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



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., 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.


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.,

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.


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.


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


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.


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.


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:



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.


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

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

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

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.

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


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!

Write It Wrong: Abusing Words for Effect


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I recently came across the Dover reprint of Ambrose Bierce’s Write It Right. While it is an interesting collection of words Bierce thought were improperly used, I cannot recommend it. It has some use as a guide to closely related words confused with one another, but for that purpose I would recommend instead SI Hayakawa’s Choose the Right Word. Otherwise, it is largely filled with Bierce’s inability to understand metaphors, or to appreciate and accept common idioms, or to accept linguistic change. To satisfy a taste for disagreeable linguistic prejudices, Strunk & White is at least equally good. But Bierce’s book reminded me of a need for an analogous list of words: those that have recently become whips in the hands of abusive politicians and their slavish media (or is it the other way round?). So, here I begin to list some of those I find both common and objectionable, and why I find them objectionable. I hope to return from time to time and enhance it.

The idea, by the way, is to deal with the misuse of words that has a political intent. Most misuses do not arise from a political ulterior motive, but from a simple lack of clear thinking about what one is saying. An example is the use of “literal” to mean metaphorical. I won’t be going into such things here. By the way, the classic treatment of the political abuse of language is George Orwell’s “Politics and the English Language”. It’s well worth reading, but times change and so does language, despite the resistance of types like Bierce.

Climate Change. The right word is “global warming”, of course, or “GW” to be informal. The case for global warming was made a long time ago, and the idea that 7 billion and more humans operating at a high-burn economically can do so without impacting on their environment is about as stupid as a dog crapping in its own den. There aren’t many dogs that stupid, but there seem to be plenty of people to match them. To cater for them, their prejudices or their special interests the media needs a special language to euphemize discussions of GW; hence, “climate change”. It sounds so natural and inevitable (as Marco Rubio says, “climate is always evolving,” in a strange metaphor for an evolution skeptic − or anyone else). However, changes are sometimes permanent, which could be scary, so let’s change it to “climate variability”, which sounds impermanent and innocuous. Oh yes, let’s. That’s what the Coal-ition Victorian government has apparently ordered its employees in the Department of Environment and Primary Industries to use (note the reorganized name: putting environmental concerns in their proper place!). It’s a pity that so many environmentalists and others who take GW seriously regurgitate this mealy-mouthed language.

Entitlement. Joe Hockey’s promised to end the “Age of Entitlement”. The problem here is not so much the word as the corruption of its meaning. Entitlements are those things one has a right to or has earned. Why would any politician want to take away things people are actually entitled to? Hockey isn’t suggesting that: he’s promising to take away things people aren’t entitled to, in his opinion, such as, in particular, his money and the money of other rich people taken away in taxes. Smokin’ Joe should really be talking about taking away people’s Unentitlements, but then perhaps people would notice that they are actually entitled to them, which could be embarrassing for him.

Exceptional. Ever since Alexis de Tocqueville wrote his fawning Democracy in America, US politicians have fallen all over themselves describing the US as exceptionally, nay, extraordinarily, nay, fantabulously wonderfulistic. It has become a sine qua non for being elected President. When I was little, I actually thought to myself “how lucky I am to have been born here.” There’s nothing exceptional about that thought: every country is full of people having it. The only things exceptional about American exceptionalism is how ordinary it is and how loud it is. The lady doth protest too much, methinks.

Genocide. It seems the murder of everybody and his uncle has become genocide in recent years. The media are constantly turning massacres into genocides, but the word means the attempted, or actual, extermination of a people, nation or ethnicity, as any decent dictionary will tell you. The bastadardization of its use makes cases of real genocide seem more acceptable, or at least more usual. It should be resisted.

