Unless you’ve been living under a rock since the new year, you have heard of the “coup attempt” at the Capitol building on Wednesday, January 6th. Upset at the fact that the election was “stolen” from Trump, his supporters showed up at the building and rushed it, causing mass chaos. But, why did they do this? Why the violence when they did not get their way in a fair election? Well, Michael Ryan, author of The Genetics of Political Behavior: How Evolutionary Psychology Explains Ideology (2020) has the answer—what he terms “rightists” and “leftists” evolved at two different times in our evolutionary history which, then, explains the trait differences between the two political parties. This article will review part of the book—the evolutionary sections (chapters 1-3).
EP and ideology
Explaining why individuals who call themselves “rightists and leftists” behave and act differently than the other is Ryan’s goal. He argues, at length, that the two parties have two different personality profiles. This, he claims, is due to the fact that the ancestors of rightists and leftists evolved at two different times in human history. He calls this “Trump Island” and “Obama Island”—apt names, especially due to what occurred last week. Ryan claims that what makes Trump different from, say, Obama, is that his ancestors evolved at a different place in a different time compared to Obama’s ancestors. He further claims using the Stanford Prison Experiment that “we may not all be capable of becoming Nazis, after all. Just some, and conservatives especially so” (pg 12).
In the first chapter he begins with the usual adaptationism that Evolutionary Psychologists use. Reading between the lines in his implicit claims, he is arguing that “rightists and leftists” are natural kinds—that is, they are *two different kinds of people.* He explains some personality differences between rightists and leftists and then says that such trait differences are “rooted in biology and governed by genes” (pg 17). Ryan then makes a strong adaptationist claim—that traits are due to adaptation to the environment (pg 17). What makes you and I different from Trump, he claims, is that our ancestors and his ancestors evolved in different places at different times where different traits would be imperative to survival. So, over time, different traits got selected-for in these two populations leading to the trait differences we see today. So each environment led to the fixation of different adaptive traits which explains the differences we see today between the two parties, he claims.
Ryan then shifts from the evolution of personality differences to… The evolution of the beaks of Darwin’s finches and Tibetan adaptation to high-altitude living (pg 18), as if the evolution of physical traits is anything like the evolution of psychological traits. His folly is assuming that these physical traits can then be likened to personality/mental traits. The ancestors of rightists and leftists, like Darwin’s finches Ryan claims, evolved on different islands in different moments of evolutionary time. They evolved different brains and different adaptive behaviors on the basis of the evolution of those different brains. Trump’s ancestors were authoritarian, and this island occurred early in human history “which accounts for why Trump’s behavior seems so archaic at times” (pg 18).
The different traits that leftists show in comparison to rightists is due to the fact that their island came at a different point in evolutionary time—it was not recent in comparison to the so-called archaic dominance behavior portrayed by Trump and other rightists. Ryan says that Obama Island was more crowded than Trump Island where, instead of scowling, they smiled which “forges links with others and fosters reciprocity” (pg 19). So due to environmental adversity, they had a more densely populated “island”—in this novel situation, compared to the more “archaic” earlier time—the small bands needed to cooperate, rather than fight with each other, to survive. So this, according to Ryan, explains why studies show more smiling behavior in leftists compared to rightists.
Some of our ancestors evolved traits such as cooperativeness the aided the survival of all even though not everyone acquired the trait … Eventually a new genotype or subpopulation emerged. Leftist traits became a permanent feature of our genome—in some at least. (pg 19-20)
So the argument goes: Differences between rightists and leftists show us that the two did not evolve at the same points in time since they show different traits today. Different traits were adaptive at different points in time, some more archaic, some more modern. Since Trump Island came first in our evolutionary history, those whose ancestors evolved there show more archaic behavior. Since Obama Island came first, they show newer, more modern behaviors. Due to environmental uncertainty, those on Obama Island had to cooperate with each other. The trait differences between these two subpopulations were selected for in their environment that they evolved in, which is why they are different today. Now today, this led to the “arguing over the future direction of our species. This is the origin of human politics” (pg 20).
