Nokia’s phone roadmap is pretty confusing (which seems to be par for the course for the telco industry admittedly) supporting both Maemo as well as different versions of Symbian everywhere (the most recent ones of which are apparently open sourced) as well as lower end “feature” phones. Apparently my Symbian version is “S60 3rd Edition”, and if you want to write apps the difference between that and “2nd edition” is a major one. S60 3rd ed is also, apparently, known as “Symbian OS 9.3″, the followup to which is “Symbian^1″ which is also known as “S60 5th Edition” (4th Edition got skipped because 4 is unlucky for some). It’s on the latest Nokia smart phones — N97, 5800 XPressMusic, etc. “Symbian^2″ and beyond will apparently be the open source versions, except that maybe it’ll be “Symbian^3″ before any phones ship with it. There was also S90 which was more advanced than S60 at the time, but then got merged back in, so now it’s obsolete and S60 is better. Apparently the way phone OSes work is that you only upgrade when you get new hardware, so all the different versions ever are still floating around on old phones.

Anyway, add ons for Symbian come packaged in “sis” files (”Symbian Installation Source” supposedly), which come signed in various different ways (some are signed for only a particular phone, in part to make it harder to write viruses, eg) and with different listed capabilities (so if your app doesn’t need to use gps or wifi, it will be blocked by the OS from doing so). When I first got the phone, the deal was you’d scour the web, and discover that you could download Python for S60 1.9.7 from garage.maemo.org — which is a SourceForge-clone that presumably was originally for Maemo stuff, but is now for anything Nokia-ish. Of course, there was no Python 1.9.7, and this is really Python 2.5.4 with miscellaneous Symbian extensions. In order to get a Python REPL prompt, you need to install both the Python_1.9.7.sis runtime and the PythonScriptShell sis, of which there are a few with different capabilities. Of course, you can only do this after unpacking the PythonForS60_1.9.7.tar.gz, which you can’t do on the phone.

Anyway, get that done and look at the docs and you can actually do something, which is kinda cool. I’ve been just plugging it into my laptop as USB mass storage and copying py scripts across, then running them from the ScriptShell menu so far, which is a bit kludgy but at least usable. So far it seems like lots of little bits of the API aren’t quite implemented for Python — I haven’t found a way to change the top right softkey hint from “Back” or “Exit” to “Save” or “Hide”; but that might just be unfamiliarity. More concerning was that when I tried to use time.mktime to get a Unix timestamp, the interpretor just crashed entirely, so it seems like there are some bugs around too. But the fact that you seem to be able to get at pretty much all the phone features (camera, gps, gsm location, sms, etc, etc) from pretty simple python still makes it a win in my book.

Shortly after I’d gotten that far, I did a random invocation of the “SW Update” app to see if there was any new stuff for me, and got informed “Python for S60 2.0″ was available for download. Neato, I thought, and went looking to see what the deal was — but there’s almost nothing out there discussing it. I tried installing it anyway, but apparently the download got cut off, and SW Update isn’t smart enough to continue or start again in that event. But deleting the partial download and trying again worked, and eventually I got me some Python for S60 2.0, which seems to be the same 2.5.4 version 1.9.7 was. The advantage, in theory, should be that programs written in python can just have a sis file that says “I need Python” and the official version will be automatically downloaded. But my first go at making that happen seems to indicate that the dependency used to be on “Python for S60″, but maybe now needs to be on “Python runtime”. Which, of course, is hardcoded into the app (ensymble), and although that’s an open source Python app (and packaged for Debian at that), it’s distributed as a base64 encoded blob so you have to go right back to the source to change the appropriate seven characters. Assuming, of course, that I’m on the right track in my guess as to what the problem is.

As far as I can tell, there’s still not much in the way of any sort of official announcement as to what’s going on with PyS60 2.0, but it seems that they’re rolling it out to some handsets, and the N86 is just lucky on that score. It’s bizarre to me that the Nokia devteam aren’t doing any bragging about getting Python for S60 up to 2.0 and into the official distribution, but I get the impression there isn’t much communication going on in general. I haven’t been able to spot the source for 2.0 either, though I haven’t exactly looked very hard.

That is why Labor will:
Provide a mandatory ‘clean feed’ internet service for all homes, schools and public
computers that are used by Australian children. Internet Service Providers (ISPs) will filter out content that is identified as prohibited by the Australian Communications and Media Authority (ACMA). The ACMA ‘blacklist’ will be made more comprehensive to ensure that children are protected from harmful and inappropriate online material.

Unless the tests somehow proved the idea utterly infeasible — slowing broadband down to modem speeds, or requiring wholesale replacement of routers and servers — there’s not really any way to weasel out of that promise. Perhaps there can be exceptions: “homes, schools and public computers” doesn’t necessarily include businesses or universities, so if you want to get the filter box removed from your uplink, there could be a legal way to arrange it, but that’s about it. But anything more than targeted exceptions would be breaking their election promises and giving up their electoral mandate.

Of course, that’s why we have two houses of parliament, and a tradition of not giving the government a majority in the upper house. And with reportedly both the Greens and the independent senators against the “clean feed” scheme, it’s all up to whether the Coalition sides with Family First and Labor to support the bill or not.

Historically, the Coalition’s been relatively good about it, as far as I can see, mostly by deferring to bureaucrats in the ABA and subsequently ACMA.

It first came up, to my knowledge, in the ABA’s “Investigation into the content of on-line services”, which started in July and had its final report of which is dated 30th June 1996 (and actually straddles the defeat of the Keating government and the election of Howard government). The “clean feed” at the time was the “Refused Access List” which was “suggested as a mechanism for restricting the availability in Australia of material that would be refused classification under the existing classification guidelines” and was “based on the possibility of identifying Universal Resource Locators host addresses and the names of newsgroups, containing Objectionable material”.

The ABA ended up recommending that “the proposal for a Refused Access List to facilitate the comprehensive blocking of Objectionable material should not proceed” as well as setting up a task force for investigating ways to limit accessibility further, and setting up an “e-mail hotline” for reports of objectionable material. The report also reacts fairly negatively to knee-jerk responses about censorship:

The RAL scheme as proposed in the issues paper drew much criticism from submitters who mistakenly considered that its intention was the censorship of classes of content which were not currently subject to any restriction. This misapprehension led other submitters to strongly oppose the RAL concept on free speech grounds, believing the proposal would result in unnecessary and additional censorship of the Internet. Some submitters expressed fear than an RAL would restrict access to a wide range of content, including political speech and hence could be subject to abuse by governments.

Since then, there’s been various industry codes of practices worked out, with procedures for filing complaints about objectionable content and getting those complaints investigated and (when appropriate) forwarded onto the police, support for and promotion of filtering products, and giving out advice like talk to your kids about their internet experiences.

On the other hand, they don’t get much credit for it, eg:

• @stephendevice: Fortunately we have an opposition who won’t stand for this shocking and misguided assault on our freedoms. Oh … damn. #nocleanfeed
• @renailemay: OK, so now the Greens and Xenophon oppose the filter, all we need to block it is the goddamn OPPOSITION #nocleanfeed #openinternet
• @Tucniak: Liberals no help to Australian public: Opposition leader @tonyabbottmhr avoids taking a #nocleanfeed [position] http://bit.ly/4W0FKB

Conceptually, it seems to me like it’d be pretty easy to get internet freedom as a core component of coalition policy — “liberal”, “liberty” and “freedom” are words that fit easily together, promoting unrestricted tubes with voluntary filters at the end-points and police enforcement doesn’t contradict any of their policies; and expecting people to take care of their own families and just coming down hard on people actually distributing child porn seems like a reasonable fit for regional Australia. But political parties are only ever going to do things if it actually helps them win elections, and whether all the online sturm and drang will even make a blip on the coalition’s radar as things stand seems really dubious.

