The Green New Deal

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We have known collectively the dangers posed by the combination of modern civilization and human population growth since at least the 1960s. During that decade Paul Ehrlich published The Population Bomb (1968), which carried forward Thomas Malthus’s argument from the 19th century that exponential population growth models apply as much to humans as to other life forms and that relaxing the natural limits on resources and their utilization would provide only temporary material comforts soon overwhelmed by an expanding population. In The Limits of Growth (1972) the Club of Rome computer modelers expanded on these ideas by developing and testing a simulation of human population and economic activity incorporating natural resources and pollution. While their model was crude by recent standards, it did behave in qualitatively sensible ways. The story it told was that however you varied the inputs, e.g., extending resource limits or slowing population growth rates, if you stayed within anything like reasonable bounds, then the model showed a collapse of the population, through impossible levels of pollution, say, sometime during the 21st century. Neither of these pivotal books dealt with anthropogenic global warming explicitly, but the message was clear and still hasn’t changed: unfettered population and economic growth, at least on the models of both we have so far adopted, will be a disaster for our species and our environment. Nothing much has changed.

Rep Alexandria Ocasio-Cortez and Sen Ed Markey’s Green New Deal (GND) seeks genuine change. It’s modeled on Franklin Delano Rooseveldt’s New Deal in the sense that FDR’s New Deal radically changed America for generations. The name also evokes the mobilization behind the World War II effort that happened shortly thereafter. The point is that radical mobilization efforts are eminently possible when the threat to a nation is existential, and human-driven climate change certainly poses an existential threat. The GND, if passed, would be a clear, resounding dual statement of intent: first, the intent to counter the threat to civilization posed by the combination of human population growth and current economic activities; second, a more local statement of intent of achieving economic and political justice for American minorities.

The bill is strictly aspirational, calling out the urgency of the situation, rather than laying out a specific pathway. It’s stated goals are not of a kind that could lead to direct actions. GND shares with Extinction Rebellion a common view of the urgency of the situation and the optimism that if there is a common will to respond, that we can do something worthwhile to diminish the worst outcomes of anthropogenic global warming.

Some of the Main Goals laid out in the GND are:

  • Guaranteed jobs with family-sustaining wages for all people of the US
  • Maximizing the energy efficiency of all existing buildings in the US
  • Moving to electric cars and high-speed rail and away from air transport
  • Universal health care
  • Moving to sustainable farming
  • Moving to 100% renewable energy

Of course, the introduction of the GND has provoked a vigorous response from opponents. The most prominent objection, perhaps, is that it would be too expensive to be practicable. Certainly, refurbishing every building in America to maximize energy efficiency can’t be cheap. The obvious rebuttal, however, has been voiced by Greta Thunberg and other young activists: inaction will be far more expensive than action. Indeed, the GND in its initial Whereas’s states that inaction will lead to $500B in lost annual economic output in the US by 2100. Such a sum applied now, on the other hand, would clearly make a strong start to doing something about climate change. Aside from that, any dollar estimate of harm is never going to be a worst case estimate, since severe climate change is fully capable not just of direct economic impacts, but also of spurring warfare and social collapse, in ways where the real valuations entirely outstrip the speculative dollar valuations of harm. The right wing who harp about the expense are simply not yet prepared to think clearly about the consequences of the choices in front of us. (In my view, it is well past time that the decision bypass the obstruction.)

The whole point of the GND is that what is practicable depends upon the context, and what is practicable in times of war is of an entirely different scale to what is practicable in normal times. We are not in normal times. This is a time of war, and our enemy is us.

 

 

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Interview on Machine Understanding

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Produced by Adam Ford:

If you are interested, some relevant references are:

Post Hoc Ergo Propter Hoc, or Correlation Implies Causation

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Wikipedia confidently explains this in its first sentence for this entry: “Post hoc ergo propter hoc (Latin: “after this, therefore because of this”) is a logical fallacy that states ‘Since event Y followed event X, event Y must have been caused by event X.’” This so-called fallacy is curious for a number of reasons. Taken literally it is a fallacy that is almost never committed, at least relative to the opportunities to commit it. There are (literally, perhaps) uncountably many events succeeding other events where no one does, nor would, invoke causality. Tides followed by stock market changes, cloud formation followed by earthquakes, and so on and so on. People do attribute causality to successive events of course: bumping a glass causing it to spill, slipping on a kitchen floor followed by a bump to the head. In fact, that’s how as infants we learn to get about in the world. Generally speaking, it is not merely temporal proximity that leads us to infer a causal relation. Other factors, including spatial proximity and the ability to recreate the succession under some range of circumstances, figure prominently in our causal attributions.

Of course, people also make mistakes with this kind of inference. In the early 1980s AIDS was attributed by some specifically to homosexual behavior. The two were correlated in some western countries, but the attribution was more a matter of the ignorance of the earlier spread of the disease in Africa than of fallacious reasoning. Or, anti-vaxxers infer a causal relation between vaccines and autism. In that case, there is not even a correlation to be explained, but still the supposed conjunction of the two is meant to confer support to the causal claim. The mistake here is likely due to some array of cognitive problems, including confirmation bias and more generally conspiritorial reasoning (which I will address on another occasion). But mistakes with any type of inductive reasoning, which inference to a causal relation certainly is, are inevitable. If you simply must avoid making mistakes, become a mathematician (where, at least, you likely won’t publish them!). The very idea of fallacies is misbegotten: there are (almost) no kinds of inference which are faulty because of their logical form alone (see my “Bayesian Informal Logic and Fallacy”). What makes these examples of post hoc wrong is particular to the examples themselves, not their form.

The more general complaint hereabouts is that “correlation doesn’t imply causation”, and it is accordingly more commonly abused than the objection to post hoc reasoning. Any number of deniers have appealed to it as a supposed fallacy to evade objections to gun control or the anthropogenic origins of global warming. It’s well past time that methodologists should have put down this kind of cognitive crime.

This supposed disconnect between correlation and causation has been the orthodox statistician’s mantra at least since Sir Ronald Fisher (“If we are studying a phenomenon with no prior knowledge of its causal structure, then calculations of total or partial correlations will not advance us one step” [Fisher, Statistical Methods for Research Workers, 1925] – a statement thoroughly debunked by many decades thereafter of causal inference based on observational data alone). While there are more circumspect readings of this slogan than to proscribe any causal inference from evidence of correlation, that overly ambitious reading is quite common and does much harm. It is unsupportable by any statistical or methodological considerations.

The key to seeing through the appearance of sobriety in the mantra is Hans Reichenbach’s Principle of the Common Cause (in his The Direction of Time, 1956). Reichenbach argued that any correlation between A and B must be explained in one of three ways: the correlation is spurious and will disappear upon further examination; A and B are causally related, either as direct or indirect causes one of the other or as common effects of a common cause (or ancestor); or as the result of magic. The latter he ruled out as being contrary to science.

Of course, apparent associations are often spurious, the result of noise in measurement or small samples. The “crisis of replicability” widely discussed now in academic psychology is largely based upon tests of low power, i.e., small samples. If a correlation doesn’t exist, it doesn’t need to be explained.

