105 Matching Annotations
  1. Jan 2024
    1. But six years after the first Learning Healthcare Project report [1] and 13 years since the IoM popularised the concept [120], no nation, region or individual healthcare provider has fully realised this promise.

      A high bar, no?

    1. How will I know if this implementation strategy had an effect via the mechanism that I think it is activating?

      So progress is measured by increasing knowledge of the mechanism rather than improved performance?

  2. Feb 2022
    1. s it did under Chavez and Maduro in Venezuela

      Comparisons to other hyper-inflationary periods are hard to accept. So many things were different then.

    2. MMT’s failure to persuade even the leftiest U.S. politicians to accede to its tenets should tell us something about its future prospects

      Again, MMT proponents have argued why this is the case, and it has nothing to do with the veracity of MMT: the "left" has taken to the same talking points as the right by arguing that the government is like a household. This isn't at all evidence against MMT.

    3. that unrestrained deficit spending

      It's not unrestrained. It's restrained by other factors.

    4. is not a falsifiable scientific theory

      It has been described by its authors not as an economic theory, but as a description of how the macro economy works. Not sure of the difference here, though.

    5. of unlimited government spending to achieve progressive ends

      All main MMT authors have argued that you can use MMT to justify increase spending, but also decreased taxes. It isn't political in nature.

    6. MMT doesn’t even use formalized mathematical models of the economy like freshwater and saltwater econ do

      In Steve Keen's book "Debunking Economics" he outlines how the mathematical framework of modern standard macro economics is way over simplified.

    7. In fact, this is also a feature of all freshwater and saltwater models of the economy. Those models deal only in real variables — asphalt and construction workers and food and cafeteria workers and so on. In fact, those models’ lack of attention to financial constraints of any kind is exactly why they failed to predict the Great Recession!


    8. They offer a package of policy prescriptions, but these prescriptions can only be learned by consulting the MMT proponents themselves.

      From what I've read, MMT proponents argue that there's only 1 actual policy that comes from MMT: the job guarantee.

  3. Feb 2021
    1. Robustness mechanisms can be challenging to build in both natural and engineered systems, because their utility isn’t obvious until something goes wrong.

      An example of the value of "scenario planning", mentioned above.

  4. Oct 2020
    1. Take a firm that fudges its numbers on quarterly earnings reports, or a high-school student who spends all her time studying specifically for a college-entrance exam rather than developing the analytical skills the test is supposed to be measuring. In these examples, a metric is introduced as a proxy for ability. Individuals in the system come to associate performance on these metrics with shareholder happiness or getting into college. As this happens, the metric becomes a target to be gamed, and as such ceases to be an objective measure of what it is purporting to assess.

      Manifests also in AI training.

    2. Instead of attempting to narrowly forecast and control outcomes, we need to design systems that are robust and adaptable enough to weather a wide range of possible futures.

      This is more difficult and requires understanding "classes" of futures, not knowing what fires you truly averted. It necessitates a change in how policy is made.

  5. Jul 2020
    1. the politics of non-violence

      Or disillusionment with the continued presence of racist policing and government policy?

    2. And this produced a law enforcement response that was massive, militarized and often corrupt.

      This says that militarized gangs caused militarized (and corrupt) police. A key assumption to this argument.

    3. This left these communities more vulnerable to drugs like first heroin and then crack cocaine.

      I thought that white use of illicit drugs like cocaine was generally and consistently on par with black use. And now opioids are a white problem more than a black one.

    4. But this did not replace lost jobs, and, the fact that the criteria for qualification favored single parents produced a counter-incentive against marriage in the black community—a systemic problem that Martin Luther King, Jr. strongly criticized.

      This is a big claim without support here. It suggests a causal relationship between "great society" programs and greater father absenteeism. There are 2 possible problems with this that I see. First, dis-incentivizing marriage doesn't necessarily incentivize absent fathers. Second, marriage rates are going down for all populations and have been since the 60s. So it's not just a "black community" thing.

    5. Conservatives tacitly admit to the relationship between flawed institutional structures and cultural formation when they criticize the social engineering of the welfare state and big liberal programs.

      Is it the "welfare state" or is it racist programs. Above was mentioned the displacement of black communities, for example. Also, the loss of jobs mentioned above is not because of "big liberal programs" but, arguably, capitalism looking for cheaper labour.