Illegals. Our political leaders of both parties are quick to denounce refugees arriving in Australian waters as illegals. This is so despite the fact that numerous legal experts have reported to them that there is nothing illegal about them. Tony Abbott, and others, then claim that its their arrival that’s illegal, rather than the people, harking back to the “queue jumping” claims of prior governments. But that claim is just as false. Refugees, by definition, have to leave the place where they and their like are being tortured, murdered or abused. That means they have to reach some place new. And that means that some place new must be reached first. So much Liberal Senator Ian Macdonald appears to accept, but you can listen to him here blithely expressing the shameful point of view that such a place could not possibly be Australia: there are millions of people waiting their turn to come to Australia in squalid refugee camps “right around the world”, but those who come to Australia first to have their claims assessed are acting illegally. The honourable Senator simply ignores the point that they have to get some place first and that international law allows for that place to be Australia. In the world view of this government, Australia is uniquely free of any legal obligations normally incumbent upon those who agree to international treaties. It is an exceptionalism that exceeds in its way that current in the US. Well, I’m sure they don’t actually believe that; they are simply posturing for a gullible public and an even more gullible media. The correct word is “refugee“.

Pro-Life. “Pro-lifers” are pro-fetus and anti-women, of course. Recently, Senator Marco Rubio, in a round about attack on the GW consensus in science, declared that there is not just a scientific consensus that life begins at conception, but that it is a unanimous consensus. This, were it true, would offer genuine support for the self-promoting label anti-women campaigners have adopted. The immediate reaction to Rubio may be that words longer than three syllables may be too long for Rubio to handle; but no, Rubio seems to have actually meant it. But there is no consensus in science or philosophy as to what life is, or when it begins or ends. Instead, there are practical rules of thumb that get used in hospitals, such as cessation of brain activity or external viability of a fetus (not conception). In any case, a claim of unanimity is defeated by a single counter-example, and since Rubio’s strange expression of ignorance there have been many scientists objecting, so the claim is immediately defeated. This, of course, won’t stop right-wing obscurantists from claiming otherwise. Let’s call these people what they are: misogynists.

Reform. This has become a favored term for change, because it has favorable connotations. To reform something is to improve it, by, for example, removing obstacles to its proper function. But a government introducing as a “reform” a reduction in taxes for the rich should not be allowed to get away with this language unless they can justify it, as with a serious argument that the taxes removed were excessive, rather than an ideological commitment to the view that all taxes for the rich are excessive.

Socialism. I was surprised to read in the Los Angeles Times recently that Obama is a socialist. It’s pretty usual for right-wing hate mongers to say such things, but I expected a once-reputable newspaper to do a little better. The correct term, in many cases of word abuse, is “social democrat”: someone who believes that there is a place for a government to support social democracy by using its power to tax for the benefit of the commonwealth. “Socialism” by contrast refers to a theory that the best way to organize society is to give the means of production and distribution entirely to the state, which is hardly a view that industry-promoter Obama shares. There aren’t many socialists left these days, as the failure of the Soviet Union and similar enterprises has been taken by most, leftists included, that socialism is at best impractical and at worst pernicious. Many right wingers fail to notice the current absence of socialists, since everyone to left of neutral looks the same to them. The rest of us needn’t encourage their fantasies, however.

Terrorism. Every attack or act of violence attributable to Arabs or Muslims can nowadays freely be called terrorism. Any similar act by others is just “random violence” or an act of lunacy (see Bernard Keane’s “Why white terrorism isn’t terrorism”). But terrorism is political violence intended to terrorize, so, for example, the explicitly political murder of two police officers in Las Vegas by a white couple was clearly an act of terrorism. Yet the language of news reports is one of “shootings” or, perhaps, at most “shooting rampage”. Terrorists aren’t white, unless they have beards and rant about the Koran, or else were French and long dead. But, once again, decrepit, old, white, male commentators need not be accommodated.

The People. The Second Amendment to the US Constitution says: “A well regulated Militia, being necessary to the security of a free State, the right of the people to keep and bear Arms, shall not be infringed.” The claim that “the people” refers to individuals is either ignorance or a lie. “The people” is a collective noun, referring to a collective. Of course, there are sentences and contexts where the phrase has the meaning gun sellers, manufacturers and nuts prefer to substitute for its actual meaning in the constitution. “The people were bamboozled by specious arguments” implies that everyone concerned was bamboozled. But there is no such usage in the second amendment. As if this wasn’t clear enough, the amendment specifically sets the context by reference to a well-regulated militia. Lone gun nuts shooting people in cinemas were not what the writers of the constitution had in mind, whatever a right-wing packed Supreme Court may say about it. We The People: Take Back Our Language!

Combat semantic abuse: don’t let Murdoch and his political minions own the debate! If you spot linguistic abuse, please make a comment; I’ll be happy to add it to my list if it fits my criteria (which are not actually politically biased, despite my own political bias).

