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May 27, 2018 | No comments

Summary Against Modern Thought: Human Happiness Does Not Come In Pleasures Of The Flesh

Previous post.

This is the . real summary against modern thought.

That Human Felicity Does Not Come In Pleasures Of The Flesh

1 Now, it is clear from what we have said that it is impossible for human felicity to consist in bodily pleasures, the chief of which are those of food and sex.

2 In fact, we have shown that in the order of nature pleasure depends on operation, and not the converse. So, if operations are not the ultimate end, the pleasures that result from them are not the ultimate end, either; nor are they concomitant with the ultimate end. It stands to reason that the operations which accompany the above-mentioned pleasures are not the ultimate end, for they are ordered to certain ends that are quite obvious: eating, for instance, to the preservation of the body, and sexual intercourse to the generation of offspring. Therefore, the aforementioned Pleasures are not the ultimate end, nor are they concomitants of the ultimate end. So, felicity is not to be located in these pleasures.

3 Again, the will is higher than sense appetite, for it moves itself, as we said above. Now, we have already shown that felicity does not lie in an act of the will. Still less will it consist in the aforementioned pleasures which are located in the sense appetite.

4 Besides, felicity is a certain kind of good, appropriate to man. Indeed, brute animals cannot be deemed happy, unless we stretch the meaning of the term. But these pleasures that we are talking about are common to men and brutes. So, felicity should not be attributed to them.

5 Moreover, the ultimate end is the noblest appurtenance of a thing; in fact, the term means the best. But these pleasures are not agreeable to man by virtue of what is noblest in him, namely, his understanding, but by virtue of his sense capacity. So, felicity should not be located in pleasures of this kind.

6 Furthermore, the highest perfection of man cannot lie in a union with things inferior to himself, but, rather, in a union with some reality of a higher character, for the end is better than that which is for the sake of the end. Now, the aforementioned pleasures consist in this fact: that man is, through his senses, united with some things that are his inferiors, that is, with certain. sensible objects. So, felicity is not to be located in pleasures of this sort.

7 Again, something which is not good unless it be moderated is not good of itself; rather, it receives goodness from the source of the moderation. Now, the enjoyment of the aforementioned pleasures is not good for man unless it be moderated; otherwise, these pleasures will interfere with each other. So, these pleasures are not of themselves the good for man. But that which is the highest good is good of itself, because what is good of itself is better than what depends on something else. Therefore, such pleasures are not the highest good for man, that is, felicity.

Notes What else can you say except Amen? Especially again in the next point.

8 Besides, in the case of all things that are predicated per se, an absolute variation is directly accompanied by a similar variation in the degree of intensification. Thus, if a hot thing heats, then a hotter thing heats more, and the hottest thing will heat the most. So, if the aforementioned pleasures were goods of themselves, the maximum enjoyment of them should be the best. But this is clearly false, for excessive enjoyment of them is considered vicious, and is also, harmful to the body, and it prevents the enjoyment of similar pleasure

9 Moreover, virtuous acts are praiseworthy because they are ordered to felicity. So, if human felicity consisted in the aforementioned pleasures, a virtuous act would be more praiseworthy when it involved the enjoyment of these pleasures than when it required abstention from them. However, it is clear that this is false, for the act of temperance is given most praise when it involves abstaining from pleasures; as a result, it gets its name from this fact. Therefore, man’s felicity does not lie in the aforesaid pleasures.

Notes This is still true in private praise. What’s publicly bruited is altogether different these days.

10 Furthermore, the ultimate end of everything is God, as is clear from what has been indicated earlier. So, we should consider the ultimate end of man to be that whereby be most closely approaches God. But, through the aforesaid pleasures, man is kept away from a close approach to God, for this approach is effected through contemplation, and the aforementioned pleasures are the chief impediment to contemplation, since they plunge man very deep into sensible things, consequently distracting him from intelligible objects. Therefore, human felicity must not be located in bodily pleasures.

11 Through this conclusion we are refuting the error of the Epicureans, who placed man’s felicity in these enjoyments. Acting as their spokesman, Solomon says in Ecclesiastes (5:17): “This therefore seemed good to me, that a man should eat and drink and enjoy the fruit of his labor, and this is his portion”; and again in Wisdom (2:9): “let us everywhere leave tokens of joy, for this is our portion, and this our lot.”

12 Also refuted is the error of the Cerinthians, for they told a fabulous story about ultimate felicity, that after the resurrection there would be, in the reign of Christ, a thousand years of carnal pleasures of the belly. Hence, they were also called Chiliasts; that is, Millenarians.

13 Refuted, too, are the fables of the Jews and the Saracens, who identified the rewards for just men with these pleasures, for felicity is the reward for virtue.

13 Saracens, i.e. Muslims.

May 26, 2018 | 13 Comments

Insanity & Doom Update XXXVI

Breaking Item #2 Ireland chooses death. Interesting the only group in favor of life were the wise, i.e. ages 65+. Letting the young vote (assuming a country insists on voting) is disastrous.

