Behavioural genetics

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Behavioural_genetics 

Behavioural genetics, also referred to as behaviour genetics, is a field of scientific research that uses genetic methods to investigate the nature and origins of individual differences in behaviour. While the name "behavioural genetics" connotes a focus on genetic influences, the field broadly investigates the extent to which genetic and environmental factors influence individual differences, using research designs that allow removal of the confounding of genes and environment. Behavioural genetics was founded as a scientific discipline by Francis Galton in the late 19th century, only to be discredited through association with eugenics movements before and during World War II. In the latter half of the 20th century, the field saw renewed prominence with research on inheritance of behaviour and mental illness in humans (typically using twin and family studies), as well as research on genetically informative model organisms through selective breeding and crosses. In the late 20th and early 21st centuries, technological advances in molecular genetics made it possible to measure and modify the genome directly. This led to major advances in model organism research (e.g., knockout mice) and in human studies (e.g., genome-wide association studies), leading to new scientific discoveries.

Findings from behavioural genetic research have broadly impacted modern understanding of the role of genetic and environmental influences on behaviour. These include evidence that nearly all researched behaviors are under a significant degree of genetic influence, and that influence tends to increase as individuals develop into adulthood. Further, most researched human behaviours are influenced by a very large number of genes and the individual effects of these genes are very small. Environmental influences also play a strong role, but they tend to make family members more different from one another, not more similar.

History

Farmers with wheat and cattle - Ancient Egyptian art 1,422 BCE displaying domesticated animals.

Selective breeding and the domestication of animals is perhaps the earliest evidence that humans considered the idea that individual differences in behaviour could be due to natural causes. Plato and Aristotle each speculated on the basis and mechanisms of inheritance of behavioural characteristics. Plato, for example, argued in The Republic that selective breeding among the citizenry to encourage the development of some traits and discourage others, what today might be called eugenics, was to be encouraged in the pursuit of an ideal society. Behavioural genetic concepts also existed during the English renaissance, where William Shakespeare perhaps first coined the phrase "nature versus nurture" in The Tempest, where he wrote in Act IV, Scene I, that Caliban was "A devil, a born devil, on whose nature Nurture can never stick".

Modern-day behavioural genetics began with Sir Francis Galton, a nineteenth-century intellectual and cousin of Charles Darwin. Galton was a polymath who studied many subjects, including the heritability of human abilities and mental characteristics. One of Galton's investigations involved a large pedigree study of social and intellectual achievement in the English upper class. In 1869, 10 years after Darwin's On the Origin of Species, Galton published his results in Hereditary Genius. In this work, Galton found that the rate of "eminence" was highest among close relatives of eminent individuals, and decreased as the degree of relationship to eminent individuals decreased. While Galton could not rule out the role of environmental influences on eminence, a fact which he acknowledged, the study served to initiate an important debate about the relative roles of genes and environment on behavioural characteristics. Through his work, Galton also "introduced multivariate analysis and paved the way towards modern Bayesian statistics" that are used throughout the sciences—launching what has been dubbed the "Statistical Enlightenment".

Galton in his later years

The field of behavioural genetics, as founded by Galton, was ultimately undermined by another of Galton's intellectual contributions, the founding of the eugenics movement in 20th century society. The primary idea behind eugenics was to use selective breeding combined with knowledge about the inheritance of behaviour to improve the human species. The eugenics movement was subsequently discredited by scientific corruption and genocidal actions in Nazi Germany. Behavioural genetics was thereby discredited through its association to eugenics. The field once again gained status as a distinct scientific discipline through the publication of early texts on behavioural genetics, such as Calvin S. Hall's 1951 book chapter on behavioural genetics, in which he introduced the term "psychogenetics", which enjoyed some limited popularity in the 1960s and 1970s. However, it eventually disappeared from usage in favour of "behaviour genetics".

The start of behavior genetics as a well-identified field was marked by the publication in 1960 of the book Behavior Genetics by John L. Fuller and William Robert (Bob) Thompson. It is widely accepted now that many if not most behaviours in animals and humans are under significant genetic influence, although the extent of genetic influence for any particular trait can differ widely. A decade later, in February 1970, the first issue of the journal Behavior Genetics was published and in 1972 the Behavior Genetics Association was formed with Theodosius Dobzhansky elected as the association's first president. The field has since grown and diversified, touching many scientific disciplines.

Methods

The primary goal of behavioural genetics is to investigate the nature and origins of individual differences in behaviour. A wide variety of different methodological approaches are used in behavioral genetic research, only a few of which are outlined below.

Animal studies

Investigators in animal behavior genetics can carefully control for environmental factors and can experimentally manipulate genetic variants, allowing for a degree of causal inference that is not available in studies on human behavioral genetics. In animal research selection experiments have often been employed. For example, laboratory house mice have been bred for open-field behaviour, thermoregulatory nesting, and voluntary wheel-running behaviour. A range of methods in these designs are covered on those pages. Behavioural geneticists using model organisms employ a range of molecular techniques to alter, insert, or delete genes. These techniques include knockouts, floxing, gene knockdown, or genome editing using methods like CRISPR-Cas9. These techniques allow behavioural geneticists different levels of control in the model organism's genome, to evaluate the molecular, physiological, or behavioural outcome of genetic changes. Animals commonly used as model organisms in behavioral genetics include mice, zebra fish, and the nematode species C. elegans.

