The right to petition doesn't specify a medium, though. If people can petition via snail mail, then that right has not been abridged en masse, even if it hasn't been enabled on a specific technology.
This is like defining legal abortion by viability, as technology keeps driving viability earlier and earlier in the pregnancy.
Does this mean that politicians can no longer use traditional one-way media? Are they forbidden from using TV or radio broadcasts where listeners aren't given an opportunity to reply on the same platform?
This seems like a ruling that depends on the specific technology being looked at, rather than any universal judicial principles.
I was just trying to say that just because weather is chaotic there are still things one can predict such as a temperature difference between winter and summer going back hundreds of years. This just shows that the claim that weather is chaotic is not sufficient to show that "climate" is impossible to predict.
But let's be specific - the predictability is proportional to the usual variability and predictability of the cycle. That is to say, we can predict day/night cycles, or seasonal cycles, because they are on the order of magnitude of 30C, and are highly regular. We're not so good at predicting AMO or ENSO, on top of the fact that the order of magnitude is less than the more predictable ones.
To be clear, the expectation that we can accurately predict global average temperature a century out, when it only varies by 1.0C, is *really* exceptional. We're looking at fourth and fifth order terms here, rather than the very impactful first and second order terms.
Unfortunately, in many areas, we need the experts to police each other.
I am agreeing with you as hard as I can - and this speaks to culture. Our scientific culture should not be one of activism, it should be one of ruthless skepticism and policing.
How do people choose the right experts especially given all the questionable science that is out there.
My heuristic is simple - ask them for a necessary and sufficient falsifiable hypothesis statement:). Those that can provide it (regardless if I can personally understand it), get past the first BS detector test:)
And it becomes hard for non-experts to tell what is good versus bad.
Again, agreeing with you as hard as I can. Bastiat once wrote about the internal inconsistency of big government democracy - the masses were assumed to have perfect judgement in deciding who their leaders should be, but once that leader was in place, the masses were assumed to have terrible judgement about their own lives and needed these perfectly chosen legislators to make laws to restrain them and mould them to perfection:)
At the same time they will look at what happened to the model, and look in a principled way to see what could be added to better match reality based on the existing laws of physics.
The problem here is that nowhere in this procedure do they challenge their own basic assumptions, or whether or not their macro application of micro laws of physics is appropriate. *That* is the key here - not to add enough things to buttress your central conceit, but to have a way to prove, or disprove, your central conceit, whatever it may be.
Put another way, you could build a model that attributed warming trends to human H2O emissions. You could build another model that attributed warming trends to human CO2 emissions. Without a necessary and sufficient falsifiable hypothesis statement for those competing models, you can extend *either* of them infinitely with additional adjustments, and never figure out which competing central conceit was correct.
It would be very limiting to restrict science to "small" models.
True, and I get your point about "all science being models" - but here's my challenge: regardless of "small" or "complex", any scientific model must have a necessary and sufficient falsifiable hypothesis. It may be that creating such hypotheses for complex models is simply too much work for it to be practicable (whereas it's trivial for "small" models), but I don't think you can get a pass on the scientific method just because you've decided to use a complex model.
Here's an interesting aside for you - there are actually some models which produce different results when run on different computers because of the underlying implementation of floating point numbers in t
What makes science amazing is that we somehow pick hypotheses that not only fit current data but also predict new surprising results.
I am agreeing with you as hard as I can:)
Personally I find it hard to believe that all the information in the universe was deterministically contained at the start. Randomness easily solve this problem.
I'll suggest to you Wolfram's cellular automata work - it's amazing how simple rules can create completely random distributions given enough steps:)
We have predictive capacity on climate from century to century and we have historical records going back millions of years to back it up.
I wouldn't be so sure of that. If you went back to 1918, looking for predictions of global average temperature in 2018, I think you'd find the vast majority of those models weren't even close - even the ones with the same central conceits as those that may have gotten it right simply by chance.
This is *especially* true if you believe climate is driven by humans - nobody in 1918 could possibly have predicted the growth in humanity and technology we have had over the past 100 years.
Now, if you wanted to assert that you can predict century to century climate with our historical records, you run into the problem that we don't have that kind of resolution - there are literally centuries upon centuries between data points in our historical proxies - it would be like trying to use a sundial to time a 100m sprint:)
I can perfectly model the behavior of some closed system. Let's say I can model it 1000 times faster than real time.
Sadly, the only way we can model reality faster than real time is by imperfectly modeling behavior:)
This isn't to say that there aren't some models that are close enough for various purposes, but I just can't start a hypothetical with "perfectly model":)
Unfortunately, most of the time we need to assume the scientists are doing a good job. Even without the statistical issues, people do not have the time to be experts in everything. We need to appeal to experts to help us make decisions. Generally the scientists do a good job of policing themselves.
Let's unpack that a bit.
Yes, most of the time we need to assume scientists are doing a good job. But we should also simultaneously assume that their good job is not perfect, and that they're humans, just like us. Either through honest error, unconscious bias, or conscious bias, they can be wrong - which is where the scientific method comes in, and the concept of a necessary and sufficient falsifiable hypothesis keeps them in check.
As for appealing to the experts to help us make decisions, avoiding this was the entire *reason* for the scientific method:). Put another way, why would we assume that we're all experts at picking out the people who are experts in fields we're not experts in? Why should I believe that I'm capable of discerning which nutritional expert is the right one, if I know nothing about nutrition?
For policing themselves, I'm afraid I'm not nearly as optimistic as you are - especially since much of science has been utterly unscientific for a long time. The incestuous peer review system that has its own internal biases and incentive structures has led to things like the reproducibility crisis in psychology, nutrition, and other fields as well. One could argue, of course, that psychology, by its very nature, is a "soft" science, not really falsifiable, but interesting and "sciencey", but the track record of scientists, especially those playing the academia game of "publish or perish", really isn't that good on the policing side
Not to quibble, but I assume you mean you want to show your hypothesis is "better" than the competing hypotheses.
Seems like a loaded word. What you want to do is start off with a huge universe of hypotheses, and winnow things down from there. The better we are at excluding things, the smaller the area in which the truth can survive exists. So, let's say we start with a billion possible hypotheses, but we find the falsification criteria for all of them except for half a dozen. Assuming the last six hypotheses don't share the same falsification criteria, it is an open question as to which one is "better" - it could be that we can never narrow things down any further than those six hypotheses, for either practical or theoretical reasons.