It’s also true that an endurring correlation between A and B is often the result of some association other than A directly causing B. For example, B may directly cause A, or there may be a convoluted chain of causes between them. Or, again, they may have a common cause, directly or remotely. The latter case is often called “confounding” and dismissed as showing no causal relation between A and B. But it is confounding only if the common cause cannot be located (and held constant, for example) and what we really want to know, say, is how much any causal chain from A to B is explanatory of B’s state. Finding a common cause that explains the correlation between A and B is just as much a causal discovery as any other.

I do not wish to be taken as suggesting that causal inference is simple. There are many other complications and difficulties to causal inference. For example, selection biases, including self-selection biases, can and do muck up any number of experiments, leading to incorrect conclusions. But nowhere amongst such cases will you find biases operating which are not themselves part of the causal story. Human experimenters are very complex causal stories themselves, and as much subject to bias as anyone else. So, our causal inferences often go wrong. That’s probably one reason why replicability is taken seriously by most scientists; it is no reason at all to dismiss the search for causal understanding.

There is now a science of causal discovery applying these ideas for data analysis in computer programs, one that has become a highly successful subdiscipline of machine learning, at least since Glymour, Scheines, Spirtes and Kelly’s Discovering Causal Structure (1987). (Their Part I, by the way, is a magnificent debunking of the orthodox mantra.)

The general application of “correlation doesn’t imply causation” to dismiss causal attributions is an example of a little learning being a dangerous thing – also known as the Dunning-Kruger effect.

 

The Sixth Extinction: A Review

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Elizabeth Kolbert’s The Sixth Extinction is a highly readable, discursive review of the state of the biosphere in the Anthropocene — i.e., now. It’s aimed at a general audience and entertains as much as it informs, relating a wide variety of anecdotes, mostly derived from Kolbert’s travels and investigations while writing this book. I think it a very worthwhile book, especially perhaps as a present for those in your life who are skeptical about global warming or science in general. Not that Kolbert is a scientist or pretends to be one, but it offers an outsiders’ view of a fair few scientists in action, chronicling the decline of many species.

Kolbert’s report is necessarily pessimistic about the general prospects for a healthy biosphere, given that the evidence of species endangerment and decline is all around and she has spent some years now documenting it. But she tries to be as optimistic as possible. She points out a variety of successes in evading or mitigating other “tragedies of the commons”, such as the banning of DDT after Rachel Carson’s warning that our springs risked going silent. Or the prominent case of the missing (well, smaller) ozone hole.

On matters that are contentious within science, Ms Kolbert aims for neutrality. For example, what killed off the megafauna — such as the marsupial lion in Australia, cave bears and saber tooth cats in America, mammoths and aurochs in Europe — that was widespread prior to the presence of homo sapiens? One school suggests that climate change, say, in the form of retreating ice sheets, was the culprit. She points out that doubts arising from the fact that the extinctions of the megafauna occurred at quite different times and, indeed, in each case shortly after the arrival of humans, militate against climate change as a sole cause. The main alternative is, of course, that these are the first extinctions due to human activity, so that the Sixth Extinction began well before the industrial age. Kolbert points out that advocates for climatic causation criticize the anthropogenic crowd for having fallen for the post hoc ergo propter hoc fallacy. But neutrality on this point is a mistake. While correlation doesn’t strictly imply direct causation, it does strictly imply direct-or-indirect causation: Hans Reichenbach in The Direction of Time made the compelling point that if there is an enduring correlation between event types (not some haphazard result of small samples and noise), then there is either a direct causal chain, a common cause, or an indirect causal chain that will explain the correlation. Everything else is magic, and science abhors magic. Given that the extinctions and the arrivals of humans fit like a hand in a glove, it is implausible that there is no causal relationship between them. As sane Bayesians (i.e., weighers of evidence) we must at a minimum consider it the leading hypothesis until evidence against it is discovered. Of course, the existence of one cause does not preclude another (even if it makes it less likely); that is, climatic changes may well have contributed to human-induced extinctions in some cases.

On a final point Kolbert again opts for neutrality: does the Sixth Extinction imply our own? Can we survive the removal of so many plants and animals that the Anthropecene should be counted as one of the Great Extinction events? Will humanity’s seemingly boundless technological creativity find us a collective escape route?

I find the enthusiasm of some futurologists for planetary escape a bit baffling. The crunch of Global Warming will be hitting civilization pretty hard within 50 years, judging by anything but the most extremely optimistic projections. The ability to deal directly with Global Warming, and the related phenomena of overutilization of earth’s resources to support around 10 billion people at an advanced economic level of activity, is possibly within our grasp, but it is very much in doubt that we will collectively grasp that option. The ability to terraform and make, say, Mars habitable in a long-term sustainable way is not within our grasp and is not in any near term prospect. Simply escaping from our own earthly crematorium is not (yet) an option. If Elon Musk succeeds in reaching Mars, he will almost certainly soon thereafter die there.

The situation on earth isn’t so dissimilar. If Global Warming leads to massive agricultural failure, the watery entombment of half the major cities on earth, unheard of droughts, floods and typhoons, resource wars and human migrations, the strain on the instruments and processes of civilization is reasonably likely to break them. If civilization comes undone, it will be impossible to avoid massive starvation and societal collapse. The dream of some to wait it out in a bunker and emerge to a new utopia thereafter is about as likely as the descendants of Musk building a new civilization on Mars. Whether the extinction of civilization entails the final extinction of humanity is a moot point. But human life after civilization will surely be nasty, brutish and short.

The best alternative is to put a stop to Global Warming now, and use the energy and human resources that effort saves to solve the remaining problems of resource depletion, habitat destruction and human overpopulation. That requires a sense of urgency and a collective will so far absent.

The Tyranny of Metrics

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In The Tyranny of Metrics, Jerry Muller presents a clear case against the current, and growing, over-reliance on KPIs and other performance metrics for assessing people’s and organizations’ work. One of the more common admonitions in AI is to be careful what you ask for, or, as Russell & Norvig put it in their incredibly popular textbook on AI, “what you ask for is what you get.” If your institutions set KPIs rewarding citations, for example, then you’re likely to end up with illicit citation rings, with pals citing each other pointlessly — pointless except for the KPI reward, of course. KPIs have a very strong tendency to replace the real objectives of an organization — education, good governance, public health, common welfare — with much lower quality ersatz objectives — student popularity, volume of memoranda, quick hospital discharges, numbers of arrests for petty crimes.

Another common problem is using absolute metrics when only relativized metrics make sense. For example, insisting that education funding reward schools whose students perform better on standardized tests can make for a good sound-bite, but has always been understood to produce “teaching to the test” — that is, a narrow educational focus that leads to ex-students poorly prepared to deal with a complex world with wide-ranging problems. But importantly it also leads to schools taking a low-risk approach to their education. Instead of seeking out and supporting students with disabilities or minority socioeconomic backgrounds, they will narrow their admissions to those already likely to perform well on standardized tests. That can really pay off for their budgets, but it’s also letting society down. The major blame should fall on the politicians who force the performance metrics on the schools in the first place. Instead of an absolute test-performance standard, a relativized standard, comparing outcome performance to initial performance, would eliminate that particular distortion of educational goals. (While doing nothing about teaching to the test, of course, which requires some other response.)

Muller reviews many of the ways in which metrics can go wrong, and he specifically considers them in some of the more socially important domains in which they do go wrong: education (secondary and tertiary), policing, medicine, finance, the military, foreign aid. His book is an excellent starting point for thinking about the general subject of work performance measurement or its particular consideration in any of these domains.

