Book Review: "Noise" by Daniel Kahneman, Olivier Sibony, and Cass Sunstein

I recently read the book Noise by Daniel Kahneman, Olivier Sibony, and Cass Sunstein. I will refer to it as the current book, because it was written after the book Thinking, Fast and Slow by Kahneman (one of the authors of the current book); I will refer to the latter book as the previous book because many concepts from the previous book are briefly reviewed in the current book, and as I reviewed the previous book in the post just before this one on this blog [LINK], I will sometimes compare some aspects of the current book to the previous book.

The current book introduces the concepts of statistical noise & statistical bias in human judgments, discusses the psychological biases that can lead to statistical biases & noise (of which statistical noise can be clearly seen even in the absence of clear information about statistical biases), demonstrates how statistical noise can lead to uncontrolled & large variations in human judgments in fields like criminal justice, medicine, forensic science, insurance claims adjustment, corporate hiring, and college admissions, explains the sorts of systematic techniques at individual & organizational levels that can be used to reduce noise in judgments, and discusses some tradeoffs that may be encountered when implementing these noise reduction strategies. The authors' discussion of many of the psychological biases that lead to statistical noise in judgments reviews concepts from the previous book, especially Systems 1 & 2.

When reading the current book, I found myself generally agreeing with the discussions of techniques to reduce noise in domains where the presence of significant statistical noise in judgments is broadly recognized as a severe problem. These techniques include aggregating predictions or evaluations that are made independently, structuring/sequencing discussions among people judging things so that their decisions don't affect each other through emergent group-based social dynamics, carefully accounting for base rates from external information when assessing various internal probabilities, and breaking up decision processes into smaller steps that are more clearly defined in their intent and in example decisions/anchors. It helped a lot that I had read the previous book first, such that even if I didn't remember every detail of every psychological bias presented in both the previous book and the current book, those things looked familiar upon reading them in the current book.

There were also a few new things that I learned from the current book. I learned about how the process of judgment feels so satisfying and infuses confidence into the person making the judgment specifically from the psychological signal of having completed the judgment, which explains why so many people who make professional judgments in many domains are so reluctant to turn their discretion over to more systematic rules or algorithms. I also learned about how simple models of human predictive judgments, when those predictive judgments are about specific outcomes, may do a better job at predicting the outcomes that are the objects of judgment than at predicting the judgments that humans would make, simply because those models lack within-person noise pervasive in human judgments.

However, my overall opinion of the current book was shaped more by the many major and minor (the latter to an appropriately lesser extent) criticisms of it. These minor and major criticisms as well as my concluding remarks will be presented in separate sections as follows after the jump; the spoiling of my concluding remarks is simply that I do not recommend this book to others.

Book Review: "Thinking, Fast and Slow" by Daniel Kahneman

I started reading the book Thinking, Fast and Slow by Daniel Kahneman in early 2024. This was initially recommended to me by a friend, and I became even more motivated to read it upon hearing positive things about it from colleagues at my previous job, as many of the subtleties described in the book are extremely relevant to the appropriate design of interviews, focus groups, and surveys of human subjects in social science research. However, because it is a long book and the middle of 2024 was made busier for me by moving back to Maryland, traveling a lot, and starting a new job (some of which I have discussed in a previous post [LINK]), I could not finish reading this book until much more recently. Because of this large gap between reading the initial 60% and remaining 40% of this book, I admit that I have since forgotten many details from the initial 60% of this book. Moreover, I started making notes to myself in this post based on that initial 60% because I assumed that I would be able to finish reading the remaining 40% soon afterwards and I would therefore remember the book as a coherent whole, but because that didn't happen, many of the notes that I have made in this post that were supposed to form the skeleton of this post now no longer make as much sense to me. For these reasons, this post may seem a bit more stilted than other book review posts in this blog and will likely seem stronger/more coherent when discussing the latter 40% of the book.