None of those hypotheses is really "better" except perhaps aesthetically.
I think you mean chaotic. The word stochastic is an adjective that describes something that was randomly determined.
Yeah, I'm mixing metaphors here - although, fun fact, you can get random sets from deterministic processes. Heck, you can argue that all random values are in fact deterministic, but simply unpredictable a priori:)
I can still say with high confidence that it will be colder in NYC on Dec 25 2100 then Jun 25 2100. (What confidence means in this context is something you could question.)
We can also say with high confidence that it will be colder in NYC on Dec 25 3:00AM than it will be at 3:00PM. The fact that we can make these predictions based on the wide variation between day and night, and summer and winter, doesn't imply that we have any predictive capacity year to year, or decade to decade, or century to century.
This is mostly a factor of what kind of swings we're talking about - we get massive swings of temperature due to seasons and day/night. The observed 1.0C/century since about 1850 simply isn't anywhere near significant enough for us to be very confident.
Worse than that, our typical "confidence" when it comes to "climate" tends to revolve around the average global temperature - something nobody actually experiences. Because our spatial resolution of our "climate" predictions aren't functionally useful, it's difficult to divine the impacts - you can have +2.0C global average temperature, and different distributions of that could mean that humans experience +10.0C, or even -10.0C. I.e., global average temperature cannot possibly give you actionable information because distributions count.
That's fine, but you also seem to want to imply things that don't fit your definition have little value. No, you need to argue the specifics of why they have little value.
Well, specifically they have little value because without the scientific method, and falsifiability, we simply cannot reach the truth. We may be inspired to think of new things, and imagine new possibilities, and yes, imagination is incredibly valuable to the scientific method, but it is not sufficient to produce scientific results.
Put another way, my lungs are incredibly important, and I hold them in high regard, but I can't actually speak out loud unless those lungs are combined with vocal chords, a nervous system, and the rest of the biological superstructure of a human. Lungs on their own might be able to make some noise, as they decay, but while they can be a *part* of a human, they are not *sufficient* to be a full human.
Science is bigger than statistics and persuasive arguments:)
However, even if one doesn't play his science game doesn't mean one's work doesn't have value.
I'm agreeing with you as hard as I can. There is great value to imagination, and the statistics that can inspire it. It is a stepping stone to the truth, but to get all the way there, you need to move past those seeds and create a necessary and suffici
Are you trying to ground things in statistics where AGW can't do repeated experiments.
No, the "repeated experiments" trope isn't actually the key to the scientific method. The phrase starts off with "testable", which gets interpreted to "test by experiment", which gets extrapolated to "test by repeated experiments".
The key to the scientific method is "testable", which should be understood as "can be falsified by a specific set of observations". Anything that can explain every conceivable observation isn't scientific - it must be able to be "put to the test", and have a risk of being shown wrong.
More importantly, you need to make the logical argument that the lack of these falsification criteria exclude other hypotheses, including the null. If five hundred different hypotheses could still be valid with the specified falsification criteria unfound, you haven't made a convincing argument that your hypothesis is correct. To do that, you want to show that not only is your hypothesis as of yet unfalsified (after trying really hard to falsify it), you want to show that other competing hypotheses are excluded.
Stochastic does not mean everything is unpredictable. It's colder every winter for as long as I can remember:)
Stochastic means that small variations in the input can have huge impacts on the output. The butterfly effect creating hurricanes and that sort of thing.
The problem with modeling stochastic phenomena is that unless you have perfect input (which we never do), you will *never* accurately predict the future in any sort of way, even if your model has every physical property of matter set correctly. A single grain of sand in the wrong place, a mismatch five thousand digits after the decimal point in an input parameter, can cause your simulation to diverge from reality.
The scientific method does not define science. I would claim it's a convenient tool to do science.
Rather than complain about what I see as a blatant contradiction here, let me ask you - what is your definition of "science" that excludes alchemy and astrology?
Sure, I would claim science is the study of how the universe works.
Astrology is the study of how the universe works. Yet, I think we would agree that it isn't science. What criteria would you use to exclude astrology from the realm of "science"?
Just because people use tricks to argue for things that aren't true doesn't mean all arguing/persuasion is "unscientific".
Of course not - there is a demarcation, which Karl Popper worked out - falsifiability. If you include that in your argument/persuasion, you're playing the science game. If you don't include that in your argument/persuasion, you may be making really cool arguments, and you might be very persuasive, but you're not playing the science game.
Biff the climate scientist gives a physical model that is based on current data and current understanding of the physics of the system. He runs his system on some super computers and comes up with a distribution of possible results. He claims that his model captures the dominate features of this physical system in terms of effects of variables of interest. He says that if in 20 years the system falls outside a predicted range then his model is probably wrong and doesn't capture some dominate features of the system. (Perhaps he forgot to model the asteroid that pulverized the planet.)
This in my book is "sciency" but not scientific. The scientific method is best understood at excluding explanations, making the area where truth exists smaller and smaller. The problems with Biff's exercise:
1) statistically, could give a wide enough range to capture all possible futures - explaining everything - without actually being accurate; 2) statistically, could give a narro
While they are somewhat falsifiable, the empirical results will be limited in generalizability and statistically weak.
Well, the model may be falsifiable, but the model itself is not a necessary and sufficient hypothesis. That is, even if a model is not falsified, it simply does not exclude the null hypothesis. Heck, the *exact* same model, given even slightly different inputs, contradicts itself - a feature of the stochastic nature of weather and climate.
Again, it looks "sciencey" but it isn't following the scientific method.
One can make meaningful predictions based on combining well verified components and using independent historical evidence to justify the models.
The problem is that any model of sufficient complexity can be tuned to match a curve - the critical question to ask is "what observations would falsify the very foundations of this model, in toto"? Not just "our central conceit is always taken to be true, and unassailable, because any falsification can be met with an ad hoc adjustment to fit the new curve".
This is the difference between astronomy and astrology - both of which use statistics, data, measurements, evidence, and convincing arguments. The trick is that astronomy includes the feature of falsifiability, whereas astrology can always come up with an ad hoc special pleading for failed models.
One can disagree and focus on a philosophical definition of science, but real "science" is about making a convincing argument using the many tools at our disposal.
Maybe if we used terms in the same way it would be easier:)
Convincing arguments using many tools is persuasion. Empirical evidence shows that there are lots of religions that are prima facie non-scientific, but very persuasive.