    1. Require anti-bias training

      I thought anti-bias training was not effective.

    2. One glaring example of Princeton’s failure to do this can be found in the Humanities Council, which was established here well over a half century ago. Its significance for scholars in the humanities at the University, as well as its international visibility, cannot be overstated.

      This paragraph seems quite balanced. It gives a specific example. They explicitly pay respect to individuals currently serving while dramatically calling out the lack of underrepresented groups in the history of the Council.

    3. Invest in the pipeline to make lasting demographic change in the graduate and undergraduate bodies.

      This seems like one of the most important things to do.

    4. Use admissions as a tool of anti-racism.

      Hopefully this just means a more racially balanced approach to finding the students of highest capacity.

    5. Elevate faculty of color to prominent leadership positions.

      This suggests that faculty of colour have been held back from leadership positions for their colour. That's a claim at a different level than the claim that anti-black racism permeates our institutions. It's a very specific claim. A lack of racial diversity might still exist even if you have hiring practices based solely on merit. This suggests that either a) they do believe hiring and promotion practices are themselves racial (i.e. not based solely on merit), or b) they believe that there are many people of sufficient merit to place in leadership positions. (There is a 3rd option: they want race to be considered above merit for some decisions.)

    1. The fact that black women outperformed their white counterparts on these measures, however, was not attributed to the punishing reach of racism against whites.

      "Among those who grow up in families with comparable incomes, black men grow up to earn substantially less than the white men. In contrast, black women earn slightly more than white women conditional on parent income." (emphasis mine)

      Black women earn slightly more than white men. Black men earn substantially less. But let's talk about the anti-white-men "racism", because that's clearly just as plausible.

      There are many possible reasons for this seeming disparity between black women and white men, including, yes, anti-black racism (e.g. black women are having to "pick up the slack" because of the demonisation of black men) but to get into these weeds requires more research and analysis, something not offered by the author. (The article doesn't even say if the difference is statistically significant!)

    2. three separate analyses

      The first article is a "working paper", so not submitted to peer review. But it has been critiqued by experts in the field as it has many problems.

      The 2nd paper indicates that "Blacks, Native Americans and Hispanics had higher stop/arrest rates per 10 000 population than white non-Hispanics and Asians". So even if the chance of death per stop isn't different, the chance of death is. (And note that we don't have all the available data on stop rates or reasons for stopping since many police organizations don't report it. And police reports have been found to be full of errors - intentional or not, so that data isn't great, either.)

      The 3rd is behind a paywall.

    3. For instance, 60 percent of blacks attribute disparities in income, jobs, and housing mainly to factors other than bias, according to a 2013 Gallup poll.

      Reading the link provided, the statement is factually accurate, but also blacks are way more likely to blame disparities in incarceration rates on discrimination. So it's not like blacks don't feel systemic racism is real. And other polls further this point: this Pew Poll from 2016 says that 70% of polled blacks say that racial discrimination is a major reason blacks "may have a harder time getting ahead than whites". Survey word choice is key, as here the wording is about blacks in general whereas the one cited is about the survey respondent specifically. There's a lot of expertise in making and interpreting survey data.

    4. A 1994 New York Times article reported that, among college graduates, black women earned slightly more money than white women did.

      The article paints a different picture.

      "Proposed explanations for the disparity included racism, an unintended consequence of affirmative action programs -- employing a woman who is black meets two minority hiring goals -- and the fact that the pool of recent college graduates from which companies can recruit black employees includes more women than men."

    5. Although black immigrants (and especially their children, who are indistinguishable from American blacks) presumably experience the same ongoing systemic biases that black descendants of American slaves do, nearly all black immigrant groups out-earn American blacks, and many—including Ghanaians, Nigerians, Barbadians, and Trinidadians & Tobagonians—out-earn the national average.

      The assumption that it's either overt racism or internal "culture" excludes the more obvious claim: the history of racial brutality has destroyed the community, stolen the cultural leaders and heroes, and destined children and children's children to poverty. This is why racism needs to be understood in the light of implicit bias (note the small "i" and "b"). Assuming the reason is "culture" is just another form of assuming it's "their fault", which is racist.

    6. It’s impossible to disentangle confounding variables like immigrant self-selection, demographic differences, and other unknown factors.

