Archive for category Science

Detecting Design

Okay, well for the life of me I couldn’t figure out what to write yesterday. Tried doing a Junk DNA post, but others have done it better (while talking about a different subject to boot). So I’ll need to come up with something different.

… how about this? I mentioned briefly in a footnote a while back the differences between top-down design and bottom-up evolution.

Believers in Intelligent Design (“It’s creationism but SCIENCEY!”) have a small number of suggested methods for ‘detecting’ design. Dembski’s filter tries to eliminate natural occurances, Behe’s irreducable complexity sets specifications for structures to meet to be the product of design, and a variety of Appeals To Common Sense that don’t really provide anything worth mentioning… but the one thing they have in common is that they all return “true” when applied to biological life.

Interestingly, mainstream science also has a number of methods for detecting, not design, but the results of design. Archeologists look for tool-marks and compare them to tools the humans of the time were known to be using, and SETI looks for mathematical regularities in radio signals known to be produced by the devices of our technological civilisations, etc. Interestingly, both of these examples are cited by IDists as evidence that meanstream science believes detecting design is possible, despite the fact that neither of them returns true when applied to biological life: DNA synthesisers don’t leave ‘tool marks’, and known mathematical regularities within organic life (for example, bilateral symmetry) are more simply explicable as the result of optimisation, not of design. Occams Razor wins again.

So why do the IDists methods return true? Partly at least this seems to be the result of confirmation bias. The Intelligent Design movement is a direct descendant of the religous creationism movements, so the design detection methods were themselves written people with the intention of confirming what they already believed. It’s no surprise that they would return the value their authors wanted them to.

But that’s not entirely it: the authors might want their algorithms to return true, but that doesn’t change the fact that the algorithms themselves do return true. So what are they detecting?

The difference seems to be that mainstream science looks for examples of human, or at least physical, design. The IDists look for any type of design, and they find it.

This isn’t actually a false positive: progressive optimisation via natural selection is a type of design. But it’s a natural, unguided form of design. Richard Dawkins dubbed it the “blind watchmaker”.

I’ll approach this with an example. Since I’ve already written about Behe’s abuse of Mullerian Interlocking Complexity, here’s William Dembski’s Explainitory Filter, in his own words:

Given something we think might be designed, we refer it to the filter. If it successfully passes all three stages of the filter, then we are warranted asserting it is designed. Roughly speaking the filter asks three questions and in the following order: (1) Does a law explain it? (2) Does chance explain it? (3) Does design explain it?

Dembski then provides an example from American law: Nicholas Caputo, a democratic senator, put the democrats in first place on the ballot papers in 40 out of 41 instances. This provides a statistical example for Dembski’s filter:

Unbeknownst to Caputo, he was not employing a reliable random process to determine ballot order. Caputo was in the position of someone who thinks she is flipping a fair coin when in fact she is flipping a double-headed coin. Just as flipping a double-headed coin is going to yield a long string of heads, so Caputo, using his faulty method for ballot selection, generated a long string of Democrats coming out on top.

In selecting the order of political parties on the state ballot, Caputo employed a reliable random process that did not favor one political party over another. The fact that the Democrats came out on top 40 out of 41 times was simply a fluke. It occurred by chance.

Caputo, knowing full well what he was doing and intending to aid his own political party, purposely rigged the ballot line selection process so that the Democrats would consistently come out on top. In short, Caputo cheated.


Naturally, the filter (and the court) disregard the first two options and rule in favor of the third.

Now let’s apply Dembski’s filter to biological life. If you read the source article linked above, and after you’ve taken an painkiller to get over the fallacy-induced headache, you’ll note that Dembski never really applies it to life himself. Instead, he cites a variety of scientific hypotheses that “get around” the necessity of a conclusion of design when applied to biological life. (most of which are actually more related to Abiogenesis than Evolution, but he disparagingly mentions Darwin (with bonus points for the shoutout to YEC’s)* so I’m going to use modern life).

So let’s find something small and cute to apply Dembski’s filter too. I know: say hi everyone!


The appearance of adorable salamanders is something that always happens, given a world like ours and 4.5 billion years of time. Uh, no. Not that we can re-run the simulation to find out, but I think we can rule out axolotyls as a physical law of the universe.

This species formed by random chance: some completely different animal, like an elephant, just up and gave birth to the first axolotyl by means of an extremely unlike complete-genome mutation. I think we can eliminate chance as well.

Ergo: Dembski’s filter concludes the axolotyl was designed.

But… at this point, Intelligent Designists smugly call it quits. “It was designed, musta had a designer.” But how was it designed? What was the ‘designer’ looking for in an axolotyl? IDists don’t look this far: they’ll tell you it’s about detecting the presense of the designer, not analysing it’s motives.

But careful examination of the axolotyls features (of any feature of any species on earth, really) reveals what the “designer” was after: ability to make new axylotyls. From the display tentacles to the body structure, all the features are either oriented around the continuation of the species, or just along for the ride: remnants of old features that were also oriented around the continuation of the species. This heavily implies the Bottom-up design that the natural process of evolution produces, not the Top-down stuff we associate with human/intelligent design.