Tony Abbott Moves against Free Speech


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In a recent blog post, Tim Wilson, the Australian Human Rights Commissioner, has defended Tony Abbott’s new rules restricting public servants in their political speech. In particular, he argues that it is not a genuine limitation of their speech and that it is a reasonable rule to impose on their employment. Here I will illustrate the process of argument analysis by a treatment of his argument. A prior caveat, however: there are always multiple, distinct ways of analysing arguments; and they will often be equally defensible. The goal of argument analysis is not to find a single, definitive argument which conclusively establishes a correct conclusion. (The plea for “proof” is a pretty good indicator of an absence of integrity in an argument!) The goal is to improve your argumentation and your thinking. Finality is a goal best reserved for the grave.

Tim Wilson’s Arguments

For the sake of brevity I will paraphrase Wilson’s arguments here. While excluding what is irrelevant to these two arguments in particular, the paraphrase is pretty accurate, as is easily determined by reference to the original. Also, I number the assertions and put them in blockquotes, although they are not literal quotes.

Argument 1: The New Rule Does Not Limit Free Speech

(1) The Department of Prime Minister and Cabinet has released new social media protocols. (2) The protocols limit the capacity of public servants to make political statements that are harsh or extreme in their criticism. (3) Employment codes are not law, and (4) so cannot constitute a legal limit on free speech. (5) Defending the universal human right of free speech is about the legal limits of speech.

Argument 2: The New Rule Is a Reasonable Employment Rule

(1) Codes of conduct provide an important civilizing role in filling gaps left by the law. For example, (2) codes of conduct restrict homophobic behavior. (3) Employment codes are not limiting, (4) since an employee may at any time resign. (5) What is specifically precluded by the new code is harsh and extreme criticism in areas that are related to their work.

I will apply the AA process only to the first argument, in order to keep this illustration of method reasonably short and clear.

Step 1: Clarify Meanings

Tim Wilson begins his post by pointing out that we should know something of what we talk about prior to opening our mouths: “Before anyone screams ‘free speech’, they should actually know what they are talking about.” The implied criticism of his critics, that they don’t know what they are talking about, is nowhere substantiated by Mr Wilson. However, the challenge is worth accepting.

So, what is free speech? Literally taken, it might be a right to say whatever you have the urge to say. In practice, however, as Wilson and every other commentator has noted, there are accepted limits upon speech. So, whatever right to speech we may be referring to is, and always has been, a limited right.

Freedom of speech as a right certainly has been recognized from long ago, for example, in the English Bill of Rights of 1689 and before that in ancient Greece, as John Milton noted in his famous defence of free speech, in Areopagitica. Free speech is recognized as fundamental in the Universal Declaration of Human Rights. It is notable also that the very first amendment in the Bill of Rights in the United States explicitly protects freedom of speech and a free press. Every democracy depends upon a free debate over public policy and principles, so attacks upon free speech are indirectly attacks upon democracy as well.

Nevertheless, it is perfectly well and widely accepted that there are proper limits on free speech. Speech that is likely to be hazardous or harmful to others is generally prohibited. Defamation and libel are also generally prohibited. And contracts may prohibit certain kinds of speech, such as the disclosure of proprietary information, as Wilson specifically notes. So, there is a real question whether Wilson’s defence of Abbott’s new rules is legitimate or not. Any reflex dismissal of it is a wrong reflex.

I have no particular unclarities about Wilson’s language, although I will return to some of the semantics later. I will also note that Wilson makes no distinction between “legal limits” on speech and “limits” on speech. That is, his post equivocates between them, attempting to support the claim that there are no limits imposed on free speech by Abbott’s actions because they do not impose any such limits in law. That inference is specious nonsense, of course.

Some Background

There is a relevant background to this issue. Tony Abbott and his government now have a track record of restricting freedom of speech and the flow of relevant public information in ways that at least suggest they fear public scrutiny of their actions. When the ABC reported on evidence of the mistreatment of refugees by the Royal Australian Navy, Abbott labeled them “Un-Australian”; many of his ministers also condemned the ABC, and they have suggested its funding and role should be curtailed. On any matters connected to dealing with refugees, Border Protection Minister Scott Morrison routinely invokes the cover of protecting military “operations” in refusing to address many questions, perhaps out of fear, for example, that smugglers might learn whether they have sent a boat to Australia. It seems likely that putting border protection and the handling of refugees under military control was, in part, designed to restrict public knowledge of the government’s activities. But, of course, issues of sovereignty and support for international law are pretty central to the public policy of a democracy. If anything is Un-Australian, it would have to be suppressing public debate about public policy.