Twin and family studies

Main article: Twin studies

See also: Heritability and Quantitative genetics § Pedigree analysis

Pedigree chart showing an inheritance pattern consistent with autosomal dominant transmission. Behavioural geneticists have used pedigree studies to investigate the genetic and environmental basis of behaviour.

Some research designs used in behavioural genetic research are variations on family designs (also known as pedigree designs), including twin studies and adoption studies. Quantitative genetic modelling of individuals with known genetic relationships (e.g., parent-child, sibling, dizygotic and monozygotic twins) allows one to estimate to what extent genes and environment contribute to phenotypic differences among individuals. The basic intuition of the twin study is that monozygotic twins share 100% of their genome and dizygotic twins share, on average, 50% of their segregating genome. Thus, differences between the two members of a monozygotic twin pair can only be due to differences in their environment, whereas dizygotic twins will differ from one another due to environment as well as genes. Under this simplistic model, if dizygotic twins differ more than monozygotic twins it can only be attributable to genetic influences. An important assumption of the twin model is the equal environment assumption that monozygotic twins have the same shared environmental experiences as dizygotic twins. If, for example, monozygotic twins tend to have more similar experiences than dizygotic twins—and these experiences themselves are not genetically mediated through gene-environment correlation mechanisms—then monozygotic twins will tend to be more similar to one another than dizygotic twins for reasons that have nothing to do with genes.

Twin studies of monozygotic and dizygotic twins use a biometrical formulation to describe the influences on twin similarity and to infer heritability. The formulation rests on the basic observation that the variance in a phenotype is due to two sources, genes and environment. More formally, ${\displaystyle Var(P)=g+(g\times \epsilon )+\epsilon }$, where ${\displaystyle P}$ is the phenotype, ${\displaystyle g}$ is the effect of genes, ${\displaystyle \epsilon }$ is the effect of the environment, and ${\displaystyle (g\times \epsilon )}$ is a gene by environment interaction. The ${\displaystyle g}$ term can be expanded to include additive (${\displaystyle a^{2}}$), dominance (${\displaystyle d^{2}}$), and epistatic (${\displaystyle i^{2}}$) genetic effects. Similarly, the environmental term ${\displaystyle \epsilon }$ can be expanded to include shared environment (${\displaystyle c^{2}}$) and non-shared environment (${\displaystyle e^{2}}$), which includes any measurement error. Dropping the gene by environment interaction for simplicity (typical in twin studies) and fully decomposing the ${\displaystyle g}$ and ${\displaystyle \epsilon }$ terms, we now have ${\displaystyle Var(P)=(a^{2}+d^{2}+i^{2})+(c^{2}+e^{2})}$. Twin research then models the similarity in monozygotic twins and dizogotic twins using simplified forms of this decomposition, shown in the table.

Decomposing the genetic and environmental contributions to twin similarity.

Type of relationship	Full decomposition	Falconer's decomposition
Perfect similarity between siblings	${\displaystyle 1.0=a^{2}+d^{2}+i^{2}+c^{2}+e^{2}}$	${\displaystyle 1.0=a^{2}+c^{2}+e^{2}}$
Monozygotic twin correlation(${\displaystyle r_{MZ}}$)	${\displaystyle r_{MZ}=a^{2}+d^{2}+i^{2}+c^{2}}$	${\displaystyle r_{MZ}=a^{2}+c^{2}}$
Dizygotic twin correlation (${\displaystyle r_{DZ}}$)	${\displaystyle r_{DZ}={\frac {1}{2}}a^{2}+{\frac {1}{4}}d^{2}+(k)i^{2}+c^{2}}$	${\displaystyle r_{DZ}={\frac {1}{2}}a^{2}+c^{2}}$
Where ${\displaystyle k}$ is an unknown (probably very small) quantity.

The simplified Falconer formulation can then be used to derive estimates of ${\displaystyle a^{2}}$, ${\displaystyle c^{2}}$, and ${\displaystyle e^{2}}$. Rearranging and substituting the ${\displaystyle r_{MZ}}$ and ${\displaystyle r_{DZ}}$ equations one can obtain an estimate of the additive genetic variance, or heritability, ${\displaystyle a^{2}=2(r_{MZ}-r_{DZ})}$, the non-shared environmental effect ${\displaystyle e^{2}=1-r_{MZ}}$ and, finally, the shared environmental effect ${\displaystyle c^{2}=2r_{DZ}-r_{MZ}}$. The Falconer formulation is presented here to illustrate how the twin model works. Modern approaches use maximum likelihood to estimate the genetic and environmental variance components.

Measured genetic variants

The Human Genome Project has allowed scientists to directly genotype the sequence of human DNA nucleotides. Once genotyped, genetic variants can be tested for association with a behavioural phenotype, such as mental disorder, cognitive ability, personality, and so on.