That confirms what biologists already knew via other lines of evidence: that the “designer” was just mutation and natural selection. It’s a shame the IDists always stop looking just before realising this conclusion.


*footnote: Dembski implying Darwin came up with the age of the earth. Charles Lyell’s remains must be generating quite a bit of torque by now (hey look, an alternative energy source!).

Thus Darwin, to prevent the probabilities from getting too small, had to give himself more time for variation and selection to take effect than many of his contemporaries were willing to grant (cf. Lord Kelvin, who as the leading physicist in Darwin’s day estimated the age of the earth at 100 million years, even though Darwin regarded this age as too low to be consonant with his theory).

“In the future, politics will be redundant. World leaders will be ultimately decided by whoever can genetically engineer the cutest species of axolotyl to use in their campaign ads.”

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The Science is Already Awesome

Loads of stuff going on behind the scenes right now: in addition to 0.5.0 (Got biomes and 3d trees done! Currently working on the interaction between the two) I’m also working hard on two parallel Species-related goals.

One has been mentioned both explicitly and implicitly several times in the last few months (I expect it to grow our audience a little), but I’m going to keep it under wraps for now because of unforseen difficulties and explosions. The other is a super-secret prototype.

So, I promised a post on science communication at the end of that comment on Bill Nye (that whole thing is still going on, btw. Ken Ham of Answers in Genesis is really pissy about it. It’s hilarious). It can’t promise the following won’t come out a little bit rambly (okay, a lot bit rambly), but here are my thoughts on the subject…

Science communication is a strange and somewhat tainted field, mostly because many of the people actively engaged in it don’t seem to realise they’re engaged in it. It’s deceptively easy to categorise the world into scientists and non-scientists, but most science communicators aren’t actually scientist communicators: they’re teachers, journalists, authors, TV personalities and (disturbingly) polititians and pundits.

There are some wonderful exceptions: scientist bloggers and writers like… (EDIT: Nevermind. I started writing this list and couldn’t stop, plus then I went researching and holy mother of cheeses there are a lot of them out there and I don’t read nearly enough of them regularly). But their audience is the people who go looking for scientist communicators: the vast majority of people don’t read proudly nerdy stuff like science blogs, and “proudly nerdy” is a good description of most scientist communicators.

In short, the general public get their info from other sources.

And those other sources usually aren’t scientists. In fact, I’d say the vast majority of the time they aren’t scientists. In general, they’re either…

a) people with an moderate understanding of science, tasked to transfer a preset curriculum of facts to a group of uninterested teenagers so they can pass their tests and promptly forget everything but the most trivial framework, or…
b) people with a barely rudimentary understanding of science, tasked to produce something they think other people with a barely rudimentary understanding of science would want to read/watch/play, or…
c) people with no understanding of science, who misheard something with sciencey sounding jargon in it and latched onto their misunderstanding as a certitude.

So that’s why we need dedicated science communicators: not just underpaid and overworked journalists tasked with getting a hyped up article about a discovery they don’t understand out in a few hours, and not just underpaid and overworked teachers tasked with making sure their students do okay on a standardised test at the end of the term. We need to expand the field.

This is especially vital in our modern society. We live in a world with uprecedented knowledge, and unprecedented access to that knowledge, yet science is still seen as an esoteric concept: the domain of nerds and geeks who use multisyllabic words like “esoteric” and “multisyllabic”. In a world where 5 minutes on wikipedia can inform anyone of things you used to need a bachelor degree to know, somehow people in general trust science even less than they used to.

So we need science communicators. I trust the scientists themselves to keep pushing at the boundaries, but if all they’re doing is pushing the boundries further and further away from the public, instead of bringing the public along for the ride, then science will suffer and society as a whole will suffer. On a societal level, education is something with no negative consequences and oh so many benefits.* Inversely, ignorance tears us all down.

*note: Okay, hypothetically, there is a level at which too many people are educated and with the surplus of skilled labour not enough are willing to do unskilled labour, and the country collapses. In reality, no society has yet reached that level: if the US had, for example, this chart would show equal levels of unemployment at all levels of education. It’s an interesting concept for science fiction to explore, though.

The alternative to an education-induced apocalypse: equally interesting.

However, we also have to be wary. Science communication is an easy thing to fail at. It requires two skill sets that, stereotypically at least, are diametrically opposed: a logical, analytic mind to understand the specifics of the science in the first place, and an ability to market yourself and your subject: to communicate enthusiasm and empathise with your audience. It requires you to be a Spock and a McCoy at the same time. (this blogs first Star Trek analogy. Oh… yeeeaaah)

To showcase this, here’s a few examples, of both successes and failures.

First of all, the Mythbusters. Indubitably a success. They might lack basic rigour, but as Zombie Feynman says: they got a whole generation interested in science. They made science cool. Ergo, if we want to communicate science, we should follow their example: sciencey stuff + funny hosts + blowing stuff up. Right?