"Science" is really something we have to agree on before we can have a scientific discussion, though. Karl Popper and his work on falsifiability and the demarcation problem represents an important bit of common ground - and without that common ground, it's *really* hard to be persuasive to someone who understands the scientific method from a first principles philosophical point of view:)
The whole point of the scientific method was, as Feynman put it, "a belief in the ignorance of experts". Whereas before, the only people allowed to comment on the world were the hallowed authorities, the scientific method, and the process of falsifiability, democratized the pursuit of knowledge by setting a table of rules that had to be *more* than simply persuasive. In the scientific method, you are supposed to challenge your own ideas - and in fact, challenge them with incredible fervor to demonstrate their strength. This is the polar opposite of looking for corroborating evidence, and our inherent human tendency to confirmation bias.
So, once someone understands the scientific method, thoroughly from first principles, every "convincing argument" of AGW simply doesn't seem quite as persuasive. Correlation is not causation, and until AGW can be stated as a necessary and sufficient falsifiable hypothesis, it's literally not science. I'm completely open to being convinced by the rules of the science game, but despite years of debate and study with some of the smartest people in the climate science field, I have never seen the following:
1) a list of observations, which if observed, mean AGW is false;
2) a logical argument that the lack of those falsifications means that AGW must be favored over all others (including the null).
So, I'm actually eating my own dogfood here - if anyone ever observes a necessary and sufficient falsifiable hypothesis statement for AGW, my hypothesis is wrong:)
Here's a fun challenge for you - find *any* climate science paper that explicitly states what observations would falsify their hypothesis. Cite and quote here if you can:)
I think it's obvious - we don't understand the system well enough to tell how it is moderated.
In the most simple form, of a bathtub with a drain, our predictions are failing - we throw in variable amounts of water into this bathtub, and the water level isn't rising as predicted - water is going missing, and not at a predictable rate.
So what we have is way smarter than a bathtub. Something reactive. Something dynamic.
My proposed necessary and sufficient falsifiable hypothesis statement for AGW hopes to identify a uniquely human fingerprint (rather than one that could be shared by other natural variations). But you are right to point out that if we have a smart bathtub, it could be smart enough to wash away the fingerprint while still being primarily affected by that fingerprinted source...but I'd count that as a pretty low probability, since I can't think of any natural phenomena that can perfectly counteract something, but still be stated as primarily affected by that thing. Open to examples though:)
So, for example, if I were going to make some sort of falsifiable hypothesis on AGW (specifically through CO2 emissions), I'd start off with something like this:
1) assume the 5-day on/2-day off work week is singularly anthropogenic (no other natural cycle has the rhythm of 5 days up, two days down) 2) assume that CO2 is mostly a well mixed gas (OCO-2 shows some really interesting points against that, but you could still apply this hypothesis to that data)
Observations which would prove our AGW via CO2 emissions false (of any given asserted proportion) - the absence of any discernible 5/2 cycle in CO2 levels, or a 5/2 cycle in CO2 levels which is small enough to be incapable of attributing CO2 fluctuations primarily to human emissions.
The logical argument - 5/2 cycles are anthropogenic by definition, and we can observe the CO2 cycle on a local level in cities on the 5/2 cycle - extrapolate that out to the rest of the globe, and we should see some sort of 5/2 cycle. Whatever we observe represents the upper limit of human contribution to CO2.
Other attributions to a "human fingerprint" often don't actually exclude non-humans - for example, the C13/14 ratio asserted to show anthropogenic origins happens during natural seasonal cycles as well (notable in the sinusoidal keeling curve). If it shows up during these natural cycles, it can hardly be asserted as uniquely human. The "work-week AGW" hypothesis, thus far, isn't explainable by any other factor than humans - unless someone can show me that there's some sort of 5/2 wobble generated in the earth by the moon or something:)
In order to play the science game, we need the necessary and sufficient falsifiable hypothesis to start off with. To wit,
1) a list of observations, which if observed, mean a hypothesis is false;
2) a logical argument that the lack of those falsifications means that a hypothesis must be favored over all others (including the null).
While playing with models while dressing in white lab coats may look "sciencey", it doesn't become scientific until it starts following the scientific method - and that means having a necessary and sufficient falsifiable hypothesis statement.
Now, there are those that would suggest that we can avoid the need for the scientific method, and simply use Bayesian analysis to reach the truth, but any statistical method that avoids the cornerstone of falsifiability opens up the world to making astrology scientific, simply based on probability distributions. There's a great opportunity for interesting discovery with Bayesian methods, but a lot more risk of false positives. In fact, given enough creativity, the false positive can be actively mined for.
I'm a white male and programs like "Outreachy" have no meaningful impact on my ability to contribute to open source projects or make a living in tech.
Well, you're white, male, rich, and established. Take a young white straight male, unestablished in the field, from a low income household, with all the disadvantages you can think of, and your "Outreachy" program has just meaningfully impacted his ability to get started in tech open source projects. Now, would this be representative of most straight white males? Perhaps not. But for the individuals you *will* impact with this (and there always will be some), you're doing a grave injustice that they have no business being the victim of. Having a poor appalachian pay for the white privilege *you* enjoy simply isn't fair - which is why we tend to decry things like racism, where we judge people based on the color of their skin rather than their individual circumstances.
Further, I don't see programs like Outreachy reversing the polarity of discrimination. All they are doing is making sure a few electrons flow in the other direction while the vast majority of electrons are still going the same direction they always have.
You're giving away the game here. Using racial, sexual orientation, and gender discrimination to "make sure" a few things flow in a specific direction *is* discrimination. It might not be widespread, or overwhelming, but rape is rape. Just because it isn't part of some massive military rape program of the Hutus doesn't make it any less rapey.
My assertion is this - there are ways of addressing both "damage" and potential discrimination without being explicitly discriminatory, even in small batches. Just like there are ways of addressing rape without just raping in the other direction. Rather than using the very tools and techniques we abhor when applied in the "wrong" direction, we should rise above the pain of the past and work towards an equitable future without becoming the enemies we fight.
I think you're trying to find some level of triviality that I would agree that outright discrimination is a good thing - but I don't think there really is any trivial discrimination, especially when you can accomplish the same goal *without* discriminatory means.
For example, let's say that only favored minorities were given a cookie for applying for a non-discriminatory position - not just given information and encouragement to apply for the job to increase their representation in the applicant pool size without denying the opening to others based on sexual orientation, gender, or skin tone. I would still object.