      These are some fundamental issues with blindly interpreting data on, say, West Indian blacks vs American blacks. Immigrant self-selection could be a major reason for any differences. Furthermore, the immigrant paradox describes how recent immigrants (1st and 2nd gen, especially) may display noted improvements in a number of outcomes relative to more established immigrants or non-immigrant populations.

    7. Indeed, cultural explanations of disparity are the exact opposite of racial-supremacist explanations for the same reason that nurture is the opposite of nature.

      Maybe this comes from a different working definition of racism, but blaming a "culture" may not be blaming the individuals within it, but it is blaming the population in the sense that it implies blame should be placed in black people for not changing their culture. It's not the racist prison and drug enforcement policies, it's the culture. It's not redlining, it's the culture. It's not prejudice in hiring practices (same resume, different name), it's the culture.

      Now culture could be a significant proximal cause in the sense that culture influences how people make decisions and place value on their struggles. But focusing on culture as an explanatory tool takes attention away from what caused culture. The whole tone of the above article is serves to draw attention away from this systemic racism. In other words, if racism attacks the culture, and the culture falters, should we really be blaming the disparity in outcome on culture?

      Finally, some have talked about how cultural reasons for black disparities are very much discussed by black communities. So they have very much accepted the culture-behaviour causal link.

    8. Vox

      This article does mention many factors that would act as barriers to entry for black people, but it also talks explicitly about how white people are much more represented in the owners and managers, which I assume is true of basketball as well, especially considering college ball.

    9. Rather than defaulting to systemic bias to explain disparities, we should understand that, even in the absence of discrimination, groups still differ in innumerable ways that affect their respective outcomes.

      Granted, differences between groups can be caused by a number of factors. But slavery et al is one of those causes. And given the magnitude of the history, likely a very significant one.

    10. Indeed, why progressives only commit the disparity fallacy in one direction is never explained.

      Well, a) the author just invented this idea, but b) because slavery.

    11. But in historical eras with far more racism, the gap was reversed. According to Sowell, “[b]lack unemployment rates were lower than that of whites in 1890 and, for the last time, in 1930.”4 Facts like these, however, are never explained in terms of discrimination in favor of blacks.

      Because there's very little evidence for that mechanism. Why posit things as a cause that are not historical? This quote demands more details. It is used here to suggest that economic outcomes overall were better for blacks than whites in 1890, something which I find highly implausible. Clearly in 1890, overt racism was still in full swing. So is there something deeper to this? Could it be, for example, that the type of job blacks had paid less and involve more hours? Without more data, this statement is a red herring.

    12. Any instance of whites outperforming blacks is adduced as evidence of discrimination. But when a disparity runs the other way—that is, blacks outperforming whites—discrimination is never invoked as a causal factor.

      But there isn't history of systemic racism against white people. "Race" was created by elites specifically to get the poor whites on their side, and so it was targeted against blacks. In other words, this statement is saying "why are differences in outcome in favour of blacks not because of the slavery that white people experienced"? Because they didn't experience it.

    13. But while psychological biases may sufficiently explain progressophobia on most other topics, our denialism about racial progress calls for a deeper explanation—an explanation in terms of widely-held beliefs about race and inequality.

      It seems strange that in the previous paragraph, he introduces a "progressive's" idea that might explain this, but them immediately says that isn't the situation here. Why introduce it, then?

  6. Nov 2019
    1. In what manner shall we deal with the people of this age, who have each chosen to follow a different religion and who each regard their own faith and religion as excelling and surpassing all the others, that we may be shielded from the onslaught of their words and deeds?

      He answers this by affirming that people have indeed forgotten justice. But the questioner is also viewing the world within the scope of a particular religion, so they could be easily within this group of the bereft. So He doesn't answer the question within the context of only the questioner's religion, but instead provides a broader view.

    2. The world hath been illumined with the splendours of His revelation, yet how few are the eyes that can behold it!

      You can behold this light from many perspectives. To truly understand it, you need to let go of your limited dogmatic ideas. Perhaps this means that prophesies within individual books should only be used to allow yourself to believe the new stuff, but once you do, you behold a wide array of splendours.

    3. “Our Books have announced that Sháh Bahrám will come, invested with manifold signs, to guide the people aright….”

      Baha'u'llah doesn't address the specifics of this idea. Instead, He seems to suggest that it's a new day and that we should let go of the old traditions. They have come to pass. Advance your thinking.

