IBE ('Inference to the best explanation' or abduction) is a popular and highly plausible theory of how we should judge the evidence for claims of past events based on present evidence. It has been notably developed and supported recently by Meyer following Lipton. I believe this theory is essentially correct. This paper supports IBE from a probability perspective, and argues that the retrodictive probabilities involved in such inferences should be analysed in terms of predictive probabilities and a priori probability ratios (...) of initial events. The key point is to separate these two features. Disagreements over evidence can be traced to disagreements over either the a priori probability ratios or predictive conditional ratios. In many cases, in real science, judgements of the former are necessarily subjective. The principles of iterated evidence are also discussed. The Sceptic's position is criticised as ignoring iteration of evidence, and characteristically failing to adjust a priori probability ratios in response to empirical evidence. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present (...) climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

It has been widely received that one of Gregor Mendel’s most important contributions to the history of genetics is his novel work on developmental information (for example, the proposal of the famous Mendelian ratios like 1:2:1, 3:1, and 9:3:3:1). This view is well evidenced by the fact that much of early Mendelians’ work in the 1900s focuses on the retrodiction (viz. the re-analysis of the pre-exist data with Mendel’s approach). However, there is no consensus on what Mendel meant by (...) development (Entwicklung). Nor is there an agreement on the interpretation of Mendel’s laws of developmental series (Entwicklungsreihe). This chapter revisits Mendel’s notions of development and developmental series. Firstly, I argue that Mendel’s use of development is greatly influenced by Gärtner’s. Secondly, I show Mendel’s work on developmental series are novel and important for its new ways of experimentation, conceputalisation, and analysis. Thirdly, I argue that Mendel’s laws of developmental information were not about heredity. (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a (...) type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

Given the difficulty of characterizing the quandary introduced in Hume’s Appendix to the Treatise, coupled with the alleged “underdetermination” of the text, it is striking how few commentators have considered whether Hume addresses and/or redresses the problem after 1740—in the first Enquiry, for example. This is not only unfortunate, but ironic; for, in the Appendix, Hume mentions that more mature reasonings may reconcile whatever contradiction(s) he has in mind. I argue that Hume’s 1746 letter to Lord Kames foreshadows a subtle, (...) but significant, shift in Hume’s reasonings regarding the relevance of “real connexions”; that the Enquiry of 1748 provides evidence for this shift; and that this shift obviates Hume’s second thoughts by reconciling the contradiction that he had in mind. In short, Hume’s letter to Kames and Enquiry supply the retrodictive keys to a systematically satisfactory account. (shrink)

This paper distinguishes between 3 meanings of reversal, all of which are mathematically equivalent in classical mechanics: velocity reversal, retrodiction, and time reversal. It then concludes that in order to have well defined velocities a primitive arrow of time must be included in every time slice. The paper briefly mentions that this arrow cannot come from the Second Law of thermodynamics, but this point is developed in more details elsewhere.

Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing scientific practice, but does so in several importantly different ways. Simulations in general, and computer simulations in particular, ought to be understood as techniques which, like many scientific techniques, can be employed in the service of various and diverse epistemic goals. We focus our attentions on the way in which simulations can function as (i) explanatory and (ii) predictive tools. We argue that a wide (...) variety of simulations, both computational and physical, are best conceived in terms of a set of common features: initial or input conditions, a mechanism or set of rules, and a set of results or output conditions. Studying simulations in these terms yields a new understanding of their character as well as a body of normative recommendations for the care and feeding of scientific simulations. (shrink)

The thesis that language is a special case of action is analysed in terms of the following three claims. First, language is presumably just as intentional as action is, in the precise sense that both involve largely automatic processing of goal-directed representations, with conscious attention essentially granting stability to the process. Second, this largely automatic processing of both language and action seems to be based on a shared generative mechanism. Third, this common process can be described as a bidirectional inferential (...) device, which at the same time allows the prediction of goals from means and the retrodiction of means from goals. These considerations converge towards the idea that language processing is based on domain-general processes which are shared with non-linguistic action. (shrink)

The key to the measurement problem is the entity at the heart of Everett's formulation, the state of the memory, defined as the record of observations. In humans, the integrated synthesis defines the perceptual reality, a projective, three-dimensional representation of the world. This 'world hologram' is the conscious point of view, the mind in Lockwood's interpretation, the 'phenomenal perspective'. As Everett demonstrates, the collapse dynamics operates only judged by the state of the memory; the physical observer remains in a superposed (...) state. An operational mind-body property dualism is defined. This field of information is multiply instantiated in the many worlds of the unitary wave function. As these are superposed and coincident, the net result is a single world hologram. The physical frame of reference on this view is the superposition of the class of instantiating worlds, a second-logical-type phenomenon. By definition determinate only where defined by observations, this is the physical ontology of Everett's relative state. Collapse on observation is inherent. The evidence is the retrodiction of the holographic universe. This is the cosmology of the relative state. As it is determinate solely where defined by the world hologram that delineates the outer boundary, entropy is defined solely by this surface. The quasi-classical world is the cosmology in the absolute state. The incompatible dynamics of quantum mechanics operate in these two different types of physical frames of reference. The linear dynamics is the time evolution of the quasi-classical worlds. On observation, judged by the state of the world hologram, the class of instantiating worlds is redefined, becoming the class of worlds in which the observed outcome occurred. Collapse effectively operates. The holographic universe of the mind updates. The dynamics operate at different levels of logical type. The measurement problem is a category error. (shrink)