Breaking Item #2 Britain begins arrests of political prisoners. We now wonder they will let him defect, or will he like Kevin Crehan mysteriously die in jail?

Item British schools are removing analog clocks from classrooms because kids can’t read them

Schools in the United Kingdom are beginning to remove analog clocks from the classroom — because students are complaining that they can’t read them, reports say.

Officials have begun replacing the traditional clocks with digital ones as children have been unable to tell the correct time on analog clocks, The Telegraph reports.

“The current generation aren’t as good at reading the traditional clock face as older generations,” Malcolm Trobe, deputy general secretary at the Association of School and College Leaders in England, told the publication. “Nearly everything they’ve got is digital so youngsters are just exposed to time being given digitally everywhere.”…

“It is amazing the number of students I am coming across in year 10, 11 and in sixth form who do not know how to tell the time,” she began. “We live in a world where everything is digital. We are moving towards a digital age and they do not necessarily have analogue watches anymore and they have mobile phones with the time on.”

How about these alternative headlines? “British schools are removing history books from classrooms because kids can’t read them.” “British schools are removing maps from classrooms because kids can’t find north”.

There is no use being sarcastic, and no use whatsoever to explain that if kids can’t do an ordinary and necessary task they should be taught that task. At school. By teachers. Yes, it’s necessary. Look at the pic atop the post. How could the poor kiddies possibly read this non-digital gauge?

Item Where Young Europeans Aren’t Religious

God bless Poland. Bóg, Honor, Ojczyzna!

Item Are you anti-GMO? Then you’re anti-science, too.

He found the scientific consensus on climate change to be compelling. But he found the evidence for the safety of GMOs to be at least as strong. “I couldn’t deny the scientific consensus on GMOs,” he writes, “while insisting on strict adherence to the one on climate change, and still call myself a science writer.”

It was, he says, “a decisive turning point in my life.” But the public debate on GMOs turned in exactly the opposite direction. Just as scientists were becoming more confident in the safety of GMOs, global anti-GMO activists, led by Greenpeace, were making the issue a hot potato (including a genetically modified insect-resistant potato cultivated in Canada). On the strength of myths (that using genetically modified seeds somehow resulted in suicides among Indian farmers) and deception (tying GMOs to autism or cancer), supermarket chains, food companies and eventually governments were frightened into anti-GMO stances. In the developing world, anti-GMO activists spread rumors that GMO consumption resulted in homosexuality and infertility.

I do not care in the least about any of these claims. I do care about scidolatry, the worship of Science. People, non-scientists in particular, wield Science like a club. Do this: Science says so. They are no different from the Saudi religious police patrolling the streets with their billy clubs looking for people not praying.

May 25, 2018 | 5 Comments

Other Practical Books On Par With Uncertainty? Reader Question

Got this email from VD. I’ve edited to remove any personal information and to add blog-standard style and links. I answered, and I remind all readers of the on-going claassre, but I thought I’d let readers have a go at answering, too.

I greatly appreciate the wealth of material contained on your website, and I am an avid reader of both your articles and papers and a consumer of your videos/lectures/podcasts on YouTube. You bring a clarity to the oft misunderstood, and—to an uncultured pleb such as myself—seemingly esoteric field of magical, complex formulae known as statistics.

I have a twofold question: First, do you have any plans to produce a textbook for students utilizing the principles within Uncertainty: The Soul of Modeling, Probability and Statistics—something along the lines of an updated Breaking the Law of Averages? I confess I have not yet read Uncertainty but assure you that it is at the top of my books-to-purchase list (although I’m under the impression much of the content therein is elucidated on your blog). If Uncertainty is the book I’m looking for then please let me know. I am also working through Breaking the Law and find it extremely helpful, lacking only in solutions to check my work.

If I simply need to go through Breaking the Law a few more times, please let me know if that’s the best route. In any event, I would appreciate a sequel that is an even better synthesis of the ideas since-developed and distilled in Uncertainty while also functioning as introductory-to-intermediate text on logical probability/objective Bayesian statistics. I appreciate your approach utilizing logic, common sense, and observation, to quantify the uncertainty for a given set of premises rather than becoming so consumed with parametrical fiddling that I forgot the nature of the problem I was trying to solve.

Second, if no new book is in the works, do you know of any good textbooks or resources for undiscerning novices such as myself for learning logical probability/objective Bayesian statistics that aren’t inundated with the baggage of frequentist ideals or the worst parts of classical statistics, baggage still dragged around by many of the currently available textbooks and outlets for learning statistics? It seems every other book or resource I pick up has at least a subset of the many errors and problems you’ve exposed and/or alluded to in your articles. If no such “pure” text exists, can you recommend one with a list of caveats? I also have found a copy of Jaynes’ Probability Theory, so I’ve added that to the pile of tomes to peruse. Since reading your blog I now make a conscious effort to mentally translate all instances of “random”, “chance”, “stochastic”, etc. to “unknown,” as well as actively oppose statements that “x entity is y-distributed (usually normally, of course!)” and recognize the fruits of the Deadly Sin of Reification (models and formulae, however elegant, are not reality).