Give everyone a cookie:)
Again, I reach this conclusion by making sure I feel the same way no matter what specific sexual orientation, gender, or skin tone group is substituted. If the KKK wants to encourage young straight white men to apply for a non-discriminatory position, in an attempt to overwhelm the applicant pool with their favored sexual orientation, gender or skin tone, so long as the position is open to even competition between applicants, I've got no problem. But if the KKK is only giving out cookies to young straight white men, it crosses the line.
At the end of the day, explicitly discriminating against people by sexual orientation, gender, or skin tone has to be predicated on the following assumptions -
1) without such restriction, your favored sexual orientation, gender or skin tone would be uncompetitive and have essentially 0% chance even if they were 99% of the applicant pool; 2) without such restriction, your favored sexual orientation, gender or skin tone would be discriminated against (even if the hiring process was supposedly non-discriminatory) and have essentially 0% chance even if they were 99% of the applicant pool.
#1 seems to be selling the applicants short, and represents the soft bigotry of low expectations.
#2 seems to be selling the HR department short, and represents a terrible assumption of discriminatory intent.
I would argue that the proper solution, if you believed either 1 or 2, would still be attacking the root problem (improving competitiveness, ensuring non-discrimination), rather than forcing discrimination into the process to try to compensate for a perceived problem.
I think the point still stands - the payment for the trainee should be non-discriminatory (along the axes of sexual attraction, gender, and skin tone).
Again, two options:
1) creating a non-exclusionary paid trainee program, and encouraging more favored sexual attraction, gender, or skin tone applicants to make a more egalitarian applicant pool;
2) creating an exclusionary paid trainee program, and discriminating against applicants who do not fit the favored sexual attraction, gender, or skin tone.
One is obviously justifiable, the other is not.
Now, perhaps you may disagree, but if stormfront.org created a paid trainee program only for straight nordic males, I'm not sure if you'd be as supportive of their discrimination - even if it directly mirrored the one you claim to be in favor of.
You can encourage more minorities to apply for a job, increasing their representation in the applicant pool, without discriminating against any other applicants.
However, if you explicitly exclude applicants based on being straight, white, and male, you're actively discriminating based on sex, sexual orientation, and gender.
Fighting discrimination with more discrimination is like fighting rape with more rape. Just stop raping.
Setup mentoring centers directly in those communities, open to anyone in the community. By sheer demographic force, you'll hit the under-represented minorities, but you'll do so without being discriminatory. Since hey, that white kid with native parents living in a ghetto neighborhood in Detroit is also disadvantaged. Certainly moreso than a rich black male from San Francisco.
The trick here is that you can't fix the problem is all you do is put up more barriers - you have to increase the pool of qualified applicants, and that's hard, slogging work through not only the educational system in the ghetto, but the culture of "keeping it real" that denigrates academic success and being "too white".
Maybe if llvm had added this, they could have avoided the SJW scare:
Our open source community prioritizes truthful communication over people’s comfort. We will act on complaints regarding:
* ‘Reverse’ -isms, including ‘reverse racism,’ ‘reverse sexism,’ and ‘cisphobia’; * Unreasonable communication of boundaries, such as “leave me alone,” “go away,” or “I’m not discussing this with you”, when open communication is required; * Refusal to explain or debate asserted concepts * Communicating in a ‘tone’ you don’t find congenial * Criticizing racist, sexist, cissexist, or other behaviors or assumptions
Literally nothing in the link you provided asserts that you are allowed to create an absolute bar towards straight white males in order to increase participation from other groups, whether or not it's called a "paid internship" or a "diversity program".
There is a real difference between outreach (increasing the number of minorities in the pool of applicants), and exclusion (excluding non-minorities from any application). One is justifiable, the other is evil.
Outreach means going to find underrepresented minorities, and encouraging them to apply for a job.
A job opening might usually get 99% cis-white male applicants, but with outreach, you could change that ratio to say, 50% underrepresented minority, 50% cis-white male. No guarantee the cis-white male won't win, but you change the pool of applicants. That's outreach.
Exclusion means opening up a job position *only* for underrepresented minorities. 100% you'll exclude cis-white males. This is immoral discrimination.
If there was a program to accept an intern, open to anyone regardless of skin tone or sexuality, and outreachy went out, and recruited just non-white non-male people to apply to that program, that would be *encouraging*.
By explicitly running a program that turned away any white males from even applying, they were going a step beyond. Rather than engaging in "encouragement", they were automatically filtering out people of an undesirable color and undesirable sex.
A good way of deciding if a policy is a good one, is for you to write it, and let other people swap out any racial or sexual orientation terms as they want to. If any formulation of your policy, when swapped around with terms defined by your ideological enemies, sounds bad, then it is.
Programs and attitudes like "Outreachy" are literally barriers to white males getting jobs in tech.
*Removing* barriers from others should not include *raising* barriers for some.
If you ever find that there is a program that hires only white males, and raises a barrier to those who aren't white males, the answer isn't to create an ideologically opposite but equally discriminatory program, the answer is to remove the discrimination at its source.
Similarly, if the problem is that some HR reps have inherent barriers to non-whites and non-males, the answer isn't to create HR reps that have inherent barriers only to white males.
You need to remove the discrimination, not simply reverse its polarity.
I think the primary reason why this story will be buried is because the response of google/youtube won't fit the narrative. 99% chance they're going to have armed guards at their campuses from here on out, aka "good guys with guns", to defend against this kind of thing. Not marches, or magazine capacity limits, or waiting periods, or banning "scary" looking guns - they're going to use the best defense against bad guys with guns.
I used to believe that, but I haven't gotten mod points for years now. I'm guessing I was blacklisted somehow, but was never informed if I had misbehaved in some manner at all.
My guess is that I shared the wrong opinions, but never cared enough to try and appeal to the gods of slashdot to rectify the situation.
But it's obviously not linear. The graph you cited showed that - it's not even constantly proportional.
Put another way, the atmosphere is the medium through which human CO2 emissions pass before being sunk by other natural sinks. Mechanically, a molecule of CO2 in the atmosphere has no idea if it is anthropogenic, or from some other source. But the other natural sinks, according to your cite, don't behave in a predictable manner - they are actively changing well out of correlation with these extra molecules of CO2.