No. None of these things were what made Mythbusters cool. I only ever saw one of the subsequent copycat shows, a series called Braniac hosted by Richard Hammond (who, for the record, is actually pretty good at communicating this stuff in more scripted shows, like documentaries), and it demonstrated quite thoroughly that “making science cool” is probably one of the worst things you can do to it. The show had it’s moments, but generally it was just a bunch of unconnected science-skits wrapped up in hype and sillyness. If you try to make science cool you fail at both science and coolness.

If you take a closer look at most episodes of Mythbusters you see fairly quickly that they’re not trying to be cool, and the moments when they are are painfully scripted. Adam, Jamie and the Build Team are by far at their best when they’re improvising, debating, making mistakes and being silly: in other words, acting like human beings. That human face, in addition to the usually excellent pacing of each episode (the show follows a pacing structure which should be familiar to anyone who has done a rudimentary literature course or remembers their high-school english), is what really made the Mythbusters popular. It was more than just a bunch of guys faffing about with science trivia and ‘splosions: it was a story, built around the scientific method.

Stand there and look cool for the camera. No, that’s constipated, try again.

Let’s take a look at another example, this time not of a success but of a failure, and not a particular work, but an entire genre. Edutainment.

For those of you who didn’t just hiss and cringe away from your computer, and thus we can assume were spared the horror of actually playing one of these games, edutainment was (and to an extent still is) the product of a bunch of people (likely older people) who saw that kids liked video games and hated being taught stuff, and thought “We can combine the two to make kids like being taught stuff!”

Unfortionately, the people put in charge of designing and making the resultant wave of educational video games didn’t understand video games. Based on the examples I’ve seen, it’s possible they didn’t understand teaching either. In some of the worst cases, I am forced to wonder if they had ever actually met a human child. For the most part, the games were what you’d get if you took a generically poor ‘memorise this’ classroom lecture and made the teacher stand behind a cardboard cutout of a cartoon character.

But it’s unconstructive (fun, but unconstructive) for me to keep insulting edutainment games without exploring why they failed. And to explore that, I need some successes to compare to. Now I’m sure that there are some edutainment games which are entertaining, but I’m not familiar with enough of them to know which ones those are. The only edutainment game I remember genuinely enjoying was an aquarium one, where you could gather fish by solving math puzzles, which appealed to my latent OCD in the same way that Pokemon did for cooler kids than I (yes, I was the kid who wasn’t cool enough to give a crap about Pokemon).

But I’m not really looking for a successful edutainment game: I’m looking for a successful game which educates. And those are surprisingly common, once you realise that games don’t have to try to educate in order to do so. This is due to a thing called Tangential Learning (yes, another link to Extra Credits. If you’re at all interested in games and you’re not watching the series, you should be).

My very first proper game, when I was in primary school, was The Incredible Machine. Anyone else remember T.I.M? It was basically a 2d Rube Goldberg Machine-maker, where you could place balls, platforms, trampolines, switches, lasers… a whole variety of things. And as a result of that game, long before I would have been capable of understanding a word with as many syllables as “algorithm”, I was making them. “The bowling ball falls to this light switch, which activates the fan, which blows the tennis ball off it’s platform…” The same logical, sequential thought patterns that game worked by would later come in handy when I was learning how to code.

When I was a little older, I picked up Sim City. Sim City taught me about complex, interacting systems in society: how doing one thing in one area could have dire consequences in another, and how the easy route (borrowing money) can get you into hot water later down the track. (although mostly what I remember learning from it is giant spider robots are bad news and that you can get money for nothing if you type F-U-N-D-S).

And just to prove that games like these aren’t a product of the past, I highly encourage everyone to check out Kerbal Space Program. For all that I thought I understood orbital physics, I never really grasped them intuitively until playing this game, which is also a whole load of fun (especially if you like explosions, and let’s face it, who doesn’t?).

At this point, you might be noticing the common thread: they’re all simulations. This means that what the game teaches you isn’t something tacked on afterwards, like a quiz or a cutscene: it’s a fundamental part of the game mechanics. By building a game around a simulation, they’ve improved both: the simulation provides depth to the game, and the game makes the simulation entertaining. And because the game mechanics revolve around the simulation, simply playing games like these tests your understanding of the simulation in a way conventional educational curriculi are simply incapable of.

This is actually a similar message to the one we took from Mythbusters earlier: you don’t have to make the science/learning fun/cool, like awesomeness is something you have to tack on to science in order to sell it, or worse: like science is mutually exclusive with awesomeness and you need to sacrifice one for the other in order to be accessable (they know know who they are). The science is already awesome: what makes a Science Communicator good is their ability to show us how awesome it truly is.

That’s what we need to get across. We shouldn’t be teaching people with games as if you can just pour information into their brain: we should be showing them how awesome the information is, letting them drink it up of their own volition, and then telling them where they can find more awesomeness of the same nature. That’s what the best communicators: the Neil DeGrasse Tysons, the David Attenboroughs, the Carl Sagans, keep telling us.

Already awesome.

And, ultimately that’s what I’m trying to do with Species: not create a game that’s awesome and scientific, but create a game that’s awesome because it’s scientific.


“The optimism, IT BURNS!”

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Why aren’t carnivores evolving?

Here’s a new type of post: analysing the behavior of Species: trying to work out why things are happening the way they are, and using what we learn to improve the mechanics for the future.