• Candidate Genes. One popular approach has been to test for association candidate genes with behavioural phenotypes, where the candidate gene is selected based on some a priori theory about biological mechanisms involved in the manifestation of a behavioural trait or phenotype. In general, such studies have proven difficult to broadly replicate and there has been concern raised that the false positive rate in this type of research is high.
• Genome-wide association studies. In genome-wide association studies, researchers test the relationship of millions of genetic polymorphisms with behavioural phenotypes across the genome. This approach to genetic association studies is largely atheoretical, and typically not guided by a particular biological hypothesis regarding the phenotype. Genetic association findings for behavioural traits and psychiatric disorders have been found to be highly polygenic (involving many small genetic effects).
• SNP heritability and co-heritability. Recently, researchers have begun to use similarity between classically unrelated people at their measured single nucleotide polymorphisms (SNPs) to estimate genetic variation or covariation that is tagged by SNPs, using mixed effects models implemented in software such as Genome-wide complex trait analysis (GCTA). To do this, researchers find the average genetic relatedness over all SNPs between all individuals in a (typically large) sample, and use Haseman–Elston regression or restricted maximum likelihood to estimate the genetic variation that is "tagged" by, or predicted by, the SNPs. The proportion of phenotypic variation that is accounted for by the genetic relatedness has been called "SNP heritability". Intuitively, SNP heritability increases to the degree that phenotypic similarity is predicted by genetic similarity at measured SNPs, and is expected to be lower than the true narrow-sense heritability to the degree that measured SNPs fail to tag (typically rare) causal variants. The value of this method is that it is an independent way to estimate heritability that does not require the same assumptions as those in twin and family studies, and that it gives insight into the allelic frequency spectrum of the causal variants underlying trait variation.

Quasi-experimental designs

Some behavioural genetic designs are useful not to understand genetic influences on behaviour, but to control for genetic influences to test environmentally-mediated influences on behaviour. Such behavioural genetic designs may be considered a subset of natural experiments, quasi-experiments that attempt to take advantage of naturally occurring situations that mimic true experiments by providing some control over an independent variable. Natural experiments can be particularly useful when experiments are infeasible, due to practical or ethical limitations.

A general limitation of observational studies is that the relative influences of genes and environment are confounded. A simple demonstration of this fact is that measures of 'environmental' influence are heritable. Thus, observing a correlation between an environmental risk factor and a health outcome is not necessarily evidence for environmental influence on the health outcome. Similarly, in observational studies of parent-child behavioural transmission, for example, it is impossible to know if the transmission is due to genetic or environmental influences, due to the problem of passive gene-environment correlation. The simple observation that the children of parents who use drugs are more likely to use drugs as adults does not indicate why the children are more likely to use drugs when they grow up. It could be because the children are modelling their parents' behaviour. Equally plausible, it could be that the children inherited drug-use-predisposing genes from their parent, which put them at increased risk for drug use as adults regardless of their parents' behaviour. Adoption studies, which parse the relative effects of rearing environment and genetic inheritance, find a small to negligible effect of rearing environment on smoking, alcohol, and marijuana use in adopted children,  but a larger effect of rearing environment on harder drug use.

Other behavioural genetic designs include discordant twin studies, children of twins designs, and Mendelian randomization.

General findings

There are many broad conclusions to be drawn from behavioural genetic research about the nature and origins of behaviour. Three major conclusions include:

1. all behavioural traits and disorders are influenced by genes
2. environmental influences tend to make members of the same family more different, rather than more similar
3. the influence of genes tends to increase in relative importance as individuals age.

Genetic influences on behaviour are pervasive

It is clear from multiple lines of evidence that all researched behavioural traits and disorders are influenced by genes; that is, they are heritable. The single largest source of evidence comes from twin studies, where it is routinely observed that monozygotic (identical) twins are more similar to one another than are same-sex dizygotic (fraternal) twins.

The conclusion that genetic influences are pervasive has also been observed in research designs that do not depend on the assumptions of the twin method. Adoption studies show that adoptees are routinely more similar to their biological relatives than their adoptive relatives for a wide variety of traits and disorders. In the Minnesota Study of Twins Reared Apart, monozygotic twins separated shortly after birth were reunited in adulthood. These adopted, reared-apart twins were as similar to one another as were twins reared together on a wide range of measures including general cognitive ability, personality, religious attitudes, and vocational interests, among others. Approaches using genome-wide genotyping have allowed researchers to measure genetic relatedness between individuals and estimate heritability based on millions of genetic variants. Methods exist to test whether the extent of genetic similarity (aka, relatedness) between nominally unrelated individuals (individuals who are not close or even distant relatives) is associated with phenotypic similarity. Such methods do not rely on the same assumptions as twin or adoption studies, and routinely find evidence for heritability of behavioural traits and disorders.

Nature of environmental influence

Just as all researched human behavioural phenotypes are influenced by genes (i.e., are heritable), all such phenotypes are also influenced by the environment. The basic fact that monozygotic twins are genetically identical but are never perfectly concordant for psychiatric disorder or perfectly correlated for behavioural traits, indicates that the environment shapes human behaviour.