I can’t give Muller five stars here, however. One area in which he goes seriously astray is the issue of transparency in our institutions. Muller quite rightly points out that in many processes we require confidentiality and that the demand for transparency may well have gone too far. In diplomacy, privacy or secrecy can be essential to achieving a reasonable outcome. Diplomacy requires compromise, and compromise requires giving away something that you’d prefer not to. If the thing given way is made public too soon, then the diplomatic transaction may well implode before any compromise can be agreed. People have secrets for good reason. Similarly, the demand for honesty in politicians (generally an unsuccessful demand to be sure, but commonly loudly made in the media nonetheless) can turn into a fetish, where politicians who legitimately change their minds are excoriated for having no position or those who bend the truth to achieve a greater good are pummelled for dishonesty. The whole point of politics is for our politicians to achieve greater goods, not lesser goods, so these kinds of admonition by Muller are well taken.

Yet Muller goes far too far in this. Wikileaks’ revelation of war crimes in Iraq was accompanied by the revelation of identities of intelligence agents around the world. Julian Assange didn’t care about the safety of those agents or the future ability of, say, the CIA to recruit other agents. That’s a failure of over-zealous transparency, for sure. But Muller gives no credit at all to the other side. For example, he fails to accept that the whistleblowing revelation of the war crimes itself was a good thing and should be legally protected. He fails to acknowledge any benefit from Freedom of Information laws. Worse still, he castigates Edward Snowden for revealing many of the secrets of the NSA. While the United States has the right to have a “no such agency”, it doesn’t have the right to have spying and anti-encryption programs of the depth and breadth of the NSA. What Snowden exposed was, and is, criminal activity endangering democracy (see, e.g., https://www.bbc.com/news/uk-45510662). I think it absurd that Muller can’t put in even one word of support for such transparency. I wish for treatments of these subjects that show better balance and judgment than Muller’s.

The New Devil’s Dictionary

Ambrose Bierce’s Devil’s Dictionary is a fine entertainment. This derivative effort is intended to be at least mildly didactic, while no doubt being a little less amusing. The abuse of language for political effect has been going on a long while, but has accelerated in recent times, with the rise to large-scale dominance of the media by right wing ideologues. Let us all do a little something about it, at least by speaking and writing properly.

Corruption

The innate behavior of liberal politicians and activists in advocating
for the commonwealth, environmental sustainability or any regulation
of markets.

Of course, the real meaning is the act of dishonest dealing in return
for money or private gain, typically taking advantage of a position of
power. The abuse of the word “corruption” has become typical of Trump
and the Murdoch press. See, e.g.,
http://www.factcheck.org/2016/10/a-false-corruption-claim/

Political Correctness

Showing respect and common decency towards minorities or disadvantaged people, especially by leftists; a refusal to demonstrate the manly virtues of machismo, sexism, misogyny, racism and other forms of bigotry.

While the term “politically correct” was used by the left in a self-deprecating way in the 1970s, it has been appropriated by the right wing since to disparage those who supposedly go over the top in avoiding embarrassment to minorities, etc. The term is typically applied in response to someone simply showing ordinary courtesy and decency, revealing the lack thereof in the critic.

Reform

Government action to hobble its own ability to protect and promote the public interest, usually promoted on the grounds that government powers are abused and harmful. The latter is often true, especially when in pursuit of “reform”.

To reform something is to improve it, by, for example, removing obstacles to its proper function. But right wing politicians and media apply it when their intent is to undermine or defeat proper function.

Socialist

Someone who opposes the obscenely rich taking full advantage of their wealth, for example, by wanting to tax them for the welfare of the commonwealth.

In the common usage in the rightwing media, many who endorse regulated capitalist markets are routinely denounced as socialists, e.g., Barack Obama and Bernie Sanders (who, admittedly, falsely labels himself a socialist, without any implied denunciation). But socialists properly understood advocate public ownership and control of the means of production and oppose capitalist markets. Any dictionary will confirm this, but rightwing media commentators are not often found referring to dictionaries or other reliable sources of information.

Analysing Arguments Using Causal Bayesian Networks

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Kevin B Korb and Erik P Nyberg

Introduction

Analysing arguments is a hard business. Throughout much of the 20th century many philosophers thought that formal logic was a key tool for understanding ordinary language arguments. They spent an enormous amount of time and energy teaching formal logic to students before a slow accumulation of evidence showed that they were wrong and, in particular, that students were little or no better at dealing with arguments after training in formal logic than before (e.g., Nisbett, et al., 1987). Beginning around 1960 a low-level rebellion began, leading to inter-related efforts in understanding and teaching critical thinking and informal logic (e.g., Toulmin, 1958).

Argument mapping has long been a part of this alternative program; indeed it predates it. The idea behind argument mapping is that while formal logic fails to capture much about ordinary argument that can help people’s understanding, another kind of syntax might: graphs. If the nodes of a graph represent the key propositions in an argument and arrows represent the main lines of support or critique, then we might take advantage of one of the really great tools of human reasoning, namely, our visual system. Perhaps the first systematic use of argument maps was due to Wigmore (1913). He presented legal arguments as trees, with premises leading to intermediate conclusions, and these to a final conclusion. This simple concept of a tree diagram representing an argument or subargument – possibly enhanced with elements for indicating confirmatory and disconfirmatory arguments and also whether lines of reasoning function as alternatives or conjunctively – has been shown to be remarkably effective in helping students to improve their argumentative skills (Alvarez, 2007).

However effective and useful argument maps have been shown to be, there is one central aspect of most arguments that they entirely ignore: degrees of support. In deductive logic there is no room for degrees of support: arguments are either valid or invalid; premises are simply true or false. While that suffices for an understanding of Aristotle’s syllogisms, it doesn’t provide an insightful account, say, of arguments about global warming and what we should do about it. Diagnoses of the environment, human diseases or the final trajectory of our universe are all uncertain, and arguments about them may be better or worse, raising or lowering support, but very few are simply definitive. An account of human argument which does not accommodate the idea that some of these arguments are better than the others and that all of them are better than the arguments of flat-earthers is one that is simply a failure. Argument mapping can not be the whole story.

Our counterproposal begins with causal Bayesian networks (CBNs). These are a proper subset of Bayesian networks, which have proved remarkably useful for decision support, reasoning under uncertainty and data mining (Pearl, 1988; Korb & Nicholson, 2010). CBNs apply a causal semantics to Bayesian networks: whereas BNs interpret an arc as representing a direct probabilistic dependency between variables, CBNs interpret an arc as representing both a direct probabilistic and a direct causal dependency, given the available variables (Handfield, et al., 2008). When arguments concern the state of a causal system, past, present or future, the right approach to argumentation is to bring to bear the best evidence about that state to produce the best posterior probability for it. When a CBN incorporates the major pieces of evidence and their causal relation to the hypothesis in question, that may already be sufficient argument for a technologist used to working with Bayesian networks. For the rest of us, however, there is still a large gap between a persuasive CBN and a persuasive argument. So, our argumentation theory ultimately will need to incorporate also a methodology for translating CBNs into a natural language argument directed at a target audience.

Example

Consider the following simple argument:

We believe that Smith murdered his wife. A large proportion of murdered wives turn out to have been murdered by their husbands. Indeed, Smith’s wife had previously reported to police that he had assaulted her, and many murderers of their wives have such a police record. Furthermore, Smith would have fled the scene in his own blue car, and a witness has testified that the car the murderer escaped in was blue.

