We present a probabilistic extension to active path analyses of token causation. The extension uses the generalized notion of intervention presented in : we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. It still succumbs to recent counterexamples by Hiddleston, because it (...) does not explicitly consider causal processes. We claim three benefits: a detailed comparison of three active-path approaches, a probabilistic extension for each, and an algorithmic formulation. (shrink)

Two interesting “no hole” spacetime properties, not being future nakedly singular) are unstable in the fine topology.

This is an English translation of Juliusz Domański’s “Fausta Socyna Explicatio primae partis primi capitis Euangelii Ioannis i egzegeza erazmiańska,” in his "Erasmiana minora. Studia i szkice o pisarstwie filozoficznym i religijnym Erazma z Rotterdamu" (Warsaw: Instytut Tomistyczny, Instytut Filologii Klasycznej UW, 2017), 337–63. Translated with the Author’s permission. The paper compares the method of Biblical interpretation used by Erasmus of Rotterdam with the method of Socinus, raising the question of the extent to which the method outlined by Socinus (...) in his Explicatio primae partis primi capitis Euangelii Joannis can be seen as continuous and and consonant with the method of Erasmus, and to what extent it should be seen as its rejection or modification. In addition, the essay outlines similarities and differences, with respect to both method and content, between Erasmus' Adnotationes and Socinus’ Explicatio. (shrink)

Computer-based argument mapping greatly enhances student critical thinking, more than tripling absolute gains made by other methods. I describe the method and my experience as an outsider. Argument mapping often showed precisely how students were erring (for example: confusing helping premises for separate reasons), making it much easier for them to fix their errors.

This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and other cases (...) that have proved problematic for causal models. (shrink)

I discuss candidates for definitions of determinism in the context of general relativistic spacetimes, and argue that a definition which does not make recourse to any particular region of spacetime should be preferred over alternatives; one such notion is discussed in detail in the light of various physical examples. The emerging picture of determinism is a pluralist one: sometimes there is no unique way of making our intuitive concept of determinism precise. Instead, what is crucial for assessment of determinism of (...) the theory are auxiliary conditions under which it counts as deterministic or indeterministic, and justification for using them in a given situation. (shrink)

I discuss candidates for definitions of determinism in the context of general relativistic spacetimes, and argue that a definition which does not make recourse to any particular region of spacetime should be preferred over alternatives; one such notion is discussed in detail in the light of various physical examples. The emerging picture of determinism is a pluralist one: sometimes there is no unique way of making our intuitive concept of determinism precise. Instead, what is crucial for assessment of determinism of (...) the theory are auxiliary conditions under which it counts as deterministic or indeterministic, and justification for using them in a given situation. (shrink)

I discuss candidates for definitions of determinism in the context of general relativistic spacetimes, and argue that a definition which does not make recourse to any particular region of spacetime should be preferred over alternatives; one such notion is discussed in detail in the light of various physical examples. The emerging picture of determinism is a pluralist one: sometimes there is no unique way of making our intuitive concept of determinism precise. Instead, what is crucial for assessment of determinism of (...) the theory are auxiliary conditions under which it counts as deterministic or indeterministic, and justification for using them in a given situation. (shrink)

We discuss three philosophically interesting epistemic peculiarities of black hole astrophysics: (1) issues concerning whether and in what sense black holes do exist; (2) how to best approach multiplicity of available definitions of black holes; (3) short (i.e., accessible within an individual human lifespan) dynamical timescales present in many of the recent, as well as prospective, observations involving black holes. In each case we argue that the prospects for our epistemic situation are optimistic.

Determinism fails easily if spacetimes with points removed from the spacetime manifold are taken to be physically reasonable representations of a way the world could be according to classical general relativity. I discuss a recently proposed condition for determining which spacetimes have holes—epistemic hole freeness—and show that epistemic hole freeness gives the correct verdict in some non-globally hyperbolic spacetimes with a closed subset removed, certain spacetimes with genuinely indeterministic features count as having an epistemic hole, which implies that the requirement (...) of epistemic hole freeness enforces a form of determinism, and there is a large class of spacetimes that intuitively are radically indeterministic and unphysical due to containing a hole, but are free from epistemic holes. I show that a few natural ways of remedying are not satisfactory. (shrink)

We present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. (...) It still succumbs to recent counterexamples by Hiddleston (2005), because it does not explicitly consider causal processes. We claim three benefits: a detailed comparison of three active-path approaches, a probabilistic extension for each, and an algorithmic formulation. (shrink)

James McAllister’s 2003 article, “Algorithmic randomness in empirical data” claims that empirical data sets are algorithmically random, and hence incompressible. We show that this claim is mistaken. We present theoretical arguments and empirical evidence for compressibility, and discuss the matter in the framework of Minimum Message Length (MML) inference, which shows that the theory which best compresses the data is the one with highest posterior probability, and the best explanation of the data.