Step 2: Identify Propositions

Already done.

Step 3: Graph the Argument

Argument 1 might be graphed as: A1

This shows its radical incompleteness. (1) is just setting context, identifying what protocols are at issue. The conclusion here is implicit, so the graph is quite fragmentary; the conclusion is in the argument’s title, so just numbering that (6) and making obvious connections we get a much better representation of the argument:A2

A few observations on graphing are in order. This graph is just a quick Google hack, but there are more sophisticated tools for the purpose, such as Austhink’s Rationale. That tool will give you some syntactic sugar that you may find useful; for example, it colors supporting links green and contrary arguments red. Here I’m inventing two small pieces of syntax: a dotted line for context setting that’s not really part of the argument; arrows joining together to show that a conjunction of premises is required for support. To be sure, (2) is also required for the inference to (6), but it is less closely associated with (4) and (5). If you have a disjunctive argument, such as “X or Y → Z”, you might want to show that clearly as well, using color or dotted lines, etc.

Step 4: Make it Valid

We now tackle the argument one subargument at a time. (3) → (4) is presumably not controversial, but it is certainly not, strictly speaking, valid. Dr Neil Thomason likes to invoke his “Rabbit Rule”: you can’t pull a rabbit out of a hat, unless it was already in there. The premise (3) doesn’t even mention limits or free speech, so it cannot be valid to conclude anything about them, as (4) does. What we need is some innocuous hidden premise to get us there, such as, (A) only laws can constitute legal limits on free speech. Since (A) is innocuous, this hasn’t revealed anything revelatory; but it is all part of the AA process.

(2) (4) (5) → (6) is much the bigger problem. First, let’s just look at (4) (5) → (6) in isolation. We have a Rabbit problem here as well: the conclusion says the new rules don’t limit free speech, whereas the premises are about legal limits only. This is not my artifact: the equivocation lies in the original, as you can see for yourself. We shall have to fix it, by some kind of bridge, that will allow a valid inference. A plausible candidate would be: (B) that which does not constitute a legal limit on free speech does not constitute a limit on free speech. From this it validly follows that there is no limit on free speech, given the premise that the new APS rules do not constitute a legal restriction on speech. There is, however, an immediate problem with (B), which is that it is obviously false. When you appear to be compelled to introduce an obvious falsehood as a missing premise, that tends to be a bad sign. There is no help to be found in Wilson’s post, since he there recognizes no distinction between legal and other limits on speech, sliding over any problem. This is where (2) comes in, at least in my thinking. It (and related text, that I have not copied) appear to be suggesting that employment codes can be legally relevant, in particular by violating the law. The laws that might be both relevant and violated here are not gone into, but the qualification that it is only harsh and extreme criticism that is being suppressed suggests some such qualification. Therefore, I shall adopt (B’) as the missing premise: (B) so long as it only limits harsh or extreme critical speech. The subargment in question then becomes (with some modest rephrasing):

(2) The new rules limit employees’ political speech that is harsh or extreme in its criticism. (3) Employment codes cannot constitute a legal limit on free speech, if they only limit harsh or extreme criticism. (5) Free speech is about the legal limits of speech. (B’) That which is not a legal limit on free speech also does not limit free speech, so long as it at most limits harsh or extreme critical speech. (6) Therefore, the new rules do not limit free speech.

Our graph at this point is:


I accept this as valid, or near enough, but that’s hardly the end of the story.