It would be pretty easy to actually make such a blip appear though — send a letter to your local Liberal/National member/candidate and senators expressing your support of Tony Smith’s scepticism of the trial, accompanied by your views on how the Internet should be regulated, along with an actual donation as thanks for their opposition to bad legislation and you’ll likely grab their attention pretty quickly.

Making a similar donation to the Greens in support of their opposition would also be obviously appropriate (even if their opposition to the clean feed doesn’t entirely square with their nomination of Clive Hamilton in the recent Higgins by-election).

Of course, all that depends on whether this issue actually matters enough to anyone to warrant more than some bitching online.

(Personally I’ve just made a small donation to both the Libs and the Greens. I wonder how many people have ever had reason to say that before…)

Presumably most people care most about increasing Q (how much useful stuff people end up producing — the more the better) and keeping P roughly constant (if prices increase a lot, you can’t buy anything unless you’ve got lots of savings; if prices decrease a lot, you can’t get any money to pay back any borrowings you made, and if you can’t predict future prices you’ll have to forego pleasures now just in case necessities cost a lot tomorrow). M doesn’t really matter much in its own right by contrast, and V effectively just measures how long people keep money around before giving it to someone else.

There’s presumably a limit to how large Q can be — 20,000 years ago no matter how much money was around, how quickly it changed hands, or how much/little people charged for things, Q wouldn’t be a high enough value to include a dozen iPhones. That limit has to be set both by both how many people are around who want things from other people, are willing to do things for other people, and what they’re actually capable of doing. But if there’s any significant unemployment, or even any useful new technology that hasn’t been widely distributed, Q probably isn’t maxed out. Which in turn is to say that people could be doing more for each other, but aren’t because they don’t have cash to pay for it.

And in turn, ways to fix that are to find ways to give these groups of people enough money to get started — so that Alice has enough money to buy something from Bob, who then can buy something from Carol, who can buy something from Dave, who can buy something from Alice, rinse and repeat. That might require giving Alice a chunk of money up front (eg a $900 stimulus payment), or Alice getting a short term loan that she can pay back when Dave pays her, or there being a way for all those transactions to require less money up front — eg, each of them paying a $900 total bill in $100 installments.

Of course, except for when Alice gets a free cheque, the above assumes that she has some immediate expectation of future work, either to pay back the loan, or to pay off each upcoming installment. And that expectation will only be there if Dave expects to have money to spend, which will only happen if Dave expects work, and so on. And each step in that will have a slight discount — if Alice is 100% confident of being able to pay Bob, Bob may be only 80% confident of getting paid, since he might justifiably worry that Alice’s confidence is misplaced. But the same discount leaves Carol with only 64% confidence of getting paid, and Dave at 51%, which means Alice should only have been 41% confident. So either someone in that chain needs to be irrationally overconfident, or the only stable solution is that everyone thinks there’s 0% chance of paying the bills and no one buys anything. That seems to me to be the best argument for stupid ideas like paying people to dig holes then fill them back up — it means Alice can decide that even if there aren’t good enough odds that Dave (or someone like him) will pay her, she can always get a lame government job to pay for what she buys from Bob.

It seems to me that something like the MV=PQ formula probably applies on personal level as well as a societal level — if you want to increase your supply of whatever you value (which is Q, at least in so far as anyone can help you with it for money), you either need to hope it magically becomes cheaper (P decreases, not so unlikely if you’re into tech), that you get more money (M increases), or you don’t let what money you have sit around as much (V increases). If you’re already living hand-to-mouth and don’t have any savings, you probably can’t do much about V — if you want more stuff, you just have to work harder or hope something magic happens. But finding ways to make your money move more quickly around the economy (and find its way back to you) probably is a good way to do things. I’m not really sure what that implies though: certainly it means not having your savings on call; probably it means finding alternative ways to let money come back to you (if it can only come back via your boss, guess who gets first pickings of whatever friends it brought back with it); and maybe it implies something about who you give it to or what you spend it on.

Of course, the other aspect of that equation is that it only really says things about instantaneous changes — money could change hands very slowly (which is roughly the same as saying people have more savings), but if that’s compensated by a lot of money in circulation, it doesn’t necessarily imply any difference in prices or GDP compared to today. But when you’re already at some point, in the immediate future the variables tend to be constrained to change at different rates: usually M won’t change very quickly (making money appear or disappear is relatively difficult and heavily regulated), V is heavily dependent on and constrained by public sentiment, and P has a tendency to be a smooth exponential, either with a low or zero exponent if you’re lucky, or a high exponent in hyperinflation.

So, if you get a sudden large drop in the supply of money for trade (which can happen if banks suddenly all significantly reduce their lending), then either the public has to respond quickly by spending their money faster, or you get a resulting drop in production, which is to say, a recession, until either the monetary supply can re-expand, or the central bank fails in its mission to ensure a stable inflation rate, and prices head downwards — ie wages drop, house prices drop, food and petrol prices drop, etc.

Further, if people spotted the signs of a recession/price deflation as a sign the economy was in trouble and they might decide to save more, spend less, or otherwise lower V, and that’s exactly as bad as decreasing the money supply was in the first place. And then there’s a good chance that the attempts to increase the money supply — low interest rates, bank bailouts and guarantees, government spending — will lead people to expect the potential for sudden price increases, which may in turn cause them to save more, just in case.

In so far as that’s an accurate description of the global financial crisis to date (which it may or may not be), it implies the only way to avoid a recession is to avoid the banks not lending in the first place (the only other options are consumer sentiment exactly counterbalancing it, or significant deflation, neither of which are really plausible). In a “functioning” market, banks shouldn’t all suddenly act the same way, without it being in the obvious interests of their customers. That is, the only reason for no one to lend is that no one wants to borrow, which in turn means either everyone already has the money they need saved and are now confident in spending it (V goes up, M goes down correspondingly), or everyone’s decided they do want to take a holiday, work less, and buy less.

But if that’s not the case, then while it’s reasonable for a few banks to stop lending for one reason or another, others (either pre-existing or new) should have picked up the slack and the profits that went with it.

Perhaps that’s the real conclusion: some countries’ banking sectors were full of banks that were too heavily invested in bad bets and not open to new players, while others were luckier. As far as I can see, that means the solution is to either ensure that at least some of your existing banks aren’t making bad bets, or make it possible for new banks to appear fairly quickly when existing banks fail.

Really, the stimulus debate seems pretty irrelevant to me — the treasury estimates put Australia’s economic growth as declining by 1.3% without the stimulus package, which barely makes a change in comparison to other countries — only moving us a couple of places down. And those other countries almost uniformly had stimulus packages of their own anyway. So if stimulus doesn’t explain most of the difference between 1% growth and 5% decline, something else must, but the question of “what?” doesn’t seem to get all that much attention.

Personally my best guess is it’s some combination of either a less unstable financial system (ie, less of a loss in down times, less of a gain in boom times) or a less affected mix of products/services in the economy (ie, we’re selling things to China who’s still buying, rather than selling to the US who isn’t), and if the government has anything to do with it, I guess less debt and waste might have made some difference; but otherwise, I’m with Treasury Secretary Ken Henry: who the heck knows?