I think maybe when you think of "physical factors", you're not really using enough imagination - you still see them as beholden to the calories in/calories out model, and you're looking at simply the calories, rather than the hormones that moderate fat accumulation.
Let's ask this question - do you believe the restaurant analogy is a good way of understanding the limitations of calories in/calories out? Is it only when applied to CO2 in/CO2 out that you feel it becomes silly?
That's an assertion. If the carbon sinks can behave in a coupled manner *with eachother*, it seems to follow that it is possible that they can behave in a coupled manner *without any outside influence*.
I'm not quite sure how you're missing this.
Let's get back to calories in/calories out, maybe this will help you.
Yes, the second law of thermodynamics apply - in order to get fat, you need more calories coming in than going out.
However, this says nothing about the cause of fat accumulation - it is merely a tautological requirement. Here's an analogy that might help:
But now imagine that instead of talking about why we get fat, we’re talking about a different system entirely. This kind of gedanken (thought) experiment is always a good way to examine the viability of your assumptions about any particular problem. Say instead of talking about why fat tissue accumulates too much energy, we want to know why a particular restaurant gets so crowded. Now the energy we’re talking about is contained in entire people rather than just the fat in their fat tissue. Ten people contain so much energy; eleven people contain more, etc.. So what we want to know is why this restaurant is crowded and so over-stuffed with energy (i.e., people) and maybe why some other restaurant down the block has remained relatively empty — lean.
If you asked me this question — why did this restaurant get crowded? — and I said, well, the restaurant got crowded (it got overstuffed with energy) because more people entered the restaurant than left it, you’d probably think I was being a wise guy or an idiot. (If I worked for the World Health Organization, I’d tell you that “the fundamental cause of the crowded restaurant is an energy imbalance between people entering on one hand, and people exiting on the other hand.”) Of course, more people entered than left, you’d say. That’s obvious. But why? And, in fact, saying that a restaurant gets crowded because more people are entering than leaving it is redundant –saying the same thing in two different ways – and so meaningless.
So yes, more CO2 in the atmosphere is because more CO2 was taken out than CO2 put in. But that's redundant - it says nothing about the cause.
The fact that CO2 sinks behave in dynamically reactive ways, rather than predictable linear ways, means we're a far cry from understanding causes at this point.
Slashdot Mirror
The right to petition doesn't specify a medium, though. If people can petition via snail mail, then that right has not been abridged en masse, even if it hasn't been enabled on a specific technology.
This is like defining legal abortion by viability, as technology keeps driving viability earlier and earlier in the pregnancy.
Does this mean that politicians can no longer use traditional one-way media? Are they forbidden from using TV or radio broadcasts where listeners aren't given an opportunity to reply on the same platform?
This seems like a ruling that depends on the specific technology being looked at, rather than any universal judicial principles.
Does this mean Yelp owners will get arrested for their policies that solicit payment to hide bad reviews?
https://www.eastbayexpress.com...
But let's be specific - the predictability is proportional to the usual variability and predictability of the cycle. That is to say, we can predict day/night cycles, or seasonal cycles, because they are on the order of magnitude of 30C, and are highly regular. We're not so good at predicting AMO or ENSO, on top of the fact that the order of magnitude is less than the more predictable ones.
To be clear, the expectation that we can accurately predict global average temperature a century out, when it only varies by 1.0C, is *really* exceptional. We're looking at fourth and fifth order terms here, rather than the very impactful first and second order terms.
I am agreeing with you as hard as I can - and this speaks to culture. Our scientific culture should not be one of activism, it should be one of ruthless skepticism and policing.
My heuristic is simple - ask them for a necessary and sufficient falsifiable hypothesis statement :). Those that can provide it (regardless if I can personally understand it), get past the first BS detector test :)
Again, agreeing with you as hard as I can. Bastiat once wrote about the internal inconsistency of big government democracy - the masses were assumed to have perfect judgement in deciding who their leaders should be, but once that leader was in place, the masses were assumed to have terrible judgement about their own lives and needed these perfectly chosen legislators to make laws to restrain them and mould them to perfection :)
The problem here is that nowhere in this procedure do they challenge their own basic assumptions, or whether or not their macro application of micro laws of physics is appropriate. *That* is the key here - not to add enough things to buttress your central conceit, but to have a way to prove, or disprove, your central conceit, whatever it may be.
Put another way, you could build a model that attributed warming trends to human H2O emissions. You could build another model that attributed warming trends to human CO2 emissions. Without a necessary and sufficient falsifiable hypothesis statement for those competing models, you can extend *either* of them infinitely with additional adjustments, and never figure out which competing central conceit was correct.
True, and I get your point about "all science being models" - but here's my challenge: regardless of "small" or "complex", any scientific model must have a necessary and sufficient falsifiable hypothesis. It may be that creating such hypotheses for complex models is simply too much work for it to be practicable (whereas it's trivial for "small" models), but I don't think you can get a pass on the scientific method just because you've decided to use a complex model.
Here's an interesting aside for you - there are actually some models which produce different results when run on different computers because of the underlying implementation of floating point numbers in t
I am agreeing with you as hard as I can :)
I'll suggest to you Wolfram's cellular automata work - it's amazing how simple rules can create completely random distributions given enough steps :)
http://mathworld.wolfram.com/E...
I wouldn't be so sure of that. If you went back to 1918, looking for predictions of global average temperature in 2018, I think you'd find the vast majority of those models weren't even close - even the ones with the same central conceits as those that may have gotten it right simply by chance.
This is *especially* true if you believe climate is driven by humans - nobody in 1918 could possibly have predicted the growth in humanity and technology we have had over the past 100 years.
Now, if you wanted to assert that you can predict century to century climate with our historical records, you run into the problem that we don't have that kind of resolution - there are literally centuries upon centuries between data points in our historical proxies - it would be like trying to use a sundial to time a 100m sprint :)
Sadly, the only way we can model reality faster than real time is by imperfectly modeling behavior :)
This isn't to say that there aren't some models that are close enough for various purposes, but I just can't start a hypothetical with "perfectly model" :)
Let's unpack that a bit.
Yes, most of the time we need to assume scientists are doing a good job. But we should also simultaneously assume that their good job is not perfect, and that they're humans, just like us. Either through honest error, unconscious bias, or conscious bias, they can be wrong - which is where the scientific method comes in, and the concept of a necessary and sufficient falsifiable hypothesis keeps them in check.