Unlike many informal arguments, this one is already simple and clear: the conclusion is stated upfront, the arguments are clearly differentiated, and there is no irrelevant verbiage. Like most informal arguments, however, it is a probabilistic enthymeme: it supports the conclusion probabilistically rather than deductively and relies on unstated premises. So, it’s hard to give a precise evaluation of it until we make both probabilities and premises more explicit, and combine them appropriately.

We can use this simple CBN to assess the argument:

Wife reported assault → Smith murdered wife → Car blue → Witness says car blue

The arrows indicate a direct causal influence of one variable on the probability distribution of the next variable. In this case, these are simple Boolean variables, and if one variable is true then this raises the probability that the next is true, e.g., if Smith did assault his wife, then this caused him to be more likely to murder his wife. (It could be that spousal assault and murder are actually correlated by common causes, but this wouldn’t alter the probabilistic relevance of assault to murder, so we can ignore the possibility here.)

First, we can do some research on crime statistics to find that 38% of murdered women were murdered by their intimate partners, and so get our probability prior to any other evidence.

Second, we can establish that 30% of women murdered by their intimate partners had previously reported to police being assaulted by those partners (based upon Olding and Benny-Morrison, 2015). Admittedly, as O. J. Simpson’s lawyer argued, the vast majority of husbands who assault their wives do not go on to murder them. However, his lawyer was wrong to claim that Simpson’s assault record was therefore irrelevant! We just need to add some additional probabilities, which a CBN forces us to find, and combine them appropriately, which a CBN does for us automatically. Suppose that in the general population only 3% of women have made such reports to police, and this factor doesn’t alter their chance of being murdered by someone else (based on Klein, 2009). Then it turns out that the assault information raises the probability of Smith being the murderer from 38% to 86%.

Third, suppose we accept that if Smith did murder his wife, then the probability of him using his own blue car is 75–95%. Since this is imprecise, we can set it at 85% (say) and vary it later to see how much that affects the probability of the conclusion (in a form of sensitivity analysis).

Fourth, we can test our witness to see how accurate they are in identifying the color of the car in similar circumstances. When a blue car drives past, they successfully identify it as blue 80% of the time. Should we conclude that the probability that the car was blue is 80%? This would be an infamous example, due to Tversky and Kahneman, of the Base Rate Fallacy — i.e., ignoring prior probabilities. In fact, we also need to know how successfully the witness can identify non-blue cars as non-blue (say, 90%) and the base rate of blue cars in the population (say, 15%). Then it turns out that the witness testimony alone would raise the probability that Smith was the murderer from 38% to 69%. Combining the witness testimony with the assault information, then the updated probability that Smith is the murderer rises to 96%.

Even this toy example illustrates that building a CBN forces one to think about how the main factors are causally related and to investigate all the necessary probabilities. Assuming the CBN is correct for the variables considered, and is built in one of many good BN software tools, it acts as a useful calculator: it combines these probabilities appropriately to calculate the probability of our conclusion. Thus, it helps prevent much of the vagueness and fallacious reasoning that are widespread, even in important legal arguments.

Alternative Techniques for Argument Analysis

Although there are genuine difficulties in using this technique, we believe that much of the resistance to it is based on imaginary difficulties, while the (italicized) rival techniques below have difficulties of their own.

In our toy example, the prose version of the argument doesn’t quantify the probabilities involved, doesn’t specify the missing premises, doesn’t indicate how the various factors are related to each other, and it’s far from clear how to compute an appropriate probability for the conclusion. The fact that the probabilities and premises aren’t specified doesn’t really make the argument non-probabilistic, it just makes it vague. Prose is often the final form of presenting an argument, but it is far from ideal for the prior analysis of an argument.

Resorting to techniques from formal logic, diagrammatic or otherwise, requires even more effort than CBN analysis, while typically losing information. It is really appropriate only for the most rigorous possible examination of essentially deductive arguments.

A more recent approach with some promising empirical backing is the use of argument maps. These are typically un-parameterized non-causal tree structures in which the conclusion is the trunk and all branches represent lines of argument leading to it. (See Tim van Gelder’s ‘Critical Thinking on the Web’.) Arguably, these are equivalent to a restricted class of Bayesian network without explicit parameters (as in the qualitative probabilistic networks of Wellman, 1990). Thus, they have many of the advantages of BNs, but they don’t provide much guidance in computing probabilities, so they can be vague and subject to the kinds of fallacious reasoning that are avoided with actual BNs. Also, as they are typically not causal, they can actually encourage misunderstanding of the scenario.

Objections

There are many common objections to the use of Bayesian networks, or causal Bayesian networks, for argumentation. Here we address some of these.

1) Bayesian network tools are difficult to use.

This is true for those who are not experienced with them. “Fluency” with BN tools requires training something on the order of the amount of training required to become a reasonably good argument analyst using any tool. (In our experience, some philosophers get fed up with Bayesian network tools when they fail to represent an argument effectively within the first ten minutes of use!)

There are other options besides training. For specific applications, easy-to-use GUIs have been developed. Also, Bayesian network tools can be (and should be) enhanced to support features that would make them easier for argument analysis, such as allowing nodes to be displayed with the full wording of a proposition which they represent. But that’s up to tool developers. In the meantime, serious argument analysts would profit from learning how to use the tools, not just for the sake of argumentation, but also for the wide range of other tasks they have been developed for, such as decision analysis.

2) BNs force you to put in precise numbers for priors and likelihoods; this is a kind of false precision. Argument maps are better because they are qualitative.

Certainly, numbers need to be entered to use the automated updating via Bayes’ theorem. As quantities, they are precise (at least to whatever limited-precision arithmetic the tool supports). That doesn’t mean that the precision need be false, meaning falsely interpreted. The user can be fully aware of their limits. Indeed, all BN tools support sensitivity analysis, the ability to test the BN’s behavior across a range of values. So, if the analyst is unsure of just what the probability of something is, she or he can try out a range of numbers to see what effect the variation has on other variables of interest. If the conclusion can be substantially weakened by pushing the probability of premises around within reasonable limits, then it’s correct to infer that the argument is not compelling, and, otherwise, the argument may be compelling. This kind of investigation of the merits of the argument — and uncertainty of our beliefs — is not possible with qualitative maps alone.

Forcing one to obtain numbers is actually an advantage, as the example above indicated: the analyst is forced to learn enough about the domain to model it effectively.

3) Where do the numbers come from?

This is an objection any Bayesian will have encountered repeatedly. Since we are here talking about causal Bayesian networks, the ultimate basis for these probabilities must be physical dispositions of causal systems. Practically speaking, they will be sourced using the same means that Bayesian network modellers use in all the applied sciences, a combination of sample data (using data mining tools) and expert opinion (see Korb and Nicholson, 2010, Part III for an introduction to such techniques).

4) Naive Bayesian networks (NBNs) have been used effectively for argument analysis and are much simpler, e.g., by Peter Sturrock (2013) in his “AKA Shakespeare”. Why not just use them?