    4. were the peoples of the world to grasp the true significance of the words of God, they would never be deprived of their portion of the ocean of His bounty

      Bounty comes from understanding the words. The Revelation is, firstly, the words (and the spiritual energy they contain).

  7. Aug 2019
    1. A notable by-product of a move of clinical as well as research data to the cloud would be the erosion of market power of EMR providers.

      But we have to be careful not to inadvertently favour the big tech companies in trying to stop favouring the big EMR providers.

    2. cloud computing is provided by a small number of large technology companies who have both significant market power and strong commercial interests outside of healthcare for which healthcare data might potentially be beneficial

      AI is controlled by these external forces. In what direction will this lead it?

    3. it has long been argued that patients themselves should be the owners and guardians of their health data and subsequently consent to their data being used to develop AI solutions.

      Mere consent isn't enough. We consent to give away all sorts of data for phone apps that we don't even really consider. We need much stronger awareness, or better defaults so that people aren't sharing things without proper consideration.

    4. To realize this vision and to realize the potential of AI across health systems, more fundamental issues have to be addressed: who owns health data, who is responsible for it, and who can use it? Cloud computing alone will not answer these questions—public discourse and policy intervention will be needed.

      This is part of the habit and culture of data use. And it's very different in health than in other sectors, given the sensitivity of the data, among other things.

    5. In spite of the widely touted benefits of “data liberation”,15 a sufficiently compelling use case has not been presented to overcome the vested interests maintaining the status quo and justify the significant upfront investment necessary to build data infrastructure.

      Advancing AI requires more than just AI stuff. It requires infrastructure and changes in human habit and culture.

    6. However, clinician satisfaction with EMRs remains low, resulting in variable completeness and quality of data entry, and interoperability between different providers remains elusive.11

      Another issue with complex systems: the data can be volumous but poor individual quality, relying on domain knowledge to be able to properly interpret (eg. that doctor didn't really prescribe 10x the recommended dose. It was probably an error.).

    7. Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.9

      AI depends on:

      • static processes - if the population you are predicting changes relative to the one used to train the model, all bets are off. It remains to be seen how similar they need to be given the brittleness of AI algorithms.
      • homogeneous population - beyond race, what else is important? If we don't have a good theory of health, we don't know.
    8. Simply adding AI applications to a fragmented system will not create sustainable change.
  8. May 2019
    1. There’s a bug in the evolutionary code that makes up our brains.

      Saying it's a "bug" implies that it's bad. But something this significant likely improves our evolutionary fitness in the past. This "bug" is more of a previously-useful adaptation. Whether it's still useful or not is another question, but it might be.

  9. Mar 2019
    1. And it’s a contributor to the emergence of an integrated social science to understand human decision-making.

      Again, economics is less a "thing". It's merging with other social and policy science.

    2. the basic tenets of economic theory still provide a solid foundation

      What are the basic tenets? If we are challenging all aspects of economic theory (rationality, perfect knowledge, static models, profit-maximization...) then what are the basic tenets that remain unchanged?

      How is economics, with all these diverse faces, different from population health? Both can include all the same forces. It's only that the latter focuses on health as a key outcome.

    3. Political economic research analyzes politicians as agents who pursue self-interest subject to their own constraints

      All motivation can be seen as "self interested" if you include morals for social betterment.

    4. The pioneer of behavioral economists was Herbert Simon, who developed the notion of bounded rationality, namely that an individual is rational, but that their ability to compute, assess, and decide are limited especially given constraints on time to make a decision.
    5. Thus each era’s models were appropriate for their time, whether Adam Smith’s or Milton Friedman’s.

      This assumes it couldn't have been done better at the time.

    1. Perhaps, he realized, these viruses don’t actually need to unite their segments in the same host cell. “If theory was saying that this is impossible, maybe the viruses just don’t do it,” he says. “And once we had this stupid idea, testing it was very easy.”

      This is different from the theory of evolution or the theory of electromagnetism. It's a smaller things, like an assumption. Evolution, also in biology, is a more encompassing set of ideas. So the theoretical framework has a hierarchy. Perhaps at the top is a Kuhnian paradigm or a Lakatosian research program.

      Does this hierarchy different between sciences, though? Like, how hard is it to take a new assumption and grow it into a fully-fledged theory? Biology is more complex than physics, with more "facts" and forms to understand. Evolution is different from electromagnetism because it doesn't limit as much. EM clearly prescribes what's possible and what isn't, whereas evolution doesn't make the distinction so clearly.