Anyway, apart from the details of exactly where it’s spent, the debate about stimulus spending seems to be about “multipliers”, where spending a dollar one way has a larger effect on the economy than spending it a different way. Richard Posner writes a good introduction in his article How I Became Keynesian:

And here is the tricky part: the increase in income brought about by an investment is greater the higher the percentage of income that is spent rather than saved. Spending increases the incomes of the people who are on the receiving end of the spending. [...] If everyone spends 90 cents of an additional dollar that he receives, then a $1 increase in a person’s income generates $9 of additional consumption ($.90 + $.81 [.9 x $.90] + $.729 [.9 x $.81], etc. = $9), all of which is income to the suppliers of consumer goods. If only 70 cents of an additional $1 in income is spent, [...] the total increase in consumption as a result of the successive waves of spending is only $1.54, and so the investment that got the cycle going will have been much less productive. In the first example, the investment multiplier–the effect of investment on income–was 10. In the second example it is only 2.5. The difference is caused by the difference in the propensity to consume income rather than save it.

In essence the conclusion is that the less “thrifty” people are (the more they spend money immediately, rather than saving it for a later day), the more productive people are, which is to say people create, use and enjoy more stuff, and the happier everyone is. So transactions with larger multipliers — ie, money that will be quickly respent — are better, and transactions with lower multipliers — money that will just be sat on — are worse.

Mathematically, that’s all fair enough — if you increase the multiplier, more stuff happens and people are happier. But changing one transaction (spending millions on schools, eg) only changes one step, it doesn’t necessarily change the whole economy. And the “multiplier” for saved money isn’t really zero either — that money usually gets invested, which is to say given to someone else to spend on the basis that they’ll give you more money back later. The reason investment has a lower multiplier than consumption is that while people might spend their fortnightly pay immediately, they’re not likely to be quite as spendthrift which a large loan.

To me though, the “speed” aspect there is what’s crucial there, not the “thrift” versus “saving” dichotomy — and that in turn seems better understood via the exchane equation: MV = PQ where M is the total amount of money in circulation ($), V is the average rate at which a dollar changes hands (Hz), P is the price of value ($/value) and Q is the rate at which value is produced. That’s also pretty much a tautology: pick a period — a day, a minute, a second — that’s short enough that no one has time to spend the money they receive; then the amount of money in circulation, M is just the sum of all the transactions, V is the inverse of the period (1/day, 1/hour, 1Hz), Q’ is the number of goods that changed hands in that time (so Q=VQ’), and the average price of goods is just M/Q’, or MV/Q.

A popular corollary of that relationship is that, assuming you hold V and Q constant, then inflation (overall changes in price) is exactly proportional to changes in the money supply — so in so far as central banks can control the money supply, they can also control inflation. Of course V and Q aren’t constant — V changes if you can access your money more quickly, which is definitely happening, and Q changes if people are more or less productive which also happens.

That has some simple implications for government spending — for instance, higher velocity is better, so stimulus programs that don’t actually get money out promptly are probably not such a great idea. Likewise, it’s probably true that focussing stimulus payments on the poor is more likely to be effective because the money will get passed on quicker (whether spent or used to pay off debt). It may also mean lots of small stimulus payments are better than a small number of larger ones, since, again, they’ll be more likely to be spent quickly.

On the other hand, given the Reserve Bank largely has control over both the money supply (M) and modifies that in order to keep prices (P) stable, that only leaves two free variables for the government to try to influence — overall velocity, which isn’t really easy to manipulate, and GDP which is even harder. So outside of the Reserve Bank’s domain, as far as I can see there’s not actually a lot of either blame or credit left to be assigned. So maybe that’s why pundits and governments are focussed on the stimulus stuff, minor though that actually seems to be in the overall scheme of things.

However when you don’t ignore the Reserve Bank (or the US Fed), there’s a bit more to say… So: to be continued.

I’ve been thinking it’d be fun to try developing code in a wiki environment — ie, web based, reasonably pretty, simple markup, potentially collaborative, and with text/links as first class elements; basically my idea of what LitProg2.0 might look like. As such, I’ve been poking at some hackable wikis — particularly ikiwiki and sputnik. Anyway, as the previous link explains I decided sputnik was the way to go, so now I have a junkcode wiki. A bit of hacking also means I have an RSS feed of new junkcode roughly as I add it, which hackers reading this might find interesting to follow. (There’s a few problems with the RSS generation — it’s not terribly efficient, but that’s probably okay with feedburner as a middleman; and sputnik seems to miscorrect for the TZ, giving timestamps 10 hours too earlier than reality, or so. But hey, it has pretty colours!)

Query: if there were an actual Richard Castle book signing, and I were there, would you wish it signed Richard Castle, or Nathan Fillion?

My fandom’s fourth wall appears to have collapsed.

Problem 6. Let a₁, a₂, …, a_n be distinct positive integers and let M be a set of n-1 positive integers not containing s = a₁ + a₂ +…+ a_n. A grasshopper is to jump along the real axis, starting at the point 0 and making n jumps to the right with lengths a₁, a₂, …, a_n in some order. Prove that the order can be chosen in such a way that the grasshopper never lands on any point in M.

Depending on your interest in such things, you might have fun trying to puzzle it out on your own. If so, you might or might not want to read the comment thread above for ideas, but be cautious about the comments to Terry’s followup post which include at least one complete proof.

Anyway, since I’m obviously not a l33t enough maths dude to come up with a proof myself, I thought it might be fun to see about converting the proof into a program (ie, “find a sequence of hops the grasshopper can take without landing on any point in M”). Something along the lines of this blog post from not too long ago. Happily it’s a constructive proof, so that’s an entirely reasonable challenge.

First, I’m going to generalise the problem slightly to make it a little easier to deal with: we’ll allow the grasshopper to start at any point, z, on the number line; and we’ll allow M to contain between zero and n-1 values, instead of exactly n-1 values, and we’ll sort the two sequences from least to greatest. (That turns out to not be any more general in practice, but will make the notation easier)

As such, we’ll define a function steps(a,m,z=0) that takes the two sorted lists of numbers, and the starting position (defaulting to 0), and returns a list of steps that when taken misses everything in m. We’ll resolve it recursively, starting with the base case of m being empty, in which case we can just take the steps in order, without worrying about hitting a mine:

def steps(a,m,z=0):
    assert(sorted(a)==a and sorted(m)==m)
    assert(len(m) < len(a))
    if m == []: return a

The ideal first step to take would be the longest possible — that’s the most likely to pass a bunch of values in m, and generally gets us moving as fast as possible. The key insight from the proof (IMO) is taking this approach, and looking at how that interacts with the various possible arrangements of the first few values of m. There are two important issues: one, the most obvious, is whether one of the values of m is hit immediately by taking the largest possible step; and two, whether the largest possible step actually passes any of the values of m. We continue on by establishing these possibilities:

    a_max = a[-1]
    m_min = m[0]

    a_max_in_m = (z+a_max) in m   # NB: an O(n) or O(lg(n)) step
    a_max_passed_any_m = (m_min < z+a_max)

The most complicated case turns out to be when a_max_in_m and a_max_passed_any_m are both True. Since we know there are at least two distinct values in m (m_min and z+a_max), we can approach that by considering two jumps together, a_other followed by a_max. This won’t hit (z+a_max), and there are at most n-2 other values in m, while there are n-1 values for a_other. So some pair of (z+a_other, z+a_other+a_max) has neither value in m, and avoids at least two members of m. That gives us an inductive step, though we have to check every element of a to find it:

    if a_max_in_m and a_max_passed_any_m:
        for i,a_other in enumerate(a):
            if z+a_other not in m and z+a_other+a_max not in m:
                z_new = z+a_other+a_max
                a_remainder = a[:i]+a[i+1:-1]
                m_remainder = [m_i for m_i in m if m_i > z_new]
                return [a_other,a_max]+steps(a_remainder, m_remainder, z_new)
        assert(False) # unreachable