NBNs for argumentation simplify by requiring that pieces of evidence be independent of each other given one or another of the hypotheses at issue. If the problem really has that structure, then there’s nothing wrong with expressing it in an NBN. However, distorting arguments into that structure when they don’t fit causes problems, rather than resolving them. In Sturrock’s case, he suggested, for example, that the Stratford Shakespeare not having left behind a corpus of unpublished writing, not having written for aristocrats for pay, and not having engaged in extensive correspondence with contemporaries are all independent items of evidence, meaning that their joint likelihood is obtained by multiplying their likelihoods together (and then multiplied again with the likelihoods of all other items of evidence he advanced). The result was that he found that the probability that the writings of Shakespeare came from the eponymous guy from Stratford ranged from 10-15 all the way down to 10-21! As Neil Thomason pointed out to us, this means that you would be more likely to encounter the author of those works by randomly plucking any human off the planet at the time (or since!), rather than arranging to meet that Will Shakespeare from Stratford! While the simplicity of NBNs is appealing, this is a case of making our models simpler than possible. Real dependencies and interrelatedness of evidence cannot be ignored.

5) Some arguments are not about causal processes, but have a structure that can only be illuminated otherwise.

Here’s a famous case:

Socrates was a human.

All humans are mortal.


Therefore, Socrates was mortal.

While Bayesian networks can certainly represent deductive arguments, they will not be causal. Furthermore, their probabilistic updating will be uninformative. A reasonable conclusion is that BNs are ill suited for analysing deductive arguments. Argument maps may or may not be helpful; at least, their lack of quantitative representation will do no harm in such cases.

This concession is not exactly painful: our advocacy of CBNs was always only about cases where causal reasoning does figure in the assessment of a thesis. Slightly more problematic are cases where the core reasoning might be claimed to be associative rather than causal. For example, yellow stained fingers are associated with lung cancer, but staining your fingers yellow is not a leading cause of lung cancer. That implies we can make meaningful arguments from one outcome to the other without following a causal chain. (The inference of a causal chain from such associations is frequently derided as the “post hoc propter hoc” fallacy.)

In such cases, however, we are still reasoning causally, and it is best to have that causal reasoning made explicit:

Yellow fingers SmokingLung Cancer

With the correct causal model, we can follow the dependencies, and we can also figure out the conditional independencies in the situation (screening off relations). Without the causal model available, we will only be using our intuitions to assess dependencies, and we will often get things wrong.

6) There are generally very many equally valid ways of modeling a causal system. How can one choose between them?

This is certainly correct. For example, between smoking and lung cancer there are a great many low-level causal processes required to damage lung cells and produce a malignant cancer. Whether we choose to model them or not depends upon our interests (pragmatics). If we are not arguing about the low-level processes, then we shall probably not bother to model them, as they would simply be a distraction. In general, there will always be multiple correct ways of modeling a causal system, meaning that the probabilistic (and causal) dependencies between the variables used are correctly represented. Which one you use will depend in part upon your argumentative purpose and in part upon your taste.

Argument Evaluation

If we are to know that our argument methods are good, we shall need methods of assessing them, built upon justifiable methods for assessing individual arguments. Arguments may be evaluated either as probabilistic predictions (if they are quantitative) or as natural language arguments or both. Here we will address quantitative evaluation. Evaluation of arguments in terms of their intelligibility, etc. we will leave to a future discussion.

One of the leading experts on probabilistic prediction in the social sciences, Philip Tetlock, has said “it really isn’t possible to measure the accuracy of probability judgment of an individual event” (Tetlock, 2015). This is not correct. To be sure, in context Tetlock points out that it is possible to measure the accuracy of probability judgments within a reference class, by accumulating the scores of individual predictions and using their average as a measure of judgment in like circumstances. Of course, if that is true, then such a measure applies equally to individual judgments within the reference class (one cannot accumulate the scores of individual predictions if there are no such scores!), so Tetlock’s point turns into the banal observation that you can “always” defend a failed probabilistic prediction. For example, if an event fails to occur that you have predicted with probability 99.9999%, you can shrug your shoulders and say “shit happens!” But actually that’s a defence that you cannot use too very often.

Tetlock suggests that the whole problem of assessing probabilistic predictions is a deep mystery. But his real problem is just the score he uses to assess predictions, namely the Brier score. It is a seriously defective measure of probabilistic predictions, and that ought to be surprising, since the real work in solving how to assess predictions was done half a century ago. But communications between the various sciences is slow and painful.

In most of statistical science an even worse measure of predictive adequacy is used: predictive accuracy. Predictive accuracy is defined as the number of correct predictions divided by the number of predictions. How can you do better in measuring predictive accuracy than using predictive accuracy? Of course, that’s why we slipped in the phrase “predictive adequacy” in place of “predictive accuracy”.

The problem with predictive accuracy is that it ignores the fact that prediction is inherently uncertain and so probabilistic. We should like our predicted probabilities to match the actual frequencies of outcomes that arise in similar circumstances. If, for example, we were using a true (stochastic) model to make our predictions, such a match would be guaranteed by the Law of Large Numbers. Predictive accuracy takes a probabilistic prediction’s modal value and effectively rounds it up to 1. For example, in measuring predictive accuracy, a probabilistic prediction that a mushroom is poisonous of 0.51 counts the same as one of 1. But that they should not be assessed as the same is obvious! The problem is what cognitive psychologists call “calibration”: if your probabilistic estimates match real frequencies on average, then you are well calibrated. Most of us are overconfident, pushing probabilities near 1 or 0 even nearer to 1 or 0. Nate Silver, for example, reports that events turning up 15% of the time are routinely said to be “impossible” (Silver, 2012). Another way of pointing this out is that predictive accuracy is not a strictly proper scoring rule, that is, it will reward the true probability distribution for events maximally, but it will also reward many incorrect distributions equally. For example, if you take every modal value and revise its probability to be maximal, you will have an incorrect distribution that is rewarded identically to the correct distribution.

Tetlock’s Brier score is strictly proper, but that doesn’t make it strictly correct. Propriety is a kind of minimum standard: if you can beat (or match) the truth with a false distribution, then the scoring function isn’t telling us what we want. Brier’s score reports the average squared deviation of the actual outcomes from the predicted outcome, so the goal is to minimize it (it is a form of root mean squared error). If we have the true distribution in hand, we cannot be beaten (any deviation from the actual probability will be punished over the long run). However, Brier’s score, while punishing deviant distributions, does so insufficiently in many cases. Consider the extreme case of predicting a mushroom’s edibility with probability 1. This will be punished when false with a penalty of 1. While such a penalty is maximal for a single prediction, in a long run of predictions, it may be washed out by other, better predictions. From a Bayesian point of view, this is highly irrational: a predicted probability of 1 corresponds to strictly infinite odds against any alternative occurring! That kind of bet is always irrational, and if it goes wrong, it should be punished by losing everything in the universe; that is, recovery should be impossible. The Brier score punishes mistakes in the range [0.9, 1] much the same, even though the shift from a prediction of 0.9 to 0.91 is qualitatively massively distinct from a shift from 0.99 to 1: a “step” from finite to infinite odds! Extreme probabilities need to be treated as extreme for a scoring function to correctly reward calibration and penalize miscalibration.

As we said, this problem has been solved some time ago, beginning with the work of Claude Shannon (Shannon and Weaver, 1949). Shannon proposed measuring information in a “message” by using an efficient code book to encode it and reporting the length of the encoding. An efficient code is one which allocates –log2 P(message) bits to all possible messages.

