Majority logic I
When I went for an interview at Oxford for my undergrad degree one of the things we had to do was a kind of logic test. It had things like “All men wear hats, some men wear ties” and you were asked what it was possible to conclude from that. Presumably they were looking for something like “Some men wear ties and hats”. One of the questions was “Most men wear ties, most men wear hats”. I said that it wasn’t possible to conclude anything from this. But it was then explained to me that if hat-wearers and tie-wearers are both in a majority, then there must be some who are both: the proper conclusion is that “Some men wear ties and hats”.
“All men” is standing in for the universal quantifier (x) and “Some men” stands for the existential quantifier (Ex). But “Most men” is awkward to put in similar terms without resorting to some sort of logic enriched with predicates relating to numerical proportions. But that sort of “statistical logic” is too complex. The fact that some predicate applies not to all but to a majority of some domain is an easily understood fact that does not need a whole logic of proportions behind it. Let’s make “Most of” a new kind of quantifier: (Mx). What properties does (Mx) have?
This is the property my interviewer at Oxford was exploiting. If most x’s are P’s and most x’s are Q’s, then some x’s must be both.
These two properties simply say that if all men wear hats then most men wear hats and if most men wear hats then some men wear hats.
Do we really need a third quantifier here? Is there some way to express “most of” in terms of universal and existential quantifiers? I’m not sure there is. Here’s a first stab. When you say “(Mx)Px” what you are really saying is:
But that’s no good, because that expression still contains an “Mx”. We are kind of going beyond standard predicate logic, but only a little bit. Perhaps Mx shouldn’t be a quantifier, but a property of predicates? But I don’t think that is a particularly satisfying suggestion. A property of predicates would be a third-order entity, and that seems extravagant given the modest goal of formalising the easily understood concept of a majority.
Soon I’ll post again and discuss the “dual” of (Mx): “Only a few men wear hats” “Hat-wearers are in a minority”.
A note on the symbolism: I wanted to use and
but I couldn’t think of a way to “invert” the letter “M” in a similar fashion. It shares the vertical symmetry of “A” but when reflected around a horizontal axis as
is, you get a “W”, which is no good if the idea is to come up with a new symbol. So I reverted to old-style quantifiers. Suggestions regarding how to “symbolise” majority are most welcome.
RIP I.J. Good
I was sad to read that I.J. Good died recently. I only heard about him a couple of weeks ago. Even from the fairly academic work of his I was reading you got a sense that he was a really interesting character with a unique sense of humour. His obituary confirms that impression.
In other news, I’m desperately trying to finish various papers and so on that need doing and I’m not really progressing particularly fast. Not that that is really news. That seems to be my default status. In the coming weeks I have to finish up short papers on the following topics:
- Frege on the definition of numbers
- Why probability is not always the best way to represent uncertainty
- Quantum Mechanics and Structural Realism
- Similarity relations and the theory-world link
Interesting topics all (apart from the first one), but it’s frustrating to have all these little bits and pieces to get done when what I really want to do is get down to a Big Project like my literature review… Although the second and fourth topics at least have some bearing on the topic of the lit rev, so that’s a bonus.
Two upcoming conferences that I will be attending:
Exciting stuff, no?
Data as a mass noun
I was told off yesterday for saying something like “Our data is incomplete…” Now, I know that “data” is a plural (as is media). But I thought it legitimate to use data as a mass noun rather than a count noun. This is, apparently, an “institutionalised mistake”. I’m not so sure. If everyone talks that way, doesn’t that make it OK? That said, I get annoyed when people say “less” when they mean “fewer”, so perhaps I shouldn’t be arguing this point… But in the case of “data”, I don’t see anything wrong with using it as a mass noun. Information, it seems to me, is a continuous mass-like thing. Perhaps the point is that data are still discrete units of information or some such… Anyway, I see nothing wrong with “the media” as a singular. I know that media is the plural of medium, but “the media” or maybe “the Media” is a way of referring to all those associated with any of the diverse media as if they were a homogenous mass. The Media is a thing – it is what feeds you entertainment, celebrity and to a lesser extent information. It does this through various media; the medium of television, the medium of radio and so on…
I am, in general, pedantic and fussy about things like this, but data as a mass noun and media as a singular noun don’t seem to bother me. I don’t really know why. Perhaps I should try and be annoyed about them, for the sake of consistency…
Dutch books
I’ve been thinking about the Dutch Book Argument (DBA) recently. I think the constraints on rational betting preference that underpin the force of the argument are unreasonable. At least they are not always reasonable. I think a better way to think of the argument is as a conditional argument: “Given these rational betting preference conditions, this is how rational degrees of belief should be constrained.” Then you can have a whole series of different DBAs with different betting preference conditions leading to different constraints on credence. It would be interesting to see how one would have to constrain betting preferences in order to have you beliefs behave like upper and lower probabilities or Dempster-Shafer belief functions…
I think this isn’t to undermine the force of the DBA, but to reinforce it. With this wider framework we can understand why people often fail to reason probabilistically. We can understand what aspects of rational betting preference are “non-probabilistic”.
