Abstract
A brief analysis of analog computation is presented, taking into account both historical and more modern statements. I will show that two very different concepts are tangled together in some of the literature—namely continuous valued computation and analogy machines. I argue that a more general concept, that of model-based computation, can help us untangle this misconception while also helping to evaluate two particularly interesting kinds of computational claims. The first kind concerns computational claims about the brain, in the spirit of Searle’s Is the Brain a Digital Computer? The second kind concerns what has recently been called analog simulation, most notably in systems reproducing effects analogous to Hawking Radiation. Some final comments discuss how a model-based notion of computation helps us understand in a more concrete way the differences found among alternative models of computation.
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Notes
- 1.
We will see shortly that von Neumann, among others, used the term organ.
- 2.
Although it may be an open question whether all models are in fact rooted in analogy or similarity, I will not focus on such an argument here.
- 3.
It is also interesting to note that a slide rule is typically called an analog computer. Under the misconception of analog computer as necessarily involving continuous variables, what role does continuity play in the use of a slide rule? It is arguable that the continuously adjustable aspect of the device is incidental to the actual use and function of the computer since outputs are also not real numbers.
- 4.
This is of course an idealization, which should not be a problem since Turing machines are idealizations—actual physical architectures of course deviate from this idealization.
- 5.
Even more so than Searle himself might have admitted.
- 6.
Attempting to mitigate or explicitly accepting the HF is a required step, since as Searle notes, “... The homunculus fallacy is endemic to computational models of cognition and cannot be removed by the standard recursive decomposition arguments.” [10, p. 36] What can be done, I argue, is to put a new spin on the issue.
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Acknowledgments
I would like to thank Bernd Ulmann for introducing me to my own errors on the foundations of analog computation, and for motivating this present analysis.
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Beebe, C. (2016). Model-Based Computation. In: Amos, M., CONDON, A. (eds) Unconventional Computation and Natural Computation. UCNC 2016. Lecture Notes in Computer Science(), vol 9726. Springer, Cham. https://doi.org/10.1007/978-3-319-41312-9_7
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