It turns out that log scores based upon Shannon’s information measure have all the properties we should like for scoring predictions. I.J. Good (1952) proposed as a score the number of bits required to encode the actual outcome given a Shannon efficient code based on the predicted outcome. That is, Good’s reward for binary predictions is:

reward

 

This is the negation of the number of bits to report the actual outcome using the code efficient for the predictive distribution plus 1. The addition of 1 just renormalizes the score, so that 0 reports complete ignorance, positive numbers predictive ability above chance and negative numbers worse than chance, relative to a prior probability of 0.5 for a binomial event. Hope and Korb (2004) generalized Good’s score to multinomial predictions.

Nothing will be able to beat the true distribution in encoding actual outcomes with an efficient code over the long run; indeed, nothing will match it, so the score is strictly proper. But the penalty for mistakes is straightforwardly related to the odds one would take to bet against the winning proposition. Infinite odds imply an outcome that is impossible, meaning in information-theoretic terms, an infinite message describing the outcome. No matter how long a sequence of predictions is scored, an infinite penalty added to a finite number of successes will remain an infinite penalty. So, irrationality is appropriately punished.

All of this refers to the usual circumstance of scoring or assessing predictions, where we know the outcome, but we are uncertain of the processes which bring it about. Supposing that we actually know how the outcomes are produced is supposing that we have an omniscient, God-like perspective on reality. But, in fact, in special cases we do have a God-like perspective, namely when the events we are predicting are the outcomes of a computer simulation that we know, because we built it. In such cases, we can score our models more directly than by looking at their predictions and comparing them to outcomes. We can simply compare a model, produced, say, by some argumentative method, with the simulation directly. In that case, another information-theoretic construct recommends itself: cross entropy (or, Kullback-Leibler divergence). Cross entropy reports the expected number of bits required to efficiently encode an outcome from the true model (simulation, above) using the learned model instead of the true model. In other words, since we have both models (true and learned) we can compare their probability distributions directly, in information-theoretic terms, rather than taking a lengthy detour through their outcomes and predicted outcomes.

In Search of a Method

CBNs are an advantageous medium for addressing other common issues in argument analysis. Active open-mindedness suggests we can minimize confirmation bias by proactively searching out alternative points of view and arguments. This can be supported by constructing CBNs with sources of evidence and lines of causal influence additional to those which might at first satisfy us, and, in particular, which might be expected to cut against our first conclusion. In view of confirmation bias (and anchoring, etc.), it might be useful to give the task of constructing an alternative CBN to a second party.

Another benefit in using CBNs is the direct computational support for assessing the confirmatory power of different pieces of evidence relative to one another, how “diagnostic” evidence is in picking out one hypothesis amongst many. While Bayes’ factors— the relative likelihood of one hypothesis to another for the evidence — have long been recommended for assessing confirmation, once coded into a CBN the diagnostic merits of evidence for the hypotheses in play is trivially computable, and computed, by the CBN itself. Hence, the merits of each line of argument can be clearly and quickly assessed, whether in isolation or in any combination.

All of the above does not provide a complete theory of argumentation using CBNs. These uses of causal Bayesian networks must sit within a larger method. This must include deciding when CBNs are appropriate and effective, and when not. When they are not effective, alternative techniques will need to be applied, such as deductive logic or argument mapping. A rich theory of argumentative context and audience analysis is needed in order to understand such issues as which lines of argument can be left implicit (enthymematic) and which sources of premises are acceptable. And guidance needs to be developed in how to translate a CBN, which only represents arguments implicitly, into an explicit formulation in ordinary language.

The required techniques in which CBN-based argumentation is embedded are largely just those employed in critical thinking and argument analysis generally. It is a substantial, but achievable, research program, ranging across disciplines, to develop these to the point where trained analysts might produce similar, and similarly effective, arguments from the same starting points.


The figure of 38% is a worldwide statistic from the WHO (“Domestic Violence”, Wikipedia). If the argument were specific to a country or region, other statistics might be more appropriate. The figure we have used is a reasonable one for the argument as stated, that is, without a specific context. Uncertainty for specific numbers can be treated via sensitivity analysis, as we discuss below.

References

Alvarez, Claudia (2007). Does philosophy improve critical thinking skills? Masters Thesis, Department of Philosophy, University of Melbourne.

Domestic violence. (n. d.). In Wikipedia, The Free Encyclopedia. Retrieved 16:49, March 29, 2016, from https://en.wikipedia.org/w/index.php?title=Domestic_violence&oldid=712521522

Good, I. J. (1952). Rational decisions. Journal of the Royal Statistical Society. Series B (Methodological), 107-114.

Handfield, T., Twardy, C. R., Korb, K. B., & Oppy, G. (2008). The metaphysics of causal models. Erkenntnis, 68(2), 149-168.

Hope, L. R., & Korb, K. B. (2004). A Bayesian metric for evaluating machine learning algorithms. In AI 2004: Advances in Artificial Intelligence (pp. 991-997). Springer Berlin Heidelberg.

Klein A. R. (2009). Practical Implications of Domestic Violence Research. National Institute of Justice Special Report. US Department of Justice. Retrieved from http://www.ncjrs.gov/pdffiles1/nij/225722.pdf

Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC press.

Nisbett, R. E., Fong, G. T., Lehman, D. R., & Cheng, P. W. (1987). Teaching reasoning. Science, 238(4827), 625-631.

Olding, R. and Benny-Morrison, A. (2015, Dec 16). The common misconception about domestic violence murders. The Sydney Morning Herald. Retrieved from http://www.smh.com.au/nsw/the-common-misconception-about-domestic-violence-murders-20151216-glp7vm.html

Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Palo Alto. CA: Morgan-Kaufmann.

Shannon, C. E., & Weaver, W. (1949). The mathematical theory of information.

Silver, N. (2012). The signal and the noise: the art and science of prediction. Penguin UK.

Sturrock, P. A. (2013) AKA Shakespeare. Palo Alto, Exoscience.

Tetlock, Philip (2015). Philip Tetlock on superforecasting. Interview with the Library of Economics and Liberty. http://www.econtalk.org/archives/2015/12/philip_tetlock.html

Toulmin, S. (1958). The Uses of Argument. Cambridge University.

Wellman, M. P. (1990). Fundamental concepts of qualitative probabilistic networks. Artificial Intelligence, 44(3), 257-303.

Wigmore, J. H. (1913). The principles of judicial proof: as given by logic, psychology, and general experience, and illustrated in judicial trials. Little, Brown.

 

 

 

Steven Pinker’s The Sense of Style

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Being a professional writer (as most academics are), I have read many books on style. Most of them are opinionated, fussy and annoying — such as Strunk and White’s most (in)famous book, The Elements of StyleSome are opinionated, fussy and amusing — such as Fowler’s A Dictionary of Modern English Usage, at least before its editing fell into others’ hands. But Steven Pinker’s recent book on style is one of the few I’ve seen that is opinionated, unfussy and well informed — and the first I’ve seen that reflects a deep understanding of both language and cognition, which is both unsurprising (Pinker is a leading cognitive linguist) and inspiring. Someone’s finally done style in style!

The Sense of Style: The Thinking Person’s Guide to Writing in the 21st Century (2014, Allen Lane) is filled (primarily) with good sense about style. To give you some idea, I’ll give you its parting message (in my own words, mostly): If you really want to improve your writing, consider the following principles.