Step 5: Counterargue

Tim Wilson’s suggestion that the right to free speech only concerns limits in law is one key issue. This certainly does reflect, for example, the first amendment to the US Constitution, which restricts what laws the US Congress may make. It also reflects the underlying motivation for many declarations about human rights in general and free speech in particular; the underlying motivation is to not tolerate governments which attack such freedoms. What it does not reflect, however, is the ability of governments to attack freedoms indirectly and implicitly. A government may, for example, attack free speech by financing those who openly support its policies and deny financing to those who openly criticize its policies. While this may not violate explicitly the Universal Declaration of Human Rights, taken to an extreme it can be just as effective and pernicious as government actions which do openly violate that Declaration. More directly, “limiting free speech” is ordinary English, not legalese: Tim Wilson has neither the right nor the ability to arrogate its meaning for his own purposes. Telling people they cannot say something is limiting free speech, whatever pathetic spin Wilson cares to put on it. The only legitimate issue is whether the limitation is warranted or not, and on that count also Wilson is very much on the wrong side.

Wilson has gone to some pains to present his view as quite moderate. The only limitation of speech is that by an employment contract, and that speech must be extreme or harsh before any cause to dismiss can be found. So reads Wilson’s blog. And no ordinary person would expect to use extreme or harsh criticism of their employers in public and get away with it. Hence, the objectors must just be more of the chattering classes, of the latte-sipping variety. But there are a few points Wilson neglected, best considered with a latte in hand.

First of all, there is pre-existing policy that current APS employees might have a reasonable expectation of being enforced. The APS employment policy states:

It is quite acceptable for APS employees to participate in political activities as part of normal community affairs. APS employees may become members of or hold office in any political party.

Clearly, it follows from this that criticism of the existing government by opposition members who are a part of the public service is legitimate and protected, whether distributed via social media or otherwise. Of course, that does not mean that “harsh” or “extreme” criticism must be protected. Or, then again, perhaps it does. Presumably, since public servants are encouraged to run for public office, they are not meant to be severely handicapped relative to the incumbents they run against. But under the new Abbott rules that is the case: Abbott and other incumbents can be as obnoxious, harsh or extreme as they like in attacking their opponents, but if their opponents are also public servants, they cannot return in kind. If I were a public servant campaigning against the likes of Abbott, I would first resign. But that is irrelevant: the fact remains that Abbott’s rules clearly violate the intent of the existing code of conduct by restricting otherwise free political speech. Unfortunately, matters are even worse than what I have just written.

The exact wording of the new rules is, in fact, relevant. Specifically, they restrict opinions posted in social media, whether acting professionally or not, which are “so harsh or extreme in their criticism of the Government, Government policies, a member of parliament from another political party, or their respective policies, that they could raise questions about the employee’s capacity to work professionally, efficiently or impartially” (my emphasis). This covers, for example, scientist public servants who may want to raise questions about George Brandis’ preposterous declamations on the climate change debate. Oh my! Were I a public servant, perhaps I would be fired tomorrow for that last sentence! It is certainly true than I hold my current political masters in contempt! Nevertheless, the standard being set here for public servants being called to account is simply absurdly low. Under what circumstances can the pack of Brandis, Abbott, Morrison, Hockey, Turnbull and the rest possibly raise questions about the professionalism of those who oppose them? I will leave it to your imagination. But if you are a public servant, you will have no difficulty answering the question and keeping your mouth firmly shut. Which is just what your masters want.

Steps 6 and 7: Consider Alternatives and Evaluate

I will illustrate these steps in the negative, by omission. As pure pedagogy it is not necessary, since it repeats the first five steps on new arguments; as a positive example, it may be necessary. I plead my case as a matter of time: I’ve taken a fair bit to do this much and need to get to other things. Perhaps, in future I shall return to this and complete it, however. Also, perhaps reader comments will help fill the gap.

I will, however, quickly comment on Wilson’s second argument. Codes of conduct may either be civilizing or barbarous. This new code might count as civilizing were the enormous leeway in its interpretation taken away. Wilson’s implicit suggestion that they are limited to work matters is at best misleading, however, since both political campaigns and scientific publications are explicitly mentioned as being circumscribed by the new rules. That the rules do not take away an employee’s right to quit work and face unemployment hardly means that employees’ rights to free speech are thereby unimpaired. A kidnap victim’s “right” to refuse an order and thereby get shot in the head doesn’t make such an event the victim’s fault, nor does its availability restore the victim’s freedom. Abbott’s rules demonstrate, as if further demonstration were needed, that all of his impulses are against transparency and freedom of speech. Barbarity is the New World Order.

