Online research in philosophy

Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, I conclude that his approach involves a version of reasoning from variety of evidence, enabling this robustness to be a confirmatory virtue..

Today's climate models are supported in a couple of ways that receive little attention from philosophers or climate scientists. In addition to standard 'model fit', wherein a model's simulation is compared to observational data, there is an additional type of confirmation available through the variety of instances of model fit. When a model performs well at fitting first one variable and then another, the probability of the model under some standard confirmation function, say, likelihood, goes up more than under each individual case of fit alone. Thus, two instances of fit of distinct variables of a global climate model using distinct data sets considered collectively will provide stronger evidence for a model than either one of the instances considered individually. This has consequences for model robustness. Sets of models that produce robust results will, if their assumptions vary enough and they each are observationally sound, provide reasons to endorse common structures found in those models. Finally, independent empirical support for aspects and assumptions of the model provides an additional confirmational virtue for climate models, contrary to what is implied by some current philosophical writing on this topic.

Though it's di cult to agree on the exact date of their union, logic and arti cial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI nd themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions via a review of six books. Encapsulated, our answer is that (i) logic and AI, despite tabloidish reports to the contraryError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMap, still enjoy matrimonial bliss, and (ii) only their future robotic o spring (as opposed to the children of connectionist AI) will mark real progress in the attempt to understand cognition..

I survey a broad variety of models with an eye to asking what kind of model each is in the following sense: in virtue of what is each of them regarded as a model? It will be seen that when we classify models according to the answer to this question, it comes to light that the notion of model predominant in philosophy of science covers only some of the kinds of models used in scientific contexts. The notion of a model predominant in philosophy of science requires that a model be related to something formal, such as equations or statements. Not all the examples provided in the brief survey in this paper fit that notion of a model. I identify another kind of model that ought to be included in philosophical and foundational studies of scientific models, which I call a “piece of the world” kind of model, to contrast with a “realm of thought” kind of model. These models also have formal methodologies associated with them, and, hence, analytic philosophers of science can embrace them without abandoning the rigor that has characterized the discipline.

Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a new account of both concrete and mathematical models, with special emphasis on the intentions of theorists, which are necessary for evaluating the model-world relationship during the practice of modeling. Although mathematical models form the basis of most of contemporary modeling, my discussion begins with more traditional, concrete models such as the San Francisco Bay model.

The Newtonian universe is usually understood to contain two classes of causal factors: universal regularitiesand initial conditions. I demonstrate that,in fact, the Newtonian universe contains no causal factors other thanuniversal regularities: the initial conditions ofany physical system are merely theconsequence of universal regularities acting on previoussystems. It follows that aNewtonian universe lacks the degree of contingency that is usually attributed to it. This is a necessary precondition for maintaining that the Newtonian universe is a block universe that exhibits no temporal development. It follows also that Newtonian physics is inconsistent, since a Newtonian universe as a whole exhibits some properties – such as the total mass of the universe – that are not determined by the laws of Newtonian physics, and that must therefore be considered contingent.

Models of psychological time / Richard A. Block -- Implicit and explicit representations of time / John A. Michon -- The evasive art of subjective time...

In both the search for ever smaller and faster computational devices, and the search for a computational understanding of biological systems such as the brain, one is naturally led to consider the possibility of computational devices the size of cells, molecules, atoms, or on even smaller scales. Indeed, it has been pointed out Braunstein, 1995] that if trends over the last forty years continue, we may reach atomic-scale computation by the year 2010 Keyes, 1988]. This move down in scale takes us from systems that can be understood (to a good enough approximation) using classical mechanics alone, to those which require a quantum mechanical understanding. Thus, it should not be surprising to nd that the idea of quantum computation is not new (see, e.g.,Error: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMap Deutsch, 1985] and Feynman, 1982])..

The use of idealized models in science is by now well-documented. Such models are typically constructed in a “top-down” fashion: starting with an intractable theory or law and working down toward the phenomenon. This view of model-building has motivated a family of confirmation schemes based on the convergence of prediction and observation. This paper considers how chaotic dynamics blocks the convergence view of confirmation and has forced experimentalists to take a different approach to model-building. A method known as “phase space reconstruction” not only reveals a lacuna in the philosophical literature on models, it also fails to conform to conventional views about how models are used to confirm a theory.

This paper studies the properties and performance of models for estimating local probability distributions which are used as components of larger probabilistic systems — history-based generative parsing models. We report experimental results showing that memory-based learning outperforms many commonly used methods for this task (Witten-Bell, Jelinek-Mercer with fixed weights, decision trees, and log-linear models). However, we can connect these results with the commonly used general class of deleted interpolation models by showing that certain types of memory-based learning, including the kind that performed so well in our experiments, are instances of this class. In addition, we illustrate the divergences between joint and conditional data likelihood and accuracy performance achieved by such models, suggesting that smoothing based on optimizing accuracy directError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMapError: Illegal entry in bfrange block in ToUnicode CMaply might greatly improve performance.