Someone is putting an idea out there to be tested in a scientific way. They are willing to be proven wrong. They are taking a risk. Someone fitting their theory to their empirical result after the fact takes no risk. Nobody can prove the theory did not come first.
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The former one tries to find causality, which is what we are after. The latter one proves nothing, just correlation.
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As well as over fitting, people also disregard data as they apply their belief reenforced bias, "I select the data to observe"
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This is so true for RL, the signal to noise ratio gets worse due to scoreboard driven research, too many hyper-parameters, and high compute power.
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As long as the idea is falsifiable. So many idea papers aren't.
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Any example?
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Einstein's general relativity paper is a good example of a falsifiable idea paper: "if I'm right you should be able to see light bending around the sun."
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I should have been specific. Any example of the non-falsifiable ones? What makes them so. Are the ideas such that we cannnot design experiments to prove/disprove them?
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String Theory is an example for today and the near future. Granted, it may change in distant future.
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What do you mean by "post-rationalized randomness and overfitting"? If an experiment serves the sole purpose of proving something it has serve their purpose and it's not overfitting right?
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Assume the "idea" that 6-sided dice always give a 6 up when thrown. You choose dice until you find biased ones; throw them until you record a streak of 10x consecutive 6s; then present these sole results to support ("prove") the initial claim. A lot of ML papers work like this.
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The place where I come from that is called fraud. had no idea.
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My example was obvious, reality less so. I notice sometimes authors themselves don't know that. It's a very heterogenous mix of engineers, data scientists, flavors of PhDs that write papers. Causes are many: lack of training in scientific method or statistics, hidden complexity…
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Maybe the ocasional promotion lol
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I think you start writing posts back. Better for you and others.
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"idea papers" cloud give you a very good questions . Or a very good definition of the problem . That's why many data science and Big data projects fails . The data no talk you need a hypothesis or idea to test .
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Idea papers with experimental evidence are the real paper.
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Unfortunate conferences don't accept idea papers or that there arent any conferences for that genre. Are there particular circles you find idea papers written/post more?
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Eventually every scientist gets to this place if they remain a scientist.
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