Category Archives: science

Mathematical models: the toxic variety

Job applications, credit ratings and the likelihood of being arrested can be affected by mathematical models. Some of the models have damaging effects.


In 1983, U.S. News & World Report – then a weekly newsmagazine in competition with Time and Newsweek – published a ranking of US universities. For U.S. News, this was a way to increase sales. Its ranking system initially relied on opinions of university presidents, but later diversified by using a variety of criteria. As years passed, the U.S. News ranking became more influential, stimulating university administrators to seek to improve rankings by hiring academics, raising money, building facilities and, in some cases, trying to game the system.

One of the criteria used in the U.S. News ranking system was undergraduate admission acceptance rates. A low acceptance rate was assumed to mean the university was more exclusive: a higher percentage of applicants to Harvard are rejected than at Idaho State.

US high school students planning further study are commonly advised to apply to at least three prospective colleges. Consider the hypothetical case of Sarah, an excellent student. She applies to Stanford, a top-flight university where she would have to be lucky to get in, to Michigan State, a very good university where she expects to be admitted, and to Countryside Tech, which offers a good education despite its ease of admission.

Sarah missed out at Stanford, as expected, and unfortunately was also rejected at Michigan State. So she anticipated going to Countryside Tech, but was devastated to be rejected there too. What happened?

The president of Countryside Tech was determined to raise his institution’s ranking. One part of this effort was a devious admissions policy. Sarah’s application looked really strong, so admissions officers assumed she would end up going somewhere else. So they rejected her in order to improve Tech’s admissions percentage, making Tech seem more exclusive. Sarah was an unfortunate casualty of a competition between universities based on the formula used by U.S. News. 


            In Australia, the U.S. News rankings are little known, but other systems, ranking universities across the globe, are influential. In order to boost their rankings, some universities hire academic stars whose publications receive numerous citations. A higher ranking leads to positive publicity that attracts more students, bringing in more income. Many students mistakenly believe a higher ranking university will provide a better education, not realising that the academic stars hired to increase scholarly productivity are not necessarily good teachers. Indeed, many of them do no teaching at all. Putting a priority on hiring them means superb teachers are passed over and money is removed from teaching budgets.


The story of U.S. News university rankings comes from an important new book by Cathy O’Neil, Weapons of Math Destruction. O’Neil started off as a pure mathematician teaching in a US university, then decided to enter the private sector where she could do something more practical as a “data scientist.” Working for a hedge fund and then some start-ups, she soon discovered that the practical uses of data analysis and mathematical models were damaging to many ordinary people, especially those who are disadvantaged. She wrote Weapons of Math Destruction to expose the misuses of mathematical modelling in a range of sectors, including education, personal finance, policing, health and voting.

A model is just a representation of a bigger reality, and a mathematical model is one that uses numbers and equations to represent relationships. For example, a map is a representation of a territory, and usually there’s nothing wrong with a map unless it’s inaccurate or gives a misleading impression.


            The models that O’Neil is concerned about deal with people and affect their lives, often in damaging ways. The model used by U.S. News, because it was taken so seriously by so many people, has distorted decisions by university administrators and harmed some students.

“Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.” (p. 21)

Another example is a model used to allocate police to different parts of a city. By collecting data about past crimes and other factors supposedly correlated with crime, the model identifies areas deemed to be at risk and therefore appropriate for more intensive policing.


This sounds plausible in the abstract, but in practice in the US the result is racially discriminatory even if the police are themselves unprejudiced. Historically, there have been more crimes in disadvantaged areas heavily populated by racial minorities. Putting more police in those areas means even more transgressions are discovered – everything from possession of illegal drugs to malfunctioning cars – and this leads to more arrests of people in these areas, perpetuating their disadvantage. Meanwhile, crimes that are not geographically located are ignored, including financial crimes of the rich and powerful.


Not every mathematical model is harmful. O’Neil says there are three characteristics of weapons of math destruction or WMDs: opacity, damage and scale. Opacity refers to how transparent the model is. If you can see how the model operates – its inputs, its algorithms, its outputs – then it can be subject to inspection and corrected if necessary. O’Neil cites models used by professional baseball clubs to recruit players and make tactical choices during games. These models are based on publicly available data: they are transparent.

In contrast, models used in many parts of the US to judge the performance of school teachers are opaque: the data on which they are based (student test scores) are not public, the algorithm is secret, and decisions made on the basis of the models (including dismissing teachers who are allegedly poor performers) are not used to improve the model.