Having dealt with that, we have the nice result that removing the smallest member of m ensures that there’s no longer anything getting in the way of taking a_max as the next step. By doing that, we can recurse into a simpler problem, and manipulate the answer we get back to come up with a solution:

    simpler = steps(a[:-1], m[1:], z+a_max)

Now we know this series of steps avoids every value of m with the possible exception of m_min, but we need to figure if it does hit m_min, and if so where. So let’s do that:

    sofar = z+a_max
    for i, s in enumerate(simpler):
        if sofar >= m_min:
            break
        sofar += s

At this point we’ve either safely passed m_min, safely reached the end of our path, or hit m_min. Avoiding m_min is great: if we managed that, we’re done.

    if sofar != m_min:
        return [a_max]+simpler

But if we didn’t, we’re still not too badly off: we have a sequence of steps, s[:i] that end on m_min, followed by steps s[i:] that safely avoid all the other values in m. But we can rearrange those steps to avoid all the mines entirely by taking step s[i] first, then steps s[1:i] and step s[0] and finally steps s[i+1:]. Since our first step, s[0], was the largest possible step, we know z+sum(s[i]+s[1:i]) is less than z+sum(s[0]+s[1:i])=m_min and thus that all those steps avoid any value in m, and we also know that z+sum(s[i]+s[1:i]+s[0])>m_min, because that is precisely z+sum(s[:i+1]) which is a step past m_min, and known to avoid all the other values of m. And at that point we’re really done:

    return [simpler[i]] + simpler[0:i] + [a_max] + simpler[i+1:]

So there you have it. (That actually turned out slightly neater than the original proof in some respects, since a couple of the cases ended up merged)

Also somewhat interesting is where the assumptions of the problem make their way into the code. The fact that m doesn’t include the total sum is relied upon to ensure simpler[i] actually exists; that each value in a is distinct is relied upon to ensure a_max is actually larger than simpler[i]; and that there are fewer distinct values in m than distinct values in a is relied upon to apply the pigeon-hole principle in the first branch.

Anyway, pretty neat, I think!

So the first interesting-tech/mediocre-business idea I have is related to popcon. I like to think a comment I made once helped inspire popcon’s existance back in the day:

I think It’d be interesting to have a debian-survey style package that when installed, informs the `project’ (stats@debian.org?)  who’s using which packages. This would allow us to get a *much* better indication on which packages’s are in fact moderately stable and tested, and which are just gathering dust; and give us a better idea of what’s appropriate for inclusion in stable and/or unreleased.

Sadly that mail disappeared from the web (it was in the archives mentioned at the bottom of one of my initial posts to debian-devel regarding (what became) the testing suite, but disappeared after an upgrade/reinstall of www.debian.org) — but it was nominally in the public domain as of late July 1998, and lo and behold, popularity-contest appeared some three months later, doing everything I’d thought of and more. (For all I know, my comment played absolutely no part in Avery’s implementation, but I still like to think it did :)

Anyway, cool as popcon (and my original idea!) is, there’re interesting ways you could extend it, getting more information, and doing more with it. You could, for instance, survey more information about packages — what version’s installed would give you hints about how many people are pulling from backports, or mixing stable and unstable, or Debian and Ubuntu; or checking conffiles against their original md5sum might give you useful information about how often the default configuration is sufficient. Or you could analyse the information more thoroughly — eg, seeing if there are any unexpected correlations between people who use particular combinations of packages, or doing a netflix-like “I see you use package foo, many other people who use it also use bar, maybe that might be worth investigating.” (I once tried to do that sort of analysis on the popcon data, but all I ended up with was a pretty animated gif, that apparently crashed some people’s browsers… Red dots were systems, blue dots packages, with a package being installed on a system implying attraction, and uninstalled applying repulsion)

You could also gather completely different data — like information about the hardware, or things like the default language or timezone, or potentially even things from logs. That would let you answer questions like “do many people run Debian on HP hardware?” or “which IBM hardware is popular with Linux users?” which might influence future hardware development or purchases; or tell you surprising things about where Linux is actually being used; or give you some feedback on questions like “is the OOM killer a common occurence?” or “is IPv6 adoption actually going anywhere?”

As well as just gathering data from otherwise passive users, you could also use the data collection as an opportunity to make introductions between users — having established you’re running Debian and have a particular Intel graphics card, you could be automatically given the address of a section of the Debian wiki that’s dedicated to issues with that card; with the idea being that you can see any helpful solutions other users have already come up with to problems you’re having, or leave your own tips for future users. The same principle potentially applies to other sorts of data: if you have an old version of wordpress installed, it might be reasonable to point you at some security alerts that apply to it, or having determined you’re running Debian on some HP server, you might get directed at some updated management software that enables some extra features.

Another interesting improvement I think you could make is to provide ways users can aggregate and anonymise their own data. Even in the age of social networks and ubiquitous transparency, managing privacy of this sort of data is important: it would be spectacularly bad to provide a website that told people exactly which machines were vulnerable to which secuirty exploit, but that’s exactly what a list of which machines have which versions of which packages installed would provide. The popularity-contest software goes to some lengths to avoid that, by identifying data against a randomly generated UUID rather than an internet address, email or username; by not storing detailed information about package versions; and by restricting who has the ability to run any detailed analysis on the data. But you can go further than that by aggregating and filtering the data even before it makes its way to a centralised server — eg, rather than have each individual machine on a network reports its statistics to Debian, you could have the information sent to a proxy server that aggregates all the packages into a single report (30 computers, 10 of which have apache, 15 of which have exim, …), thus removing certain correlations (do all the machines running apache also run exim? or do none of them?), and potentially filtering things like the UUID (which might reveal something about the random number generator, particularly given Debian’s recent issue with randomness…) popcon version (which gives an indication what version of Debian is in use, and in some cases how recently it’s been updated) or timestamp (that may give away that the machine has been down). And if you’re running a network that’s intended to be somewhat locked down, it might be more reasonable to have computers reporting to a machine that you control, rather than one just out their in the wild.

So that, in very rough terms, is the spec for this project, which is currently going by the name “pageant” (ie, a popularity contest that takes itself a bit more seriously…) The technical goal is to provide a pageant client that people can run on their systems, which can report potentially arbitrary information to a central server and can receive and present relevant snippets of advice related to that information; a pageant proxy that can intermediate and filter pageant clients to provide a slighter higher level of anonymity/privacy; and a pageant server that can collect the data, provide relevant advice to clients, and analyse the data. I think it’s feasible to do an interesting job of that, that should go a little further than existing programs, and be usable by actual people, though I suspect the server side will have to be a bit beta-ish to be finished within a  week or so.

The business goal, obviously, is to turn some of the hypothetical benefits touched on above into actual income, ideally without turning it into a vast NSA-like data hoarding corporate conspiracy. I figure there’s a few reasonable ways to approach that:

• First, I figure that providing the same information other systems currently do at no charge makes sense: so getting basic stats on how many Debian users have nickle installed, or Ubuntu users have network-manager, or Fedora users have a Synaptics touchpad should be free.
• Second, I figure providing further analysis for companies and researchers should probably be possible, and cost something: probably more depending on how complicated the analysis is. Possibly there could be an extra fee for the analysis to not be also made available to the public; that could be entertaining.
• Third, I figure that it probably should be possible for companies to at least provide advice to users of their hardware through the system, and that at least in some cases, that probably should be for a fee. I’m not sure if there’s a line in there somewhere between necessary advice (security updates?), helpful tips (here’s some non-free drivers for that hardware?), or outright advertising (buying our hard drives will give you 200% better performance!) that might mean “advice” should vary between free, paid and blocked. An approach might be to say distros’ advice is free, other people pay.
• Fourth, I think it would be interesting to allow users to optionally pay a fee to register their hardware. This could have a couple of benefits: it provides a low-maintenances way to discourage ballot stuffing — it’s not at all difficult to hack up popcon to pretend you have thousands of servers running your favourite package to try to bias the statistics, but it’s somewhat harder to come up with even a few dollars thousands of times; and possibly more interestingly, it provides an easy means to link a small payment for “using Linux” with the software that’s being used — so distributing 80%-90% of those fees to the authors of the software that’s actually being used might be an efficient way of helping support free software development.

