I am often told that I should keep my nose out of other science domain's business because they know more than I do. However, I think when they start building their science on top of computers, I start getting a say again. Here's what concerns me about this increasing use of computers:<p>It seems like the vast bulk of these simulations are iterative, and therefore subject to mathematical chaos. How many of these researchers have any clue what door they are walking through? How many of them know what a strange attractor is? I'm sure the answer is non-zero; I'm equally sure the answer is nowhere near 100%.<p>Small errors cascade even if you consider a non-chaotic classical model. (That is, not that there is such a thing as an iterative model that is not potentially subject to chaos, but rather than even if you don't understand chaos you can see that small errors can cascade. Chaos just makes it worse, and weirder.) A simulation will have bugs like any other large problem. A non-programmer approaches bugs by banging on the program until it seems to generate expected results. (About 50-80% of programmers do that too.) Therefore, many of these simulations are simply reflections of the simulator's expected result, due to the effect of the researcher's selection mechanism running on the results of the simulations they run. How do we verify that this is not the primary factor in the result of the simulation? <i>This need not be conscious</i>. It need not be ideological, either; I can easily envision a simulation that "should" return a boring or trivial result being monkeyed with until it produces something "interesting", because the simulators think the boring result should not obtain.<p>A lot of algorithms you can use in these simulations are fundamentally unstable when used iteratively; some exacerbated by floating point errors, some mathematically unstable even with perfect real numbers. How many of these simulations use something unstable without even realizing it, given that it could take a professional mathematician to work out whether that's the case? Even algorithms thought to be stable and reliable can fall apart under pathological situations, and one of the odd things about mathematics is just how often you end up hitting those pathological situations when programming; far more often than it seems like should be the case.<p>In information theory terms, a simulation can not contain more information that the sum total of the input data and the content of the simulation algorithm. How many simulators understand the full implications of that statement? <i>I</i> sure don't understand the <i>full</i> implications of that, but what I do understand makes me pause a bit. Very simple simulations with rules that can be verified and initial data that is very solid I can deal with; for instance, I like the cell-automata based social theories that show the spread of information or political views or something, especially when it is clear the researchers understand that it's only an approximation. But as the initial data starts getting sketchy or the simulation grows enormous, I start getting nervous about the actual information content of the output. Just because the output <i>appears</i> to be information doesn't prove that it is. It is vitally necessary to be able to check the simulation against <i>real data</i>. For instance, physical simulations of, say, cars crashing can be verified. How many simulations can actually be verified, though? Frequently the reason computers were reached for in the first place is the inability to do the real experiment. Any simulation that can't be verified should be <i>presumed</i> worthless by default. How often does that happen? (It's 20-f'ing-10 and "the computer said it, it must be right" still runs rampant through our culture....)<p>And of course there's the whole reproducibility issue, where the <i>absolute bare minimum</i> for science would be to publish the <i>full</i> simulation program, <i>all</i> data, the necessary invocation and compile instructions to bring the two together, and all necessary information to understand the input and the output. Clearly, this is not something that fits in a journal paper, but how often does this happen at all?<p>No, I am not referring to any specific discipline here and in particular I'm not actually referring to climate science. I'm nervous about the whole movement towards simulations in general.<p>Note that I'm not reflexively against the idea. Meet these bars and I'm happy; give me enough data for reproducibility and verify that your simulation is in fact simulating something real and corresponds to reality and I am happy. (Many physical simulations fit in here.) But as more disciplines jump in I am concerned that these bars are not well understood, and I'm seeing ever more press releases about simulations that can't possibly meet these bars.