The second feature of WMDs is damage. Baseball models are used to improve a team’s performance, so there’s little damage. Teacher performance models harm the careers and motivation of excellent teachers.

The third feature is scale. A model used in a household to decide on when to spend money can, at the worst, hurt the members of the household. If scaled up to the whole economy, it could have drastic effects.

Cathy O’Neil

O’Neil’s book is engaging. She describes her own trajectory from pure mathematician to disillusioned data scientist, and then has chapters on several types of WMDs, in education, advertising, criminal justice, employment, workplaces, credit ratings, insurance and voting. Without a single formula, she tells about WMDs and their consequences.

The problems are likely to become worse, because data companies are collecting ever more information about individuals, everything from purchasing habits to opinions expressed on social media. Models are used because they seem to be efficient. Rather than reading 200 job applications, it is more efficient to use a computer program to read them and eliminate all but 50, which can then be read by humans. Rather than examining lots of data about a university, it is more efficient to look at its ranking. Rather than getting to know every applicant for a loan, it is more efficient to use an algorithm to assess each applicant’s credit-worthiness. But efficiency can come at a cost, including discrimination and misplaced priorities.

My experience

Earlier in my career, I did lots of mathematical modelling. My PhD in theoretical physics at the University of Sydney was about a numerical method for solving the diffusion equation, applied to the movement of nitrogen oxides introduced into the stratosphere. I also wrote computer programmes for ozone photochemistry in the stratosphere, among related topics. My initial PhD supervisor, Bob May, was at the time entering the field of mathematical ecology, and I helped with some of his calculations. Bob made me co-author of a paper on a model showing the effect of interactions between voters.

During this time, I started a critical analysis of models for calculating the effect of nitrogen oxides, from either supersonic transport aircraft or nuclear explosions, on stratospheric ozone, looking in particular at the models used by the authors of two key scientific papers. This study led eventually to my first book, The Bias of Science, in which I documented various assumptions and techniques used by the authors of these two papers, and more generally in scientific research.

While doing my PhD, some other students and I studied the mathematical theory of games – used for studies in economics, international relations and other topics – and ran an informal course on the topic. This enabled me to later write a paper about the social assumptions underpinning game theory.

In the following decade, as an applied mathematician at the Australian National University, I worked on models in astrophysics and for incorporating wind power in electricity grids. Meanwhile, I read about biases in models used in energy policy.

I had an idea. Why not write a book or manual about mathematical modelling, showing in detail how assumptions influenced everything from choices of research topics to results? My plan was to include a range of case studies. To show how assumptions affected results, I could program some of the models and then modify parameters and algorithms, showing how results could be influenced by the way the model was constructed and used.

However, other projects took priority, and all I could accomplish was writing a single article, without any detailed examples. For years I regretted not having written a full critique of mathematical modelling. After obtaining a job in social science at the University of Wollongong, I soon discontinued my programming work and before long was too out of touch to undertake the critique I had in mind.

I still think such a critique would be worthwhile, but it would have quite a limited audience. Few readers want to delve into the technical details of a mathematical model on a topic they know little about. If I were starting today, it would be more illuminating to develop several interactive models, with the user being able to alter parameters and algorithms and see outcomes. What I had in mind, decades ago, would have been static and less effective.

What Cathy O’Neil has done in Weapons of Math Destruction is far more useful. Rather than provide mathematical details, she writes for a general audience by focusing on the uses of models. Rather than looking at models that are the subject of technical disputes in scientific fields, she examines models affecting people in their daily lives.

Weapons of Math Destruction is itself an exemplar – a model of the sort to be emulated – of engaged critique. It shows the importance of people with specialist skills and insider knowledge sharing their insights with wider audiences. Her story is vitally important, and so is her example in showing how to tell it.

“That’s a problem, because scientists need this error feedback – in this case the presence of false negatives – to delve into forensic analysis and figure out what went wrong, what was misread, what data was ignored. It’s how systems learn and get smarter. Yet as we’ve seen, loads of WMDs, from recidivism models to teacher scores, blithely generate their own reality. Managers assume that the scores are true enough to be useful, and the algorithm makes tough decisions easy. They can fire employees and cut costs and blame their decisions on an objective number, whether it’s accurate or not.” (p. 133)


Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (London: Allen Lane, 2016)

Brian Martin

Daily data: be sceptical

Be careful about data you encounter every day, especially in the news.