The book is a lengthy exposition of novel ideas in psychology & behavioral economics that were empirically validated by the author, most often in conjunction with his longtime academic collaborator Amos Tversky. The concluding chapter does a good job of recapitulating the main ideas of the book. Most of the book explores various facets of individual & group-based human behavior based on the idea that there are effectively 2 modes through which individuals process information, which the author refers to as Systems 1 & 2. System 1 "thinks fast", making snap judgments based on limited information, heuristics, and a bit of laziness, and is the aspect of thinking that drives most day-to-day reactions & decisionmaking, while System 2 "thinks slow", making more deliberate judgments with more of an effort to gather all relevant information but must in turn be consciously engaged and ultimately disengages from mental fatigue (in favor of System 1) if engaged for too long. The book also considers how individuals' typical behaviors when faced with outcomes that are certain competing with outcomes that have known or unknown probabilities deviate from behaviors idealized by microeconomic theories of expected utility, notably that while the commonly observed behavior choosing a certain gain with a lower value than the expected value of an uncertain gain can be explained to some degree by expected utility theory, the commonly observed behavior of choosing a gamble on losing outcomes with an expected loss of larger magnitude than a different certain loss cannot be explained by expected utility theory; this partly explains the risks that people take in business and can be explained in turn by how people in their perceptions tend to overestimate probabilities that are close to but not exactly 0 and underestimate probabilities that are close to but not exactly 1. Finally, the book partly explains notions of hedonic adaptation (the idea that one's sense of well-being is generally similar in many different good or bad medium- or long-term circumstances by adapting to those circumstances) by distinguishing how people rate pleasure or pain when experiencing those things versus in hindsight and shows how people's conceptions of their identities & well-being in the past, present, and future are intimately tied to their actual memories and their abilities to form & retain memories. These aspects of self-conception as well as perceptions of probability can also be tied to Systems 1 versus 2, as many seemingly shortsighted decisions or perceptions can be explained by System 1 making snap judgments lazily & using heuristics based on incomplete information.

Especially as I read the latter 40% of the book, I came to appreciate how many of the ideas of this book had permeated into other things that I had read & heard from others and that I had internalized into my own worldview & view of myself. Professionally, I could see how so many aspects of framing could be important when designing surveys & focus groups. Personally, I could see how especially as I have aged, I have in many cases consciously chosen to not worry too much about certain details and instead make decisions based on lazier heuristics because I didn't feel that the results of spending more mental energy making a decision based on System 2 would be worth the effort. At the same time, I have become more consciously aware of how my memories of things in my own life can be affected by the passage of time and by more recent events in my own life, and I have become more consciously aware of the deep entanglement between my perceptions of my own memories and the narratives that shape my perceptions of my own life & of the world. I thus feel more proud of maintaining detailed personal diaries where I take note (using System 2 as much as possible when considering things outside of the current moment) of how I feel about various things in the moment as well as in hindsight and carefully consider how & why my thoughts & feelings about different events in or aspects of my life have evolved over time. Moreover, I have become more aware over time of when I might be vulnerable (through System 1) to the power of suggestion or to a subconscious desire to align with groupthink, though given that it is System 1, I am not necessarily aware of these things until later (thinking about these things through System 2). Finally, especially over the last several years, I have come to see many things at a very broad conceptual/philosophical level, whether the experiences in my own life, the evolution of different aspects of human society, or the expansion of human knowledge, in terms of perdurantism [LINK from Wikipedia]; although I am not philosophically sophisticated enough to be able to think through & defend all of its implications, it intuitively makes sense to me to think about personal identities, feelings, people, and other things that can be said to exist, in terms of their existence in spacetime and not just in space at specific instants of time. Because of my philosophical inclination in this way, I was particularly pleased to see the author discuss the idea of time-integrated pleasure or pain and of looking at changing identities or overall life courses in terms of spacetime.

Although this book is not technical at the level of an academic journal article, it is fairly technical compared to most nonfiction books aimed at the general public, so I would say that it is aimed at a well-educated reader. That said, I do think that it is written with reasonable clarity for non-academic audiences. Additionally, the book covers many topics, and it is recommended to bear in mind the headings of sections that comprise groups of chapters, because otherwise, it is easy to lose track of the narrative of the book, especially because the book is long enough that I suspect that it would be impossible for most readers (even those who read books, including more technical nonfiction books, relatively quickly) to finish this book in one sitting. I would say that the concluding chapter is a nice way to reinforce the main points of the book in the reader's mind and that the details of each chapter can be treated as a reference when needed as opposed to forming a perfectly coherent narrative in the progression of chapters in the book.

It is important to remember that some aspects of this book are out of date. In some cases, that is just because this book was published in 2011 and had been written over many years before that; for example, the author gives an example of estimating the likelihood of choosing a particular major in college, but that example uses base rates that seem to be quite out-of-date. In other cases, the book is out of date because it is based on academic experimental work in psychology & behavioral economics, and other studies may find contradictory (either null or opposite) results to those presented in this book. The Wikipedia article about this book [LINK] discussed how most of the results from most of the studies discussed in one chapter (as an example) have been found to be not replicable, with the author afterwards admitting to putting too much faith in those studies and therefore falling prey to the same biases as those discussed in that chapter & elsewhere in the book. As a slightly different example, later parts of the book discuss the ideas of nudge theory and its seeming successes in public policy, but the Wikipedia article about nudge theory [LINK] has pointed out that later studies & meta-analyses have found that after correcting for publication biases in favor of positive results & against null results, nudging does not yield statistically significant (non-null) effects on human behavior; in this case, one of the primary researchers (who is named in this book as a collaborator of the author & pioneer of nudge theory) has made some counterarguments that I don't find convincing.

