June 28, 2010
Newcomb’s paradox posits a game with a transparent box containing $1 and an opaque box containing either $0 or $1,000. A Player is offered the choice between only the opaque box or both boxes. Before the Player makes this choice, a Predictor has attempted to predict the Player’s choice. The Predictor puts $1,000 into the opaque box, if the prediction is that only this box will be chosen. Otherwise, the Predictor puts $0.
The Predictor neither has a time machine nor some gift for backward causation. It is assumed, however, that the Predictor is rather reliable, though not necessarily infallible. Should the Player choose the opaque box only or both boxes? Read the rest of this entry »
June 22, 2010
There’s a debate in linguistics on the extent to which humans acquire language via learning and statistical generalization and the extent to which language acquisition aided by innate biases. Theoretical arguments have been devised to demonstrate that general-purpose learning would require to much data and that innate bias is necessary. An eprint claims to provide mathematical and empirical support against such impossibility results and in favor of the possibility of language acquisition through general-purpose learning. The learning method being advanced is a Minimum Description Length type method.
A. S. Hsu, N. Chater and P. M. B. Vitanyi. The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis. arXiv:1006.3271 (2010)
June 21, 2010
A while back I noted an interesting paper about the arrow of time and memory loss in quantum mechanics. Unfortunately for that idea a new commentary by Jennings and Rudolph [PRL 104:148901, 2010] refutes it by exhibiting a counter-example.
June 21, 2010
A new survey sheds more light on who the anthropogenic global warming contrarians are. The abstract is self-explanatory and the fulltext is open access.
W. R. L. Anderegg et al. Expert credibility in climate change. PNAS, doi: 10.1073/pnas.1003187107
Abstract: Although preliminary estimates from published literature and expert surveys suggest striking agreement among climate scientists on the tenets of anthropogenic climate change (ACC), the American public expresses substantial doubt about both the anthropogenic cause and the level of scientific agreement underpinning ACC. A broad analysis of the climate scientist community itself, the distribution of credibility of dissenting researchers relative to agreeing researchers, and the level of agreement among top climate experts has not been conducted and would inform future ACC discussions. Here, we use an extensive dataset of 1,372 climate researchers and their publication and citation data to show that (i) 97–98% of the climate researchers most actively publishing in the field support the tenets of ACC outlined by the Intergovernmental Panel on Climate Change, and (ii) the relative climate expertise and scientific prominence of the researchers unconvinced of ACC are substantially below that of the convinced researchers.
April 23, 2010
A remarkable eprint just appeared: T. Bolognesi, Causal sets from simple models of computation, arXiv:1004.3128.
In the theory of relativity, the relation “event A happened before event B” is not a total ordering relation. When events have space-like separation, the relation “before” is observer-dependent. By restricting attention to a partial ordering relation, that only applies to pairs of events with time-like separation, an observer-independent relation is obtained.
The theory of relativity is concerned with continuous, smooth manifolds. Computation is usually conceived of in terms of a discretized state space. To connect some aspects of the two, the new eprints defines a partial ordering relation on computational events (e.g. state transitions in a Turing machine) that represents a “before” relation. The resulting visualizations of the partial ordering relations as graphs are intriguing. Whether or not one believes this sort of mathematical structure will have useful applications in physics, the eprint is worth a read just for its visualizations.
April 6, 2010
Proponents of Bayesian epistemology have invented several different quantitative measures of how available evidence bears on different hypotheses. These measures are referred to as measures of evidential support, coherence, confirmation, or justification, depending a bit on the precise significance ascribed to them. Presently, Bayesians disagree over which of these measure is the most useful, but a new paper by Shogenji provides very appealing alternative measure and, with some luck, may even settle this debate.
At first sight, it might appear that the posterior probability of a hypothesis conditional on observed evidence is the perfect measure for a Bayesian. However, a hypothesis sometimes has a high posterior probability simply by virtue of a high prior probability, without necessarily being support/confirmed/justified by virtue of the observed evidence. Read the rest of this entry »
January 4, 2010
More thoughts about Dembski and Marks’ project: