Ceteris Paribus Laws

Laws of nature take center stage in philosophy of science. Laws are usually believed to stand in a tight conceptual relation to many important key concepts such as causation, explanation, confirmation, determinism, counterfactuals etc. Traditionally, philosophers of science have focused on physical laws, which were taken to be at least true, universal statements that support counterfactual claims. But, although this claim about laws might be true with respect to physics, laws in the special sciences (such as biology, psychology, economics etc.) appear to have—maybe not surprisingly—different features than the laws of physics. Special science laws—for instance, the economic law “Under the condition of perfect competition, an increase of demand of a commodity leads to an increase of price, given that the quantity of the supplied commodity remains constant” and, in biology, Mendel's Laws—are usually taken to “have exceptions”, to be “non-universal” or “to be ceteris paribus laws”. How and whether the laws of physics and the laws of the special sciences differ is one of the crucial questions motivating the debate on ceteris paribus laws. Another major, controversial question concerns the determination of the precise meaning of “ceteris paribus”. Philosophers have attempted to explicate the meaning of ceteris paribus clauses in different ways. The question of meaning is connected to the problem of empirical content, i.e., the question whether ceteris paribus laws have non-trivial and empirically testable content. Since many philosophers have argued that ceteris paribus laws lack empirically testable content, this problem constitutes a major challenge to a theory of ceteris paribus laws

Inference, Explanation, and Asymmetry

Explanation is asymmetric: if A explains B, then B does not explain A. Tradition- ally, the asymmetry of explanation was thought to favor causal accounts of explanation over their rivals, such as those that take explanations to be inferences. In this paper, we develop a new inferential approach to explanation that outperforms causal approaches in accounting for the asymmetry of explanation

(WP 2018-02) Extending Behavioral Economics’ Methodological Critique of Rational Choice Theory to Include Counterfactual Reasoning

This paper extends behavioral economics’ realist methodological critique of rational choice theory to include the type of logical reasoning underlying its axiomatic foundations. A purely realist critique ignores Kahneman’s emphasis on how the theory’s axiomatic foundations make it normative. I extend his critique to the theory’s reliance on classical logic, which excludes the concept of possibility employed in counterfactual reasoning. Nudge theory reflects this in employing counterfactual conditionals. This answers the complaint that the Homo sapiens agent conception ultimately reduces to a Homo economicus conception, and also provides grounds for treating Homo sapiens as an adaptive, non-optimizing, reflexive agent

A Bayesian approach to counterfactual analysis of structural change

In this paper, we develop a Bayesian approach to counterfactual analysis of structural change. Contrary to previous analysis based on classical point estimates, this approach provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. We apply the Bayesian counterfactual analysis to examine the sources of the volatility reduction in U.S. real GDP growth in the 1980s. Using Blanchard and Quah’s (1989) structural VAR model of output growth and the unemployment rate, we find strong statistical support for the idea that a counterfactual change in the size of structural shocks alone, with no corresponding change in propagation, would have produced the same overall volatility reduction that actually occurred. Looking deeper, we find evidence that a counterfactual change in the size of aggregate supply shocks alone would have generated a larger volatility reduction than a counterfactual change in the size of aggregate demand shocks alone. We show that these results are consistent with a standard monetary VAR, for which counterfactual analysis also suggests the importance of shocks in generating the volatility reduction, but with the counterfactual change in monetary shocks alone generating a small reduction in volatility.Monetary policy ; Econometrics

Counterfactually robust inferences, modally ruled out inferences, and semantic holism

It is often argued that inferential role semantics (IRS) entails semantic holism as long as theorists fail to answer the question about which inferences, among the many, are meaning-constitutive. Since analyticity, as truth in virtue of meaning, is a widely dismissed notion in indicating which inferences determine meaning, it seems that holism follows. Semantic holism is often understood as facing problems with the stability of content and many usual explanations of communication. Thus, we should choose between giving up IRS, to avoid these holistic entailments, and defending holism against this charge, to rescue IRS. I try to pursue the second goal by analyzing certain patterns of counterfactual reasoning. Wilfrid Sellars and Robert Brandom claim that, to defend IRS, content-constitutive inferences are those counterfactually robust. While it is difficult to assess the goodness of such a view, it nonetheless entails that counterfactually non-robust inferences (which I call “modally ruled out inferences”) are not content-constitutive. If this is true, and if we take certain remarks about the grasp of concepts on board, there is a way to restrict the scope of the holism entailed by IRS to the extent of reshaping problems with the stability of content

Mind, Matter, Morals - The Epistemic Condition in Causal Judgment

In this thesis, we explore the role of theory of mind, broadly construed, in peo- ple’s causal reasoning and inferences from causal explanations. While research in causal cognition has acknowledged the influence of agents’ knowledge states on how causal they are judged, the theoretical implications of these findings remain unclear. This thesis aims to provide an empirical and theoretical account of the essential function of epistemic states in causal thinking and inference. In chapter 2, we demonstrate how epistemic states mediate a prominent find- ing in causal cognition research — people’s preference for deviant causal agents. Various studies in causal cognition research find that people have the tendency to attribute increased causality to atypical actions. In a series of experiments, we find that this abnormal causal preference is driven by the epistemic states of the causal agents. In chapter 3, we show more generally what role causal agents’ epistemic states and epistemic actions play for people’s causal judgments. We develop and test an account that integrates epistemic states into counterfactual theories of causation: agents’ epistemic states influence the target of intervention in people’s counterfac- tual reasoning. In chapter 4, we investigate whether other people’s epistemic states not only influence causal judgments, but also play a role for the inferences we draw from their causal explanations. In sum, this thesis shows how people take into account the mental states of an agent in both causal judgment and inference. We situate these findings in the broader context of causal and counterfactual theories

Understanding and Coming to Understand

Many philosophers take understanding to be a distinctive kind of knowledge that involves grasping dependency relations; moreover, they hold it to be particularly valuable. This paper aims to investigate and address two well-known puzzles that arise from this conception: (1) the nature of understanding itself—in particular, the nature of “grasping”; (2) the source of understanding’s distinctive value. In what follows, I’ll argue that we can shed light on both puzzles by recognizing first, the importance of the distinction between the act of coming to understand and the state of understanding; and second, that coming to understand is a creative act

The physical mandate for belief-goal psychology

This article describes a heuristic argument for understanding certain physical systems in terms of properties that resemble the beliefs and goals of folk psychology. The argument rests on very simple assumptions. The core of the argument is that predictions about certain events can legitimately be based on assumptions about later events, resembling Aristotelian ‘final causation’; however, more nuanced causal entities (resembling fallible beliefs) must be introduced into these types of explanation in order for them to remain consistent with a causally local Universe