The nature of this environmental influence, however, is such that it tends to make individuals in the same family more different from one another, not more similar to one another. That is, estimates of shared environmental effects (${\displaystyle c^{2}}$) in human studies are small, negligible, or zero for the vast majority of behavioural traits and psychiatric disorders, whereas estimates of non-shared environmental effects (${\displaystyle e^{2}}$) are moderate to large. From twin studies ${\displaystyle c^{2}}$ is typically estimated at 0 because the correlation (${\displaystyle r_{MZ}}$) between monozygotic twins is at least twice the correlation (${\displaystyle r_{DZ}}$) for dizygotic twins. When using the Falconer variance decomposition (${\displaystyle 1.0=a^{2}+c^{2}+e^{2}}$) this difference between monozygotic and dizygotic twin similarity results in an estimated ${\displaystyle c^{2}=0}$. It is important to note that the Falconer decomposition is simplistic. It removes the possible influence of dominance and epistatic effects which, if present, will tend to make monozygotic twins more similar than dizygotic twins and mask the influence of shared environmental effects. This is a limitation of the twin design for estimating ${\displaystyle c^{2}}$. However, the general conclusion that shared environmental effects are negligible does not rest on twin studies alone. Adoption research also fails to find large (${\displaystyle c^{2}}$) components; that is, adoptive parents and their adopted children tend to show much less resemblance to one another than the adopted child and his or her non-rearing biological parent. In studies of adoptive families with at least one biological child and one adopted child, the sibling resemblance also tends be nearly zero for most traits that have been studied.

Similarity in twins and adoptees indicates a small role for shared environment in personality.

The figure provides an example from personality research, where twin and adoption studies converge on the conclusion of zero to small influences of shared environment on broad personality traits measured by the Multidimensional Personality Questionnaire including positive emotionality, negative emotionality, and constraint.

Given the conclusion that all researched behavioural traits and psychiatric disorders are heritable, biological siblings will always tend to be more similar to one another than will adopted siblings. However, for some traits, especially when measured during adolescence, adopted siblings do show some significant similarity (e.g., correlations of .20) to one another. Traits that have been demonstrated to have significant shared environmental influences include internalizing and externalizing psychopathology, substance use and dependence, and intelligence.

Nature of genetic influence

Genetic effects on human behavioural outcomes can be described in multiple ways. One way to describe the effect is in terms of how much variance in the behaviour can be accounted for by alleles in the genetic variant, otherwise known as the coefficient of determination or ${\displaystyle R^{2}}$. An intuitive way to think about ${\displaystyle R^{2}}$ is that it describes the extent to which the genetic variant makes individuals, who harbour different alleles, different from one another on the behavioural outcome. A complementary way to describe effects of individual genetic variants is in how much change one expects on the behavioural outcome given a change in the number of risk alleles an individual harbours, often denoted by the Greek letter ${\displaystyle \beta }$ (denoting the slope in a regression equation), or, in the case of binary disease outcomes by the odds ratio ${\displaystyle OR}$ of disease given allele status. Note the difference: ${\displaystyle R^{2}}$ describes the population-level effect of alleles within a genetic variant; ${\displaystyle \beta }$ or ${\displaystyle OR}$ describe the effect of having a risk allele on the individual who harbours it, relative to an individual who does not harbour a risk allele.

When described on the ${\displaystyle R^{2}}$ metric, the effects of individual genetic variants on complex human behavioural traits and disorders are vanishingly small, with each variant accounting for ${\displaystyle R^{2}<0.3\%}$ of variation in the phenotype. This fact has been discovered primarily through genome-wide association studies of complex behavioural phenotypes, including results on substance use, personality, fertility, schizophrenia, depression, and endophenotypes including brain structure and function. There are a small handful of replicated and robustly studied exceptions to this rule, including the effect of APOE on Alzheimer's disease, and CHRNA5 on smoking behaviour, and ALDH2 (in individuals of East Asian ancestry) on alcohol use.

On the other hand, when assessing effects according to the ${\displaystyle \beta }$ metric, there are a large number of genetic variants that have very large effects on complex behavioural phenotypes. The risk alleles within such variants are exceedingly rare, such that their large behavioural effects impact only a small number of individuals. Thus, when assessed at a population level using the ${\displaystyle R^{2}}$ metric, they account for only a small amount of the differences in risk between individuals in the population. Examples include variants within APP that result in familial forms of severe early onset Alzheimer's disease but affect only relatively few individuals. Compare this to risk alleles within APOE, which pose much smaller risk compared to APP, but are far more common and therefore affect a much greater proportion of the population.

Finally, there are classical behavioural disorders that are genetically simple in their etiology, such as Huntington's disease. Huntington's is caused by a single autosomal dominant variant in the HTT gene, which is the only variant that accounts for any differences among individuals in their risk for developing the disease, assuming they live long enough. In the case of genetically simple and rare diseases such as Huntington's, the variant ${\displaystyle R^{2}}$ and the ${\displaystyle OR}$ are simultaneously large.