0. Don’t pay attention to the anal-retentive Ms Thistlebottoms of the world who insist that splitting infinitives is evil or that punctuation is always thus-and-so. They are more often wrong than right.

1. Look things up. Humans are doubly cursed with fallible memories and overconfidence in their beliefs. Strong convictions about the meanings of words, correct usage, and how things hang together, in both yourself and others, are only weak indicators of truth. Dictionaries and thesauri (I looked it up) should be consulted when there is doubt.

2. Make sure your arguments are sound. Verify your sources and test your arguments before publishing them, if you can. If you can’t, then learn something about argument analysis.

3. Don’t confuse an anecdote or your personal experience for good evidence for a general proposition. A cold winter doesn’t mean global warming is unreal, contrary to a large number of dimwits active on Twitter. If this causes you problems, learn something about science and scientific method.

4. Avoid false dichotomies. Everyone has some impulse towards characterizing their enemies as subhuman or evil. Black and white exhausts the usual color spectrum. Try to see a little better than your neighbor or interlocutor. Try to avoid the “fundamental attribution error”, that whatever someone has done or said can only be due to their internal nature, their essence as a subhuman. Conservatives are not necessarily evil (or stupid), nor are liberals.

5. Follow Ann Farmer’s tagline: “It isn’t about being right, it’s about getting it right.” Don’t distract yourself with ad hominem arguments, focus on the reasoning in arguments.

Pinker brings considerations of cognitive science to bear on questions of language and communication that are quite useful. One example is his treatment of the “Curse of Knowledge”: the tendency to assume that your audience is at a level similar to your own, so that things you take for granted are also well known to them. Pinker argues that this is the major cause of incomprehensible prose. The phrase was invented by economists trying to explain why some market players don’t take advantage of others’ ignorance, because they act as though unaware of the others’ ignorance, since they do not share it. But related difficulties with empathy and understanding are well studied in young children and primates by cognitive scientists attempting to understand how people model and reason about the mental states of others. People mature into an understanding of the mental states of others, unless they are handicapped. But we all retain some tendency to assume more knowledge than we ought in our readers and listeners. And this explains more than a fair share of bad student reviews of lecturers who can’t stop talking well above the level of comprehension of their students.

The Curse of Knowledge is, like many of the biases cognitive psychologists study, very hard to cure. As Donald Rumsfeld infamously said, there are known unknowns and unknown unknowns, and the unknown unknowns are the hardest to deal with. Since the Curse concerns states of mind we are not directly familiar with, we have to apply some imagination to cope with them. Being aware of the problem is a first step, after which, as Pinker writes, you can see it all around you: acronyms that are full of meaning to only some of the many people who encounter them (e.g., ADE, CD, VLE, IELTS, ATAR); a walking sign that tells you how long a walk takes, but not whether it is round-trip or one-way; innumerable gadgets with obscure combinations of buttons required to get things done; and innumerable computer applications likewise. If you notice the many ways other people’s knowledge is being used to stymie you, you may acquire a taste for catering to other people’s ignorance when you are communicating with them.

Perhaps the majority of style books document most closely the prejudices of their authors, rather than drawing upon much evidence. Pinker, rather more sensibly, refers to evidence both from the history of English usage and from cognitive science. This often clarifies matters of style, whether they are in dispute or not. For example, instead of simply stating that parallel constructions are often stylistically neat and dropping in a few illustrations, Pinker shows how parallel constructions aid the reader in parsing sentences and how failures of parallelism (“stylistic variations”) interfere.

The “Great War” of style is between Prescriptivists and Descriptivists. Prescriptivists believe that proper grammar and style can be codified in a set of rules, and good writing is a matter of finding the right ones and adhering to them. Linguists, lexicographers and good writers don’t agree with them. There is no algorithm for good writing — not yet, anyway. Linguists can trace the historical and pre-historical relations between families of languages because of two things: there is continuity in the way language is used, and there is continuous change. Were the Prescriptivists to win their war, languages would cease to be useful, for nothing will stop the world from changing. On the other hand, Pinker is not so drenched in his descriptive studies of human cognition to not see advantages in some prescriptions. Take the word “disinterested”. Pinker points out that its earliest uses were in the sense of one being uninterested, rather than in the sense of allowing an impartial judgment. The Oxford English Dictionary, at any rate, gives quotes from 1631 for the former and 1659 for the latter. A pure Descriptivist must accept both usages, but Pinker quite sensibly points out that “uninterested” works perfectly well for a lack of interest, so reserving “disinterested” for a compact way of expressing the second sense makes sense. As a language must strike a balance between its own past and its current surroundings, neither pure Prescriptivists nor pure Descriptivists can capture its essence.

When I read, I almost always find something to disagree about, and Pinker’s book, despite being first rate, is no exception. So in conclusion, I would like to register one complaint with Mr Pinker. While I think most of his judgments about language and style are right, and often well grounded with evidence, there is a principle which he ignores, that I have held to over my career: language is first and foremost spoken and only secondarily written. If you find yourself tempted to write something which you cannot imagine yourself speaking in any circumstances, then you are being tempted into a stylistic error. For a few examples: the use of the genitive apostrophe, according to Pinker, always demands a following “s” (except for some historical special cases). But this (according to me!) is wrong. People say “Bayes’ Theorem”, not “Bayeses Theorem”, and so it should be written with no trailing “s”. Similarly, acronyms are pronounced a certain way, and how the indefinite article is used with them depends upon that. So, if someone writes “an NBN connection”, that means in their idiolect “NBN” is spelled out when speaking; if they write “a NBN connection”, that either means that they say “NBN” in some other way, perhaps like “nibbin” or that they are not thinking about what they are writing. Many people miswrite the indefinite article, or English more generally, by not reflecting on how they speak.

The advice to read aloud your writings before finalizing them is not idle advice.

How to Do a PhD

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I recently read an article in the Guardian reporting to students “top tips” from experts (supervisors) on how to finish a PhD. While many of the tips are useful, it occurred to me that many other important points are missing, including some that, after decades of research, seem pretty obvious to me. So here I produce my own list, in a kind of rough chronological order (I put quotes around the few I’ve lifted from the above article):

1. Sort out why you’re doing a PhD. What stops more students from completing a PhD than anything else is an eventual failure of purpose, in particular a slacking of motivational drive. If you don’t have a clear purpose from the beginning, one that will stand the test of time, you are probably wasting your time. You can test out your motivation by doing research prior to commencing a PhD, in an honours project, summer internship or something similar. It may also help you obtain a scholarship.

2. Find the right supervisor. Your relationship with you supervisor may be the most important one during your PhD. Ask around other students and ex-students before committing yourself to anyone. There’s a lot of emphasis on getting the right supervisor, the right “Master” in the traditional master-apprentice model, that is, someone who knows the craft best. That’s the easy part, since academics routinely reveal their understanding, or misunderstanding, of their fields by publishing their thoughts. Learning about their eccentricities and how they may affect you as a student is harder, but important. You may luck out by landing with the best possible researcher while sticking yourself with a supervisor who ends up undermining you, blocking you, usurping your work or simply harassing you for years. It’s best to avoid ever landing there, but if you have, you may have a chance to change supervisors; in that case take it. For that reason, it is risky to start a PhD at an institution that has only one potential supervisor for you.