Anyway, that’s the project! My notes have a few other things in them worth mentioning — there’s a couple of not entirely little complications in a few of the above ideas, for one — but this is already long enough, and it’s not like I can’t blog again later. Even though there’s a few similar projects around (popcon and smolt in particular) I’m planning on taking a NIH approach and starting from scratch, on the basis that current stuff is mostly pretty basic to reimplement, and getting an architecture I’m comfortable with is pretty important in making it appropriately generic. As always, helpful tips, questions and/or any general encouragement appreciated, either by email or the comment link…

I have what I guess is a similar collection of interesting little ideas that I think would be worth seeing through, with many of them not really seeing the light of day. If I had any sense, I’d be keeping them all in a wiki, but in reality I tend to just use a collection of TODO files in my home directory. Fortunately, that’s not really the problem for me: I’m mostly able to either track down or reinvent the nuggets of ideas, and I don’t usually forget ideas entirely, however long I’ve put them aside.

What I have been having difficulty with is actually getting them finished — I’ve got a bunch of neat ideas to the point where I think I see how to finish them — ie, right up to the point where the next bit is Hard Work. And apparently I’m no longer at the point where I like doing complicated coding gymnastics just to prove I can. And beyond that, the overall motivation, ie that it’d be kinda cool when finished, just isn’t enough to actually get stuff done.

(And the worst part about trying to motivate yourself to do anything hard, is that when you don’t succeed n times, that becomes an additional reason not to succeed at attempt n+1. But pfft, we’ve addressed that, right?)

(Of course, the worst part for you, dear reader, is having to skim through all this filler I’m writing while trying to avoid getting to the actual point…)

One aspect to my motivations these days is that I’m interested in business and entrepreneurship these days — and hey, I figure it’d be a lot easier to deal with having lots of Fantastic Ideas if you happened to have a profitable business with a bunch of employees you could tell to implement them for you. (I’m sure there are other complications of some sort in that plan, but hey) And having decided to pass on the exciting sounding BootUpCamp next month in favour of the inaugural (and much more local) kernel.conf.au and I guess the forthcoming Brisbane BarCamp now too, I’m left lacking an awesome opportunity to hone said interests.

Being the sort of person who likes looking for mass-kills when considering stones and birds, an idea came to me. If I want to try starting some businesses just for practice, and have a bunch of kinda cool ideas I want to finish, why not turn them into mediocre business ideas, build them, and see how it goes? Adds an extra reason to actually get the cool ideas implemented (it’s a business necessity!), makes them slightly more challenging (can’t just be useful, but have to be at least potentially profitable), and means that even having the business part fail completely is still an objective win (because there’s a kinda cool creation that’s at least functional, if not finished), as well as being a learning experience (which of course is also a win!). And if the business part happens to be successful, well, there’s all those wins, plus some extra cash!

Great in theory! In practice, the abject terror it inspires is something of a drawback — though also possibly motivating in its own way. Anyway, I figure sometime in the near future I’m going to try running a handful of projects through roughly the following formula:

1. Pick a neat idea I’ve been putting off, that shouldn’t take too long to actually get up and running (say a week or two)
2. Work out how it improves the world — who would be better off, and why
3. Work out a plausible way of charging some-or-all of those people before they get some-or-all of that benefit.
4. Blog about that, on the basis that (a) it gives me a huge incentive to actually finish in a timely manner, a la WoBloMo, and (b) if I’m really luck someone comments or emails with an even better business model.
5. Write the software and whatnot.
6. Setup as simple a charging mechanism as possible.
7. Publish both.
8. Take a breath.
9. Go back to step 1.

My initial idea was to commit myself to doing one of those each week for about a month starting yesterday, but while that might be plausible for the coding part (or might not be, too), it’s a bit too daunting for the business part. So I’m thinking I’ll just be starting “soon”, and aiming to get each project “launched” within a week or two, and seeing how that goes. There’s a few other daunting bits too, like setting things up to automatically deal with payments, and there’s a few aspects to some of the ideas that require confusing things like setting up websites… But hey, learning experience!

Anyway, that’s the theory. Whether it ends up bearing any resemblance to practice, I guess we’ll see…

As an INTP, I generally try to make “rational” decisions — which is to say, ones I can rationalise and explain and logically support. That in turn is something I can rationalise, explain and support: I’m fairly good at logic, and I’ve been taught lots of ways of analysing problems that people have discovered over centuries, which helps make better decisions. But the counter-argument to that is that it’s still easy to make mistakes, and mistaken logic can lead you to all sorts of bad ideas; a lot of deep, rational thought went into eugenics, for example. So for me, I like to keep a handle on that at least partly by trying to keep things aligned with my emotional response. If something doesn’t feel right, that’s a good time to look back through the logic, because there’s probably a mistake. If you can’t find a mistake, and that doesn’t make you feel better, it’s a good time to be cautious in other ways; if you do feel better, that probably means you’ve got a better understanding than you did before and definitely means you’ll be able to act on the ideas more effectively; and if you do find a mistake, well, you get a chance to fix it.

Going the other way — rationalising whatever you already feel — isn’t so effective to my mind; it’s often easier to make an apparently logical argument that’s actually wrong, than to work out how it’s wrong. That can be useful as a defense mechanism to rebuff challenges to what you want to do, but you can generally rationalise anything without much effort so it’s not actually adding information or improving your decision, and if you infer from the fact you’ve come up with a rationalisation that your decision is the only rational one to make, you can end up with a closed mind to better alternatives, leaving you with a worse decision than if you’d just continued going with your gut feeling. Personally, I tend to take that a fun game: take a completely subjective and illogical response to something (eg, “orange is the best colour”), then “logically” and “conclusively” prove it’s the only justifiable response. At the very least, it’s a good way to keep a little humility about the value of a good argument.

Logically investigating something (”I like orange. Hmm, is there some way to tell what the best colour is?”) you had a gut feeling about is a different matter entirely, of course — and as you think about it, if the analysis leads you to find something different to what you expected (”that’s odd, I think I just proved chartreuse is the best colour”), which might lead you to investigate different definitions for your terms (”perhaps orange is best in some ways, and chartreuse is better in others”) if it doesn’t lead you to change your opinions (”oh. my. god. this chartreuse cape is to die for!”)

Of course, if you’re not naturally comfortable with coming up with logical arguments, or trained enough to do them well, you’ve probably got other, more personally appropriate, ways of coming up with decisions anyway, and maybe none of this applies. But hey, that’s not my problem.

The other advantage of keeping your feelings in accord with your thoughts is that it tends to be more motivating — “passionate” tends to be a decent description both of someone pretty emotional and of someone pretty motivated and active, and there’s no point to making good, rational, decisions without acting on them. In some respects, the more intense the emotion the better; it’s easy to want to quit doing something difficult that’s not immediately rewarding, no matter how logically you’ve convinced yourself that it’s a good idea, but it’s a lot harder to shake off broiling rage, true love, or abject terror, eg.

