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.As I argued in chapterone, this metaphorical approach may be dangerous and in the end counterproductive.For reasons of space, even the problems selected in this chapter are only brieflyintroduced and not represented with adequate depth, sophistication, and significance.These macroproblems are the hardest to tackle but also the ones that have the greatestinfluence on clusters of microproblems, to which they can be related as theorems tolemmas.I have listed some microproblems whenever they seemed interesting enoughto deserve being mentioned explicitly but, especially in this case, the list is far fromexhaustive.Some problems are new, others are developments of old problems, and insome cases they have already been addressed.I have avoided listing old problems thathave already received their due philosophical attention.I have not tried to keepa uniform level of scope.Some problems are very general, others more specific.Allof them have been chosen because they well indicate how vital and useful the newparadigm is, in a variety of philosophical areas.I have organized the problems into five groups.The analysis of information and itsdynamics is central to any research to be done in the field, so the review starts from there.After that, problems are listed under four headings: semantics, intelligence, nature, andvalues.This is not a taxonomy of families, let alone of classes.I see them more like four points ofour compass.They can help us to get some orientation and make explicit connections.I would not mind reconsidering which problem belongs to which area or furtherproblems that need to be addressed.After all, the innovative character of PI may forceus to change more than a few details in our philosophical map.What I do hope is that thefollowing map, limited as it is, will be better than no map at all.And now, to work.2.3 AnalysisLet us start by taking the bull by the horns:P1 The elementary problem: What is information?This is the hardest and most central problem in PI and this book could be read as a longanswer to it.Information is still an elusive concept.This is a scandal not by itself, butbecause so much basic theoretical work relies on a clear analysis and explanation ofinformation and of its cognate concepts.Information can be viewed from three perspectives: information as reality (e.g.aspatterns of physical signals, which are neither true nor false), also known as environmen-tal information; information about reality (semantic information, alethically qualifiable);and information for reality (instructions, like genetic information, algorithms, orders, orrecipes). OPEN PROBLEMS IN THE PHILOSOPHY OF INFORMATION 31Many extensionalist approaches to the definition of information as reality or aboutreality provide different starting points for answering P1.The following list containsonly some of the most philosophically interesting or influential.They are not to betaken as necessarily alternative, let alone incompatible.I shall discuss them more indetail in chapters four and five, but here is a quick overview:1.the information theory approach (mathematical theory of codification andcommunication of data/signals, Shannon and Weaver (1949 rep.1998) definesinformation in terms of probability space distribution;2.the algorithmic approach (also known as Kolmogorov complexity, Li andVitanyi (1997)) defines the information content of x as the size in bits of thesmallest computer program for calculating x (Chaitin (2003));3.the probabilistic approach (Bar-Hillel and Carnap (1953), Bar-Hillel (1964),Dretske (1981)) defines semantic information in terms of probability space andthe inverse relation between information in p and probability of p;4.the modal approach defines information in terms of modal space and in/consis-tency: the information conveyed by p is the set of possible worlds excluded by p;5.the systemic approach (situation logic, Barwise and Perry (1983), Israel and Perry(1990a), Devlin (1991)) defines information in terms of states space and consis-tency: information tracks possible transitions in the states space of a system;6.the inferential approach defines information in terms of inferences space:information depends on valid inference relative to a person s theory or epistemic state;7.the semantic approach (defended in this book) defines information in terms ofdata space: semantic information is well-formed, meaningful, and truthful data.Each extentionalist approach can be given an intentionalist reading, by interpreting therelevant space as a doxastic space, in which information is seen as a reduction in thedegree of uncertainty or level of surprise in an informee, given the state of informationof that informee.Information theory in (1) approaches information as a physical phenomenon,syntactically.It is not interested in the usefulness, relevance, meaning, interpretation,or aboutness of data, but in the level of detail and frequency in the uninterpreted data(signals or messages).It provides a successful mathematical theory because its centralproblem is whether and how much data, not what information is conveyed.Thealgorithmic approach in (2) is equally quantitative and solidly based on probabilitytheory.It interprets information and its quantities in terms of the computationalresources needed to specify it.The remaining approaches address the question whatis semantic information?.They seek to give an account of information as semanticcontent, usually adopting a propositional orientation (they analyse examples like Thebeer is in the fridge ).Do information or algorithmic theories in (1) and (2) provide thenecessary conditions for any theory of semantic information? Are all the remainingsemantic approaches mutually compatible? Is there a logical hierarchy? Do any of theprevious approaches provide a clarification of the notion of data as well? Most of the 32 THE PHILOSOPHY OF INFORMATIONproblems in PI acquire a different meaning depending on how we answer this cluster ofquestions.Indeed, positions might be more compatible than they initially appearowing to different interpretations of the concept(s) of information involved
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