With these caveats in mind, I would still recommend this book to anyone interested in these ideas and with the patience to carefully consider them, though this may partly reflect my own biases in how I view issues of identity & the world. Follow the jump to see my other assorted & disjointed thoughts about this book.

Book Review: "The Drunkard's Walk" by Leonard Mlodinow

I've recently reread the book The Drunkard's Walk by Leonard Mlodinow, which is a book about many ways that probabilistic phenomena occur in daily life and what the consequences are for understanding individual & collective decisions. I say "reread" because the first time I read it was in high school (as I recall, although I don't remember exactly when, though I did mention it in a review for a different book, saying then that I didn't finish it because I didn't find it as engaging as the book in that review); a few days ago, I happened to see it on top a stack of books, and I figured it would be nice to reread for a few reasons. First, I have learned a lot of science, and my worldview has developed & matured a lot, since I was in high school, so I thought it would be good to see how this book would hold up in my view in that context. Second, I figured it would be nice to read a book about probabilistic phenomena, as it wasn't something that I had to worry much about in my college studies or in my PhD work (which is a little ironic, given that van der Waals forces and radiative heat transfer are phenomena of statistical thermodynamic fluctuations, but it turns out that certain mathematical formulations hide all essential randomness), it will be relevant to my postdoctoral work as I get more into travel surveys with associated statistical analysis, concepts like base rate fallacies are relevant for things like false positive result rates for tests associated with this coronavirus (please note that I am not a public health expert, and please consult governmental public health agencies for guidance with respect to this ongoing pandemic), and I've been thinking over the last several months about how many of the conceptual quandaries associated with quantum mechanics can actually be tied to questions of whether probability is emergent versus fundamental.

The book is not too long, and it is a quick & engaging read. The author uses many interesting examples to motivate the discussion of fallacious reasoning in the context of probability as well as ways that probability enters daily life even in areas where people expect more determinism. There are also many interesting historical anecdotes about the development of probability theory, especially how ancient Greece and certain medieval European societies believed that any discussion of uncertainty would go against their conceptions of a pure & deterministic universe (whatever the prime mover might be). Also, in the tenth chapter, there is an interesting discussion of how the development of chaos theory itself is an example of the unpredictable & seemingly random nature of human life (though I didn't like the conflation of chaos theory itself with probability, as chaos is a separate mathematical phenomena that can emerge in purely deterministic systems). Additionally, in the tenth chapter, I appreciated how the author is careful to state that determinism is a bad model only of human behavior (at individual & societal levels) and makes no claim about the applicability of determinism to the universe at large, and how the author makes a call for humility and for rewarding people based on their character instead of perpetuating beliefs that people who are successful are wholly responsible for their successes while people who are in marginalized circumstances are somehow rightfully being punished for past mistakes. Overall, I think the book does a good job of achieving its purpose of illustrating to lay readers how ubiquitous probabilistic phenomena are in even seemingly deterministic aspects of daily life.

Before getting into other criticisms, I should point out that my copy of this book has several printing errors (mostly missing words) and a few typographical errors, but these occurred maybe once every 10 pages (based on an instinctive guess), so these therefore didn't affect my understanding of the book. Also, the author errs in claiming that Germanic rule in the Dark Ages (commonly understood to be the medieval period) preceded the ancient Roman civilization, but this again doesn't undercut the overall argument.

