Abstract
In the last couple of decades, a logician or a philosopher has run a risk whenever he has put the term ‘information’ into the title of one of his papers. In these days, the term ‘information’ often creates an expectation that the paper has something to do with that impressive body of results in communication theory which was first known as theory of transmission of information but which now is elliptically called information theory (in the United States at least).1 For the purposes of this paper, I shall speak of it as statistical information theory. I want to begin by making it clear that I have little to contribute to this statistical information theory as it is usually developed.
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Bibliography
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References
For this theory, see e.G. [27]. A popular survey is given in [7].
On this subject, cf. [1], Chapters 15-18; also [7], pp. 229-255.
E.G., [1], p. 242.
It is therefore natural that such expected values or estimates of information as [2] should play an important role in this theory. Its statistical character is not the only reason, however, for the prevalence of expressions similar to [2]; they have a place in the semantic theory, too.
I shall draw on the works I have published elsewhere. See [12-18],
The term land the idea] goes back all the way to Boole.
Cf. [1], p. 307.
24] passim.
An example to this effect is given by Carnap in [5]. Further examples are found in my survey [16], especially the expected content p[A] — p[h], which is as likely to vary with p[h[e] as with 1 — p[/r].
See e. g. [9-10].
See e. g. [21-22].
This is in effect surmised by Hempel in [9], p. 77.
The pioneering work in this area has been done by Rudolf Carnap. See [3-4]. Cf. also [26], especially Carnap’s own contribution, pp. 966-998.
The line of thought presented here is not Carnap’s.
This measure was for a while preferred by Carnap to other probability measures, and it appears to be an especially simple one in certain respects.
Cf. my criticism of Carnap in the early pages of [12].
This was the course followed in [12].
It was proposed in [13].
This question was asked by Hintikka and Pietarinen [18], whose line of thought we shall here follow.
The choice of an appropriate utility here is a very tricky matter. Further comments on the subject are made in [16].
Notice, however, that other kinds of results might be forthcoming if we considered other kinds of relative information instead of the added surprise value in [16].
See the last few pages of [12] and [13].
These informal arguments can easily be reconstructed on the basis of the distinctions between different kinds of information discussed in [16].
For the following, cf. [4] and [14].
See e. g. Nelson Goodman [8] or E. Nagel [23], Ch. 4.
See e. g. Nelson Goodman [8] or E. Nagel [23], Ch. 4. 28 See de Finetti’s classical paper reprinted in [20].
A preliminary report on some of the simplest cases is given in [11].
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Hintikka, J. (1970). On Semantic Information. In: Hintikka, J., Suppes, P. (eds) Information and Inference. Synthese Library, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-3296-4_1
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