If you watch the news, you are exposed to all sorts of numbers, intended to provide information. Some might be reliable, such as football scores, but with others it’s harder to know, for example the number of people killed in a bomb attack in Syria, the percentage of voters supporting a policy, the proportion of the federal budget spent on welfare, or the increase in the average global temperature.

Should you trust the figures or be sceptical? If you want to probe further, what should you ask?

To answer these questions, it’s useful to understand statistics. Taking a course or reading a textbook is one approach, but that will mainly give you the mathematical side. To develop a practical understanding, there are various articles and books aimed at the general reader. Demystifying Social Statistics gives a left-wing perspective, a tradition continued by the Radstats Group. Joel Best has written several books, for example Damned Lies and Statistics, providing valuable examinations of statistics about contested policy issues. The classic treatment is the 1954 book How to Lie with Statistics.

Most recently, I’ve read the recently published book Everydata by John H. Johnson and Mike Gluck. It’s engaging, informative and ideal for readers who want a practical understanding without encountering any formulas. It is filled with examples, mostly from the US.


            You might have heard about US states being labelled red or blue. Red states are where people vote Republican and blue states are where people vote Democrat. Johnson and Gluck use this example to illustrate aggregated data and how it can be misleading. Just because Massachusetts is a blue state doesn’t mean no one there votes Republican. In fact, quite a lot of people in Massachusetts vote Republican, just not a majority. Johnson and Gluck show pictures of the US with the data broken down by county rather than by state, and a very different picture emerges.

ed, blue and in-between states

            In Australia, aggregated data is commonly used in figures for economic growth. Typically, a figure is given for gross domestic product or GDP, which might have grown by 2 per cent in the past year. But this figure hides all sorts of variation. The economy in different states can grow at different rates, and different industries grow at different rates, and indeed some industries contract. When the economy grows, this doesn’t mean everyone benefits. In recent decades, most of the increased income goes to the wealthiest 1% and many in the 99% are no better off, or go backwards.

The lesson here is that when you hear a figure, think about what it applies to and whether there is underlying variation.

In the Australian real estate market, figures are published for the median price of houses sold. The median is the middle figure. If three houses were sold in a suburb, for $400,000, $1 million and $10 million, the median is $1 million: one house sold for less and one for more. The average, calculated as total sales prices divided by the number of sales, is far greater: it is $3.8 million, namely $0.4m + $1m + $10m divided by 3.

The median price is a reasonable first stab at the cost of housing, but it can be misleading in several ways. What if most of those selling are the low-priced or the high-priced houses? If just three houses sold, how reliable is the median? If the second house sold for $2 million rather than $1 million, the median would become $2 million, quite a jump.

sydney-houses sydney-house-expensive
Is the average or median house price misleading?

            In working on Everydata, Johnson and Gluck contacted many experts and have used quotes from them to good effect. For example, they quote Emily Oster, author of Expecting Better: Why the Conventional Pregnancy Wisdom is Wrong, saying “I think the biggest issue we all face is over-interpreting anecdotal evidence” and “It is difficult to force yourself to ignore these anecdotes – or, at a minimum, treat them as just one data point – and draw conclusions from data instead.” (p. 6)

Everydata addresses sampling, averages, correlations and much else, indeed too much to summarise here. If Johnson and Gluck have a central message, it is to be sceptical of data and, if necessary, investigate in more depth. This applies especially to data encountered in the mass media. For example, the authors comment, “We’ve seen many cases in which a finding is reported in the news as causation, even though the underlying study notes that it is only correlation.” (p. 46) Few readers ever check the original research papers to see whether the findings have been reported accurately. Johnson and Gluck note that data coming from scientific papers can also be dodgy, especially when vested interests are involved.

The value of a university education

For decades, I’ve read stories about the benefits of a university education. Of course there can be many sorts of benefits, for example acquiring knowledge and skills, but the stories often present a figure for increased earnings through a graduate’s lifetime.


            This is an example of aggregated data. Not everyone benefits financially from having a degree. If you’re already retired, there’s no benefit.

There’s definitely a cost involved, both fees and income forgone: you could be out earning a salary instead. So for a degree to help financially, you forgo income while studying and hope to earn more afterwards.