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation (...) of Bayesian networks can provide the desired criterion of probabilistic causality. (shrink)

The paper compares the method of Biblical interpretation used by Erasmus of Rotterdam with the method of Socinus, raising the question of the extent to which the method outlined by Socinus in his Explicatio primae partis primi capitis Euangelii Joannis can be seen as continuous and and consonant with the method of Erasmus, and to what extent it should be seen as its rejection or modification. In addition, the essay outlines similarities and differences, with respect to both method and content, (...) between Erasmus' Adnotationes and Socinus’ Explicatio. (shrink)

The inherent complexity of modern financial systems, and their basis in human behavior, make it hard to establish unequivocally the “who, what, where, when, and why” of the global financial crisis. However, after almost a decade, we know enough to discard much of what has, in the meantime, become folk wisdom about the causes of the debacle, including Wall Street bonus culture, bankers’ greed, the moral hazard of “too big to fail,” government intervention (“too much government”) and deregulation (“not enough (...) government”). While each of these factors may have played some role, the crisis can be much more accurately described as one of ignorance than malevolence. The common thread tying the emergence of the bubble to the disastrous consequences of its bursting is the misunderstanding of correlation and causation links in three key domains: economic policy, financial modeling, and the regulatory framework. These misunderstandings helped fuel the housing bubble and introduced fragility into the system, making it vulnerable to the bubble’s subsequent, unavoidable burst. (shrink)

We present a minimum message length (MML) framework for trajectory partitioning by point selection, and use it to automatically select the tolerance parameter ε for Douglas-Peucker partitioning, adapting to local trajectory complexity. By examining a range of ε for synthetic and real trajectories, it is easy to see that the best ε does vary by trajectory, and that the MML encoding makes sensible choices and is robust against Gaussian noise. We use it to explore the identification of micro-activities within a (...) longer trajectory. This MML metric is comparable to the TRACLUS metric – and shares the constraint of abstracting only by omission of points – but is a true lossless encoding. Such encoding has several theoretical advantages – particularly with very small segments (high frame rates) – but actual performance interacts strongly with the search algorithm. Both differ from unconstrained piecewise linear approximations, including other MML formulations. (shrink)

Recent scholarship has underscored the possibility that capital-adequacy regulations may have contributed to the 2008 financial crisis by privileging highly rated asset-backed securities, such as mortgage-backed securities. The same regulations gave an even more privileged position to sovereign debt, leading to the question of whether they helped cause the European sovereign-debt crisis. There is little question that the regulations encouraged European banks to invest in European governments' bonds, but it does not appear that this encouragement led to more government borrowing (...) by significantly reducing the yield governments needed to pay on their debt. However, it does appear that capital-adequacy regulations—along with post-2008 liquidity regulations—concentrated sovereign risk in the European banking system, aggravating the sovereign-debt problem by requiring government bailouts that, in turn, required the issuance of more sovereign debt, further jeopardizing the governments' fiscal position and banks' solvency. Exacerbating the situation, in the midst of a panic, the European Banking Authority imposed new capital requirements on banks and effectively forced them to realize mark-to-market losses on sovereign exposures. (shrink)

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. With it we demonstrate (...) the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991). (shrink)

Paper presented to the Twenty-seventh Hume Society Conference, 26 July 2000, Williamsburg, Virginia. -/- At the time I thought there was a stronger link between Maclaurin and Hume, but in discussions at and after the meeting, decided Hume was not taking his mechanics out of Maclaurin’s Account. Although I still have found Maclaurin useful in interpreting Hume -- see Sapadin 1997 for a discussion of popular Newtonianism in Hume's day -- I suspect my draft suffers somewhat from ambivalence. There are (...) still similarities, and possible avenues of influence, arguing that Hume was not ignorant of the new mechanics, but it also becomes clear that he did not understand it: although he adopts the Newtonian measure of force, he misapplies it. (shrink)

Part of our fascination with the Maya can be attributed to the fact that they were literate . . . that is, the Classic Maya possessed a visible language that consisted of letters and a grammar, and one of the products of their literacy was the book. (Aveni 1992b, p.3).

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—understanding causality. Bayesian networks have recently been used by Judea Pearl in a new approach to understanding causality (Pearl, 2000). Part of understanding causality is understanding causal interaction. Bayes nets can represent any degree of causal interaction, and researchers normally try to limit interactions, usually by replacing the full CPT with a noisy-OR function. But we show that noisy-OR and another common model are merely special cases of the general linear (...) systems definition of noninteraction. However, they apply in different situations, and we can measure the degree of causal interaction relative to any such model. (shrink)