    1. such as scope, simplicity, fruitfulness, accuracy

      Theories can be measured according to multiple metrics. The current default appears to be predictive accuracy, but this lists others, such as scope. If theory A predicts better but narrower and theory B predicts worse (in A's domain) but much more broadly, which is a better theory?

      Others might be related to simplicity and whatnot. For example, if a theory is numerical but not explanatory (such as scaling laws or the results of statistical fitting) this theory might be useful but not satisfying.

    2. Like in evolution, the process does not change toward some fixed goal according to some fixed rules, methods or standards, but rather it changes away from the pressures exerted by anomalies on the reigning theory (Kuhn 1962, 170–173). The process of scientific change is eliminative and permissive rather than instructive.

      This is similar to evolution: not guided, but not random. Does this view contradict the idea of progression?

      It also suggests a complex dynamic system that possess path dependence and environmental interaction.

    1. The IP metaphor has had a half-century run, producing few, if any, insights along the way.

      The value of an analogy is in the fruit it bears. If no insights have resulted, the analogy is weak.

    2. Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it. This is perhaps the most egregious way in which the IP metaphor has distorted our thinking about human functioning.

      Again, this doesn't conflict with a machine-learning or deep-learning or neural-net way of seeing IP.

    3. No ‘copy’ of the story is ever made

      Or, the copy initially made is changed over time since human "memory" is interdependent and interactive with other brain changes, whereas each bit in computer memory is independent of all other bits.

      However, machine learning probably results in interactions between bits as the learning algorithm is exposed to more training data. The values in a deep neural network interact in ways that are not so obvious. So this machine-human analogy might be getting new life with machine learning.

    4. The IP perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight

      I don't see how this is true. The IP perspective depends on algorithms. There are many different algorithms to perform various tasks. Some perform reverse-kinematic calculations, but others conduct simpler, repeated steps. In computer science, this might be dynamic programming, recursive algorithms, or optimization. It seems that the IP metaphor still fits: it's just that those using the metaphor may not have updated their model of IP to be more modern.

    5. just as we now view the hydraulic and mechanical metaphors to be silly

      We certainly view them as silly when compared to our modern perspective, but viewed from the knowledge of the day, it isn't silly. You could also argue that early models of celestial mechanics (that god's pulled the sun across the sky, or something) were silly.

    6. either replaced by another metaphor or, in the end, replaced by actual knowledge

      You can argue that all knowledge is a metaphor of some type. All knowledge is, at best, a simplified map from the intellectual/logical to the empirical. It's just that the metaphor/map might be written in human language, mathematical language, or algorithmic language.

  10. Jan 2019
    1. Bahá’u’lláh states that the knowledge of God is revealed through His Manifestation, Who has an innate awareness of the human condition and the social order

      On behalf of the Universal House of Justice: Baha'u'llah's innate knowledge is of the human reality.

      Is it also of the animal, vegetable, and mineral realities?

    1. This is the meaning of the “Day of Resurrection,” spoken of in all the scriptures, and announced unto all people. Reflect, can a more precious, a mightier, and more glorious day than this be conceived, so that man should willingly forego its grace, and deprive himself of its bounties, which like unto vernal showers are raining from the heaven of mercy upon all mankind?

      I think this meaning is that "Resurrection" is the return of a Manifestation in another human frame. And this is stated to be clearly more glorious than the literal interpretations of past scripture.

      Why is it clearly more glorious?

      1. Everyone has access. And it leads to empowerment.
      2. It allows us to keep science, which is pretty awesome.
      3. It doesn't allow us to just wait for the rapture - see point 1 about empowerment.
      4. It allows us to see all religions as united in spirit.
      5. Related to point 3 and 4, it allows us to unite with non-religious people.
      6. All of this without "doing violence to the facts".
    1. Should a man wish to adorn himself with the ornaments of the earth, to wear its apparels, or partake of the benefits it can bestow, no harm can befall him, if he alloweth nothing whatever to intervene between him and God, for God hath ordained every good thing, whether created in the heavens or in the earth, for such of His servants as truly believe in Him.

      Definition of attachment.

      Also, if your actions are due to addiction but you're consciously trying to counter it (or even, if you don't have help in quitting, just disliking it), perhaps that's different from allowing something to "intervene between him and God".