The trick, then, is if you’ve found something that inspires that sort of emotion, to make sure it’s working in the same direction as the goals you’ve carefully and logically examined. That can be really easy: if your primary emotion is that you care deeply about helping people, seeing someone who’s had a bad day or week or year get a break and maybe break a smile is a good way to keep yourself working in a charity or a hospital, if that happens to be what you want to do. But it’s often not — maybe you’re overwhelmed by anger at the stupid bureaucratic nonsense that’s getting in the way of your hospital helping people, or maybe you’d like to help out in a soup kitchen but you’re terrified of violence in the area.

But, at least sometimes, those can be harnessed too. “Use your anger” isn’t exactly “use the force”, but it still gets some 40,000 hits on google offering useful advice. My feeling (which I wish I’d had earlier than today, but oh well) is there’s probably similar ways to grab most of those emotions, and turn them into allies, rather than just trying to figure out ways of making them go away.

• frustration, anger, hate: Figure out exactly what it is that’s the deserving object of your ire, and find ways to harm it. There’s lots of entirely reasonable ways to hurt things: a death blow, divide and conquer, death by a thousand cuts, subversion and betrayal; and most entirely reasonable ways of contributing to the good of society can be rephrased into something that’s more acceptable to anger. Annoyed by ignorance on the Internet? Deal it a death blow by creating a site like Wikipedia or snopes; create a debating forum so ignorant people are fighting each other instead of you, and maybe learning something as a result; contribute to Wikipedia or snopes or just help your friends avoid spreading urban myths;  find a group of people who seem particularly ignorant, join them, become well-educated in their customs, befriend them, and then help them get access to all the knowledge they’ve been missing.

• worry, fear, terror: Be thorough. If you’re worried anyway, you’re going to naturally be thinking of every single way every single thing can go wrong, so take advantage of that and do something about each of those things you think of. Maybe it seems more rational to ignore your worries, and just charge ahead (and maybe it is), but there’s an equally good chance that will just make you worry more and make you less effective, whereas if you actually nervously go around making sure everything is absolutely perfect, you’re at least spending your time contributing to your goals. And every little thing you do fix up is one less thing to worry about, so it’s possible you might end up naturally less worried anyway. Probably not, of course…

• affection, appreciation, love: Dedicate your work, do it in appreciation, or in honour, and make it something that’s worthy of the object of your admiration, whether that be a person or an idea. It’s always tempting to cut corners or strive for something other than your absolute best, but much less so when what you’re doing is making a devout offering to something or someone you care about.

• pride, arrogance, narcissism: You think you’re the best, so do something that demonstrates it. Repeat.

• greed, envy, lust: Be a free-market capitalist — get more of what you want by doing more of what other people want.

• embarrassment, guilt, shame: Accept, apologise, and then do something worthwhile to atone?

• indifference, apathy, sloth: No idea. (Is this an emotion, or the absence of emotion? If the latter, find an actual emotion? If you can’t, just try to avoid watching Dexter for lifestyle tips?)

Anyway, there’s my thought for the day. YMMV. The following quote may or may not add support to the thesis presented:

Peace is a lie, there is only passion.
Through passion, I gain strength.
Through strength, I gain power.
Through power, I gain victory.
Through victory, my chains are broken.
The Force shall free me.

— The Sith Code

As far as I can tell, Twitter is a flakier, crappier knockoff of Facebook, that has even less monetization potential than Facebook.

Meanwhile, identi.ca is an open source knock-off of twitter, FriendFeed is a knockoff of Facebook, LinkedIn is knocking-off a little bit of both of them, everything’s getting comments and tags and automatic recommendations, and everyone and their lolcat is starting up their own social network of some sort or another. It’s all very confusing. Anyway, as a snapshot in time, the social media thingies I’m on at the moment:

• blogging — almost finished my fifth year of irregular blogging; now with comments enabled, and using email-address based gravtars so people can have an identity while commenting. Still like it.
• microblogging — have accounts on twitter and identi.ca under the userid “ajtowns”, with the twitter feed syndicated into the sidebar of my blog. Mostly used for trivial techy comments or link sharing — things too banal or already known, or just not fleshed out enough, to be worth blogging. The two accounts are hooked up via ping.fm, so they mostly get the same content. I’ve got both because twitter’s popular in general, and identi.ca’s the “alternative” version for the open source crowd, and the easiest way to follow other people’s comments via those services is if you’re signed up.
• google reader — in practice I mostly just use this to read other blogs, but I occassionally use the “share” button that shares entries with friends in my (fairly minimal) google contact list. Not really convenient, since it’s a nuisance to share random webpage that you get to by following links, so I’m probably switching to tweeting interesting tech things instead
• facebook — good for connecting up with non-tech friends and procrastination. Some (but not all) of my techy friends link this with their microblog accounts, so I get the same updates there and here, and any responses/comments they get then get further split. Don’t much like that, hope someone will fix it. Also the only way I ever know anyone’s birthday.
• youtube — also procrastination
• linkedin — really good for figuring out who some tech person is, haven’t tried
• stackoverflow — seems to be better than IRC and forums for getting useful answers to programming questions atm, and answering programming questions is kinda fun too

Having one or more social media accounts seems to be (becoming) a significant part of the way business networking gets done now too — with an @reply/comment and a friend/follow instead of  some idle chat and swapping business cards. Don’t know whether I think that’s a good thing or not, but it seems useful to be aware of anyway.

As far as contributing to the fads go, I like to think I provide useful content to a few of these (blogging, microblogging, linkedin, stackoverflow), I pay my own way for blogging (they’re my thoughts and I’d like to keep them, thanks), and I’ve followed some ads on facebook (though I don’t think actually purchased anything as a result).

Anyway, that’s my take — whether my contribution is enough to justify my slice of the computing power associated with keeping those sites running, I don’t know, but they’re currently all of some value to me. I suspect my blog is the only one of them I’m willing to keep going at all costs (especially since I know at worst I can just move it to paper).

First, at its most basic $42B divided by 7.4M households means infrastructure costs of about $5,700 per household. That seems like a lot of cash, when (non-Telstra) ADSL2+ upfront fees are only $130, with no contract. It’s covering businesses too though, and potentially is a qualitative change to the service compared to ADSL coverage (whether due to speed or reliability). For me, that’s way more than the government should be committing to this; if people really think fibre speeds are worth $6000 per household, let an ISP sell it to them privately. I’d completely support having local neighbourhoods able to vote to have fibre (or high speed wireless or similar) installed locally, paid for by an increase in everyone’s rates, eg.

But hey, the point isn’t to see whether it’s worth going ahead, it’s to see what it’ll end up costing when/if it does. Infrastructure development is apparently going to be funded by a bond issue, which means the government sells bits of paper with “$1000 treasury bond” written on it, with a “coupon” rate that’s currently around 5.75%, and a maturity date (up to around ten years away). The government will then pay $28.75 every six months to the bond holder, until the maturity date, at which point they’ll give them the full $1000. Over a ten year period, that’s a total of $1,575 paid out. The initial price is just whatever the government can get, which might be more than $1000 or less, but certainly won’t be $1,575. If I’m reading the RBA’s numbers right, the current price for a $1000 treasury bond with a coupon of 5.75% that mature in 2021 is about $1105.42.