Where this book falls short is in fulfilling its purpose of diving deeper into the implications of such randomness for human behavior at individual and societal levels; the author's sloppy treatment of human behavior is a recurring problem throughout the book. In particular, there are a few related broad issues that come up at various points through the book. The first is the question of how to reconcile the apparent randomness of daily events (including the phenomenon of regression to the mean) believed to be deterministic with the real phenomena of collective self-fulfilling prophecies (including emergent segregation of social groups to reinforce outcomes that are believed to be deterministic even if they are not, thereby reinforcing determinism in itself). The second is the treatment of things like superstitions as examples of self-fulfilling prophecies, even if the superstitions have no effects in their contents but may change mindsets enough to change outcomes. The author doesn't do a good job of addressing many of these issues throughout the book, and only partially acknowledges the power of self-fulfilling prophecies at the end of the book (in the tenth chapter); the author makes it seem like a slow & methodical build-up to a satisfying conclusion, but frankly, the discussion of these issues could have been a lot more clear & concise and could have come much sooner in the book. The discussion of superstition in particular is rife with condescension, as the author never acknowledges how superstitions may change mindsets & lead to self-fulfilling prophecies but instead summarily dismisses them as silly relics mostly of a bygone era, reinforced by statements about how science and religion were irreparably separated with the trial of Galileo with no nuanced discussion of how religious beliefs (even if not organized religious institutions per se, to the extent that was the case before Galileo) played a role in motivating scientific discoveries even after Galileo. Another example is how the author glibly dismisses many claims of clusters of environmentally-caused cancer; it may well be true that some cases are due to biased statistical analysis after the fact, but it doesn't really address why inequitable outcomes seem to occur so frequently in this context, and it doesn't do justice to the gravity of the problem. (UPDATE: I recognize that my argument against the author's treatment of the incidence of environmentally-caused cancer can easily be dismissed as an overly emotional reaction that is not justified by the statistics, so it is worth clarifying that further. My concern is that the statistical arguments that claim that environmentally-caused cancer is not really a problem, and that those who claim it is a problem only do so by drawing arbitrary boundaries after the fact to inflate apparent concentrations of carcinogens in specific areas, may themselves be riven with the same sorts of bias that are perpetuated in situations like machine learning determining the provision of health care, but are cast in a way that seems "neutral" and therefore "superior" to "emotionally-driven" arguments.) Furthermore, although there is discussion of both the failures of superficial statistical arguments in favor of DNA testing in the criminal justice system and of the way that Bayesian analysis can systematically codify learning of new information in terms of probabilities, there is little discussion of how these issues can combine in toxic ways to perpetuate existing societal biases under the veneer of formal Bayesian analysis (as occurs with machine learning now); I admit that I wouldn't have been thinking about this as much had I not read and reviewed the book Weapons of Math Destruction by Cathy O'Neil, but similar examples were already available at the time that the book that I review in this post was being written. I really see an essential condescension and a lack of humility throughout the book in these discussions of human behavior, masked by the pithy & irreverent writing, that are at odds with the author's own calls for humility & deeper understanding.

There are other aspects of human behavior that this book fails to adequately capture; these may technically be beyond the scope of this book, but I think they are worth noting anyway, as they speak to larger problems with the ability of people (even those well-trained in STEM fields) to really understand probability theory. The second chapter goes over many examples of how, in the technical language of probability, given events \( A \) and \( B \), certain questions can be framed such that laypeople and professional specialists (particularly doctors & lawyers) fall into the trap of believing that \( \operatorname{Pr}(A \cap B) > \operatorname{Pr}(A) \) even though the opposite is mathematically always true. However, I can already see that the phrasing of many of those questions, particularly the way that events \( A \) & \( B \) are juxtaposed (especially if \( B \) is additional information that may be relevant to the assessment of \( A \)), may make people believe either that what should be interpreted as \( \operatorname{Pr}(A) \) is actually \( \operatorname{Pr}(A \cap \neg B) \), in which case \( \operatorname{Pr}(A \cap B) > \operatorname{Pr}(A \cap \neg B) \) could in fact be true, or that what should be interpreted as \( \operatorname{Pr}(A \cap B) \) is actually the conditional probability \( \operatorname{Pr}(A|B) \), in which case \( \operatorname{Pr}(A|B) > \operatorname{Pr}(A) \) could in fact be true. This speaks more to the way that natural human language is unsuited to the subtleties of the language of probability theory, yet rather than address these possibilities, the author again leaves the discussion there, implying disdain for people who are too stupid to know better. Another problem is that throughout the book, the author raises the question of how to determine whether a particular sequence of observations of outcomes for a process that may be random reflects a specific probability distribution model, but never clearly explains how to do this in practice, instead only giving hints about this through various examples. This is related to the question of why one may prefer an explanation based on probabilities than based on deterministic phenomena, particularly for small sample sizes. For this, I will give an example. Consider exactly 5 observations of an event, which has binary outcomes (either success or failure), for which no other observations are made, and for which in all of those 5 observations, success occurs every single time. Intuitively, laypeople might be inclined to believe that there is a deterministic cause of this, while if a probability theorist were to initially believe that this is consistent with a binomial distribution with \( (N, p) = (5, 0.6) \) but then later revise this to \( (N, p) = (5, 0.99) \), laypeople could reasonably wonder why this would be justified, and why the probability theorist refuses to believe in the possibility of some deterministic causal relationship. Of course, this is a contrived example, I understand why causation needs to be proved as an alternative to a null hypothesis, and I understand that probability distributions closer to uniform probabilities are favored as those that maximize entropy (which essentially means that subject to certain known constraints, the probability distribution that best reflects the state of ignorance about a system is closest to uniform), but the author does not properly explain these points. Finally, the broadest problem with this book is that the author only superficially acknowledges the issue that if every calculation in probability theory or statistics, whether of a certain event happening, a string of events being a true "hot streak", or a model fitting data correctly, is itself a probability, then the aforementioned disconnect of this language of probability from natural human language makes it difficult to translate probabilities into robust rules for deterministic (usually binary) decisions that laypeople must make; this is related to the idea that in game theory, a single person playing a single-shot game cannot play a mixed strategy, and the concept of a mixed strategy only makes sense in the context of observing a large ensemble of independent players, possibly playing repeatedly. Perhaps asking the author to address this problem is too much, but I still feel like such failures diminish the book in comparison to its hype.