As for appealing to the experts to help us make decisions, avoiding this was the entire *reason* for the scientific method :). Put another way, why would we assume that we're all experts at picking out the people who are experts in fields we're not experts in? Why should I believe that I'm capable of discerning which nutritional expert is the right one, if I know nothing about nutrition?
For policing themselves, I'm afraid I'm not nearly as optimistic as you are - especially since much of science has been utterly unscientific for a long time. The incestuous peer review system that has its own internal biases and incentive structures has led to things like the reproducibility crisis in psychology, nutrition, and other fields as well. One could argue, of course, that psychology, by its very nature, is a "soft" science, not really falsifiable, but interesting and "sciencey", but the track record of scientists, especially those playing the academia game of "publish or perish", really isn't that good on the policing side
Seems like a loaded word. What you want to do is start off with a huge universe of hypotheses, and winnow things down from there. The better we are at excluding things, the smaller the area in which the truth can survive exists. So, let's say we start with a billion possible hypotheses, but we find the falsification criteria for all of them except for half a dozen. Assuming the last six hypotheses don't share the same falsification criteria, it is an open question as to which one is "better" - it could be that we can never narrow things down any further than those six hypotheses, for either practical or theoretical reasons.
None of those hypotheses is really "better" except perhaps aesthetically.
Yeah, I'm mixing metaphors here - although, fun fact, you can get random sets from deterministic processes. Heck, you can argue that all random values are in fact deterministic, but simply unpredictable a priori :)
We can also say with high confidence that it will be colder in NYC on Dec 25 3:00AM than it will be at 3:00PM. The fact that we can make these predictions based on the wide variation between day and night, and summer and winter, doesn't imply that we have any predictive capacity year to year, or decade to decade, or century to century.
This is mostly a factor of what kind of swings we're talking about - we get massive swings of temperature due to seasons and day/night. The observed 1.0C/century since about 1850 simply isn't anywhere near significant enough for us to be very confident.
Worse than that, our typical "confidence" when it comes to "climate" tends to revolve around the average global temperature - something nobody actually experiences. Because our spatial resolution of our "climate" predictions aren't functionally useful, it's difficult to divine the impacts - you can have +2.0C global average temperature, and different distributions of that could mean that humans experience +10.0C, or even -10.0C. I.e., global average temperature cannot possibly give you actionable information because distributions count.
Well, specifically they have little value because without the scientific method, and falsifiability, we simply cannot reach the truth. We may be inspired to think of new things, and imagine new possibilities, and yes, imagination is incredibly valuable to the scientific method, but it is not sufficient to produce scientific results.
Put another way, my lungs are incredibly important, and I hold them in high regard, but I can't actually speak out loud unless those lungs are combined with vocal chords, a nervous system, and the rest of the biological superstructure of a human. Lungs on their own might be able to make some noise, as they decay, but while they can be a *part* of a human, they are not *sufficient* to be a full human.
Science is bigger than statistics and persuasive arguments :)
I'm agreeing with you as hard as I can. There is great value to imagination, and the statistics that can inspire it. It is a stepping stone to the truth, but to get all the way there, you need to move past those seeds and create a necessary and suffici
No, the "repeated experiments" trope isn't actually the key to the scientific method. The phrase starts off with "testable", which gets interpreted to "test by experiment", which gets extrapolated to "test by repeated experiments".
The key to the scientific method is "testable", which should be understood as "can be falsified by a specific set of observations". Anything that can explain every conceivable observation isn't scientific - it must be able to be "put to the test", and have a risk of being shown wrong.
More importantly, you need to make the logical argument that the lack of these falsification criteria exclude other hypotheses, including the null. If five hundred different hypotheses could still be valid with the specified falsification criteria unfound, you haven't made a convincing argument that your hypothesis is correct. To do that, you want to show that not only is your hypothesis as of yet unfalsified (after trying really hard to falsify it), you want to show that other competing hypotheses are excluded.
Stochastic means that small variations in the input can have huge impacts on the output. The butterfly effect creating hurricanes and that sort of thing.
The problem with modeling stochastic phenomena is that unless you have perfect input (which we never do), you will *never* accurately predict the future in any sort of way, even if your model has every physical property of matter set correctly. A single grain of sand in the wrong place, a mismatch five thousand digits after the decimal point in an input parameter, can cause your simulation to diverge from reality.
Rather than complain about what I see as a blatant contradiction here, let me ask you - what is your definition of "science" that excludes alchemy and astrology?
Astrology is the study of how the universe works. Yet, I think we would agree that it isn't science. What criteria would you use to exclude astrology from the realm of "science"?
Of course not - there is a demarcation, which Karl Popper worked out - falsifiability. If you include that in your argument/persuasion, you're playing the science game. If you don't include that in your argument/persuasion, you may be making really cool arguments, and you might be very persuasive, but you're not playing the science game.
This in my book is "sciency" but not scientific. The scientific method is best understood at excluding explanations, making the area where truth exists smaller and smaller. The problems with Biff's exercise:
1) statistically, could give a wide enough range to capture all possible futures - explaining everything - without actually being accurate;
2) statistically, could give a narro
Well, the model may be falsifiable, but the model itself is not a necessary and sufficient hypothesis. That is, even if a model is not falsified, it simply does not exclude the null hypothesis. Heck, the *exact* same model, given even slightly different inputs, contradicts itself - a feature of the stochastic nature of weather and climate.
Again, it looks "sciencey" but it isn't following the scientific method.
The problem is that any model of sufficient complexity can be tuned to match a curve - the critical question to ask is "what observations would falsify the very foundations of this model, in toto"? Not just "our central conceit is always taken to be true, and unassailable, because any falsification can be met with an ad hoc adjustment to fit the new curve".
This is the difference between astronomy and astrology - both of which use statistics, data, measurements, evidence, and convincing arguments. The trick is that astronomy includes the feature of falsifiability, whereas astrology can always come up with an ad hoc special pleading for failed models.
Maybe if we used terms in the same way it would be easier :)
Convincing arguments using many tools is persuasion. Empirical evidence shows that there are lots of religions that are prima facie non-scientific, but very persuasive.