Additional general findings

In response to general concerns about the replicability of psychological research, behavioral geneticists Robert Plomin, John C. DeFries, Valerie Knopik, and Jenae Neiderhiser published a review of the ten most well-replicated findings from behavioral genetics research. The ten findings were:

1. "All psychological traits show significant and substantial genetic influence."
2. "No behavioral traits are 100% heritable."
3. "Heritability is caused by many genes of small effect."
4. "Phenotypic correlations between psychological traits show significant and substantial genetic mediation."
5. "The heritability of intelligence increases throughout development."
6. "Age-to-age stability is mainly due to genetics."
7. "Most measures of the 'environment' show significant genetic influence."
8. "Most associations between environmental measures and psychological traits are significantly mediated genetically."
9. "Most environmental effects are not shared by children growing up in the same family."
10. "Abnormal is normal."

Criticisms and controversies

Behavioural genetic research and findings have at times been controversial. Some of this controversy has arisen because behavioural genetic findings can challenge societal beliefs about the nature of human behaviour and abilities. Major areas of controversy have included genetic research on topics such as racial differences, intelligence, violence, and human sexuality. Other controversies have arisen due to misunderstandings of behavioural genetic research, whether by the lay public or the researchers themselves. For example, the notion of heritability is easily misunderstood to imply causality, or that some behavior or condition is determined by one's genetic endowment. When behavioral genetics researchers say that a behavior is X% heritable, that does not mean that genetics causes, determines, or fixes up to X% of the behavior. Instead, heritability is a statement about genetic differences correlated with trait differences on the population level.

Historically, perhaps the most controversial subject has been on race and genetics. Race is not a scientifically exact term, and its interpretation can depend on one's culture and country of origin. Instead, geneticists use concepts such as ancestry, which is more rigorously defined. For example, a so-called "Black" race may include all individuals of relatively recent African descent ("recent" because all humans are descended from African ancestors). However, there is more genetic diversity in Africa than the rest of the world combined, so speaking of a "Black" race is without a precise genetic meaning.

Qualitative research has fostered arguments that behavioural genetics is an ungovernable field without scientific norms or consensus, which fosters controversy. The argument continues that this state of affairs has led to controversies including race, intelligence, instances where variation within a single gene was found to very strongly influence a controversial phenotype (e.g., the "gay gene" controversy), and others. This argument further states that because of the persistence of controversy in behavior genetics and the failure of disputes to be resolved, behavior genetics does not conform to the standards of good science.

The scientific assumptions on which parts of behavioral genetic research are based have also been criticized as flawed. Genome wide association studies are often implemented with simplifying statistical assumptions, such as additivity, which may be statistically robust but unrealistic for some behaviors. Critics further contend that, in humans, behavior genetics represents a misguided form of genetic reductionism based on inaccurate interpretations of statistical analyses. Studies comparing monozygotic (MZ) and dizygotic (DZ) twins assume that environmental influences will be the same in both types of twins, but this assumption may also be unrealistic. MZ twins may be treated more alike than DZ twins, which itself may be an example of evocative gene-environment correlation, suggesting that one's genes influence their treatment by others. It is also not possible in twin studies to completely eliminate effects of the shared womb environment, although studies comparing twins who experience monochorionic and dichorionic environments in utero do exist, and indicate limited impact. Studies of twins separated in early life include children who were separated not at birth but part way through childhood. The effect of early rearing environment can therefore be evaluated to some extent in such a study, by comparing twin similarity for those twins separated early and those separated later.

Human behavioral ecology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Human_behavioral_ecology 

Human behavioral ecology (HBE) or human evolutionary ecology applies the principles of evolutionary theory and optimization to the study of human behavioral and cultural diversity. HBE examines the adaptive design of traits, behaviors, and life histories of humans in an ecological context. One aim of modern human behavioral ecology is to determine how ecological and social factors influence and shape behavioral flexibility within and between human populations. Among other things, HBE attempts to explain variation in human behavior as adaptive solutions to the competing life-history demands of growth, development, reproduction, parental care, and mate acquisition.

HBE overlaps with evolutionary psychology, human or cultural ecology, and decision theory. It is most prominent in disciplines such as anthropology and psychology where human evolution is considered relevant for a holistic understanding of human behavior or in economics where self-interest, methodological individualism, and maximization are key elements in modeling behavioral responses to various ecological factors.

Evolutionary theory

Human behavioral ecology rests upon a foundation of evolutionary theory. This includes aspects of both general evolutionary theory and established middle-level evolutionary theories, as well. Aspects of general evolutionary theory include:

• Natural selection, the process by which individual organisms with favorable traits are more likely to survive and reproduce.
• Sexual selection, the theory that competition for mates between individuals of the same sex results in differential mating and reproduction.
• Kin selection, the changes in gene frequency across generations that are driven at least in part by interactions between related individuals, and
• Inclusive fitness, the sum of an individual's own reproductive success, (natural and sexual selection), plus the effects the individual's actions have on the reproductive success of that individual's kin, (kin selection).