The big problem with calculations about benefits is that they don’t compare like with like. They compare the lifetime earnings of those who obtained degrees to the lifetime earnings of those who didn’t, but these groups aren’t drawn randomly from a sample. Compared to those who don’t go to university, those who do are systematically different: they tend to come from well-off backgrounds, to have had higher performance in high school and to have a greater capacity for studying and deferred gratification.

Where’s the study of groups with identical attributes, for example identical twins, comparing the options of careers in the same field with and without a degree? Then there’s another problem. For some occupations, it is difficult or impossible to enter or advance without a degree. How many doctors or engineers do you know without degrees? It’s hardly fair to calculate the economic benefits of university education when occupational barriers are present. A fair comparison would look only at occupations where degrees are not important for entry or advancement, and only performance counts.

A final example

For those who want to go straight to takeaway messages, Johnson and Gluck provide convenient summaries of key points at the end of each chapter. However, there is much to savour in the text, with many revealing examples helping to make the ideas come alive. The following is one of my favourites (footnotes omitted).


Americans are bad at math. Like, really bad. In one study, the U.S. ranked 21st out of 23 countries. Perhaps that explains why A&W Restaurants’ burger was a flop.

As reported in the New York Times Magazine, back in the early 1980s, the A&W restaurant chain wanted to compete with McDonald’s and its famous Quarter Pounder. So A&W decided to come out with the Third Pounder. Customers thought it tasted better, but it just wasn’t selling. Apparently people thought a quarter pound (1/4) was bigger than a third of a pound (1/3).

Why would they think 1/4 is bigger than 1/3? Because 4 is bigger than 3.

Yes, seriously.

People misinterpreted the size of a burger because they couldn’t understand fractions. (p. 101)

John H. Johnson

Mike Gluck

John H. Johnson and Mike Gluck, Everydata: The Misinformation Hidden in the Little Data You Consume Every Day (Brookline, MA: Bibliomotion, 2016)

Brian Martin

An orchestrated attack on a PhD thesis

Judy Wilyman, an outspoken critic of the Australian government’s vaccination policy, undertook a PhD at the University of Wollongong. She graduated in December 2015.

On 11 January, her PhD thesis was posted on the university’s digital repository, Research Online. On the same day, anticipating an attack on Judy and the thesis, I posted a document titled “Judy Wilyman, PhD: how to understand attacks on a research student“, which turned out to be remarkably accurate in characterising the nature of the attack, which commenced within 24 hours.

The attack included a series of biased articles in The Australian by journalist Kylar Loussikian, numerous hostile blogs and tweets, a one-sided Wikipedia page, and a petition. Never before have I heard of such an outpouring of rage over the award of a PhD in Australia.


As a sociologist, this phenomenon is fascinating in its assumptions and motivations. I am hardly a neutral observer: I was Judy’s principal supervisor at the University of Wollongong, and quite a bit of the outrage has been directed at me, my supervision and my research. On the other hand, I have considerable inside knowledge, enabling insight about the claims being made.

Given the volume of hostile commentary about Judy’s thesis, it is not possible for me to undertake a comprehensive analysis of it in a short time. Therefore my observations here are preliminary. Rather than try to provide detailed evidence to document my generalisations, I merely illustrate them with a few comments made by signers of the petition against the university and the PhD. Down the track, I hope to provide a more detailed response, including to some of the treatments that address matters of substance.

SAVN attacks

The outrage over Judy becoming Dr Wilyman can best be understood by studying the operations of the group now calling itself Stop the Australian (Anti)Vaccination Network or SAVN. Since 2009, SAVN has been attempting to censor and discredit any public criticism of vaccination, using misrepresentation, ridicule, complaints and harassment, as I have documented in a series of articles. SAVN’s agenda has been to cleanse public discourse of dissent about vaccination. Judy Wilyman has been one of SAVN’s many targets.


Judy had been under attack by SAVNers for several years. Therefore, I and others at the University of Wollongong correctly assumed there would be a hostile response to her graduation. Consider two hypotheses for how I and university officials would behave in this situation.

Hypothesis 1. We would push through a sub-standard thesis.

Hypothesis 2. We would take extra care to ensure that the thesis was of requisite quality and that all university processes were followed carefully. This would include sending the thesis to technical experts and choosing external examiners of high standing.

To me, it beggars belief that anyone would believe hypothesis 1, especially given that outsiders lack information about the operation of university processes. Yet in practice it seems that many outsiders, based on limited knowledge, assume that the thesis must be no good, my supervision was inadequate and the university was derelict.