Eventually, I’d like to integrate this sort of questioning into the game mechanics themselves: make science itself a game mechanic. As an example: say you want to know what a specific gene does. The game could require you to earn a certain amount of “data points”, or something equally abstract, and then spend them on that gene to discover it’s function. And that would make for a decent game mechanic, encouraging you to earn data points, which I assume you’d get by performing actions like creature sampling. But it wouldn’t be science, it’d be economics. And no offence to economists and EVE Online players, but unless your entire country is falling into disrepair because of too many short-term investments and cuts by the rich and powerful, economics is boring.

Alternatively, you could take a creatures genome, apply heavy mutation to that gene specifically (via a targetable radiation gun or something equally awesome), then clone a creature from that genome and see what’s changed. You could then go back and label the gene. Observation, hypothesis, experimentation and conclusion: that’s science, turned into a gameplay mechanic. And best of all, the reward is the logical result of the experiment: you can now target that feature specifically when you manipulate genetics.

Of course, the game can take care of some of the slower bits: for instance, an ‘autocompare’ between parent and child, which detects and names the heavily mutated gene for you, would keep the game moving better than if the player had to visually inspect for differences, and then go back and type in the function of the mutated gene themselves. Afterall, I’m not interested in data entry, I’m interested in science. (On the other hand, if naming gene functions is your thing, this would tie in well with the ‘naturalist mode’ idea that Reprieve suggested)

Anyway, I’ve managed to go off track before I even got on the track in the first place. I don’t even think I ever even [i]saw[/i] the track: I’ve been doing donuts in the carpark this whole time. Shorter me: I’ll be looking for any opportunity I can to integrate science and gameplay. In the meantime, I can still do science on the game out here in meta-space.

And what I want to know today is: why aren’t carnivores evolving?

Play the game for a bit and the best you’ll probably get are omnivores, despite all the giant piles of delicious meat lying around. Something’s going on: hunting and scavenging should be viable strategies, so why don’t carnivores survive?

Part of this is simply the power of stupid. Creatures are often too dumb to seek out plants, let alone other creatures, let along dead other creatures. Rest assured I’m working on this for 0.4.1, and future versions will have significant AI improvements on top of it.

But it’s not just that. You may have noticed that probably the most viable strategy among the creatures is to find a large tree and just sit there eating it, staying at max energy and pumping out 10 or more babbies until it’s gone, like the Quiverful movement’s vision of the ideal woman.

So why doesn’t this work for carnivores?

Is it just simple math? I’ve been meaning to check the math for this post, but haven’t gotten around to it (and I don’t have the game in front of me right now durn it), but I’m pretty sure it isn’t: meat energy is highly dependant on the life of the dead creature, but everything they eat during their life is converted to meat, so there should be loads of it in each meat pile.

But there’s another factor. You see, creatures have an ‘eat rate’ value, which differs between vegetation and meat. The malleable, delicious, high-protein meat can be consumed much more rapidly than the tough, horrible, inefficient plants.

Seriously. We really appreciate it.

In theory, this serves as a reward: unlike grazing, creatures can get a lot of energy in a short amount of time from meat. In practice, it became the opposite: the above strategy, where creatures maintain their energy at max by eating from one source until it’s completely consumed, works for only as long as the source lasts. And meat doesn’t last much time at all.

This is an interesting phenomenamnicon in it’s own right: the ecosystem actually encourages slow-eating creatures, which is the opposite of my expectations. But it makes the game less interesting and actually discourages evolution, so I believe a few changes are in order.

There are two approaches I could take to this:

– The balancing approach. Consuming meat faster is preventing carnivores from developing, so I’ll make them consume meat slower. I’m not going to take this approach, partly because it’s illogical, but mainly because it utterly and unforgivably violates my primary design goal. I’m developing the environment, not the evolution: I shouldn’t be ‘balancing’ selection pressures to make the things that I find interesting develop. That’s not what the game is about! Aargh!

– The expanding approach. It’s not consuming meat faster that’s preventing carnivores from developing: it’s that I’ve failed to model the animals eating behavior correctly. It’s logically impossible for them to continue to eat when their stomach’s completely full, so why are they doing so? I need to fix that fundamental bug, rather than working within it to achieve outcomes I percieve as desirous.

Of course, I probably won’t be able to fix it immediately. Making them make a new decision when full is a relatively simple thing to implement, but it will lead to a whole bunch of new bugs to test for, and will have significant implications for their survival (making it much harder on the early, blind creatures, and much easier on the sighted random creatures). So I’ll probably hold off implementing it until 0.5.0, which will also implement grazing, a feeding strategy that doesn’t depend on sight and so makes it easier on the starting creatures.

(EDIT) Actually, you know what guys? Screw everything I just said, I’m implementing it anyway. I might as well: it’s not like I’m not fixing AI bugs already, and with those fixes I’ve already completely screwed over the game balance.

(EDIT Mk2) What the flip. The blind starting creatures are surviving better now? How are they… I don’t even… what is this? What the hell even is this? YOUR GOD DEMANDS TO KNOW.