Middle-level evolutionary theories used in HBE include:

• The theory of parental investment, which predicts that the sex making the largest investment in lactation, nurturing and protecting offspring will be more discriminating in mating and that the sex that invests less in offspring will compete for access to the higher investing sex.
• Parent–offspring conflict, which predicts that because the genetic interests of parents and offspring are not identical, offspring will be selected to manipulate their parents in order to ensure higher investment, and that, conversely, parents will be selected to manipulate their offspring.
• The theory of reciprocal altruism, a form of altruism in which one organism provides a benefit to another in the expectation of future reciprocation.
• The Trivers–Willard hypothesis, which proposes that parents should invest more in the sex that gives them the greatest reproductive payoff (grandchildren) with increasing or marginal investment.
• r/K selection theory, which, in ecology, relates to the selection of traits in organisms that allow success in particular environments. r-selected species – in unstable or unpredictable environments – produce many offspring, any individual one of which is unlikely to survive to adulthood, while K-selected species – in stable or predictable environments – invest more heavily in fewer offspring, each of which has a better chance of surviving to adulthood.
• Evolutionary game theory, the application of population genetics-inspired models of change in gene frequency in populations to game theory.
• Evolutionarily stable strategy, which refers to a strategy, which if adopted by a population, cannot be invaded by any competing alternative strategy.

Basic principles

Ecological selectionism

Ecological selectionism refers to the assumption that humans are highly flexible in their behaviors. Furthermore, it assumes that various ecological forces select for various behaviors that optimize humans' inclusive fitness in their given ecological context.

The piecemeal approach

The piecemeal approach refers to taking a reductionist approach as opposed to a holistic approach in studying human socioecological behavior. Human behavioral ecologists assume that by taking complex social phenomena, (e.g., marriage patterns, foraging behaviors, etc.), and then breaking them down into sets of components involving decisions and constraints that they are in a better position to create models and make predictions involving human behavior. An example would be examining marriage systems by examining the ecological context, mate preferences, the distribution of particular characteristics within the population, and so forth.

Conditional strategies

Human behavioral ecologists assume that what might be the most adaptive strategy in one environment might not be the most adaptive strategy in another environment. Conditional strategies, therefore, can be represented in the following statement:

• In environmental context X, engage in adaptive strategy A.
• In environmental context Y, engage in adaptive strategy B.

The phenotypic gambit

The phenotypic gambit refers to the simplifying assumption that complex traits, such as behavioural traits, can be modelled as if they were controlled by single distinct alleles, representing alternate strategies. In other words, the phenotypic gambit assumes that "selection will favour traits with high fitness ...irrespective of the particulars of inheritance."

Modeling

Theoretical models that human behavioral ecologists employ include, but are not limited to:

• Optimal foraging theory, which states that organisms focus on consuming the most energy while expending the least amount of energy.
• Life history theory, which postulates that many of the physiological traits and behaviors of individuals may be best understood in relation to the key maturational and reproductive characteristics that define the life course.
• Sex allocation theory, which predicts that parents should bias their reproductive investments toward the offspring sex generating the greatest fitness return.
• The polygyny threshold model, which suggests that polygyny is driven by female choice of mates who control more resources relative to other potential mates in the population.

Evolutionarily stable strategy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Evolutionarily_stable_strategy 

 

Evolutionarily stable strategy
A solution concept in game theory
Relationship
Subset of	Nash equilibrium
Superset of	Stochastically stable equilibrium, Stable Strong Nash equilibrium
Intersects with	Subgame perfect equilibrium, Trembling hand perfect equilibrium, Perfect Bayesian equilibrium
Significance
Proposed by	John Maynard Smith and George R. Price
Used for	Biological modeling and Evolutionary game theory
Example	Hawk-dove

An evolutionarily stable strategy (ESS) is a strategy (or set of strategies) that is impermeable when adopted by a population in adaptation to a specific environment, that is to say it cannot be displaced by an alternative strategy (or set of strategies) which may be novel or initially rare. Introduced by John Maynard Smith and George R. Price in 1972/3, it is an important concept in behavioural ecology, evolutionary psychology, mathematical game theory and economics, with applications in other fields such as anthropology, philosophy and political science.

In game-theoretical terms, an ESS is an equilibrium refinement of the Nash equilibrium, being a Nash equilibrium that is also "evolutionarily stable." Thus, once fixed in a population, natural selection alone is sufficient to prevent alternative (mutant) strategies from replacing it (although this does not preclude the possibility that a better strategy, or set of strategies, will emerge in response to selective pressures resulting from environmental change).

History

Evolutionarily stable strategies were defined and introduced by John Maynard Smith and George R. Price in a 1973 Nature paper. Such was the time taken in peer-reviewing the paper for Nature that this was preceded by a 1972 essay by Maynard Smith in a book of essays titled On Evolution. The 1972 essay is sometimes cited instead of the 1973 paper, but university libraries are much more likely to have copies of Nature. Papers in Nature are usually short; in 1974, Maynard Smith published a longer paper in the Journal of Theoretical Biology. Maynard Smith explains further in his 1982 book Evolution and the Theory of Games. Sometimes these are cited instead. In fact, the ESS has become so central to game theory that often no citation is given, as the reader is assumed to be familiar with it.

Maynard Smith mathematically formalised a verbal argument made by Price, which he read while peer-reviewing Price's paper. When Maynard Smith realized that the somewhat disorganised Price was not ready to revise his article for publication, he offered to add Price as co-author.