"Science" is really something we have to agree on before we can have a scientific discussion, though. Karl Popper and his work on falsifiability and the demarcation problem represents an important bit of common ground - and without that common ground, it's *really* hard to be persuasive to someone who understands the scientific method from a first principles philosophical point of view :)
The whole point of the scientific method was, as Feynman put it, "a belief in the ignorance of experts". Whereas before, the only people allowed to comment on the world were the hallowed authorities, the scientific method, and the process of falsifiability, democratized the pursuit of knowledge by setting a table of rules that had to be *more* than simply persuasive. In the scientific method, you are supposed to challenge your own ideas - and in fact, challenge them with incredible fervor to demonstrate their strength. This is the polar opposite of looking for corroborating evidence, and our inherent human tendency to confirmation bias.
So, once someone understands the scientific method, thoroughly from first principles, every "convincing argument" of AGW simply doesn't seem quite as persuasive. Correlation is not causation, and until AGW can be stated as a necessary and sufficient falsifiable hypothesis, it's literally not science. I'm completely open to being convinced by the rules of the science game, but despite years of debate and study with some of the smartest people in the climate science field, I have never seen the following:
1) a list of observations, which if observed, mean AGW is false;
2) a logical argument that the lack of those falsifications means that AGW must be favored over all others (including the null).
So, I'm actually eating my own dogfood here - if anyone ever observes a necessary and sufficient falsifiable hypothesis statement for AGW, my hypothesis is wrong :)
Here's a fun challenge for you - find *any* climate science paper that explicitly states what observations would falsify their hypothesis. Cite and quote here if you can :)
I think it's obvious - we don't understand the system well enough to tell how it is moderated.
In the most simple form, of a bathtub with a drain, our predictions are failing - we throw in variable amounts of water into this bathtub, and the water level isn't rising as predicted - water is going missing, and not at a predictable rate.
So what we have is way smarter than a bathtub. Something reactive. Something dynamic.
My proposed necessary and sufficient falsifiable hypothesis statement for AGW hopes to identify a uniquely human fingerprint (rather than one that could be shared by other natural variations). But you are right to point out that if we have a smart bathtub, it could be smart enough to wash away the fingerprint while still being primarily affected by that fingerprinted source...but I'd count that as a pretty low probability, since I can't think of any natural phenomena that can perfectly counteract something, but still be stated as primarily affected by that thing. Open to examples though :)
So, for example, if I were going to make some sort of falsifiable hypothesis on AGW (specifically through CO2 emissions), I'd start off with something like this:
1) assume the 5-day on/2-day off work week is singularly anthropogenic (no other natural cycle has the rhythm of 5 days up, two days down)
2) assume that CO2 is mostly a well mixed gas (OCO-2 shows some really interesting points against that, but you could still apply this hypothesis to that data)
Observations which would prove our AGW via CO2 emissions false (of any given asserted proportion) - the absence of any discernible 5/2 cycle in CO2 levels, or a 5/2 cycle in CO2 levels which is small enough to be incapable of attributing CO2 fluctuations primarily to human emissions.
The logical argument - 5/2 cycles are anthropogenic by definition, and we can observe the CO2 cycle on a local level in cities on the 5/2 cycle - extrapolate that out to the rest of the globe, and we should see some sort of 5/2 cycle. Whatever we observe represents the upper limit of human contribution to CO2.
Other attributions to a "human fingerprint" often don't actually exclude non-humans - for example, the C13/14 ratio asserted to show anthropogenic origins happens during natural seasonal cycles as well (notable in the sinusoidal keeling curve). If it shows up during these natural cycles, it can hardly be asserted as uniquely human. The "work-week AGW" hypothesis, thus far, isn't explainable by any other factor than humans - unless someone can show me that there's some sort of 5/2 wobble generated in the earth by the moon or something :)
In order to play the science game, we need the necessary and sufficient falsifiable hypothesis to start off with. To wit,
1) a list of observations, which if observed, mean a hypothesis is false;
2) a logical argument that the lack of those falsifications means that a hypothesis must be favored over all others (including the null).
While playing with models while dressing in white lab coats may look "sciencey", it doesn't become scientific until it starts following the scientific method - and that means having a necessary and sufficient falsifiable hypothesis statement.
Now, there are those that would suggest that we can avoid the need for the scientific method, and simply use Bayesian analysis to reach the truth, but any statistical method that avoids the cornerstone of falsifiability opens up the world to making astrology scientific, simply based on probability distributions. There's a great opportunity for interesting discovery with Bayesian methods, but a lot more risk of false positives. In fact, given enough creativity, the false positive can be actively mined for.
Well, you're white, male, rich, and established. Take a young white straight male, unestablished in the field, from a low income household, with all the disadvantages you can think of, and your "Outreachy" program has just meaningfully impacted his ability to get started in tech open source projects. Now, would this be representative of most straight white males? Perhaps not. But for the individuals you *will* impact with this (and there always will be some), you're doing a grave injustice that they have no business being the victim of. Having a poor appalachian pay for the white privilege *you* enjoy simply isn't fair - which is why we tend to decry things like racism, where we judge people based on the color of their skin rather than their individual circumstances.
You're giving away the game here. Using racial, sexual orientation, and gender discrimination to "make sure" a few things flow in a specific direction *is* discrimination. It might not be widespread, or overwhelming, but rape is rape. Just because it isn't part of some massive military rape program of the Hutus doesn't make it any less rapey.
My assertion is this - there are ways of addressing both "damage" and potential discrimination without being explicitly discriminatory, even in small batches. Just like there are ways of addressing rape without just raping in the other direction. Rather than using the very tools and techniques we abhor when applied in the "wrong" direction, we should rise above the pain of the past and work towards an equitable future without becoming the enemies we fight.
I think you're trying to find some level of triviality that I would agree that outright discrimination is a good thing - but I don't think there really is any trivial discrimination, especially when you can accomplish the same goal *without* discriminatory means.
For example, let's say that only favored minorities were given a cookie for applying for a non-discriminatory position - not just given information and encouragement to apply for the job to increase their representation in the applicant pool size without denying the opening to others based on sexual orientation, gender, or skin tone. I would still object.
Give everyone a cookie :)
Again, I reach this conclusion by making sure I feel the same way no matter what specific sexual orientation, gender, or skin tone group is substituted. If the KKK wants to encourage young straight white men to apply for a non-discriminatory position, in an attempt to overwhelm the applicant pool with their favored sexual orientation, gender or skin tone, so long as the position is open to even competition between applicants, I've got no problem. But if the KKK is only giving out cookies to young straight white men, it crosses the line.