(EDIT Mk3) Okay, it looks like this might be one of those hilariously unintuitive results the Anthropomorphic Personification Of Evolution is so fond of. I could explain what I think is causing it, but that would deprive you guys of the challenge of working it out yourself. So come on: tell me in the comments why they’re reproducing better now that they leave tree’s alone when they’re full! I’ll post what I think is the answer a few days from now.


“Phenomenon”: singular. “Phenomena”: plural. And the “Phenomenamnicon” is a book used to summon a particularly vague eldritch abomination. Geez, it’s not that hard to remember!

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Artificial DNA

Naturally, in a game like Species, the genetic code and the format of the genetic code is going to come up. Of course the player is going to want to see and manipulate the genetic code directly. But that means giving the creatures a genetic code to manipulate in the first place. That’s… well, it’s a bit harder than it sounds…

Here in reality, DNA strings are made out of four molecules: A, T, G and C.

[/pedant: technically DNA strings are made out of pairs of these molecules (AT, TA, GC and CG), which form the double-helix ‘ladder’ shape we’re all familiar with, but it’s easier to represent the code by just reading the molecules up one side of the ladder]

Now here’s the first place people get caught up: DNA isn’t a “code”. It doesn’t really abstract or represent anything: it’s just a long string of molecules, one after the other. When a transcription molecule runs along the edge of the string, it transcribes each set of 3 molecules (codon) it comes to into basic amino acids through molecular processes, and those acids later go on to form proteins.

We often use human terms like “code” and “compile”, “instruct” and “transcribe”, a lot when we’re talking about DNA, and those terms are often misused by anti-evolutionists to imply that these structures had to have been designed. This is purely a semantic argument, but it can be a convincing one: these terms all imply slightly more than what they refer to. The thing to remember is that they’re analogies. The transcription molecule isn’t reading a codon into memory and outputting an acid based on that code: it’s simply reacting to the molecules it comes across, and that reaction happens to result in a specific amino acid.

As one example of a difference, genetic code is a remarkably poor and inefficient way to store amino-acid information, even before we hit the whole “junk dna” issue. Codons have 3 characters, so there are 64 codons. But there’s only 20 amino acids. This means that there are multiple codons for every amino acid. Then, just to make it even more confusing, some amino acids can be created by as many as 6 different codons, while others can only be created by two.

From a purely design perspective, especially a design perspective motivated by a belief in an omnipotent creator of the universe and all associated laws (they know who they are), this is stupid. It’s like inventing the decimal numeric system and then deciding to have 4 symbols for “7”, or making a binary system with 12 symbols for 0 and 8 symbols for 1. It makes no sense.

But from a biological and molecular standpoint, this redundancy is probably responsible for life’s ability to develop into so many and varied forms. Any codon will create an amino acid: returning to the programming analogies, there are no exception-inducing combinations that will make the code crash or stop responding. The molecular compiler is infinitely robust.

And an infinitely robust compiler might be significantly less efficient, but it’s also far more versatile.

I’ve gone rather significantly off-topic, haven’t I? One moment…

[Drags post back to topic by its neck…]

The genetics in Species differ from real life DNA in that they aren’t stored as a series of characters. This is another case where I had to balance the limitations of a computer simulation with sticking strictly to reality. An accurate simulation of genetic code and amino acid generation, while interesting from an academic standpoint, wasn’t the game I had in mind. In addition, computers have all sorts of problems with string manipulation and memory management. It’s not impossible, but representing a creature’s genetic values as a string would mean far less CPU for other aspects of the game (like simulating and drawing more creatures)

So instead, the internal “genetic code” of creatures in Species is actually a list of numbers.

But that doesn’t mean I don’t have a genetic code in the game. In this case though, and unlike real life, the genetic code is quite literally a “code”: it is generated from the original number list using a fairly simple cryptographic key. It can be used to reconstruct the original numbers and thus clone the creature.

So the genetic code you see in game is not the actual genetic values used to simulate the creatures, but it does represent them. So what are the practical effects of this?

Mainly, they make direct genetic manipulation highly inadvisable for a variety of reasons, which I’m about to go list. But first, I’ll say this: I want direct genetic manipulation in Species. Maybe not immediately, but definitely within a few versions of the alpha. So these problems are all things I need to sort out. Now, on with the show…

1. Crashing.

The most obvious problem is that the compiler is not completely robust. With the right sequence of characters, it can be made to crash: for example, a number with two decimal points, or none. This is something I’m going to have to invest time in. Making the compiler robust enough to compile a gene list from any sequence of characters would be a great advantage for the game, because it would pave the way towards direct genetic manipulation.

2. Sensitivity.

A less-obvious result of this system is that making small, direct changes to the genetic code could have ridiculous effects on creature physiology. For instance, moving the decimal place could instantly make a Godzilla-like creature. Since mutations are normally applied to the numbers, and not the code, this doesn’t happen in the games ecosystem: but editing the code directly? Anything goes.

There are several artificial solutions to this problem. I could restrict what codons can be changed: make it so the player can only move decimal places by small amounts. Or I could give artificial ranges to genetic values, so if they go outside the arbitrary ranges the creature dies when it’s born. A third way would be to make the direct manipulation work on the numbers the code represents, rather than the code itself.