Models of evolution
Ryan then discusses four models of evolution: (1) the standard model, where “natural selection” is the main driver of evolutionary change; (2) epigenetic models like Jablonka’s and Lamb’s (2005) in Evolution in Four Dimensions; (3) where behavioral changes change genes; and (4) where organisms have phenotypic plasticity and is a way for the organism to respond to sudden environmental changes. “Leftists and rightists“, writes Ryan, “are distinguished by their own versions of phenotypic plasticity. They change behavior more readily than rightists in response to changing environmental signals” (pg 29-30).
In perhaps the most outlandish part of the book, Ryan articulates one of my now-favorite just-so stories. The passage is worth quoting in-full:
Our direct ancestor Homo erectus endured for two million years before going extinct 400,000 years ago when earth temperatures dropped far below the norm. Descendants of erectus survived till as recently as 14,000 years ago in Asia. The round head and shovel-shaped teeth of some Asians, including Vladimir Putin, are an erectile legacy. Archeologists believe erectus was a mix of Ted Bundy and Adolf Hitler. Surviving skulls point to a life of constant violence and routine killing. Erectile skulls are thick like a turtle’s, and the brow’s are ridged for protection from potentially fatal blows. Erectus’ life was precarious and violent. To survive, it had to evolve traits such as vigilant fearfulness, prejudice against outsiders, bonding with kin allies, callousness toward victims, and a penchant for inflexible habits of life that were known to guarantee safety. It had to be conservative. 34 Archeologists suggest that some of our most characteristic conservative emotions such as nationalism and xenophobia were forged at the time of Homo erectus. 35 (pg 33-34)
It is clear that Ryan is arguing that rightists have more erectus-like traits whereas leftists have more modern, Sapiens traits. “The contemporary coexistence of a population with more “modern” traits and a population with more “archaic” traits came into being” (pg 37). He is implicitly assuming that the two “populations” he discusses are natural kinds and with his “modern” “archaic” distinction (see Crisp and Cook 2005 who argue against a form of this distinction) he is also implying that there is a sort of “progress” to evolution.
Twin studies, it is claimed, show “one’s genetically informed psychological disposition” (Hatemi et al, 2014); they “suggest that leftists and rightists are born not made” while a so-called “consensus has emerged amongst scientists: political behavior is genetically controlled and heritable” (pg 43). But, Beckway and Morris (2008), Charney (2008), and Joseph (2009; 2013) argue that twin studies can do no such thing due to the violation of the equal environments assumption (Joseph, 2014; Joseph et al, 2015). Thus, Ryan’s claims of the “genetic origins” of political behavior rest on studies that cannot prove or disprove “genetic causation” (Shulitziner, 2017)—but since the EEA is false we must discount “genetic causation” for psychological traits, not least because it is impossible for genes to cause/influence psychological traits (see argument (iii)).
The arguments he provides are a form of inference to best explanation (IBE) (Smith, 2016). However, this is how just-so stories are created: the conclusion is already in mind, and then the story is crafted using “natural selection” to explain how a trait came to fixation and why it currently exists today. The whole book is full of such adaptive stories. Claiming that we have the current traits we do in the distributions they are in in the “populations” because they were, at a certain point in our evolutionary history, adaptive which then led to the individuals with those traits passing on more of their genes, eventually leading to trait fixation. (See Fodor and Piattelli-Palmarini, 2010).
Ryan makes such outlandish claims such as “Rightists are more likely than leftists to keep their desks neat. If in the distant past you knew exactly where the weapons were, you could find them quickly and react to danger more effectively. 26” (pg 45). He talks about how “time-consuming and effort-demanding accuracy of perception [were] more characteristic of leftist cognition … leftist cognition is more reflective” while “rightist cognition is intuitive rather than reflective” (pg 47). Rightists being more likely to endorse the status quo, he claims, is “an adaptive trait when scarce resources made energy management essential to getting by” (pg 48) Rightist language, he argues, uses more nouns since they are “more concrete, an anxious personalities prefer concrete to abstract language because it favors categorial rigidity and guarantees greater certainty” while leftists “use words that suggest anxiety, anger, threats, certainty, resistance to change, power, security, and conformity” (pg 49). There is “a connection between archaic physiology and rightist moral ideology” (pg 52). Certain traits that leftists have were “adaptive traits [that] were suited to later stage human evolution” (pg 53). Ryan just cites studies that show differences between rightists and leftists and then uses some great leaps and mental gymnastics to try to mold the findings as being due to evolution in the two different time periods he describes in chapter 1 (Trump and Obama Island).