The rush to condemn the thesis and the university can be understood this way: opponents assume it is impossible to undertake a scholarly critique of vaccination policy (or at least impossible for Judy to do so). Therefore, they condemn everyone involved in the process.

Furthermore, opponents do not acknowledge that scholars can differ in their evaluation of evidence and arguments. Instead, in various scientific controversies, including the vaccination debate, dissident experts are subject to attack.


Within media studies, there is a well known and widely discussed view that mass media do not tell people what to think, but are quite influential in determining what people think about. The articles by Kylar Loussikian in The Australian apparently were highly influential in getting a lot of readers to think about Judy Wilyman’s PhD. Their agenda was set by the mass media yet, as noted within agenda-setting research, few readers realised their focus of attention had been so influenced.


Associated with media agenda-setting is the significance of framing, which is about the perspective from which people see an issue. Loussikian’s articles framed the issue as about shortcomings of a PhD thesis and the credibility of the student, the supervisor, the examiners and the university. This frame was adopted by most (though far from all) commentators.

It is an interesting thought experiment to consider the likely response to a differently framed set of articles about the thesis, in which the central issue was an attack on academic freedom by SAVN over a number of years. However, The Australian was unlikely to adopt this frame. Indeed, a couple of years earlier, an Australian journalist had adopted SAVN’s agenda against Judy.

Assumptions about scholarship

Many of the attackers seem to have assumed that scholarship and criticism of vaccination are incompatible. How else could they justify condemning the university? An alternative view is to support current Australian government vaccination policy while accepting that it can be subject to a scholarly critique.


SAVNers for years have proclaimed that there is no debate about vaccination, by which they mean that there are no valid objections to the dominant view. To acknowledge that a scholarly critique is possible is to accept there is something to debate. Apparently this possibility is so threatening that it must be met by denigration and abuse.

Looking at the thesis

In “Judy Wilyman, PhD” I anticipated the sorts of attacks that would be made. This was not difficult: I simply listed the methods that had been used previously. Here’s what I wrote in a section titled “What to look for in criticism”:

When people criticise a research student’s work, it is worth checking for tell-tale signs indicating when these are not genuine concerns about quality and probity but instead part of a campaign to denigrate viewpoints they oppose.

  1. They attack the person, not just their work.
  2. They concentrate on alleged flaws in the work, focusing on small details and ignoring the central points.
  3. They make no comparisons with other students or theses or with standard practice, but rather make criticisms in isolation or according to their own assumed standards.
  4. They assume that findings contrary to what they believe is correct must be wrong or dangerous or both.

The attacks on Judy’s research exhibit every one of these signs. Her opponents attack her as a person, repeatedly express outrage over certain statements she has made while ignoring the central themes in her work, make no reference to academic freedom or standard practice in university procedures, and simply assume that she must be wrong.

My preliminary observation is that most of the hostile commentary about the thesis exhibits one or more of these signs.


There have been numerous derogatory comments made about Judy, me and the university, most without providing any evidence and many based on misrepresentations of the thesis. Proponents of evidence-based medicine might ponder whether it is legitimate to condemn a thesis without reading it, condemn a supervisor without knowing anything about what happened during the supervision process, and condemn a university without having any information about the operation of university procedures. (Tell-tale sign 1)

Some of the opponents of the thesis have referred to comments made by Judy in other contexts. Likewise, questions have been raised about some of my other research. This is the technique of attacking the person in order to discredit their work. (Tell-tale sign 1)

When raising concerns about a piece of research, the normal scholarly route is to send them to the author, inviting a reply, not to immediately publicise them via journalists. An alternative is to submit them to a scholarly journal for publication, in which case many editors would invite the author to reply.

Alleging there are errors in a piece of work does not on its own challenge the central arguments in the work. For this, addressing those arguments directly is necessary. Very few of the critics of Judy’s thesis have addressed any of its central themes. (Tell-tale sign 2)

The intensive scrutiny of Judy’s thesis on its own does not enable a judgement of its quality, because it is necessary to benchmark against other comparable theses. None of her critics has attempted a similarly intensive scrutiny of any other thesis, much less a set of theses large enough to enable a fair assessment of her work. Experienced examiners have assessed many theses, as supervisors and/or examiners, and are well placed to make the required judgements about quality. This is in stark contrast to outside critics, many of whom lack any experience of thesis supervision or examination. (Tell-tale sign 3)