Without hyping my own credentials, I admit that it is possible that my reaction to this book is more of a reflection of my greater experience with STEM, humanities, and social science fields and with science education/communication compared to when I was in high school. Furthermore, just as this book exhorts, I cannot be overcome by either positivity bias or negativity bias; it would only be fair to take the good & bad parts of this book together as appropriate, without believing that one outdoes or cancels the other. Given this, I can't really make a strong recommendation that readers should or should not read this book.

Book Review: "Weapons of Math Destruction" by Cathy O'Neil

I've recently read Weapons of Math Destruction by Cathy O'Neil. It is a short but dense exposition into the various ways that computer algorithms can determine the courses of people's lives and exacerbate existing societal inequities and biases/prejudices, in areas like education, civic engagement, education, health at the workplace, and many others. It argues that while many algorithms used in big data can be used for good, many instead widen inequalities and reinforce various cycles of poverty because of their opacity, lack of accountability, poor use of statistics, and lack of critical examination by those in charge (who instead use results and predictions generated by such algorithms to fire workers, deny opportunities to potential employees, financially prey on poor people, and so on, making such predictions self-fulfilling prophecies as only confirmatory data is fed back in); in particular, many of these algorithms and models use questionable proxies to predict certain attributes or behaviors (especially when the desired attributes are hard to quantify but the proxies are easy), these models are rarely transparent in what inputs are collected and how they are manipulated to produce outputs, and further research and fine-tuning are rarely performed to correct models that most humans would recognize produce incorrect results (but which computers would miss). It concludes that extensions of existing regulations on use of health and financial data are needed to curtail the misuse of such algorithms, and that simultaneously data scientists need to be scrupulous about the ways that their work is used and developed.

I rather enjoyed reading this book: it's pretty fast-paced, yet gives many detailed examples of the abuse of these algorithms to form a compelling narrative. Additionally, it in many ways follows the book The Attention Merchants by Tim Wu (which I have previously reviewed), because as that book shows the various ways that companies collect and sell customer data, this book shows the various ways that data can be used for the benefit of those companies (even if that works against some of those customers). There are only two issues that I have with this book. One is that the few times that politics comes up, the author's political bias (in favor of liberals in the US) is obvious; perhaps this is just due to the nature of the author's passionate crusade against abuse of algorithms and for institutional action uplifting poor and marginalized people, as that would necessitate regulation of such mathematical instruments, which would be (and has been) loudly opposed by large corporations maintaining their short-term profits and long-term status quo through these algorithms as well as the conservative politicians that they support. The other is that there aren't too many examples of big data and related algorithms truly working toward greater socioeconomic equity, especially when such algorithms are finding patterns that wouldn't be found by humans; while I get that the author is trying to build a brief but dense narrative warning against the excesses and abuses of such algorithms (as she professes herself to not be a big data evangelist), I would have liked to see more nuanced examples of proper uses of big data, because as this book stands, it seems just as one-sided/polemical as uncritical big data evangelism. Overall, I certainly feel like I got a better sense of the potential dangers of unchecked and uncritical use of algorithms to shape the economy and society. Plus, now that I'm about halfway through my PhD, I've started to think more about the sorts of jobs that I'd like to take after I finish. I've decided that I don't want to go into finance because (as mentioned in this book too) I'm not comfortable with playing with other people's money, as it is too easy to be seduced by mathematical simplicity and elegance into doing questionable things. That said, one thing (among the many) that has caught my fancy has been studies of policy problems (especially as related to STEM fields, but as they affect ordinary people); however, the story in this book about the role of the Mathematica Policy Research company in developing the arbitrary and statistically unsound metrics for evaluating teachers in DC public schools has made me realize that I'll need to make sure if I end up joining a policy research organization/consultancy/think tank that the organization that I join is responsible and transparent about the data that it collects and processes as much as possible.