I have not read one page in this book that does not have some kind of adaptive just-so story attempting to explain certain traits/behaviors between rightists and leftists in evolutionary terms. Ryan uses the same kind of “reasoning” that Evolutionary Psychologists use—have your conclusion in mind first and then craft an adaptive story to explain why the traits you see today are there. Ryan outright says that “[t]raits are the result of adaptation to the environment” (pg 17), which is a rare—strong adaptationist—claim to make.
His book ticks off all of the usual EP things: strong adaptationism, just-so storytelling, the claim that traits were selected-for due to their contribution in certain environments at different points in time. The strong adaptationist claims, for example, are where he says that erectus’ large brow “are rigid for protection from potentially fatal blows” (pg 34). Such strong adaptationist claims imply that Ryan believes that all traits are the result of adaptation and that they, as a result, are still here today because they all serve a function in our evolutionary past. His arguments are, for the most part, all evolutionary and follow the same kinds of patterns that the usual EP arguments do (see Smith, 2016 for an explication of just-so stories and what constitutes them). Due to the problems with evolutionary psychology, his adaptive claims should be ignored.
The arguments that Ryan provides are not scientific and, although they give off a veneer of being scientific by invoking “natural selection” and adaptationism, they are anything but. It is just a long-winded explanation for how and why rightists and leftists—liberals and conservatives—are different and why they cannot change, since these differences are “encoded” into our genome. The implicit claim of the book, then, that rightists and leftists are two different—natural—kinds, lies on the false bed of EP and, therefore, the arguments provided in the book fail to sway anyone that does not believe such fantastic storytelling masquerading as science. While he does discuss other evolutionary theories, such as epigenetic ones from Jablonka and Lamb (2005), the book is largely strongly adaptationist using “natural selection” to explain why we still have the traits we do in different “populations” today.
‘Health inequalities are the systematic, avoidable and unfair differences in health outcomes that can be observed between populations, between social groups within the same population or as a gradient across a population ranked by social position.’ (McCartney et al, 2019)
Health inequities, however, are differences in health that are judged to be avoidable, unfair, and unjust. (Sudana and Blas, 2013)
Asking “Is X racist?” is the wrong question to ask. If X is factual, then making the claim cannot be racist (facts themselves cannot be racist). But, one can perform a racist action—either consciously or subconsciously—on the basis of a fact. Facts themselves cannot be racist, but one can use facts to be racist. One can hold a belief and the belief can be racist (X group is better than Y group at Z), but systemic racism would be the result (the outcome) of holding said belief. (Some examples of systemic racism can be found in Gee and Ford, 2011.) Someone who holds the belief that, say, whites are more “intelligent” than blacks or Jews are more “intelligent” than whites could be said to be racist—they hold a racist belief and are making an invalid inference based on a fact (blacks score 15 points lower in IQ tests compared to whites so blacks are less intelligent). Truth cannot be racist, but truth can be used to attempt to justify certain policies.
I have argued that we should ban IQ tests on the basis that, if we believe that the hereditarian hypothesis is true and it is false, then we can enact policies on the basis of false information. If we enact policies on the basis of false information, then certain groups may be harmed. If certain groups may be harmed, then we should ban whatever led to the policy in question. If the policy in question is derived from IQ tests, then IQ tests must be banned. This is one example on how we can use a fact (like the IQ gap between blacks and whites) and use that fact for a racist action (to shuttle those who perform under a certain expectation into certain remedial classes based on the fact that they score lower than some average value). Believing that X group has a higher quality of life, educational achievement, and life outcomes on the basis of IQ scores—or their genes—is a racist belief but this racist belief can then be used to perform a racist action.
I have also discussed different definitions of “racism.” Each definition discussed can be construed as having a possible action attached to it. Racism is an action—something that we perform on the basis of certain beliefs, motivated by “what can be” possible in the future. Beliefs can be racist; we can say that it is an ideology that one acts on that has real causes/consequences to people. Truth can’t be racist; people can can use the truth to perform and justify certain actions. Racism, though, can be said to be a “cultural and structural system” that assigns value based on race; further, actions and intent of individuals are not necessary for structural mechanisms of racism (e.g., Bonilla-Silva, 1997).