    2. Know ye that by “the world” is meant your unawareness of Him Who is your Maker, and your absorption in aught else but Him. The “life to come,” on the other hand, signifieth the things that give you a safe approach to God, the All-Glorious, the Incomparable.

      The next world isn't another world. It's a way of being in this world. Using this idea alone, no surviving memory or individuality is required after death.

    3. Say: Teach ye the Cause of God, O people of Bahá, for God hath prescribed unto every one the duty of proclaiming His Message, and regardeth it as the most meritorious of all deeds.

      The discussion in book 6 after this quote explores what a duty is. It presents the idea that, like eating, laws in the Faith to pray or teach are not arbitrary rules, but statements about our nature, and conducive to our growth. This implies that they should also be sources of joy.

    4. Say: Should your conduct, O people, contradict your professions, how think ye, then, to be able to distinguish yourselves from them who, though professing their faith in the Lord their God, have, as soon as He came unto them in the cloud of holiness, refused to acknowledge Him, and repudiated His truth?

      What's the difference between a professed Baha'i who (purposefully?) breaks the laws, and people who do not accept the next Manifestation when They come?

      How does this fit with, for example, alcohol addiction? Is that conduct that is contradicting someone's professions? Maybe not if your professions are "alcohol is bad, we shouldn't do it, but I am addicted and trying to recover", as opposed to "Baha'u'llah says alcohol is bad, but I drink anyway and don't see it as a problem".

    5. Beware lest ye contend with anyone, nay, strive to make him aware of the truth with kindly manner and most convincing exhortation. If your hearer respond, he will have responded to his own behoof, and if not, turn ye away from him, and set your faces towards God’s sacred Court, the seat of resplendent holiness.
    1. A comment at the bottom by Barbara H Partee, another panelist alongside Chomsky:

      I'd like to see inclusion of a version of the interpretation problem that reflects my own work as a working formal semanticist and is not inherently more probabilistic than the formal 'generation task' (which, by the way, has very little in common with the real-world sentence production task, a task that is probably just as probabilistic as the real-world interpretation task).

    2. There is a notion of success ... which I think is novel in the history of science. It interprets success as approximating unanalyzed data.

      This article makes a solid argument for why statistical and probabilistic models are useful, not only for prediction, but also for understanding. Perhaps this is a key point that Noam misses, but the quote narrows the definition to models that approximate "unanalyzed data".

      However, it seems clear from this article that the successes of ML models have gone beyond approximating unanalyzed data.

    3. But O'Reilly realizes that it doesn't matter what his detractors think of his astronomical ignorance, because his supporters think he has gotten exactly to the key issue: why? He doesn't care how the tides work, tell him why they work. Why is the moon at the right distance to provide a gentle tide, and exert a stabilizing effect on earth's axis of rotation, thus protecting life here? Why does gravity work the way it does? Why does anything at all exist rather than not exist? O'Reilly is correct that these questions can only be addressed by mythmaking, religion or philosophy, not by science.

      Scientific insight isn't the same as metaphysical questions, in spite of having the same question word. Asking, "Why do epidemics have a peak?" is not the same as asking "Why does life exist?". Actually, that second question can be interested in two different ways, one metaphysically and one physically. The latter interpretation means that "why" is looking for a material cause. So even simple and approximate models can have generalizing value, such as the Schelling Segregation model. There is difference between models to predict and models to explain, and both have value. As later mentioned in this document, theory and data are two feet and both are needed for each other.

    4. This page discusses different types of models

      • statistical models
      • probabilistic models
      • trained models

      and explores the interaction between prediction and insight.

    5. Chomsky (1991) shows that he is happy with a Mystical answer, although he shifts vocabulary from "soul" to "biological endowment."

      Wasn't one of Chomsky's ideas that humans are uniquely suited to language? The counter-perspective espoused here appears to be that language emerges, and that humans are only distinguished by the magnitude of their capacity for language; other species probably have proto-language, and there is likely a smooth transition from one to the other. In fact, there isn't a "one" nor an "other" in a true qualitative sense.

      So what if we discover something about the human that appears to be required for our language? Does this, then, lead us to knowledge of how human language is qualitatively different from other languages?

