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I like the term "mathiness" for this, because it seems like exactly the same kind of thing that's complained about in economics under that name. Namely, you cl
by evanpw 11y ago
I like the term "mathiness" for this, because it seems like exactly the same kind of thing that's complained about in economics under that name.
Namely, you claim (necessarily without proof) that a real-world phenomenon is analogous to a certain mathematical model. Then you prove some things about the model. Finally, you claim that the things you've proved about the model are also true about the real world. It doesn't matter how rigorous the mathematical steps in the middle are, the strength of this kind of argument is determined by the first step: how strong is the analogy between the mathematical system and real life?
In physics, we can build models where the analogy is very strong: when we try out new situations in both the model and in real life, we get the same answer in both. In (macro-)economics, we typically do something weaker, where we start with a set of models determined by intuition, and then choose the one that gives results most similar to real life in the situations we've already seen. Prediction of future events is really difficult.
It seems like the approach in Taleb's paper is even weaker. We choose a model based on pure analogy, explicitly disclaim the usefulness of evidence ("While evidentiary approaches are often considered to reflect
adherence to the scientific method in its purest form, it is apparent that these approaches do not apply to ruin problems"), and then attack anyone who says that we've done something other than prove a mathematical theorem about the real world.
It's too bad, because underneath all of the "mathiness" is one of the strongest arguments against GMOs that I've heard (and I say that as a GMO-eater). It just should have been an opinion piece in the NYT rather than a research article on the arxiv.
- pcrh 11y agoFrom the point pf view of a biologist, his argument against GMO is pretty weak. He is essentially saying that we don't know, therefore there could be a large negative consequence. However, he has failed to grasp, or educate himself sufficiently, that gene transfer between organisms is in fact a common occurrence in natural ecosystems [0]. Further, at this moment at least, the genes that are being transferred artificially are in general not genes that would cause havoc if they somehow became mobilized from the species into which they were introduced. By way of example, the protein Bt confers resistance to insect predation,it is originally derived from a bacterium that killed insects in a parasitic cycle [1]. Plants, generally, have hundreds or thousands of ways of resisting insect predation, and adding one more to the mix is not going to devastate any ecosystem, at least not in the long term. [0]https://en.wikipedia.org/wiki/Horizontal_gene_transfer https://en.wikipedia.org/wiki/Horizontal_gene_transfer [1]https://en.wikipedia.org/wiki/Bacillus_thuringiensis https://en.wikipedia.org/wiki/Bacillus_thuringiensis
- littletimmy 11y agoThe argument that GMOs are OK because something similar happens in nature is a poor argument. Just because there exists in nature Process X that leads to Outcome Y, does not mean that a man-made Process Z that also leads to Outcome Y is good. Nature has a way of eliminating failures; humans have a way to offload failures off to someone else.
- dvanduzer 11y agoHumans are part of nature, and you might be confusing arguments with logic. The effective aspect of the scientific method that separates it from scientism is falsifiability. The comparison for falsification here is "GMO farming" versus "status quo farming"; nowhere is nature involved.
- Tycho 11y agoI think the argument is that 'status quo farming' engages in localised tinkering in a way which is similar to what occurs in nature, whereas GMOs are examples of top-down engineering which will be imposed across the whole planet.
- evanpw 11y agoI am not a biologist, but I think of the ecological risk of GMO as something akin to AI risk: Not a serious risk at the current level of technology or in the near future, not sufficiently dangerous to stop research or forego the benefits of today's versions, but serious enough that it would be good for a few people to be thinking about it right now.
- a_bonobo 11y ago> but serious enough that it would be good for a few people to be thinking about it right now. Of course, GM-technology is a huge area of tools, tools which are being used in all kinds of situations and outcomes: modifying bacteria to create insulin or washing powder ingredients (both of these have been done on an industrial scale for decades now); modifying plants to have a better defense against various pests (being done with Bt cotton and soy IIRC), or just to glow in the dark (there's a kickstarter), etc. These are all more or less "good" outcomes of GM, but you could also use GM for "bad" - you could make maize express a human neurotoxin and spread that plant in order to attack a country (a not very directed attack). The only thing that's stopping you is that it's still very hard, expensive and time-consuming to do so.
- DyslexicAtheist 11y agoI'm no mathematician or physicist but have often wondered of Godhart's Law[0] during testing of complex distributed systems. If we consider that Quantum Theory also says qbits are both off/on at the same time until measured, one might conclude the result of any data-science experiment becomes outdated as soon as it is performed. Aren't all such (big-)data models (e.g. predicting natural disasters, stock-markets, global-warming, effects on GMO, ...) an attempt on measuring "real life"? And so can not be measured accurately because as soon as you take your snapshot data points and events inside the model will have moved on to a new state? Not that we shouldn't try to build these theories but accept them as theories or "soft-science" and not facts. As soon as we have simplified a complex hard to understand system into a chart we tend to ignore that these "facts" represent at best a snapshot of reality (one version of it) from a specific point in time but certainly not the future. [0]https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law