Another common failing in supervisors is an urge to claim ownership, or co-authorship, of anything one of their students writes, regardless of their own contribution. At my institution “honorary authorship” violates regulations; at every institution it violates integrity. You should be able to discover whether a potential supervisor suffers from this ethical malady before signing up.

Less extreme difficulties are more common. It can be difficult establishing the right degree of autonomy, for example. You and your supervisor may have different ideas about what your project is and, more problematically, what your role in deciding its direction and content is. Usually these are sorted out implicitly, but it may be worthwhile to treat them explicitly and at the beginning.

3. Learn about the process of doing a PhD. Most students just do (or try to do) a PhD. Many fail who may have benefited from taking the process of doing a PhD as a subject of study in its own right. I teach and do argument analysis. I take it as a part of that effort that I ought to be spending some of my time learning something new about writing and argument analysis. PhD students ought, I think, to similarly take their own efforts seriously enough to study and think about them.

So, for an example, you will have done one or more literature searches in your area of interest (duh!). Try doing a literature search in the area of: how to do a PhD. <break> If you haven’t yet found at least the following web sites, you haven’t tried hard enough (I provide links, in case that’s intentional):

Of course, the meta-effort of learning about the process and the effort of the process itself needs to be in balance, with the overwhelming bulk on the latter. But a little bit of looking around before you dive in will pay off.

By the way, if you look at these sources and at some of my own writings (e.g., Research Writing in Computer Science — which is actually quite general), you will see that I don’t endorse everything the references above have to say.

4. Governments and institutions insist that PhDs take three years, or four years, or whatever, to complete. What’s pretty certain is that scholarships take three years, or four years, or whatever, to complete. PhDs take whatever time the research takes, plus whatever time being distracted by the need to earn additional money takes away. Some outside work is positively useful, such as research or tutoring in the same field as your PhD. This kind of work should be pursued, in moderation. It’s best to avoid other work until you finish your PhD, if you can. But be prepared for a longer haul than is advertised.

5. Build up a network with other students. The pain, stress and tedium of years of work on a PhD can be ameliorated by a little sharing. You might consider structuring the network on a regular meeting, like a biweekly brown bag discussion of readings or research.

6. Don’t “write up” your thesis. Most people, including supervisors, talk about a “writing up” period: after the research is over, the experiments are finished and the ideas are exhausted, it’s time to put it down on paper. This is a mistaken attitude about both research and writing. Writing is an integral part of thinking, of clarifying, testing and improving ideas. You should be writing from the beginning of your research to the end. A part of this is avoiding perfectionism: you should rewrite what you write, but not endlessly; instead, set at an end and meet it. There are always other things to move onto.

7. Enjoy your time as a PhD student. It won’t return. There are certain freedoms that you have with your time and effort. Use them to establish work habits, and break habits, that are useful in both achieving your targets and avoiding burn out.

8. “Write the introduction last.” This is generic writing advice that applies to anything with an introduction. You may also want to write your introduction first, which is simply a free choice. You should always (re)write it last, as, unless your writing is trivial, you will have learned something during the writing and changed your mind about something along the way.

More generally: since writing a PhD requires a fair bit of writing, go and learn something about how to write. My paper Research Writing in Computer Science is an OK starting point, with references to argument analysis, writing methods and style guides, but does need updating.

9. Publish and present along the way. Publishing good work is nearly essential for obtaining academic work after a PhD. The older model of publishing papers out of a completed dissertation, whatever its merits, will be more likely to lead to a prolonged period of unemployment. Attempting to publish, successful or not, will also usually provide valuable expert feedback along with any rejection letters. Of course, getting published also provides some evidence that your contribution to knowledge is real.

Giving seminars and presentations at conferences and neighboring institutions is an essential part of the whole process. Presenting complex material intelligibly is almost the definition of an academic lecturer, and the more practice you can get the better. Seminars also provide valuable opportunities for getting intelligent criticism of your ideas, and especially criticism from those with distinct points of view from your supervisors and peers at your home institution. Attending conferences regularly, whether local, regional or international, will help you build up connections that may last, supporting collaborations or providing job opportunities throughout your career.

Another worthwhile activity might be to assist your supervisor or other academics to write a grant application. As writing grants is an onerous, and in many respects a time-wasting, exercise you can probably find an opportunity within your general area of research — i.e., an academic is unlikely to turn away a qualified volunteer. The benefit to the volunteer is an increased understanding of the process, the gratitude of an academic, and possibly post-doctoral employment if the grant is successful.

10. “Make sure you meet the PhD requirements for your institution.” This is a no-brainer, but I suppose some students violate regulations on occasion. In my experience university bureaucrats tend to see their mission in life to be enforcing regulations rather than meeting the real goals of their institutions (e.g., graduating students), so you violate regulations at risk. As Tara Brabazon wrote, “Bureaucracy is infinite.”

11. “Prepare for the viva” as you would for a job interview or a politician would for a campaign debate. Take care of the basics, such as knowing how you will start out, e.g., summarizing the thesis and its contributions. Figure out five questions your examiners will most likely ask you and prepare answers. Practice them in a mock viva with fellow students or supervisors. If nothing else, a mock viva should reduce your stress level in your real viva. (Australians don’t have vivas, presumably because getting external examiners to sail over from England takes too long — which is related to the delay Americans have between Presidential elections and the swearing in, as a couple of months are needed for the horse ride to Washington, D.C. In any case, similar considerations to the above apply to candidature reviews, etc., which do occur in Australia.)

I Support BDS

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I have decided to support the “Boycott, Divestment and Sanctions” (BDS) program against the state of Israel. The goal of the program is to bring an end to illegal occupation of Palestinian lands by Israel, allow Palestinians to live in their homes unmolested and free from military or militia attacks and to bring fully equal democratic rights to Palestinians living inside Israel. Israel is occupying Palestinian land and operating there in ways violating international law, including building its wall of separation and allowing Jewish settlements to displace Palestinians. At least until Israel begins to abide by international law, I shall abide by the general provisions of BDS, meaning I shall not knowingly purchase Israeli goods, invest in Israeli companies, visit or support Israel as a state. I look forward to the day when I can reverse my position.

Boycotts are, clearly, blunt instruments. However, they have been effective in the past. Furthermore, I have been informally using them all my life, avoiding wherever possible, for example, the use or purchase of Microsoft products, on such grounds as that they demonstrate unethical business practices, including even stupid practices, such as refusing to support their own prior customers after developing new versions of software. Of course, I do use Microsoft products, because others keep forcing me to do so; but I minimize my use of them, and I encourage others to do the same. Similarly, I do not support PayPal or Amazon, because they’ve acted against one of the few who have directly supported principles of democratic freedom as they apply in the Internet age, Edward Snowden. Boycotts are blunt, but effective instruments, and I will continue to use them.

I’m willing to listen and respond to sensible counterarguments. But arguments that the UN or the International Court are the homes of conspiracies against Israel or the US needn’t be put; they are stupid and pointless. Arguments that Hamas is an evil organization will probably be coherent and correct, but also red herrings. The issue in question is Israel and its actions.

And, by the way, the state of Australia is also systematically violating international law, by, for example, refusing to receive and properly process the claims of refugees, as required by the Refugee Convention. I abhor the actions of my own government and would welcome a BDS movement against it, since I think pressure to change its policies, or better yet its government, are most welcome!