The Process of Argument Analysis

To do a thorough analysis of an argument requires a certain discipline. The best approach I know of is due to Michael Scriven and specifically his book Reasoning (1976), which is out of print. Here I present my own version of this process, in compressed form, in seven steps. Roughly, the idea is to first build up the argument into its strongest possible form and then to try to tear the argument asunder. The result should be a good understanding of both the strengths and weaknesses of the argument as it was stated.

I present this as a process for analysing someone else’s argument as it might appear in some ordinary text. However, it may be applied elsewhere, for example, to your own arguments, with a view to improving them. Also, I present this as a kind of ideal. Since in reality we are all constrained by time, it’s unlikely that anyone will continually apply all of the steps to every argument of interest. It’s worthwhile applying all of them to some arguments, however.

The AA Seven Step Program

1. Clarify Meanings.

The first step to critiquing an argument is to understand it. Words that are new to you, or used in unusual ways, might require you to use a dictionary or an encyclopedia. This step may require a certain amount of detective work, for example, learning more about the author and the argument’s history, so as to understand the context and to disambiguate some of the expressions used. Lewis Carroll’s Humpty Dumpty was of course wrong to say that a word means “just what I choose it to mean”, but that doesn’t mean that what authors think they mean is irrelevant. This is also a step where there may be some opportunity to identify whether equivocation is playing some role in the argument; that is, to identify whether some words or phrases are being used in multiple senses, and perhaps misleadingly.

As Tim Wilson, the Australian Human Rights Commissioner, writes: “Before anyone screams ‘free speech’, they should actually know what they are talking about.”

In this, Tim Wilson is exactly right. He writes this, however, in the context of a defence of his Prime Minister’s recent move to restrict the freedom of political speech by public servants. I shall post an analysis of this issue soon after this post, partly to debunk Wilson’s posturing and partly to illustrate the methods explained here.

2. Identify Propositions.

Propositions are assertions about the world, ruling some possible states of affairs in or out. In an argument, some one or more propositions will be premises — assumptions of the argument — while some one or more other propositions will be final conclusions. In between will be intermediate conclusions that are derived, directly or indirectly, from the premises and which are further used to derive other propositions. The rest of the argument will be, in effect, chaff — rhetorical flourishes, irrelevancies, noise. In this step all the relevant propositions should be identified and tentatively identified as premise, intermediate conclusion, final conclusion.

Propositions and sentences may or may not correspond. A sentence may easily contain many propositions. For example, the sentence “Boat people harbor terrorists and criminals and are not our kind of people” one might find three propositions. Propositions may also be spread over multiple sentences, where the sentences are complete grammatically, but somehow incomplete conceptually.

There are, of course, certain words and phrases which may introduce or indicate a role. Statements about observation, testimony, etc., would tend to suggest that premises are being discussed, while “thus” and “therefore” would tend to indicate conclusions. This kind of syntactical marker won’t carry us very far, however, since meanings can be stretched (I can “observe” a conclusion) and, importantly, intermediate conclusions are both premises and conclusions, giving rise to both kinds of marker. The real objective in tagging the propositions is to make the best sense of the argument as possible, so while graphing the argument (in the next step) you may well decide on a different way of classifying propositions as conclusions and premises.

3. Graph the Argument.

Graphing an argument, with each proposition appearing as a node and inferential steps as arrows relating premises and conclusions, is usually a useful exercise. It forces you to make the argumentative steps explicit. The most common way arguments go wrong is by leaving some, or much, of the reasoning implicit, where, unexamined, its imperfections remain unexposed. Per Louis Brandeis, “sunlight is said to be the best of disinfectants.”

While graphing you will need to think about which premises go together to support which conclusions. The goal here will not to be to make these subarguments (parents and their immediate children) valid, but they should be put together so as to be as strong as possible, given the propositions actually in hand. If they are not, you have done a bad job.

Argument mapping has become more popular as computer tools for doing it have become available. For example, Tim van Gelder’s Rationale is widely used in teaching critical thinking. A good alternative, especially once the basics of argument analysis and mapping have been learned, is to use Bayesian networks for laying out arguments and assessing their merits. Bayesian networks have the distinct advantage over pure mapping tools that they can reflect the degrees of strength that premises confer on conclusions. Netica would be a good place to start investigating Bayesian nets for argument analysis, having a relatively friendly GUI; although there is a licence fee, the free download can be used for small maps without any licence.