So what’s that mean? To get $42B now at that rate, you have to issue just under 38 million bonds. You then have to pay each person who holds one $57.50 per year, and then pay them $1000 in 2021. That’s $2.185B per year, and $38B in 2021. If you’re balancing the budget, you’re thus hoping to collect $300 per year from every household ($25 per month) over the eleven year period for the coupon costs; and you also have to come up with $38B from somewhere. If you’re going to do a Telstra again and just sell the infrastructure you built, then hopefully it’ll be worth $38B (or more) at that point  and you’re okay. If you’re hoping to have built a public asset, you’ll want to have collected roughly $3B per year more than that, so you’ll be able to pay off your bond holders and keep the infrastructure, which means about $700 per year for every household, or about $60 per month.

This is averaged over every household in range of the NBN, including ones that don’t have computers or don’t want access to the internet. That’s likely inaccurate: if it’s paid for by people who use it, and only one in ten of the people who can, do, then that’s $250 to $600 per month instead. On the other hand, if it’s taken out of income tax/GST receipts, it’ll be a different group of people who end up paying for it, and working out how that’ll actually affect tax rates or consumption or other government projects is beyond my ken really.

Those are, presumably, just infrastructure costs, so additionally you presumably need to factor in maintenance fees, tech support, external bandwidth, and other costs too — as Simon pointed out in his post, effectively all your $60/month is getting you is the equivalent of the copper wires we already take for granted and pay Telstra about $20/month for (whether directly or not); running actual data over it is an add-on either way. Going on Internode’s charges, getting 40G of data a month would be an additional $55/month (if you want less than that, I’d presume you don’t care about the NBN anyway). It’s possibly slightly worse than that, in that the $20 that Telstra gets also covers routine network maintenance after the service is initially setup, while it’s not clear the $42B (and hence $25-$60/month) does. And of course, while $42B is a very Hitchhikers numbers, as a government project it might blow its budget and require additional financing later, so multiply it out for that reason as you see fit too.

So by my count, that means retail prices are something like $25-$60 (infrastructure) plus $? (infrastructure maintenance) plus $0-$540 (unused capacity costs borne by early adopters) plus $55 or more (data, service) plus $? (corruption, waste, budget blowouts, profit), which sums to a monthly retail broadband fee between $80 or more and $700 or more.

That sounds like it’s in the right ballpark for the scale they’re considering — about $80/month for low-end fibre sounds plausible if you don’t get forced to try to provide it outside of major population areas (ie, the new 90% of the population target, not the old 98%), but only if there’s a high takeup in the area, and it’s implemented with a lot of competence. If there’s low takeup, then you get to multiply the cost accordingly; and if there’s problems in the implementation, you get to add to the cost, and lower the adoption when people avoid it.

Of course, the more likely scenario is the budget doesn’t get balanced, and the final $38B is either rolled over into ongoing debt (”we need to come clean on $38B of bonds? let’s issue more bonds then and pay the old debts with the new debts! ponzi scheme? what’s that?”), taken out of taxpayers’ hides, or we have a round of inflation so that $38B is barely enough for a morning coffee. The other issue is that $42B worth of bonds would almost double the amount of debt Australia currently has on issue, which could easily affect the price we get for our debt — and that we’d have to issue more bonds than I’ve estimated above or have a higher coupon to get the same cash now, with corresponding increases in the prices needed to keep the budget balanced.

(And of course, if there really is a credit crisis, and people aren’t willing to loan money, issuing $42B of bonds wouldn’t be possible. If there’s just a credit crisis for private borrowers, this probably just makes it worse by giving even less reason for people to loan money to anyone who doesn’t have their own mint and tax agency)

If you’re looking for actual interesting content, you might like to check out On Topology, by John Moeller who did a better (though still imperfect) job keeping consistent on his blog, with some interesting posts on hyperspheres amongst other things.

So snarky comments notwithstanding, I’m sticking with my assessment of the quota behaviour as weird and confusing, but it’s nice to have a pretty picture tracking just how weird and confusing it is. Hopefully tomorrow Internode will be all “haha, it was all just a practical joke, April fools!” and give me another 40GB to play with. Since the first of the month is my usual rollover day, I’m quietly optimistic.

Somehow the blocks of downloads that almost used up the remainder of my quota yesterday just vanished! Awesome. Especially since there apparently wasn’t any downloading happening during those blocks. Apparently there’s rumours that the quota exempt stuff is sometimes added to your usage as it happens, then deducted later, so maybe that’s what happened.

The red “variance” line is how much expected quota usage differs from actual usage — by the above I would’ve expected to have used 2.5GB more than I actually have so far. (The expected usage is just a constant rate that will exhaust the entire quota just in time for it to rollover)

Over the next twelve months, what single development/activity/project is going to improve Debian’s value the most? By how much? How will you be involved?

There have only been a couple of replies so far, the first of which was from Russ Allbery, who took issue with the way I’d chosen to focus on growth. Which lead to some interesting thoughts; or at least, I find them interesting.

The examples I gave talked about how much that would improve Debian from users’ perspective in percentage terms — this would make Debian three times as good for one out of every ten of our users, eg. That, in turn, implies an exponential rate of growth: if you can consistently improve at a given percentage over a given timeframe, whether that’s 100% a month or 1% a year, you’ll eventually be doing better than anything that can’t remain exponential, given enough time. On the other hand, if you can’t maintain exponential growth, you won’t be able to maintain any given percentage either — linear growth, eg, will give you percentages that drop rapidly: 100%, then 50%, then 33%, then 25%, 20%, 17%, 14%, 12.5%, 11%, 10%, etc.

The interesting thing is how that looks from a user’s perspective. If a user’s already got a working system — running Debian, or Ubuntu, or anything else — what’s the incentive to either upgrade or switch to another distribution? One is that ongoing support might disappear, so you can’t get security support, or Oracle will stop answering your calls because your OS is too old and they just can’t be bothered anymore. That’s a mostly negative approach though: you’re not expecting any benefit, so you just want to minimise the pain of upgrading. New features, behaviour changes, all that stuff is a cost, because you’re just looking to keep doing what you were doing before. The other reason to upgrade is exactly the opposite: that there are new features, or new ways of doing things that are a real benefit to you personally. Perhaps it’s a bunch of small things — a little less power usage, a few less errors here and there, less obnoxious popups, a faster boot, fewer typos around the place, some fixed bugs, some more documentation — that just add up to a more pleasant experience. Perhaps it’s one or two big things — you can replace your last Windows box, eg. Perhaps it’s something that pretty much only matters to you — you changed your name to an unpronouncable symbol, and finally your preferred font has a unicode glyph included that you can use as your real name in your email program.

But it seems to me, that if you’re going to provide a new version of your software and you want users to be happy about upgrading, then there are two things to focus on: making the changeover completely unnoticable, and making the upgrade give an appreciable benefit to an significant number of users. And if you’re going for the latter path, then that does require a percentage improvement: for x% of your users, they’re experience using the new software has to be y% more pleasant than using the old software. (And if you decide to go exclusively for the former path, there’s an easy solution: don’t change the software at all — you can’t notice changes that aren’t there)

And note that that’s actually the right answer, too: if you aren’t improving your users’ lives, you shouldn’t be releasing new versions. Upgrades come at a cost, even if they’re nominally free: some things break, you need to learn new things, and you often get forced to upgrade other things too. If you’re only making things 0.0001% better, it’s probably better to delay the upgrade until there’s a bunch of improvements that can be combined to actually make the cost worthwhile.