Why is there such a hue and cry over Judy’s thesis? Many theses tackling controversial topics or taking non-standard positions are published every year. Many of the critics of the thesis apparently believe no thesis proposal critical of vaccination should be accepted at an Australian university, and that for such a thesis to be passed necessarily reflects adversely on the university. The thinking behind this seems to be based on the assumption that criticism of Australian government vaccination policy is dangerous and should be censored. (Tell-tale sign 4)

I care. I believe in freedom of thought and speech, however this unscientific bullshit has to stop. It’s endangering lives — Kate Hillard, Broome, Australia

The net effect of these techniques is striking. A group of campaigners, with a well-established agenda of attacking critics of vaccination, sets out to discredit a thesis. Disdaining accepted scholarly means of critique, they feed material to a journalist. They take sentences from the thesis out of context and assert they are wrong, going public before offering the author an opportunity to reply. They ignore the central themes of the thesis. They show no awareness of scholarly expectations in the field, instead asserting the superiority of their own judgements over those of the examiners. Based on this charade of intellectual critique, they then condemn the thesis, the student, the supervisor and the university in an orchestrated campaign.

The role of expertise

SAVNers and quite a few other commentators state or assume that vaccination policy is a scientific issue, rather than one including a complex mixture of science, ethics and politics. These commentators then jump to the conclusion that only scientific experts are qualified to make judgements about vaccination policy. There is a contradiction in their discourse, though, because few of these commentators themselves have relevant scientific expertise, yet they feel entitled to make pronouncements in support of vaccination. So their assumption is that anyone, with relevant credentials or not, can legitimately support vaccination policy but no one without relevant scientific expertise is entitled to criticise it. They ignore the significance of policy expertise.


This is a familiar theme within scientific controversies: critics of the epistemologically dominant view are dismissed because they are not suitably qualified. There is another way to look at policy issues: all citizens should be able to have an input, especially those with a stake in the outcomes. This participatory view about science policy has been well articulated over several decades, but few of those commenting about Australian vaccination policy even seem to recognise it exists.

Many opponents of the thesis and critics of the university have declared this issue is not about academic freedom but about academic standards. This claim would be more convincing if these opponents had ever made scholarly contributions about academic freedom or if they were not making self-interested judgements about their own behaviour. Their actions show their agenda is suppression of dissent.

The SAVN message

What is the implication of SAVN’s campaign against Judy Wilyman? And why do SAVNers and others continue to attack the University of Wollongong despite lacking any concrete evidence of any shortcomings in the university’s processes? There is one underlying message and two audiences. The message is that no university should consider allowing a research student (or at least an outspoken research student) to undertake a study critical of vaccination.

The first audience is the University of Wollongong. The second audience is other universities, which are being warned off critical studies of vaccination, or indeed of any other medical orthodoxy, by the example being set by the attack on the University of Wollongong.

There is also another message, which is along the lines of “Don’t mess with SAVN. We will launch a barrage of abuse, ridicule and complaints, and use our connections with the media and the medical profession, to assail anyone who crosses us.”

The original reason I became involved in the Australian vaccination debate is that I saw SAVN’s agenda as dangerous to free speech. If adopted more widely, SAVN’s approach would stifle discussion on a range of issues.

I am therefore buoyed by the support I’ve received from my colleagues, including senior figures, at the University of Wollongong, who believe in the importance of open debate and of scholarship that challenges conventional wisdom.

It is apparent that academics and universities need to do more to explain what they do and to explain the meaning and significance of academic freedom.


See also my other writings about attacks on Judy and her thesis.

Evolution of a different kind

Humans are dominating evolutionary processes, according to synthetic biologists. But who is dominating decision-making about human futures?


It’s a summer day in suburbia, and the lawnmowers are going. As well as cutting grass, residents plant shrubs, remove or poison weeds, and in generally intervene to make their yards look the way they want.

We hear all the time about the importance of evolution in the origin and development of species. The usual idea is that some species thrive and others die out. When there is a mutation or gene recombination favourable to survival, it will spread, so the combination of genetic variation and environmental pressure leads to genetic changes and eventually to species changes.

However, what happens daily in suburbs goes against the standard thinking, because humans are making the decisions about which species survive, by introducing desired plants and getting rid of unwelcome ones. This is a type of “unnatural selection,” according to Juan Enriquez and Steve Gullans in their book Evolving Ourselves (2015). Suburban gardeners might be small players in global evolution, but then think of urbanisation and agriculture, including interventions such as irrigation, fertilisers and pesticides, now covering a significant proportion of the earth’s surface. The areas for traditional natural selection are limited and are being replaced by human-influenced selection.