I currently work to some degree as an analyst in Business Intelligence/Operations for a [large] company—a field where uncertainty, risk, and accurate predictive modeling are of paramount importance—and confess my grasp of mathematics and statistics is often lacking (I am in the process of reviewing my high school pre-calculus algebra and trigonometry so I can finally have a good-spirited go at calculus and hopefully other higher math). I think my strongest grasp at this point is philosophy (which I studied in undergrad with theology and language), and then logic and Boolean algebra, having spent a bit of time in web development and now coding Business Intelligence solutions. It’s the math and stats part that’s weak. If only I could go back 10 years and give myself a good talking to; hindsight’s 20-20 I suppose.

While not aiming to be an actuary by any measure, I want to be able to understand statements chock full of Bayesian terminology like the following excerpt from an actuarial paper on estimating loss. I want to discern whether such methods and statistics are correct:

“We will also be assuming that the prior distribution (that is, the credibility complement, in Bayesian terms) is normal as well, which is the common assumption. This is a conjugate prior and the resulting posterior distribution (that is, the credibility weighted result) will also be normal. Only when we assume normality for both the observations and the prior, Bayesian credibility produces the same results as Bühlmann-Straub credibility. The mean of this posterior normal distribution is equal to the weighted average of the actual and prior means, with weights equal to the inverse of the variances of each. As for the variance, the inverse of the variance is equal to the sum of the inverses of the within and between variances (Bolstad 2007).” (Uri Korn, “Credibility for Pricing Loss Ratios and Loss Costs,” Casualty Actuarial Society E-Forum, Fall 2015).

I understand maybe 25% of the previous citation.

My end goal is to professionally utilize the epistemological framework given on your blog and in Uncertainty. I want to be able to do modeling and statistics the right way, based on reality and observables, without the nuisances of parameters and infinity if they are not needed. I deal with mostly discrete events and quantifications bounded by intervals far smaller than (-infinity, +infinity) or (0, infinity),

I appreciate any advice you could share. Thank you sir!

Cordially,
VD

May 24, 2018 | 3 Comments

Manipulating the Alpha Level Cannot Cure Significance Testing

Nothing can cure significance testing. Except a bullet to the p-value.

(That sound you heard was from readers pretending to swoon.)

The paper is out and official—and free!: “Manipulating the Alpha Level Cannot Cure Significance Testing“. I am one (and a minor one) of the—Count ’em!—fifty-eight authors.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

My friends, this is peer-reviewed, therefore according to everything we hear from our betters, you have no choice but to believe each and every word. Criticizing the work makes you a science denier. You will also be reported to the relevant authorities for your attitude if you dare cast any doubt.

I mean it. Peer review is everything, a guarantor of truth. Is it not?

Or do we allow the possibility of error? And, if we do, if we are allowed to question this article, are we not allowed to question every article? That sounds mighty close to Science heresy, so we’ll leave off and concentrate on the paper.

Now I am with my co-authors a lot of the way. Except, as regular readers know, I would impose my belief that null hypothesis significance testing be banished forevermore. Just as the “There is some good in p-values if properly used” folks would impose their belief that there is some good in p-values. Which there is not.

Another matter is “effect size”, which almost always means a statement about a point estimate of a parameter inside an ad hoc model. These are not plain-English effect sizes, which implies causality. How much effect x has on y. But statistical models can’t tell you that. They can, when used in a predictive sense, say how much the uncertainty of y changes when x does. So “effect size” is, or should, be thought of in an entirely probabilistic way.

The conclusion we can all agree with:

It seems appropriate to conclude with the basic issue that has been with us from the beginning. Should p-values and p-value thresholds, or any other statistical tool, be used as the main criterion for making publication decisions, or decisions on accepting or rejecting hypotheses? The mere fact that researchers are concerned with replication, however it is conceptualized, indicates an appreciation that single studies are rarely definitive and rarely justify a final decision. When evaluating the strength of the evidence, sophisticated researchers consider, in an admittedly subjective way, theoretical considerations such as scope, explanatory breadth, and predictive power; the worth of the auxiliary assumptions connecting nonobservational terms in theories to observational terms in empirical hypotheses; the strength of the experimental design; and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

Bonus Disguising p-values as “magnitude-based inference” won’t help, either, as this amusing story details. Gist: some guys tout massaged p-values as innovation, are exposed as silly frauds, and cry victim, a cry which convinces some.

Moral: The best probability is probability, and not some ad hoc conflation of probability with decision, which is what all “hypothesis tests” are.