], [2002]). 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 Introduction Glennan’s Account of Causality Objections to Glennan’s Account Glennan’s Replies Ontological Symmetry? Explanatory Symmetry? A Solution to the Explanatory Regress The Prospects of Glennan’s Account