Evolving ourselves

Then there is molecular biology, in which scientists insert genes in places where they would not occur naturally, creating new genetic sequences. Enriquez and Gullans call this “non-random mutation,” and say it is replacing the random mutation that was the basis of Darwinian natural selection though evolution on earth, until recently.

Evolving Ourselves is an entertaining ride through the science and social implications of humans taking over their own evolution and that of other species. Enriquez and Gullans have a lot of fun in the way they write, and cover an astounding array of topics, for example allergies, autism, diet, obesity, reproduction and the decline in human violence. Some of the developments are here today, some are experimental and some are speculation. If Enriquez and Gullans’ thinking becomes reality, so-called synthetic biology will have impacts comparable to information technology, and indeed will interact with it in significant ways. As I will discuss later, a key omission in their treatment is the question of who makes decisions about synthetic biology.


DNA is the basis for inheritance. According to previous conventional teaching in biology, the environment does not affect DNA. That it might was rejected as heresy, called Lamarckism.

uan Enriquez

Enriquez and Gullans describe how this perspective is outdated. They say that humans have four genomes, or genetic systems. The first is DNA, in cells, the basis of traditional genetics. The genes in DNA are not affected by the environment, but their expression can be affected in a process called epigenetics, that can have long-lasting effects on species. Enriquez and Gullans start with the example of the famine in the Netherlands in the end of World War II, triggered by a Nazi blockade. Although Dutch DNA was not directly affected, the effects of the famine were experienced through several generations by chemical tags that can activate or deactivate individual genes. Research on epigenetics is booming.

The discoveries kept piling on; in 2013, a Cornell team demonstrated that epigenetics, not gene code, was a critical factor when trying to figure out when and why a tomato ripens. Similar epigenetic effects were discovered in worms, fruit flies, and rodents; a creative and slightly meanspirited experiment let mice smell sweet almonds and then shocked their feet. Soon mice were terrified of the smell of almonds. When these mice reproduced, the kids were never shocked, but they were still quite afraid of the same smell. So were the grandkids. The brains of all three generations had modified “M71 glomeruli,” the specific neurons sensitive to that type of smell. (p. 69)


The implication is that the massive environmental changes in human lives – including processed food, sedentary lifestyles, watching small screens, and chemicals in the environment – can be affecting human evolution, epigenetically through genes being switched on and off over generations.

Part of the environment that affects humans is microbes: bacteria, viruses and parasites. Microbes have caused more deaths than all wars. Enriquez and Gullans describe an ongoing war against microbes, through four stages: vaccines, antiseptics, antibiotics and antivirals. These massive wars against microbes have largely been successful, but there is a fightback, for example antibiotic-resistant infections.


The story for Enriquez and Gullans is about evolution, and so they introduce the third human biome: the huge number of microscopic organisms that live on us. These organisms, which interact with us, have their own collective genome. What’s interesting is that we are changing human evolution by changing the microbiome, including through the wars against microbes.

Then there is the human virome, composed of viruses that live in cells and in bacteria in the microbiome. Virome DNA is the fourth human genome, along with the core DNA genome, epigenome and microbiome DNA. The environment affects these four genomes through Diet, Enriched environment (information and so forth), Stress, Toxins, Infections, Nurturing and “You” (human decision-making), giving the acronym DESTINY. Humans are driving evolution by changing their environment, affecting the endocrine system, the nervous system and the immune system, all of which affect expression in the genomes.

One top of this, molecular biologists are inserting genes in all sorts of unusual places, with the potential to dramatically alter the usual pattern of evolution. Rather than evolution occurring by natural selection applied to genetic changes that also occur “naturally”, molecular biologists are practising a type of “unnatural selection” by making the choices for genetic change. Enriquez and Gullans say these developments are occurring in a wide range of labs. There is no centralised control, but overall the outcome is a different sort of evolution, a form of human-instigated “fast evolution”. Indeed, the technology and skills for genetic transformation are becoming accessible to people outside the scientific mainstream, in what can be called do-it-yourself synthetic biology.

synthetic biology

Wild-sounding futures

Enriquez and Gullans make a good case that humans are dramatically altering their evolutionary path, very rapidly, through the two processes of unnatural selection and non-random mutation. From this they move on to other possible developments, some of them sounding like science fiction. They discuss them as real possibilities, giving evidence that makes them sound just around to corner, or maybe a few decades away.