Can Politicians Outsmart the Internet?

I was watching The Daily Show with Jon Stewart at home last night, and Jon Stewart's guest for that show was Dan Rather, the former CBS Evening News anchor who had come to promote his new book. They talked about Rather's childhood, the perception of a liberal bias in the media, and how politicians have outsmarted TV.

The Daily Show with Jon Stewart	Mon - Thurs 11p / 10c
Dan Rather
www.thedailyshow.com
Daily Show Full Episodes Political Humor & Satire Blog The Daily Show on Facebook

I would like to take a brief moment to discuss what Rather had to say about liberal bias in the media. He said that in his time as a TV journalist, he found that among his colleagues, there was an even split between those who identified as being politically conservative and those who identified as being politically liberal; he further explained away the idea of liberal bias in the news media as being the creation of the conservative journalists who were really just admitting that not all journalists were conservative like them. Yet I can't help but ask, why is it only conservative journalists who raise the issue of a liberal bias? If there was an essentially equal distribution of political viewpoints in Rather's time, shouldn't there have been an equal stink raised by liberal journalists about a conservative bias? Or were conservative journalists more likely to feel victimized for their political beliefs even before FOX News came on the scene?

I do agree with Rather that today's journalists have no guts, and that is because politicians have bent TV journalism to suit their wills. And I think that if there does exist a liberal bias in TV news, then it has evolved to reflect this as well. For instance, it has recently become known that Walter Cronkite was way more unabashedly liberal than anyone could have imagined. He used his liberal bias to actually ask tough questions of politicians with whom he disagreed. Now, many journalists like the majority of those on CNN use their liberal bias to ask stupid, uninformative "gotcha" questions just to make conservative politicians look bad to boost ratings; for a concrete example, see Wolf Blitzer trying to trap Mitt Romney a few months ago by asking him really silly questions about the movie The Hunger Games.

But the bigger question is, can politicians bend the Internet similarly to TV and print media? I think not. The Internet really is fundamentally different from TV and print media because it is a two-way medium, whereas TV and print are one-way media. Hence, TV and print can be used as mouthpieces for politicians or journalists with an agenda, and no one can really do anything about that except for perhaps writing letters to the editor. The Internet has a far more diverse set of viewpoints than TV and print could ever have just because it is open to anyone and everyone to publish anything and everything, rather than being restricted to a particular set of journalists. I mean, the fact that I can write this up on my own blog and have other people see it, leave their own comments, and possibly write their own blog posts in response rather than me simply disagreeing in my own head after watching that interview on TV shows how truly different the Internet is. Sure, politicians could manipulate the New York Times, the Washington Post, and other mainstream news publications that have online sites, but they can't possibly begin to manipulate every single online news source out there, because so many of them are from alternative, nontraditional sources of news.

I think the best example of how politicians will continue to be powerless against the Internet until they have a deep working understanding of how it works comes in the SOPA debate that happened many months ago. There, politicians seemed to take pride in their ignorance of how the Internet works, and for once, that totally backfired on their efforts to massively censor it. The traditional news sources by and large simply parroted the words coming out of politicians' mouths about SOPA, but people knew better that time because the tech companies (of all sizes) behind the websites people use frequently showed exactly why SOPA would be so harmful. Furthermore, alternative news sources, especially technology-related news sites that were not beholden to any politicians or political parties, had excellent news pieces about why SOPA would severely curtail the goings-on of the Internet. Through these efforts, thousands upon thousands of people petitioned Congress and the President to stop SOPA, and only after politicians realized the magnitude of the backlash did SOPA and PIPA stall and die.

Another way that politicians will never be able to outsmart the Internet lies in the Streisand effect. Most Internet users route around restrictions by any means possible on the Internet. Before the Internet, if a politician said or did something controversial or embarrassing, that politician could hope to survive politically by manipulating the TV and print news media to downplay its significance or, more directly, expunge mentions of the incident from the media. Now, however, if a politician tries that and attempts to remove controversial or embarrassing material from the Internet, users will simply copy and reupload said material elsewhere. The name for the term comes from the time when Barbra Streisand's house was accidentally shown in some news piece. When Streisand tried to have it removed from the Internet, users simply copied and uploaded that picture to even more news sites, so the effort to have it removed was completely counterproductive in that it had the exact opposite effect of what was intended. Thus, as long as politicians believe that they can manipulate the Internet in the same way that they have manipulated TV and print news media, the Internet will always outsmart politicians.