We can, furthermore, use facts about differences between races in health outcomes and say that certain rationalizations of certain outcomes can be construed as racist. “It’s in the genes!” or similar statements could be construed as racist, since it implies that certain inequalities would be “immutable” on the basis of a strong genetic determination of disease.
Racism is indeed a public health issue. For instance, physicians can hold biases on race—just like the average person. For instance, differences in healthcare between majority and minority populations can said to be systemic in nature (Reschovsky and O’Malley, 2008). This needs to be talked about since racism can and is a determinant of health—as many places in the country are beginning to recognize. Racism is rightly noted as a public health crisis because it leads to disparate outcomes between whites and blacks based on certain assumptions on the ancestral background of both groups.
Quach et al (2012) showed that not receiving referrals to a specialist is discriminatory—Asians, too were also exposed to medical discrimination, along with blacks. Such discrimination can also lead to accelerated cellular aging (on the basis of measured telomere lengths where shorter telomeres indicate a higher biological compared to chronological age; Shammas et al, 2012) in black men and women (Geronimus et al, 2006; 2011; Schrock et al, 2017; Forrester et al, 2019). We understand the reasons why such discrimination on the basis of race happens, and we understand the mechanism by which it leads to adverse health outcomes between races (chronic elevation in allostatic load leading to higher than normal levels of certain stress hormones which will, eventually, lead to differences in health outcomes).
The idea that genes or behavior lead to differences in health outcomes is racist (Bassett and Graves, 2018). This can then lead to racist actions—that their genetic constitution impedes them from being “near-par” with whites, or that their behavior is the cause of the health disparities (sans context). Valles (2018: 186) writes:
…racism is a cause with devastating health effects, but it manifests via many intermediary mechanisms ranging from physician implicit biases leading to over-treatment, under-treatment and other clinical errors (Chapman et al. 2013; Paradies et al. 2015) to exposing minority communities to waterborne contaminants because of racist political disenfranchisement and neglect of community infrastructure (e.g., the infamous Flint Water Crisis afflicting my Michigan neighbors) (Krieger 2016; Sherwin 2017; Michigan Civil Rights Commission 2017).
There is a distinction between “equity” and “equality.” For instance, to continue with the public health example, take public health equality and public health equity. In this instance, “equality” means giving everyone the same thing whereas “equity” means giving individuals what they need to be the healthiest individual they can possibly be. “Strong equality of health” is “where every person or group has equal health“, while weak health equity “states that every person or group should have equal health except when: (a) health equality is only possible by making someone less healthy, or (b) there are technological limitations on further health improvement” (Norheim and Asada, 2009). But we should not attempt to “level-down” people’s health to achieve equity; we should attempt to “level up” people’s health, though. That is, it is impossible to reach a strong health equality (making all groups equal), but we should—and indeed, have a moral responsibility to—attempt to lift up those who are worse-off. Poverty is what is objectionable, inequality is not. It is impossible to achieve true equality between groups, but we can—and indeed we have a moral obligation to—lift up those who are in poverty, which is, also a social determinant of health (Braveman and Gottlieb, 2014; Frankfurt, 2015; Islam, 2019).
We achieve health equity when all individuals have the same access to be the healthiest individuals they can be; we achieve health equality when all health outcomes are the same for all groups. Health equity is, further, the absence of avoidable differences between different groups (Evans, 2020). One of these is feasible, the other is not. But racism does not allow us to achieve health equity.
The moral foundation for public health thus rests on general obligations in beneficence to promote good health. (Powers and Faden, 2006: 24)
Social justice is not only a matter of how individuals fare, but also about how groups fare relative to one another whenever systemic racism is linked to group membership. (Powers and Faden, 2006: 103)
…inequalities in well-being associated with severe poverty are inequalities of the highest moral urgency. (Powers and Faden, 2006: 114)
Public health is directly a matter of social justice. If public health is directly a matter of social justice, and if health outcomes due to discrimination are caused by social injustice, then we need to address the causes of such inequalities, which would be for example, conscious or unconscious prejudice against certain groups.