I don’t really like any of these options: they restrict the player’s freedom to type whatever they want into the genetic field. The option also exists to make the compiler itself make changes in the background before generating the creature from them, to give the illusion of freedom but make sure ridiculous numbers don’t get out of hand (indeed, this is what will happen to the aforementioned numbers with two decimal points), but I’m not too fond of that idea either: it feels like cheating because no matter what I do the min-max ranges are going to be arbitrary.

There is a third option, though I’m not sure whether it will help: encode the numbers differently. Currently the cryptogram is simple: assign a different codon to each possible number and symbol and write the code out directly. It’s possible that a different means of encoding (for instance, prefixing the number with it’s exponent?) would be less sensitive to direct manipulation.

I’d love to hear anyone’s idea’s for this by the way: in fact, all of these are problems I haven’t definitively solved yet.

3. Order

The genetic code is just a list of numbers: it’s the order of the numbers that decides which gene they affect. This makes the entire system extremely sensitive to insertion and deletion mutations: a “9” won’t affect much at the very end of a 7-decimal number, but an insertion mutation could easily push it into the front of the next gene’s number with obvious consequences. Even worse, inserting or deleting a single character rather than two would completely change the meaning of the codons after it, and probably result in some sort of missingno equivalent.

Thankfully, this is something I have already taken a few steps to alleviate. I have stop codons in place: an insertion mutation early in the torso segment, although it will (probably dramatically) affect the entirety of torso, will have no effect on any feature after that because there is a stop codon at the end of the torso segment.

I’m considering even taking this a step further and including stop codons between many genes, to further reduce the impact of insertion mutations.

4. Mutation

As I mentioned in point 2, mutations in Species act on the numbers behind the genetic code, not the letters in front of it. This isn’t really a problem, but it can look strange when you’re examining the genetic code. I’ll show you what I mean with a quick diagram:

Parent: Gene 1: 1.0000000 AGTATGTGTGTGTGTGTG
Child : Gene 1: 1.0128639 AGTATGAAAGCAGGACCG

With a tiny change (~0.01) the entire genetic string now looks completely different. This is especially noticeable in game, when you’re looking at the “species average”.

Fixing this is actually a very interesting task, from a mathematical perspective. Currently, the mutation amount of each gene is determined by a simple random number generator, giving a nice even probability distribution. My initial thoughts were to simply round to the highest significant digit, so 0.465… would come out as 0.5, while 0.007324… would come out as 0.007. Mathematically inclined readers may have already worked out the problem with this, but if not why not see if you can work it out before moving on…

Worked it out? What this does is gives a 90% probability that a specific codon/digit will be the one modified, and a 99% probability that it will be one of the first two. You’d likely never see some of the later digits mutated, and that’s not the way mutation works.

My second idea was to take a ‘per codon’ approach to mutation: each mutation randomly picks and affects a single digit of the number.

Parent: Gene 1: 1.0000000 AGTATGTGTGTGTGTGTG
Child : Gene 1: 1.0080000 AGTATGTGCATGTGTGTG

From a genetic perspective, this looks pretty good. Genetic differences between parents and children are much smaller and more logical. But from a numbers standpoint… can you work out the unintended consequences in this case? It’s a bit more complicated than the last one.

Here’s my problem: ~86% of mutations are now going to have an effect of less than 0.1: 71% will have less than 0.01 effect. All I’ve really done is replaced the constant probability curve of the random number generator with an exponential one, so the majority of mutations are now ridiculously small.

This is an interesting situation: in some ways it’s actually a good thing. It means that creatures can have higher mutation tolerances and rates, because the majority of mutations are going to be tiny with the occasional large ones mixed in. On the other hand, it may slow down evolution. Based on what I’ve seen in Species (and what I’ve read in reality), I believe “lucky mutants” have a far lower influence on population change than the slow-but-constant adaptation of the entire population.

I’m currently considering a mix-and-match approach: apply both of these methods and hope that the individual digit changes mask the fact that certain codons are far more susceptible to mutation than others. But I’m also open to suggestions: if you’ve got any other ideas, I’d love to hear them!


“Molecular Genetics doesn’t lend itself particularly well to comedy, does it?”


Mutations Don’t Increase Information

Warning: the following contains high levels of derision, for which Qu apologises. He is apparently not the biggest fan of the Intelligent Design movement. Either that or he’s just grumpy today.


Another fun argument from the antievolutionists who fancy themselves smarter than your average bear, this one has become much more common since the rise and subsequent fall of the Intelligent Design movement and the release of Nazi propaganda film Expelled: No Intelligence Allowed*. It’s patently absurd on the very face of it, but that doesn’t stop it from getting about. Here’s a quick, dirty refutation:

AGCT => point mutation => ACCT

This mutation could have all sorts of effects, but it has clearly changed the information content. Of course, how it has changed the information content depends on how you define “information” (something the anti-evolutionists are very careful never to do). There are fewer characters used, so it’s got less information (by one definition). But it’s now more ordered, and it could replace the word “Act” and be legible to an English-reading person, so it’s got more information (by another definition). And there’s still the same number of digits, so it’s got the same amount of information (by yet another definition). But that’s not particularly important for the argument. What is important, is this:

ACCT => point mutation => AGCT

The above can be applied to almost any mutation. Point mutations can be undone by new point mutations, addition and copy mutations can be undone by deletions, deletions can be undone by additions and copies.