The concept was derived from R. H. MacArthur and W. D. Hamilton's work on sex ratios, derived from Fisher's principle, especially Hamilton's (1967) concept of an unbeatable strategy. Maynard Smith was jointly awarded the 1999 Crafoord Prize for his development of the concept of evolutionarily stable strategies and the application of game theory to the evolution of behaviour.

Uses of ESS:

• The ESS was a major element used to analyze evolution in Richard Dawkins' bestselling 1976 book The Selfish Gene.
• The ESS was first used in the social sciences by Robert Axelrod in his 1984 book The Evolution of Cooperation. Since then, it has been widely used in the social sciences, including anthropology, economics, philosophy, and political science.
• In the social sciences, the primary interest is not in an ESS as the end of biological evolution, but as an end point in cultural evolution or individual learning.
• In evolutionary psychology, ESS is used primarily as a model for human biological evolution.

Motivation

The Nash equilibrium is the traditional solution concept in game theory. It depends on the cognitive abilities of the players. It is assumed that players are aware of the structure of the game and consciously try to predict the moves of their opponents and to maximize their own payoffs. In addition, it is presumed that all the players know this (see common knowledge). These assumptions are then used to explain why players choose Nash equilibrium strategies.

Evolutionarily stable strategies are motivated entirely differently. Here, it is presumed that the players' strategies are biologically encoded and heritable. Individuals have no control over their strategy and need not be aware of the game. They reproduce and are subject to the forces of natural selection, with the payoffs of the game representing reproductive success (biological fitness). It is imagined that alternative strategies of the game occasionally occur, via a process like mutation. To be an ESS, a strategy must be resistant to these alternatives.

Given the radically different motivating assumptions, it may come as a surprise that ESSes and Nash equilibria often coincide. In fact, every ESS corresponds to a Nash equilibrium, but some Nash equilibria are not ESSes.

Nash equilibrium

An ESS is a refined or modified form of a Nash equilibrium. (See the next section for examples which contrast the two.) In a Nash equilibrium, if all players adopt their respective parts, no player can benefit by switching to any alternative strategy. In a two player game, it is a strategy pair. Let E(S,T) represent the payoff for playing strategy S against strategy T. The strategy pair (S, S) is a Nash equilibrium in a two player game if and only if for both players, for any strategy T:

E(S,S) ≥ E(T,S)

In this definition, a strategy T≠S can be a neutral alternative to S (scoring equally well, but not better). A Nash equilibrium is presumed to be stable even if T scores equally, on the assumption that there is no long-term incentive for players to adopt T instead of S. This fact represents the point of departure of the ESS.

Maynard Smith and Price specify two conditions for a strategy S to be an ESS. For all T≠S, either

1. E(S,S) > E(T,S), or
2. E(S,S) = E(T,S) and E(S,T) > E(T,T)

The first condition is sometimes called a strict Nash equilibrium. The second is sometimes called "Maynard Smith's second condition". The second condition means that although strategy T is neutral with respect to the payoff against strategy S, the population of players who continue to play strategy S has an advantage when playing against T.

There is also an alternative, stronger definition of ESS, due to Thomas. This places a different emphasis on the role of the Nash equilibrium concept in the ESS concept. Following the terminology given in the first definition above, this definition requires that for all T≠S

1. E(S,S) ≥ E(T,S), and
2. E(S,T) > E(T,T)

In this formulation, the first condition specifies that the strategy is a Nash equilibrium, and the second specifies that Maynard Smith's second condition is met. Note that the two definitions are not precisely equivalent: for example, each pure strategy in the coordination game below is an ESS by the first definition but not the second.

In words, this definition looks like this: The payoff of the first player when both players play strategy S is higher than (or equal to) the payoff of the first player when he changes to another strategy T and the second player keeps his strategy S and the payoff of the first player when only his opponent changes his strategy to T is higher than his payoff in case that both of players change their strategies to T.

This formulation more clearly highlights the role of the Nash equilibrium condition in the ESS. It also allows for a natural definition of related concepts such as a weak ESS or an evolutionarily stable set.

Examples of differences between Nash equilibria and ESSes

Cooperate Defect Cooperate 3, 3 1, 4 Defect 4, 1 2, 2 Prisoner's Dilemma

A B A 2, 2 1, 2 B 2, 1 2, 2 Harm thy neighbor

In most simple games, the ESSes and Nash equilibria coincide perfectly. For instance, in the prisoner's dilemma there is only one Nash equilibrium, and its strategy (Defect) is also an ESS.

Some games may have Nash equilibria that are not ESSes. For example, in harm thy neighbor (whose payoff matrix is shown here) both (A, A) and (B, B) are Nash equilibria, since players cannot do better by switching away from either. However, only B is an ESS (and a strong Nash). A is not an ESS, so B can neutrally invade a population of A strategists and predominate, because B scores higher against B than A does against B. This dynamic is captured by Maynard Smith's second condition, since E(A, A) = E(B, A), but it is not the case that E(A,B) > E(B,B).