4. Make it Valid.

This is perhaps the most interesting and challenging of the steps. First, however, a qualification: many arguments are intrinsically or intentionally inductive (probabilistic). Their premises are meant to make a conclusion probable, and not certain. For a trivial example consider a classic enumerative induction: In our history the sun has risen every morning; therefore, tomorrow the sun also rises. There is no certainty, but plenty of probability. Good inductive arguments are already good arguments and don’t need to be made valid. Of course, inductive arguments can also be rendered valid, for you can always add a premise such as “Those things which are probable are true.” But that is really a pointless step. You may as well simply make it as good an inductive argument as you can.

To make each subargument valid, enthymemes (hidden premises) will need to be found and filled in. They shall have to be sufficient to render the conclusion necessarily true, given all of its premises (which is what a valid argument is). In general, they should not be more than sufficient. That is, you should not be building a “Straw Man” — an argument which asserts far more than what its author meant. For example, if an argument’s validity requires that some boat people are terrorists, you wouldn’t want to fill in as hidden premise the assertion that all boat people are terrorists.

This is often called the Principle of Charity. To be sure, charity can be taken too far; for example, if the argument already states that all boat people are terrorists, then, even if the argument doesn’t need it, a fair presentation will include it. Attending to what the author wrote, what the author meant, what the author implied or connoted by what was written, and what the author thought was meant, are all a part of filling in the argument.

Counterexampling is a key technique for this step: imagine some possible world where all of the stated premises are true and yet somehow, perhaps amazingly, the conclusion is false. That is a possible world demonstrating that the argument is not yet valid. You need to add some premise which will make the conclusion false and try again. For example, suppose someone asserts that Gertie, being a swan, must be white. Then we should try to imagine a possible world that includes black swans (that doesn’t take much imagination, since we live in such a possible world:-), in order to note that the argument is assuming that all swans are white.

5. Counterargue.

Having the best version of the argument that we can produce before us, we should now criticize it. Since it is now valid, we are hardly going to be able to criticize the inferential steps. But what we have done in making it valid is to expose all of the argument’s weaknesses in its premises, if there are any weaknesses. So we can now canvass the premises, new (hidden) and old (explicit), for those we might find implausible. Generally, arguers prefer to hide their arguments’ weaknesses, consciously or not, and so the implausibilities will be found in the previously hidden premises, now exposed. We can follow our judgments of implausibility, hunting down and constructing the best arguments we can against those premises, applying the Seven Steps recursively as often as we may need. The result of this step should be a pretty thorough accounting of the merits and demerits of the argument.

6. Consider Alternatives.

If you want a good understanding of the issue at hand, then you will need to survey the relevant literature at least for the main alternative points of view and run them through the first five steps as well. That said, the end of Step 5 is a natural stopping point: the argument may already be assessed on its own terms. If you have built a Bayesian network for it, for example, you may be able to assess precisely the weight given to its conclusion by its premises.1 If you extend the network to a Bayesian decision network with actions and utilities, you will be able to assess relevant actions or interventions, as well.

While you can stop with Step 5, gathering alternative arguments and mapping them should not be considered frosting on the cake. Even though an argument in isolation can be assessed on its own merits, considering the alternatives, especially those put by those who are your ideological contraries, will often lead to a reassessment of your analytical work to this point. Indeed, if you are an open-minded Fox, perhaps they will typically lead to a reassessment.

7. Evaluate.

With multiple argument maps in front of you — or better still, Bayesian networks modeling the main relevant arguments — you can interrogate them to find which conclusions are genuinely supported by the available premises. The premises by this time should include only those which are themselves reasonably well justified. In particular, the weak premises from the initial argument should have been recursively exposed as weak by you having drilled deeper than them (so they are no longer premises); hence, they should no longer be conferring any phony support on the original conclusion.

I shall be illustrating the Seven Step program, or the products of it, on this blog with many arguments. I rather enjoy ripping the common arguments in political speech to shreds.

1 Of course, the precise probabilities entered into a Bayesian network representing an argument may or may not be read over-precisely. If they are rough estimates — corresponding, say, to high, medium and low — then you should just be using the network to assess the more general aspects of the argument, rather than precise probabilities. Bayesian nets can be used to reason Bayesianly, whether or not the probabilities are precise, contrary to canards presented by some anti-Bayesians.