Certain inequalities between groups are, therefore, due to systemic racism which is an action which can be conscious or unconscious. But which inequalities matter most? In my view, the inequalities that matter most are inequalities that impede an individual or a group from having a certain quality of life. Racism can and does lead to health inequalities and by addressing the causes for such actions, we can then begin to ameliorate the causes of structural racism. This is more evidence that the social can indeed manifest in biology.
Holding certain beliefs can lead to certain actions that can be construed as racist and negatively impact health outcomes for certain groups. By committing ourselves to a framework of social just and health, we can then attempt to ameliorate inequities between social class/races, etc. that have plagued us for decades. We should strive for equity in health, which is a goal of social justice. We should not believe that such differences are “innate” and that there is nothing that we can do about group differences (some of which are no doubt caused by systemically racist policies). Health equity is something we should strive to do and we have a moral obligation to do so; health equality is not obligatory and it is not even a feasible idea.
If we can avoid health certain outcomes for certain groups on the basis of beliefs that we hold, then we should do so.
The use of polygenic scores has caused much excitement in the field of socio-genomics. A polygenic score is derived from statistical gene associations using what is known as a genome-wide association study (GWAS). Using genes that are associated with many traits, they propose, they will be able to unlock the genomic causes of diseases and socially-valued traits. The methods of GWA studies also assume that the ‘information’ that is ‘encoded’ in the DNA sequence is “causal in terms of cellular phenotype” (Baverstock, 2019).
For instance it is claimed by Robert Plomin that “predictions from polygenic scores have unique causal status. Usually correlations do not imply causation, but correlations involving polygenic scores imply causation in the sense that these correlations are not subject to reverse causation because nothing changes the inherited DNA sequence variation.”
Take the stronger claim from Plomin and Stumm (2018):
GPS are unique predictors in the behavioural sciences. They are an exception to the rule that correlations do not imply causation in the sense that there can be no backward causation when GPS are correlated with traits. That is, nothing in our brains, behaviour or environment changes inherited differences in DNA sequence. A related advantage of GPS as predictors is that they are exceptionally stable throughout the life span because they index inherited differences in DNA sequence. Although mutations can accrue in the cells used to obtain DNA, like any cells in the body these mutations would not be expected to change systematically the thousands of inherited SNPs that contribute to a GPS.
This is a strange claim for two reasons.
(1) They do not, in fact, imply causation since the scores derived from GWA studies which are associational and therefore cannot show causes—GWA studies are pretty much giant correlational studies that scan the genomes of hundreds of thousands of people and look for genes that are more likely to be in the sample population for the disease/”trait” in question. These studies are also heavily skewed to European populations and, even if they were valid for European populations (which they are not), they would not be valid for non-European ethnic groups (Martin et al, 2017; Curtis, 2018; Haworth et al, 2018).
(2) The claim that “nothing changes inherited DNA sequence variation” is patently false; what one experiences throughout their lives can most definitely change their inherited DNA sequence variation (Baedke, 2018; Meloni, 2019).
But, as pointed out by Turkheimer, Plomin and Stumm are assuming that no top-down causation exists (see, e.g., Ellis, Noble, and O’Connor, 2011). We know that both top-down (downward) and bottom-up (upward) causation exists (e.g., Noble, 2012; see Noble 2017 for a review). Plomin, it seems, is coming from a very hardline view of genes and how they work. A view, it looks like to me, that derives from the Darwinian view of genes and how they ‘work.’
Such work also is carried out under the assumption that ‘nature’ and ‘nurture’ are independent and can therefore be separated. Indeed, the title of Plomin’s 2018 book Blueprint implies that DNA is a blueprint. In the book he has made the claim that DNA is a “fortune-teller” and that things like PGSs are “fortune-telling devices” (Plomin, 2018: 6). PGSs are also carried out based on the assumption that the heritability estimates derived from twin/family/adoption studies tell us anything about how “genetic” a trait is. But, since the EEA is false (Joseph, 2014; Joseph et al, 2015) then we should outright reject any and all genetic interpretations of these kinds of studies. PGS studies are premised on the assumption that the aforementioned twin/adoption/family studies show the “genetic variation” in traits. But if the main assumptions are false, then their conclusions crumble.