Featured Comments: Week of 2010 December 19

Unfortunately, there were no comments on articles from the previous week (2010 December 12), so there was no accompanying "Featured Comments" article. That can be attributed to the fact that I wrote very few posts that week. Anyway, there were a whole bunch of comments this past week, so I can only post a couple.

Review: Slackware 13.1

There were many comments about this, so I'll post a couple, as well as my responses to them.
An anonymous reader writes, "You never even talked about this distro whatsoever. You just complained about what stuff you were used to that slackware didnt have. if every distro was the same then there would be one distro. Slackware is one of the most stable distros still surviving. You want a extremely stable distro here it is. While i agree that there is no dependacy checking, if you are going to slackware you are usually semi good at the command line and usually know how to install a program anyhow. so its "your" job to do it."
Reader Hannes Worst had this to say: "I think it's an ultimately unfair review. It only states the preferences of the writer and nothing about Slackware. It's like someone allergic to fruit writes a review about apples. From the first sentence on prejudice is shown. When you aren't capable of exploring and researching Slackware, don't write a review about it."
To the anonymous reader, I ask, if complaining about what isn't in the distribution isn't saying something about the distribution itself, then what is? Also, the last point leads me into my response to Hannes Worst.
I may not have made it clear in the review itself, but relatively speaking, I am a newbie to Linux. I like using Linux a lot, but I still have neither the skills nor the patience to work with something like Slackware for a long time just to get it working. I had a feeling that my experience wouldn't turn out so well for this very reason. But I wanted to test this feeling, so I did; I then wrote about the experience. Please tell me: what exactly is wrong with that? I figured that I made it abundantly clear that my issues with Slackware were never meant to reflect poorly on Slackware; quite the opposite: they reflect poorly on me as a Linux user.
I hope all that is cleared up. Next!

Familiarity Breeds Fondness, not Contempt

Reader T_Beermonster had this to say: "Probably the big one for me is the package manager. I've just got so used to APT over the years that I find other (probably equally good) systems don't feel right. Kind of like getting into someone else's car, the biting point is wrong and the seat is the wrong hight." I feel the same way as well, but I must ask, what about when familiar front-ends are applied to different back-ends? For example, PCLinuxOS uses RPMs in the back-end, but its GUI package manager is the familiar Synaptic. How would you feel about using that there? Or am I missing the point entirely?

Bad Experiences are Forever

Reader T_Beermonster wrote a long diatribe against Apple's Macs: "I have a visceral loathing of Apple Macs - not the hardware which in recent times has been quite attractive looking (if overpriced/underspecced). It's the OS."

FOLLOW-UP: Linux and Breakfast Cereals

Commenter twitter had this to say: "Diversity and choice are good, restrictions are bad. Despite the differences between distributions, they all share the same core of free software and all of it tailored to a wide variety of hardware architectures. That means that users get the software they want on the platform they want. Skype is difficult because it is not free software and the company has to do all the hard work of packaging things themselves. If you want Skype to work as well as Mozilla, ask Skype to liberate their code and rely on an honest service model that does not demand undue power over users."

Well, that's all for this week. I hope all the confusion surrounding my motivations regarding the Slackware review are cleared up, and I sincerely apologize for not delivering adequately to those who were expecting a more substantive level appropriate for an intermediate or advanced Linux user trying Slackware. As always, if you like the content, please continue commenting and subscribing. Finally, happy holidays!

Q: What do Bing and George W. Bush have in common?