Mutations can be undone by subsequent mutations. This is a fact, an observation, a truth, something indisputable. It happens. So if an anti-evolutionist claims that mutations can decrease total information content, then they’re also claiming the inverse: that mutations can increase it.

There is a second even more patently absurd variant of this argument that says “mutations can’t be beneficial”, and usually cites things like sterility and cancer. Yes, cancer. The mutation that causes individual cells to reproduce very rapidly. I would hope I didn’t have to explain that a multicellular organisms “cancer” is a single-celled organisms “beneficial mutation”, but I’ve long since stopped being that naive. The moment you assume a certain level of minimum-intelligence amongst others, one of them will go out of their way to prove you wrong.

“There are two things in the universe that are infinite: the universe, and human stupidity, and I’m not sure about the former.”
– Albert Einstein

Now, for the sake of fairness, I should point out that the people making this argument do have a glimmering of a smidgeon of a speck of a fraction of a gram of an interesting point, though it’s highly doubtful they actually noticed it. The point in question is this: most mutations are indeed harmful or neutral. Very few are beneficial.

Now for an denialist, that’s enough. Case closed, end of story, entire theory of evolution completely disproven because we found some small problem and didn’t bother/didn’t want to/aren’t capable of thinking about it in any depth. For a scientist, it’s a challenge. Why are most mutations harmful? Why doesn’t this stop anything from evolving?

The answer to the first question is a combination of factors, but the primary one is this: we’re already using most of the beneficial mutations for our environment. We’ve stabilised and plateau’d, at least in comparison to the rapid evolution associated with population explosions. I’m actually seeing this in action with Species right now: creatures will very quickly develop simple legs and features, but evolution will then slow down significantly.

The second question, ”Why doesn’t this stop anything from evolving?” can be answered with two words: natural selection. Natural selection works as a ratchet: allowing movement to the left, but not the right. If a beneficial mutation occurs, it will help its host survive and result in it being kept and propagated throughout the population. The hundreds of negative mutations that also occurred were not kept: their hosts didn’t survive, so they were culled from the population.

Not that any of this means anything to the propaganda artists. “Mutation doesn’t increase information” is a useful soundbite because, as I’ve just demonstrated, it needs quite some time to demonstrate to be false. In the time it takes a scientist to refute it, the denialist can have repeated it and other soundbites like it to hundreds of people.

After all, why drop a perfectly good persuasive soundbite just because it’s dishonest?

So… yeah… meh, I dunno… this post kinda got out of hand… whatever…

*footnote: Oh dear, I’m sorry. Expelled features lots of images and references to nazi’s and is a propoganda film. These two things are not connected in any way. Well, okay, they are, but not in the way my wording implied. I apologise for any confusion my completely accidental wording may have caused, which is more than the films producers have done.

Uh oh… he got through an entire post without referencing time travel, eldritch abominations, zombie armies, orbital weapons platforms or the end of the world. That’s a bad sign. I’d better go check the cynical bastards not about to do something stupid, like join a secret society or a cult or a political party or all three…

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It’s just a theory!

Easily (and sadly) one of the more common claims amongst the… err.. “less intellectual” of the anti-evolutionist crowd, “It’s just a theory!” is roughly analogous, at least from my perspective, to “I don’t understand science, therefore Evolution is false!” or if I’m feeling less generous “Call me a moron again, I like it!”

For those looking for a simple, fast response to this claim: “So is [insert germ theory/atomic theory/the theory of gravity and/or the theory of general relativity]” suffices. Depending on the tone used, this simple statement can be expressed as anything from polite confusion (“But, hang on, isn’t gravity “just a theory” as well?”) to pure, scornful, unadulterated sarcasm (“By god, you’re right! What have we been doing teaching germ theory to the poor innocent children all this time? Teach the controversy!”).

For a more complex answer, you have to realise the common misconception this claim stems from: that “theory” in science means the same thing as it does in general usage. In general usage, “theory” can refer to anything from “If I keep working this hard I might just get a pay raise” (false) to “maybe if I show that person my gigantic genitals they’ll sleep with me” (false) to “I bet I can grow an aquatic super-soldier clone army by combining cuttlefish DNA with human DNA” (true). In scientific usage, it is just a wee bit different.

Many people believe that an idea starts as a “theory”, gathers evidence to become a “fact”, and finally gets proven to become a “law”. This flawed idea also explains a variant of the “it’s just a theory” argument, where the user claims they support teaching evolution, but not “as fact”. It is also part of the reason things like the Laws of Thermodynamics are so treasured by certain subgroups of evolution-denialism: they are seen as the immutable truths of science, the pillars on which the Lab Coat Of Knowledge sits, all shiny-white and glowing.