  • Individuals could be bioengineered for extraordinary athletic abilities, in conjunction with designer drugs, generating complex challenges for sporting authorities.
  • Drugs could be developed that enter cells and change your DNA, allowing for all sorts of therapies and capacities.
  • 3D optical neural implants could become standard, allowing interfacing with databases. This would potentially allow interventions into people’s brains, for example to reduce violence.
  • Body parts, such as arms or eyes, could be cloned and grown externally, so people become composed of organs they didn’t have at birth.
  • People’s bodies can be reinvigorated so they can live far longer, perhaps centuries.
  • Humans could be cloned.
  • Human minds could be downloaded and uploaded into new bodies.
  • The human species could differentiate into multiple species.


Enriquez and Gullans provide plausible pathways to each of these possibilities. For example, concerning cloned organs, they write:

Researchers at Harvard’s dental school have already rebuilt copies of many people’s teeth in small glass dishes. And if you can rebuild teeth using the genetic instructions in every person’s body, and you can decipher the instructions and create the right scaffolds, eventually you can rebuild any human organ. (pp. 178-179)

Steve Gullans
Steve Gullans

Who decides?

Evolving Ourselves is written in a breezy style, with ideas from synthetic biology presented in an accessible fashion. Enriquez and Gullans note the social implications of synthetic biology and possible objections to it, but they basically see the push of biology into all sorts of applications as proceeding in labs around the world in an unstoppable way, with the implications needing to be addressed. They mainly point to positive outcomes, trying to make the craziest possibilities seem plausible, indeed inevitable. (The assumption that technology has an inevitable momentum is called technological determinism.)

A key omission is any systematic discussion of the political economy of synthetic biology. In a typical fashion, the developments are seen as something “we” are doing and therefore “we” need to consider the implications. The role of vested interests, the possibilities for malign use, the lack of citizen oversight and the potential for exacerbating inequality in an unjust world are not addressed in any depth.

Consider this statement by Enriquez and Gullans.

The quiver of instruments we have created to redesign and drive fast evolution is so powerful, effective, and dominant that we are not going to give them up, or even curb them much. (p. 217)

who are we title

The use of “we,” referring to humans, skates over the fact that decisions will be made by only a few people, not in a participatory process involving everyone. Their statement that “we are not going to give them up” is a clear articulation of technological determinism. This reminds me of the early hype about nuclear power, which was seen both beneficial and inevitable, despite its historical connections with nuclear weapons.

Enriquez and Gullans continue:

We will continue evolving bacteria, plants, animals, and ourselves to our particular desires. So now is the time to ask: Having put ourselves in charge of our own evolution and that of other species, what will we choose to do with this extraordinary power? (p. 218)

Who are we?

Again note the use of “we,” hiding huge differences in power over human destiny, and the assumption that synthetic biology is unstoppable (“We will continue …”).

Enriquez and Gullans make synthetic biology sound amazing, attractive and mostly positive, once the spectacular possibilities are considered. But it is also possible that the technology will be used for purposes many will oppose. Military researchers look at biotechnology as a potential tool against enemies, just as they look at every branch of science, indeed funding and monitoring research in numerous fields.

It is salutary to remember that citizen campaigners have challenged and altered technological trajectories. In the 1970s there were plans for fleets of hundreds of supersonic transport aircraft. Due to opposition, only a few such SSTs – the Concorde and the Tupolev Tu-144 – were ever produced, and they fly no more. On a bigger scale, peace movements around the world have been instrumental in preventing nuclear war and in pushing for a shrinking of arsenals, though the goal of nuclear abolition is far from being achieved.

Then there are movements for “appropriate technology,” in the widest sense, promoting energy, transport and other technological systems that serve human needs and are under the control of local communities rather than imposed by governments and corporations.

Whether synthetic biology turns out to be largely beneficial or a catastrophe for the world will depend, to a large extent, on citizens being involved in discussions and campaigns about what sort of world is desirable. So read Evolving Ourselves for an entertaining view of future possibilities, but replace the authors’ assumptions about inevitability with a parallel perspective that might be called Deciding Ourselves.

Brian Martin

Thanks to Jason Delborne, David Mercer and Peter Taylor for valuable comments.