A: They are both their own self-proclaimed "deciders".
(Ex-)President George W. Bush became infamous for this as he essentially said that he would let his inner circle work out the details and he would simply say "yes" or "no". This is probably the most explicit admission I have heard from him or anyone else in his Administration that he deferred to (now Ex-)Vice President Dick Cheney, rather than the other way around.
Bing, Microsoft's rebranded (it's technically not new except for the name and the idea of a changing background), proclaims itself to be a "decision engine" rather than a search engine. It's implying that it will decide for you what is best for what you want rather than letting you decide. It seems to be taking on the search engine Google and the computational engine Wolfram|Alpha (both of which I happen to like A LOT) at the same time, and failing miserably at both.
My dad and I both complain that Bing is utter crap compared to Google. For most of what we search (in common) every day, Bing returns fewer AND less relevant results than Google consistently. That means it didn't happen after 1-2 searches. That means it happened after more than 20 different searches. This is bad.
What's worse is that Bing's algorithm seems to have been altered by Microsoft to promote itself. For example, any search on Google on "why Microsoft sucks" or "why Windows sucks" will return a litany of correct results going on for many pages. This is understandable because there are a LOT of internet users who are active in communities that oppose Windows and Microsoft's business model as well as many more users who are simply frustrated by crappy Microsoft software.
The same search terms in Bing will return results as if you had typed "why Apple sucks" for the same number of pages; if you're lucky, you'll get the desired results at the very end, and if you're not, there will be no relevant results. This is also understandable as there are many frustrated Mac/other Apple product users (though Apple and its fanbois would like you to not think so!), but that's only if the search terms included "Apple" in terms of "Microsoft". The bias is pretty blatant here.
Similarly, Google searches for "Windows fixes" and such return relevant information generated by regular users on how to fix something on Windows and why it's a problem with Windows. Similar Bing searches will magically replace "Windows" with "Mac" or "Linux", and if you're really lucky, you'll get a few results that are official fixes from Microsoft (which generally don't solve the entire problem anyway).
I had written off Bing earlier, but after a bit of thought and self-reflection I figured that might not be fair. After reading this, I can conclude it is certainly fair. I use Linux Mint almost all the time on my computer (I still have Windows XP for the occasional use) and my aim is to avoid learning about the command line and use the OS like a non-computer-knowing Windows user, only using the terminal by copying and pasting commands from Internet results. Thus, I would need to search for my problem online and use the (absolutely marvelous) forums for help. Google comes in very handy for this; Bing would mysteriously hide these results in favor of "why Windows is better" or something similar. Thus, my dislike has a very practical side to it too, so you can't accuse me of one-sidedly hating on Microsoft.
The most rational reason I have seen for this behavior is that Microsoft (somewhat correctly) assumes that most of Bing's users use Windows, so official Microsoft fixes and the like are all they ever expect to see and all they want; this makes sense but it still doesn't explain the magical replacement of Microsoft-related terms with competitors' names.
I thus mourn the loss of Yahoo! Search (which was really a decent search engine on its own and a great competitor to Google), as it now redirects all searches to Bing search; furthermore, as far as I know, Microsoft has no plan to incorporate the superior Yahoo! search technology into Bing, so all it wants to do is bring in that greater traffic from Yahoo! and keep a crappy search engine.
Of course, it's a "decision engine", so it'll decide for you what's best and what's crap; rather than have that happen, you decide.

For those of you who know me pretty well, you know I hate Microsoft in general. I don't think they have ever come up with an original piece of software; they always derive their work from other sources (which is laudable and promoted among the free software community...) but always take the credit for themselves and charge exorbitant prices for their products. Furthermore, their recent support of free software standards has been nominal at best; the whole OOXML thing was proven to have many proprietary parts and sections that essentially gave Microsoft full control of development in open violation of open standards. Then, Bill Gates decided to purchase and release all of the Feynman lecture videos to the public online; this is great as my passion is physics and I really like (in a totally straight way) Richard Feynman, but Gates decided to do it in Microsoft's proprietary Silverlight format instead of the free Ogg-Theora standard or even the proprietary but much more widely used Shockwave/Flash format. This means that essentially only Windows users can see it, and while Mac and Linux users have Moonlight, an adaptation of Silverlight for non-Windows OSs, Moonlight is crippled because Silverlight makes it so, meaning that Mac and Linux users really can't see these videos anyway. Then, very recently, Microsoft supported a particular software/architecture-thingy (I'm not exactly sure what it was) and GPL'ed it. Releasing code under the GPL means that they have to respect the openness of the code and allow people to modify and rerelease (under the GPL) the code too. This was laudable at first until a third party found out that Microsoft had included their own licenses that allowed the use of proprietary code and almost entirely disallowed modifying or even viewing the underlying code; all of these things constituted huge violations of the GPL. Thus, Microsoft completely undermines its commitment to openness through its continuing backhanded tactics; openness to them means openness only to them.
That said, Microsoft does actually make some decent products. I use OpenOffice.Org because it suits all my needs and is free software, but for more extensive documents and spreadsheets and more content-filled presentations, Microsoft Office 2007 is the way to go. Though I don't like the de facto non-support for ODF (yes, it was originally a separate extension and then was made into a software update, but both forms of support worked horribly if at all), the Ribbon UI is really nice and makes a lot of sense (I didn't like it at first but that was probably just because I was wary of Microsoft, so now I like it a lot), and all the new features are really useful. I hear OO.O is coming out with a Ribbon competitor for the next release, but I've seen a screenshot and it looks terrible compared to Microsoft's version. Also, the XBOX 360 is a great system (though still buggy today compared to the PS3 and the Wii) with amazing graphics, games, and the legendary XBOX Live. Furthermore, Microsoft's hardware, like its mice, keyboards, and webcams, are of very high quality and last for a very long time.

Moral of the story: don't Bing and decide, just decide not to use Bing!