That’s not to say that the DBA isn’t without its flaws. Some elements that concern me are:
- Using betting behaviour as a proxy for belief
- Existence of exactly specific numerical credence (and utility)
- Reasoning as calculating expected utilities
- The idealisations involved in discussing “ideal rational agents”: utility maximising, purely self interested, perfect calculating agents…
Enumerating structure on sets, again.
The number of partitions on an N-membered set is called its “Bell number“. The number of strict total orders, I’m not sure about that… There is some relevant information here.
Well it’s good to see those problems solved, and I’m glad they weren’t trivial… Here is a more general issue. How many relations are there on a set? How many transitive? And so on.
So, a relation is simply a set of ordered pairs. So there are 2^X possible relations if there are X ordered pairs. For a set with N elements, there are N^2 ordered pairs. Thus 2^(N^2) relations on N elements. To give you an idea of scale, there are 65,536 relations on the 4 element set. But there are only 15 partitions on the 4 element set (not 18 as I said before; I counted each 2+2 partition twice.) So the properties of being transitive, reflexive and symmetrical must seriously cut down the number of relations! The only thing I’ve concluded in answer to this question is that there is an upper bound on the number of possible total relations on N elements: 2^(N^2) – 2^([n^2]/2). This is because the property of being total means that the size of the set of ordered pairs has to be big enough to contain at least one of (a,b) or (b,a) for every a,b. So small sets of pairs are ruled out.
Ennumerating structures on sets
I have a question: for a set of N elements, how many different ways are there of partitioning it? In other words, how many distinct equivalence relations are there on a set? Similarly, how many distinct strict total orderings are there? How many weak partial orderings?
All of these things (equivalence relations, orderings…) are determined by sets of ordered pairs on the set. So is it easier to calculate numerical formulae for each type of structure on the set, or is it easier to demonstrate that, for example there are more weak partial orderings than there are partitions… It’s trivial, for example, to show that there are at least as many weak partial orderings as there are strict total ones, since each strict total order is equivalent to a weak partial order.
There could be quite a lot of partitions. A partition is a subset of the power set. Or an element of the power set of the power set. So the absolute upper bound on partitions for a 4 element set is 2^(2^4) = 65,536. Obviously this is a ridiculously high figure, but I can’t see an easier way to get closer to the actual number. I am no good at combinatorics. But for the 4 element set you could probably just list all the possible partitions. First list all the ways the sets could break down:
- 1,1,1,1
- 2,1,1
- 2,2
- 3,1
- 4
Then list the number of ways of splitting up the 4 elements, A,B,C,D into those sets.
- Only one {1,1,1,1} partition
- Six {2,1,1}
- Six {2,2}
- Four {3,1}
- One {4}
For a total of 18 partitions. So there are more partitions than there are subsets. But for the 2 element set, the power set has 4 elements, whereas there are only two possible partitions. My guess is for bigger sets there will be more partitions than there are subsets.
As for strict total orderings, I think there will be N! of them. Because there are N choices for the maximal element, N-1 choices for the next “biggest” and so on. So the 4 element set has 24 strict total orderings on it. N! > 2^N for N>3. As for weak partial ordering, there’s probably even more of them…
What other kinds of structure can one impose on a set? If one thinks of graph theory in terms of set theoretic structure, what then? In undirected graphs, the edges of a graph are pairs of elements of the set of vertices. I just did a little googling and learned that there is something called “graph ennumeration” which is where one studies the number of graphs with a certain number of vertices that have certain properties. Much like the classification of finite groups, I suppose. So for example there are no asymmetric graphs with less than 5 vertices. (Or possibly 6?)
Anyway, that’s just what I was thinking about this morning, when I should probably have been trying to understand the game theory lecture…
The end of time…
This New Scientist article made me wonder about time. The latest super-clocks are sensitive to differences in height (actually differences in gravitational field) small enough that their accuracy would be affected if they were placed on a slightly lower table, for example. This is madness. Maybe we’ve reached a point where time doesn’t really mean all that much any more. On those kind of scales, at that sort of precision, maybe time just stops being a useful concept. Like the length of a coastline stops being a useful concept when you measure it too accurately. (Because it’s a fractal). Or temperature; if you keep zooming in, you reach a point where the “temperature” of the volume becomes meaningless – if you zoom in enough that the volume contains maybe one atom or even no atoms, then surely temperature is unhelpful. Perhaps the same thing happens to time when we start trying to subdivide it into 10^-18ths of a second or whatever it is they are doing…
Here’s another article on time where some people seem to be reaching the same conclusions… I’m not sure these articles will be available – I’m on the campus network and they might have some sort of subscription to the magazine…
Titanoboa – Weighs a tonne and eats crocodiles
I just read about the Titanoboa. This is a snake that lived soon after the Dinosaurs died out. It was huge. I don’t think that quite does justice to it. It has been estimated that these things weighed upwards of 1,100kg and were 13 metres long. That’s a snake taller than your house weighing as much as your car! This thing was a metre wide! HUGE! The craziest part was that these things probably ate crododiles. Let me say that again; it ATE CROCODILES! You could not make up something like that up.