Back in reality, really real realistic scientists would stare at the above diagram with the expression of one trying valiantly to fathom just how anyone could possibly believe that. And then they’d laugh hysterically at your ignorance. Unless they were polite scientists (through selective breeding and various indoctrination techniques we finally managed to produce one of these. Unfortunately, attempts to introduce it into the wild populations were… unsuccessful. And messy).

Fact: A fact is an observation, a data point, something we can point to and say “that happens”. That things fall at a rate of 9.80665 m/s on earth is a fact. A fact never changes: it is an aspect of reality that must be accounted for. No matter how much we refine and change the theory of Gravity, it will remain true that things fall at a rate of 9.80665 m/s on the surface of the earth.

Law: A law is a mathematical representation of a physical phenomena. That things are subjected to the force of gravity at a rate of of Gm1m2/r2 is a law. A law, contrary to the above misconception of science, can change when our measurement tools get better and we discover that the law is only mostly correct. This is what happened to Newton’s laws (like the one above), and we’re now seeing it happen to Einstein’s laws.

A fact does not become a law: they are two entirely separate creatures. Laws can be derived from and supported by a number of facts, however.

Theory: A scientific theory is something far bigger and more important than either of these: it’s an explanation that ties together dozens, if not hundreds or even thousands of facts and more than a few laws as well. A theory grows in detail and scope as facts are added to it over time, and its strength is in them: new facts can change the specifics of the theory, but it takes something radical and contradictory to overthrow it entirely. And to make it even harder for the incoming theory, it has to provide an explanation for everything the old theory did.

If you want the scientific term for a drunken guess, that would be “hypothesis” (though, in all fairness, not all hypothesis’ are drunken guesses). Evolution has not been a hypothesis since Charles Darwin was first realising it: by the time he released Origin of Species it was a fully-fledged Theory, and it has only gotten stronger in the 150 years since then, thanks at least in part to the untiring efforts of its detractors attempting and failing to disprove it.


For the record there isn’t a warlike civilisation of cepholopod-men off the coast of Florida and it wasn’t us who put them there.

PS: We’re still having technical difficulties of the “computer == x_X” variety, so todays illustrations are brought to you by letter’s “F” and “U”.

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Non Chonological

Also in the murky primordial soup that makes up societies perception of evolution are the claims of the denialists: those who, for religous, political or financial reasons, peddle lies and falsehoods to undermine the claims of science. I’m not going to try to dignify these lies by pretending they are honest mistakes, or alternative interpretations of the evidence. Those who originally came up with them knew they were lying, and even if they didn’t: this is the information age. If they couldn’t spare the time to fact check their claims, if they weren’t willing to listen to the many internet skeptics who are drawn to their beacons of stupidity like peppered moths to flame, they were being willfully ignorant or worse.

So to summarise: if you believe and repeat the claims of the denialists, you are misinformed. But if you are one of those who make up the claims of the denialists in the first place, you are a liar. And as much as my tactful nature is telling me not to post this, that accusing actual people of actual, willful dishonesty is too actually offensive and not accommodating enough for a polite conversation with my opponents, I’m not going to water it down. I’m not going to sacrifice clarity for nicey niceness and ponies. This needs to be said, and it needs to be said in an authoritive and commanding tone of voice. Since I’m not actually capable of that, I’m just going to put in all-caps:


There are a rather rediculous number of them out there, both the lies and the liars, so I’ll be contributing my personal action to the noble and valiant fight for truth by either screaming wildly and frothing at the mouth, or bashing my head repeatedly against the nearest solid object. This seems like both a constructive and intellectually valid debate tactic.

Alternatively, I suppose could just go and savagely refute some of them on a random blog on the internet. You know, to save on head trauma costs and time wasted in a mental asylum…

Unleash the teddy bear.

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Non Chronological

Fun fact: this blog isn’t only about Game Development! Who knew?

A common response to creationists and creationism which I’ve seen during my debates (don’t worry, I’m not going to start ragging on religion: just the anti-evolution sentiment rampant in some minority groups), is to reference the “mountains of evidence” for evolution. Now, technically, this is a respectable response: 150 years worth of supporting data certainly qualifies as ‘mountains’ in my opinion. But the problem is that it’s not particularly persuasive: whether or not the evidential alps exist doesn’t matter one iota if nobody’s going to be inspired to go climbing them

So that’s why specifics are called for. Observations explained elegantly by the standard model, predictions based on it that turned out to be true, contradictions that cannot be reconciled by any other extant hypothesis or theory. They’re not rare, but they can be hard to extract from the mass of technical vocabulary written by scientists who don’t rate yet another piece of evidence for evolution very highly. The scientists already know about the ridiculously huge mountain of data they are simply adding to. Proving an already-proved theory doesn’t really excite them.

But with so much misinformation, quote-mining, selective-reporting, false-claims, hoaxes, wilful ignorance and, when all else fails, blatant lying out there, on the internet and in real life, such observations do excite me. Mostly because I’m kinda egotistical and I like being right.

So I’ll pick and choose some of the more interesting items here and there to discuss, to add a bit of variety to this blog and maybe to stir up a bit of trouble.

What does he mean “kinda”?

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