Indeed, lifestyle factors are better indicators of one’s disease risk compared to polygenic scores, and so “This means that a person with a “high” gene score risk but a healthy lifestyle is at lower risk than a person with a “low” gene score risk and an unhealthy lifestyle” (Joyner, 2019). Janssens (2019) argues that PRSs (polygenic risk scores) “do not ‘exist’ in the same way that blood pressure does … [nor do they] ‘exist’ in the same way clinical risk models do …” Janssens and Joyner (2019) also note that “Most [SNP] hits have no demonstrated mechanistic linkage to the biological property of interest. By showing mechanistic relations between the proposed gene(s) and the disease phenotype, researchers would, then, be on their way to show “causation” for PGS/PRS.
Nevertheless, Sexton et al (2018) argue that “While research has shown that height is a polygenic trait heavily influenced by common SNPs [7–12], a polygenic score that quantifies common SNP effect is generally insufficient for successful individual phenotype prediction.” Smith-Wooley et al (2018) write that “… a genome-wide polygenic score … predicts up to 5% of the variance in each university success variable.” But think about the words “predicts up to”—this is a meaningless phrase. Such language is, of course, causal when they—nor anyone else—has shown that such scores are indeed casual (mechanistically).
What these studies are indexing are not causal genic variants for disease and other “traits”, they are showing the population structure of the population sampled in question (Richardson, 2017; Richardson and Jones, 2019). Furthermore, the demographic history of the sample in question can also mediate the stratification in the population (Zaidi and Mathieson, 2020). Therefore, claims that PGSs are causal are unfounded—indeed, GWA studies cannot show causation. GWA studies survive on the correlational model—but, as has been shown by many authors, the studies show spurious correlations, not the “genetics” of any studied “trait” and they, therefore, do not show causation.
One further nail-in-the-coffin for hereditarian claims for PGS/PRS and GWA studies is due to the fact that the larger the dataset (the larger the number of datapoints), there will be many more spurious correlations found (Calude and Longo, 2017). When it comes to hereditarian claims, this is relevant to twin studies (e.g., Polderman et al, 2015) and GWA studies for “intelligence” (e.g., Sniekers et al, 2017). It is entirely possible, as is argued by Richardson and Jones (2019) that the results from GWA studies “for intelligence” are entirely spurious, since the correlations may appear due to the size of the dataset, not the nature of it (Calude and Longo, 2017). Zhou and Zao (2019) argue that “For complex polygenic traits, spurious correlation makes the separation of causal and null SNPs difficult, leading to a doomed failure of PRS.” This is troubling for hereditarian claims when it comes to “genes for” “intelligence” and other socially-valued traits.
How can hereditarians show PGS/PRS causation?
This is a hard question to answer, but I think I have one. The hereditarian must:
(1) provide a valid deductive argument, in that the conclusion is the phenomena to be explained; (2) provide an explanans (the sentences adduced as the explanation for the phenomenon) that has one lawlike generalization; and (3) show the remaining premises which state the preceding conditions have to have empirical content and they have to be true.
An explanandum is a description of the events that need explaining (in this case, PGS/PRS) while an explanans does the explaining—meaning that the sentences are adduced as explanations of the explanans. Garson (2018: 30) gives the example of zebra stripes and flies. The explanans is Stripes deter flies while the explanandum is Zebras have stripes. So we can then say that zebras have stripes because stripes deter flies.
Causation for PGS would not be shown, for example, by showing that certain races/ethnies have higher PGSs for “intelligence”. The claim is that since Jews have higher PGSs for “intelligence” then it follows that PGSs can show causation (e.g., Dunkel et al, 2019; see Freese et al, 2019 for a response). But this just shows how ideology can and does color one’s conclusions they glean from certain data. That is NOT sufficient to show causation for PGS.
PGSs cannot, currently, show causation. The studies that such scores are derived from fall prey to the fact that spurious correlations are inevitable in large datasets, which also is a problem for other hereditarian claims (about twins and GWA studies for “intelligence”). Thus, PGSs do not show causation and the fact that large datasets lead to spurious correlations means that even by increasing the number of subjects in the study, this would still not elucidate “genetic causation.”