At the end of the day, explicitly discriminating against people by sexual orientation, gender, or skin tone has to be predicated on the following assumptions -
1) without such restriction, your favored sexual orientation, gender or skin tone would be uncompetitive and have essentially 0% chance even if they were 99% of the applicant pool;
2) without such restriction, your favored sexual orientation, gender or skin tone would be discriminated against (even if the hiring process was supposedly non-discriminatory) and have essentially 0% chance even if they were 99% of the applicant pool.
#1 seems to be selling the applicants short, and represents the soft bigotry of low expectations.
#2 seems to be selling the HR department short, and represents a terrible assumption of discriminatory intent.
I would argue that the proper solution, if you believed either 1 or 2, would still be attacking the root problem (improving competitiveness, ensuring non-discrimination), rather than forcing discrimination into the process to try to compensate for a perceived problem.
I think the point still stands - the payment for the trainee should be non-discriminatory (along the axes of sexual attraction, gender, and skin tone).
Again, two options:
1) creating a non-exclusionary paid trainee program, and encouraging more favored sexual attraction, gender, or skin tone applicants to make a more egalitarian applicant pool;
2) creating an exclusionary paid trainee program, and discriminating against applicants who do not fit the favored sexual attraction, gender, or skin tone.
One is obviously justifiable, the other is not.
Now, perhaps you may disagree, but if stormfront.org created a paid trainee program only for straight nordic males, I'm not sure if you'd be as supportive of their discrimination - even if it directly mirrored the one you claim to be in favor of.
You can encourage more minorities to apply for a job, increasing their representation in the applicant pool, without discriminating against any other applicants.
However, if you explicitly exclude applicants based on being straight, white, and male, you're actively discriminating based on sex, sexual orientation, and gender.
Fighting discrimination with more discrimination is like fighting rape with more rape. Just stop raping.
Setup mentoring centers directly in those communities, open to anyone in the community. By sheer demographic force, you'll hit the under-represented minorities, but you'll do so without being discriminatory. Since hey, that white kid with native parents living in a ghetto neighborhood in Detroit is also disadvantaged. Certainly moreso than a rich black male from San Francisco.
The trick here is that you can't fix the problem is all you do is put up more barriers - you have to increase the pool of qualified applicants, and that's hard, slogging work through not only the educational system in the ghetto, but the culture of "keeping it real" that denigrates academic success and being "too white".
I noticed that too.
Maybe if llvm had added this, they could have avoided the SJW scare:
Literally nothing in the link you provided asserts that you are allowed to create an absolute bar towards straight white males in order to increase participation from other groups, whether or not it's called a "paid internship" or a "diversity program".
There is a real difference between outreach (increasing the number of minorities in the pool of applicants), and exclusion (excluding non-minorities from any application). One is justifiable, the other is evil.
Outreach means going to find underrepresented minorities, and encouraging them to apply for a job.
A job opening might usually get 99% cis-white male applicants, but with outreach, you could change that ratio to say, 50% underrepresented minority, 50% cis-white male. No guarantee the cis-white male won't win, but you change the pool of applicants. That's outreach.
Exclusion means opening up a job position *only* for underrepresented minorities. 100% you'll exclude cis-white males. This is immoral discrimination.
If there was a program to accept an intern, open to anyone regardless of skin tone or sexuality, and outreachy went out, and recruited just non-white non-male people to apply to that program, that would be *encouraging*.
By explicitly running a program that turned away any white males from even applying, they were going a step beyond. Rather than engaging in "encouragement", they were automatically filtering out people of an undesirable color and undesirable sex.
A good way of deciding if a policy is a good one, is for you to write it, and let other people swap out any racial or sexual orientation terms as they want to. If any formulation of your policy, when swapped around with terms defined by your ideological enemies, sounds bad, then it is.
Programs and attitudes like "Outreachy" are literally barriers to white males getting jobs in tech.
*Removing* barriers from others should not include *raising* barriers for some.
If you ever find that there is a program that hires only white males, and raises a barrier to those who aren't white males, the answer isn't to create an ideologically opposite but equally discriminatory program, the answer is to remove the discrimination at its source.
Similarly, if the problem is that some HR reps have inherent barriers to non-whites and non-males, the answer isn't to create HR reps that have inherent barriers only to white males.
You need to remove the discrimination, not simply reverse its polarity.
I think the primary reason why this story will be buried is because the response of google/youtube won't fit the narrative. 99% chance they're going to have armed guards at their campuses from here on out, aka "good guys with guns", to defend against this kind of thing. Not marches, or magazine capacity limits, or waiting periods, or banning "scary" looking guns - they're going to use the best defense against bad guys with guns.
I used to believe that, but I haven't gotten mod points for years now. I'm guessing I was blacklisted somehow, but was never informed if I had misbehaved in some manner at all.
My guess is that I shared the wrong opinions, but never cared enough to try and appeal to the gods of slashdot to rectify the situation.
But it's obviously not linear. The graph you cited showed that - it's not even constantly proportional.
Put another way, the atmosphere is the medium through which human CO2 emissions pass before being sunk by other natural sinks. Mechanically, a molecule of CO2 in the atmosphere has no idea if it is anthropogenic, or from some other source. But the other natural sinks, according to your cite, don't behave in a predictable manner - they are actively changing well out of correlation with these extra molecules of CO2.
I think maybe when you think of "physical factors", you're not really using enough imagination - you still see them as beholden to the calories in/calories out model, and you're looking at simply the calories, rather than the hormones that moderate fat accumulation.
Let's ask this question - do you believe the restaurant analogy is a good way of understanding the limitations of calories in/calories out? Is it only when applied to CO2 in/CO2 out that you feel it becomes silly?
That's an assertion. If the carbon sinks can behave in a coupled manner *with eachother*, it seems to follow that it is possible that they can behave in a coupled manner *without any outside influence*.
I'm not quite sure how you're missing this.
Let's get back to calories in/calories out, maybe this will help you.
Yes, the second law of thermodynamics apply - in order to get fat, you need more calories coming in than going out.
However, this says nothing about the cause of fat accumulation - it is merely a tautological requirement. Here's an analogy that might help:
http://garytaubes.com/inanity-...
So yes, more CO2 in the atmosphere is because more CO2 was taken out than CO2 put in. But that's redundant - it says nothing about the cause.
The fact that CO2 sinks behave in dynamically reactive ways, rather than predictable linear ways, means we're a far cry from understanding causes at this point.
Capito?