Getting back to the original question, it seems to me like it’s also fair to focus first on the things that are going to provide the biggest benefit; though obviously there’s plenty of room for debate over whether three small things are better than one big thing, or how to compare a short term benefit with a longer term benefit. But ultimately, I think it’s fair to say that when you multiply all the improvements all your contributors are working on, scaled by the proportion of users they affect and how much they affect those users, you want to end up with something similar to Moore’s law: that is your project becoming twice as “useful” every eighteen months. Maybe it’s a different time frame, maybe it’s three years or five years, but if it’s not in the ballpark, then you’re basically not doing your users any favours.

So how do you get there? There’s a few ways of getting that sort of growth. You can keep your userbase constant, and improve your quality. That has the benefit that you’ll likely get more users as well, so do better than you expect, but eventually you’ll hit a wall because your users will be near enough to completely happy that you just can’t make things that much better. Alternatively, you can maintain your quality and expand your userbase; which largely means finding new things to do, rather than doing the current things better. For open source development, that has the benefit that it can increase your contributor base too — if you have one contributor for every twenty users, then if your userbase increases by 100% every eighteen months, so do your contributors. And if each of those contributors is focussed on individually linear growth — improving the system enough that twenty new users will adopt it, for instance — that will rebound back into sustainable exponential growth.

Now, there are limits to that sustainability: eventually you run out of people (or just match the rate at which the population is growing, anyway), or hit the absolute limits of a quality experience. But that just means one of three things: your project should expand into other areas that still usefully contribute to humanity at a significant level, people should stop spending much time on your project and work on other projects that usefully contribute to peoples’ well-being, or the human race has pretty much hit the absolute limits of its potential. Those seem like big calls to me, and at least in the areas that interest me, there still seems like plenty of potential for big improvements.

So at the moment there are three responses to my original mail, from Russ, from Raphael Hertzog, and from (DPL candidate) Stefano Zacchiroli. And they’ve all pretty much avoided the “By how much?” part of the question. If it’s really fair to expect Debian to improve significantly (by 10%, 100%, 300%, whatever) over the course of a year — and as I’ve argued above, I think it is — not making estimates of how much benefit things will actually result in seems both a bad way to establish the project’s priorities, and somewhat disconnected from the usual philosophy of wanting to measure performance and results, that we expect from scientific and engineering endeavours.

Anyway, it seems a useful way of looking at conflicts to me: are you actually growing the organisation, or are you just spending all your effort getting other people to move in the direction you want, when they don’t actually care?

You could probably extend the analogy to cover forking too — stretching the band so far it snaps, then tying it back together again. I don’t know if that’s very helpful though… Also, there’s probably more than two degrees of freedom, so a hypersphere is probably technically a better model, but, well…

The other comparison that can be made to a few years ago is more of an absence. When Andrew was on the committee, at least in the year we had in common, he and Pia had a habit of butting heads, more or less on this topic. From where I sat, it was mostly entertaining: a real live dialectic, noble scholars jousting on the field of ideas wearing their philosophys’ favours on their arms — though I gather for both Andrew and Pia it was mostly just frustrating. Particularly since there was something of an impedance mismatch in their roles within the organisation, rather than being a debate between people with equal responsibilities that someone else gets to adjudicate. To attempt to paraphrase Pia’s line of thinking (and without the benefit of it having been reiterated just a few days ago), I’d say her view was that sustainability is very much a secondary issue compared to activity and actually getting things done; that Linux Australia is a volunteer community, so make use of that and get people to do things for free so you don’t have to worry about how much money you have, and reward that contribution with kudos and appreciation, and ultimately if your organisation is doing great things, people will find a way to keep it going one way or another anyway.

I’m personally more biassed towards Andrew’s focus than Pia’s — I’d rather work on the multiplier between effort and results, than increasing efforts with the same multiplier. But not completely so: there’s no point having huge results for very little effort if nobody’s putting in any effort, after all, and there’s no point having an organisation that can sustain itself forever, but that never actually does anyone any good. So for me, it seems valuable to keep the other side of the argument close to mind.

(We discussed a bunch more practical stuff on Sunday, but I couldn’t very well have posted about that the day before, which was the WoBloMo post I’m meant to be making up for here…)

A day late for WoBloMo (unless you go by Hawaii time…) but I figured I shouldn’t blog ’til I’d actually voted…

A couple more thoughts on Sunday’s post. In comments, Brendan Scott asks “Why would a trader extrapolate against their estimate v valuation?” But there’s actually a broader question — why would anyone trade at all? The initial scenario provided infinite supply at $500 per item, and gave a randomly chosen demand at $500 per item, which was then naturally fully satisfied. If they thought it was a good idea to buy more at more than $500, they should have bought upfront, and why would they want to sell something they just paid $500 for, for less than $500? Worse, the only difference between the traders is the input they get from the random generator: their strategies are explicitly the same. So no trader can potentially be smarter than another, and profit from their stupidity, they can only profit if the random number generator gives them a lucky number, and someone else an unlucky number. So I don’t think there’s a good answer to “why would they do this?” — participating in this market is fundamentally a mistake, the way most people view the world. For instance, it ends up with a 74% chance that you’ll have less money than you started with, and starts off with perfectly equal wealth amongst all the participants, and ends up with the wealthiest individuals having over $60,000 while the poorest have less than $10.

In a real market, as JD points out you have different people having different information — though of course you’d have to actually have something to have information about. If you decided the assets were batches of ten barrels of oil ready for delivery in twenty-four months time, different people would have better or worse estimates of the value, and depending on other changes in the economy, the underlying value would change too (maybe someone discovers a cheap oil replacement and it drops, maybe there’s a war and it rises).

An interesting theory that I’d never heard of until David Pennock posted about it the other day is the “Kelly Criterion”. Given an estimate of your odds of success, and how much you’ll make, it will tell you the optimal amount to risk to get the biggest advantage from compounding returns. The idea is if you’ve got an almost sure thing, and you only risk a few dollars on it, you won’t make much; but if you continually risk everything, even on sure things, you’ll eventually lose it all, and that’s no good either. The Kelly criterion makes that idea precise, telling you exactly how much of your resources you should commit, assuming you can come up with a reasonable estimate of your odds.

The original paper was from 1956 byJohn Kelly, apparently in collaboration with Claude Shannon, and as you might therefore expect, came from an information theoretic approach, rather than an economics one. The idea was that you have a secret channel that tells you exactly what to bet on, but unfortunately it’s not a clear channel, and sometimes you mishear what you’re being told and thus bet wrong. Fortunately you’re clever enough to figure out how often this is likely to happen, and thus you can work out when to follow the tips and how much to invest in them, which gives you the aforementioned Kelly criterion. But the signal you get doesn’t have to actually be from the future, it just has to be correct predictably often. If you want to apply that to your intuition, your astrologer, or a groundhog’s shadow, that’s fine, though the lower your odds of success, the lower the amount you’ll be encouraged to invest, and thus the lower your optimal returns will be.

But that’s only relevant if you’ve got an actual meaningful signal and a chance to actually profit, which isn’t what I gave my poor automated traders. If I had, an optimal system would’ve rewarded folks with the best signal, provided useful information for someone, and transferred physical wealth from people who wanted information to people who had it. Redoing the marketplace so that was actually possible would provide a much more interesting endgame.

Nevertheless, it’s interesting to me that even without any fundamentals at all, or any complicated trading techniques, you can pretty easily get behaviour that looks like a bunch of otherwise intelligent people bidding themselves into bubbles and then crashes. If you looked at those graphs as the price of oil or milk or similar, you’d naturally go looking for a cause for the price changes: but at heart, there actually wasn’t one in that case, it was just a combination of coin flips, that happened to be more or less likely, due to trader’s habits, and how much they could happen to afford at the time. You can only reasonably fix that by changing habits, and probably the only way to do that is to bankrupt folks with bad habits so they stop it…