      Can probabilistic models account for qualitative differences? If a very low, but not 0, probability is assigned to a given event that we know is impossible from our theory-based view, that doesn't make our probabilistic model useless. "All models are wrong, some are useful." But it seems that it does carry with it an assumption that there are no real categories, that categories change according to the needs, and are only useful in describing things. But the underlying nature of reality is of a continuum.

  11. jasss.soc.surrey.ac.uk jasss.soc.surrey.ac.uk
    1. To Guide Data Collection

      This seems to be, essentially, that models are useful for prediction, but prediction of unknowns in the data instead of prediction of future system dynamics.

    2. Without models, in other words, it is not always clear what data to collect!

      Or how to interpret that data in the light of complex systems.

    3. Plate tectonics surely explains earthquakes, but does not permit us to predict the time and place of their occurrence.

      But how do you tell the value of an explanation? Should it not empower you to some new action or ability? It could be that the explanation is somewhat of a by-product of other prediction-making theories (like how plate tectonics relies on thermodynamics, fluid dynamics, and rock mechanics, which do make predictions).

      It might also make predictions itself, such as that volcanoes not on clear plate boundaries might be somehow different (distribution of occurrence over time, correlation with earthquakes, content of magma, size of eruption...), or that understanding the explanation for lightning allows prediction that a grounded metal pole above the house might protect the house from lightning strikes. This might be a different kind of prediction, though, since it isn't predicting future dynamics. Knowing how epidemics works doesn't necessarily allow prediction of total infected counts or length of infection, but it does allow prediction of minimum vaccination rates to avert outbreaks.

      Nonetheless, a theory as a tool to explain, with very poor predictive ability, can still be useful, though less valuable than one that also makes testable predictions.

      But in general, it seems like data -> theory is the explanation. Theory -> data is the prediction. The strength of the prediction depends on the strength of the theory.

  12. Jul 2018
    1. The empty brain

      To look at what the brain manifests isn't enough. You can find a machine analogy for most of what the brain manifests, but that doesn't mean the analogy is valid, If the brain is actually a computer, then it must not only behave like one, but also response to counter-factuals (experiments) like one. It doesn't. So either the brain is a much more complex computing machine than we ever have, or it is better thought of as something else.

    2. Think how difficult this problem is. To understand even the basics of how the brain maintains the human intellect, we might need to know not just the current state of all 86 billion neurons and their 100 trillion interconnections, not just the varying strengths with which they are connected, and not just the states of more than 1,000 proteins that exist at each connection point, but how the moment-to-moment activity of the brain contributes to the integrity of the system. Add to this the uniqueness of each brain, brought about in part because of the uniqueness of each person’s life history, and Kandel’s prediction starts to sound overly optimistic. (In a recent op-ed in The New York Times, the neuroscientist Kenneth Miller suggested it will take ‘centuries’ just to figure out basic neuronal connectivity.)

      An example of a problem that is both hard to solve and hard to verify. More than NP-hard.

    3. there is no reason to believe that any two of us are changed the same way by the same experience.

      Experiential changes are highly sensitive to the brain's starting conditions - a feature of complex systems.

    4. This might sound complicated, but it is actually incredibly simple, and completely free of computations, representations and algorithms.

      Actually, this sounds exactly like an algorithm, though one we clearly are conscious of. You could build a robot to do this, and in fact, some have. See Perceptual Control Theory by William Powers.

    5. no image of the dollar bill has in any sense been ‘stored’ in Jinny’s brain

      There was research using machine learning to "see" pictures that people imagined. It was able to somewhat decode the measurements of brain activity at various locations outside the head and reconstruct something similar to what was being thought of. True, this isn't a memory, but it could be that the "thought" is "stored" in a pattern of electric potentials, and we just can't decode it. A computer stores information bits without any interaction (ideally). Clearly, human memory isn't so orthogonal. But why couldn't it be some type of encoding into a measurable pattern where, whether it's to save space, improve recall through association, or whatever else?

    6. By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.

      Our most complex machine is always used as an analogy for the brain.

    7. Predictably, just a few years after the dawn of computer technology in the 1940s, the brain was said to operate like a computer, with the role of physical hardware played by the brain itself and our thoughts serving as software.

      This is a very interesting set of correlations, but could it be that our understanding of intelligence is improving precisely because our machines are improving? Our analogies are getting better all the time. Perhaps the current one is "good enough".

    8. knowledge

      We don't develop knowledge ever?

    9. software

      Biological systems of course don't have "software", but isn't this just what gives something it's behaviours and reactions?