C D C 2, 2 1, 2 D 2, 1 0, 0 Harm everyone

Swerve Stay Swerve 0,0 −1,+1 Stay +1,−1 −20,−20 Chicken

Nash equilibria with equally scoring alternatives can be ESSes. For example, in the game Harm everyone, C is an ESS because it satisfies Maynard Smith's second condition. D strategists may temporarily invade a population of C strategists by scoring equally well against C, but they pay a price when they begin to play against each other; C scores better against D than does D. So here although E(C, C) = E(D, C), it is also the case that E(C,D) > E(D,D). As a result, C is an ESS.

Even if a game has pure strategy Nash equilibria, it might be that none of those pure strategies are ESS. Consider the Game of chicken. There are two pure strategy Nash equilibria in this game (Swerve, Stay) and (Stay, Swerve). However, in the absence of an uncorrelated asymmetry, neither Swerve nor Stay are ESSes. There is a third Nash equilibrium, a mixed strategy which is an ESS for this game (see Hawk-dove game and Best response for explanation).

This last example points to an important difference between Nash equilibria and ESS. Nash equilibria are defined on strategy sets (a specification of a strategy for each player), while ESS are defined in terms of strategies themselves. The equilibria defined by ESS must always be symmetric, and thus have fewer equilibrium points.

Vs. evolutionarily stable state

In population biology, the two concepts of an evolutionarily stable strategy (ESS) and an evolutionarily stable state are closely linked but describe different situations.

In an evolutionarily stable strategy, if all the members of a population adopt it, no mutant strategy can invade. Once virtually all members of the population use this strategy, there is no 'rational' alternative. ESS is part of classical game theory.

In an evolutionarily stable state, a population's genetic composition is restored by selection after a disturbance, if the disturbance is not too large. An evolutionarily stable state is a dynamic property of a population that returns to using a strategy, or mix of strategies, if it is perturbed from that initial state. It is part of population genetics, dynamical system, or evolutionary game theory. This is now called convergent stability.

B. Thomas (1984) applies the term ESS to an individual strategy which may be mixed, and evolutionarily stable population state to a population mixture of pure strategies which may be formally equivalent to the mixed ESS.

Whether a population is evolutionarily stable does not relate to its genetic diversity: it can be genetically monomorphic or polymorphic.

Stochastic ESS

In the classic definition of an ESS, no mutant strategy can invade. In finite populations, any mutant could in principle invade, albeit at low probability, implying that no ESS can exist. In an infinite population, an ESS can instead be defined as a strategy which, should it become invaded by a new mutant strategy with probability p, would be able to counterinvade from a single starting individual with probability >p, as illustrated by the evolution of bet-hedging.

Prisoner's dilemma

	Cooperate	Defect
Cooperate	3, 3	1, 4
Defect	4, 1	2, 2
Prisoner's Dilemma

A common model of altruism and social cooperation is the Prisoner's dilemma. Here a group of players would collectively be better off if they could play Cooperate, but since Defect fares better each individual player has an incentive to play Defect. One solution to this problem is to introduce the possibility of retaliation by having individuals play the game repeatedly against the same player. In the so-called iterated Prisoner's dilemma, the same two individuals play the prisoner's dilemma over and over. While the Prisoner's dilemma has only two strategies (Cooperate and Defect), the iterated Prisoner's dilemma has a huge number of possible strategies. Since an individual can have different contingency plan for each history and the game may be repeated an indefinite number of times, there may in fact be an infinite number of such contingency plans.

Three simple contingency plans which have received substantial attention are Always Defect, Always Cooperate, and Tit for Tat. The first two strategies do the same thing regardless of the other player's actions, while the latter responds on the next round by doing what was done to it on the previous round—it responds to Cooperate with Cooperate and Defect with Defect.

If the entire population plays Tit-for-Tat and a mutant arises who plays Always Defect, Tit-for-Tat will outperform Always Defect. If the population of the mutant becomes too large — the percentage of the mutant will be kept small. Tit for Tat is therefore an ESS, with respect to only these two strategies. On the other hand, an island of Always Defect players will be stable against the invasion of a few Tit-for-Tat players, but not against a large number of them. If we introduce Always Cooperate, a population of Tit-for-Tat is no longer an ESS. Since a population of Tit-for-Tat players always cooperates, the strategy Always Cooperate behaves identically in this population. As a result, a mutant who plays Always Cooperate will not be eliminated. However, even though a population of Always Cooperate and Tit-for-Tat can coexist, if there is a small percentage of the population that is Always Defect, the selective pressure is against Always Cooperate, and in favour of Tit-for-Tat. This is due to the lower payoffs of cooperating than those of defecting in case the opponent defects.

This demonstrates the difficulties in applying the formal definition of an ESS to games with large strategy spaces, and has motivated some to consider alternatives.

Human behavior

The fields of sociobiology and evolutionary psychology attempt to explain animal and human behavior and social structures, largely in terms of evolutionarily stable strategies. Sociopathy (chronic antisocial or criminal behavior) may be a result of a combination of two such strategies.

Evolutionarily stable strategies were originally considered for biological evolution, but they can apply to other contexts. In fact, there are stable states for a large class of adaptive dynamics. As a result, they can be used to explain human behaviours that lack any genetic influences.