    10. rules

      A reflex isn't a rule?

    1. No statistical procedure allows one to somehow see a mundane, taken-for-granted observation in a radically different and new way.

      What makes machine learning unique, perhaps, is that it learns. The question should be about the nature of that learning rather than whether it's possible qualitatively. What about machine learning is not going to match human learning?

    2. But intelligence and rationality are more than just calculation or computation, and have more to do with the human ability to attend to and identify what is most relevant.

      Can this be modelled well with calculation, though? Perhaps the limitation is that we don't know how to code relevance-finding, nor can we train it (in situations that interact with the analog world rather than being explicitly in the simulated).

    3. Kahneman concluded his aforementioned presentation to academics by arguing that computers or robots are better than humans on three essential dimensions: they are better at statistical reasoning and less enamoured with stories; they have higher emotional intelligence; and they exhibit far more wisdom than humans.

      A little over-the-top?

    4. But, while Kahneman calls for large-scale replications of priming studies, the argument here is not that we need more studies or data to verify that people indeed miss blatantly obvious gorillas. Instead, we need better interpretation and better theories.

      More data vs more theories

      Humans are biased by our theories (though not totally). But isn't that the goal of science, to collectively question our assumptions and experiments? We need to attempt to falsify our theories not only by questioning the experiments and repeating them, but also by questioning the theories used to interpret data.

    5. The worry is that the growing preoccupation of many behavioural scientists – across psychology, economics and the cognitive sciences – with blindness and bias causes scientists to look for evidence of human blindness and bias.

      That's not irrational: If someone found gold someplace, I would want to look there, too. A global optimization algorithm will require both trusting your recent results (if you are finding more good stuff here, keep searching here) and distrusting them (to avoid finding a local optimum).

      But this is different because it isn't just about finding the objective optimum, but about interpreting the data itself. If the search strategy is not in data but in data interpretation, though, this analogy might hold. The scientific movement, composed of many scientists, might eventually find the truth, though individuals scientists might get distracted by perceived truth. But what's the measure of truth if not how it manifests in action? ("Judge the tree by the fruit") We thought the geocentric picture was best, but when a small group of people showed that the heliocentric view was much better (in prediction - i.e. a type of scientific action), eventually, the phase-change happened and we left the local optimum.

    6. How you interpret the finding depends on what you are looking for.

      Yes, but not totally. "All things are relative, but some more than others."

    7. After all, many of the most significant scientific discoveries resulted not from reams of data or large amounts of computational power, but from a question or theory.

      There are examples of how data has led to theory as well, Tycho Brahe being a solid one.

    8. ‘omniscience in the observer’
    9. To illustrate, consider Isaac Newton.

      But there are examples of where our theory has led us astray, the heliocentric vision of the universe being an example. If not for that attachment to previous thinking, we might have learned more quickly about the heliocentric truth.

      'Even as He hath revealed: "As oft as an Apostle cometh unto you with that which your souls desire not, ye swell with pride, accusing some of being impostors and slaying others."' - Kitab-i-Iqan

    10. However, computers and algorithms – even the most sophisticated ones – cannot address the fallacy of obviousness. Put differently, they can never know what might be relevant.

      One goal of systems science and modelling, to explore what might be relevant and give us better heuristics.

    11. At the other extreme we have behavioural economics, which focuses on human bias and blindness by pointing out biases or obvious things that humans miss.
    12. So, given the problem of too much evidence – again, think of all the things that are evident in the gorilla clip – humans try to hone in on what might be relevant for answering particular questions. We attend to what might be meaningful and useful

      Consumat, heuristics - actually, this does work with thinking fast and slow. But maybe the divide isn't so clear - a spectrum?

    13. ‘blind to the obvious, and that we also are blind to our blindness’
    14. building on Herbert Simon’s 1950s work on bounded rationality
    15. In other words, there is no neutral observation.

      Similar to the objectivism/subjectivism divide and social construction of truth/positivist scientific method.

      Part of meditative practices is sometimes a focus of the mind, or an absence and an "insistent" self. Perhaps it's simply quieting part of the questioning mind in order to make room for a better side, but maybe it's about removing one's preconceived notions.

    16. The implication (contrary to psychophysics) is that mind-to-world processes drive perception rather than world-to-mind processes.

      Maybe both? Causal feedback.