Pre-print	version	of: Serrelli	E	(2016).	Evolutionary	genetics	and	cultural	traits	in	a	'body	of	theory' perspective. In Panebianco F, Serrelli E, eds.	Understanding cultural traits.	A multidisciplinary perspective on cultural diversity. Springer, Switzerland, Chapter	11.	ISBN	978-3-319-24347-4 DOI 10.1007/978-3-319-24349-8_11 Print	ISBN 978-3-319-24347-4 Online	ISBN 978-3-319-24349-8 Chapter	11 Evolutionary	Genetics	and	Cultural	Traits in	a	'Body	of	Theory'	Perspective Emanuele	Serrelli The fact that methods of evolutionary genetics can deal with culture and cultural	traits	has	recently	become	one	argument	to	some	hyper-enthusiastic claims	that	the	social	sciences	could	be	unified	by	an	evolutionary	approach.1 In this paper, while I try to explain how culture is indeed tractable by evolutionary genetics, I adopt a less pretentious attitude. The basis of this attitude are the well-defined accumulations of scientific knowledge I call bodies of theory, made of mathematical methods, mathematical models and painfully achieved knowledge about these models. Bodies of theory are deposited in the traditions of the sciences along with notions and achievements about particular topics. Bodies of theory, whose usefulness depends	on	generality,	get	expanded	over time,	and	sometimes	get	modified to study	new	problems, new	domains,	where, in turn, they	promote	definite ways	of	constructing	problems	and	understanding	things.	I	treat	evolutionary genetics as a body of theory that had inherited and radically transformed Darwin's	way of thinking of characters. The 'body' still includes long-lasting mathematical tools that were invented by statisticians and people such as Francis Galton in the nineteenth century, but the real boost was the mathematical theory of Mendelian populations elaborated since the late 1910s. In the 1970s, evolutionary genetics was pushed by some empirical problems towards incorporating learned – or "cultural" – behaviors into its matemathical	models,	generating	a	way	of	describing	culture	in	terms	of	traits. 1	Connection: Note that, in almost every discipline, evolution has nothing to do with the concept	of	progress.	Refer	to	the	introduction	of	Chapter	3	for	a	commentary	and	further	links. The	separation	of	evolution	and	progress	is	discussed	at	length,	in	Section	13.3,	with	particular reference	to	'cultural	evolution'. 2 Before telling this story from the	beginning, I introduce,	with a	minimum	of technicalities, the encounter of evolutionary genetics with cultural transmission. In the end, I	will argue for a	humbler view	of the relationship between evolutionary genetics and culture, and, accordingly, for a less demanding	epistemology	of	cultural	traits. 11.1 What	would	you	Like	for	Breakfast?	Cultural Transmission	and	Evolutionary	Genetics The following example aims to illustrate why, in the 1970s, a scientific discourse started to emerge that treated things like the lactase gene and things like italian-style breakfast in a unified way, that is, as genetic and cultural traits.	The	discourse, scaffolded	by	mathematical	modeling,	updated the notion of 'characters' that was present in the tradition of biology since Darwin. Food customs	are	one	of the	most celebrated	kinds	of cultural	differences. Breakfast,	for	example,	is	highly	variable	across	countries.	Italians	are	known for their cult of coffee, cappuccino, and various combinations of coffee and milk. It is not rare that people from different ethnic, linguistic, or national groups praise or disapprove each other for the kind of food or cooking method. Fortunately, food is also a	matter of curiosity and happy exchange and cultural hybridization. In sum, feeding customs are culture. But	what if human	organisms,	more	specifically	adult	people,	were	definitely	partitioned into	milk-digesting	and	non-milk-digesting	people?	And	what	if	milk-digesting people were very unevenly distributed in different regions of the world? Could	breakfast	habits in	different "cultures"	be	affected	by	people's	varying capacity	to	digest	milk? Young mammals synthesize an enzyme called lactase that hydrolyzes the main carbohydrate of milk, lactose, into glucose and galactose. In this way they process their diet which is essentially composed by milk. After the weaning, mammals usually reduce lactase production. In humans, instead, many individuals continue to express lactase throughout adult life, and are thus able to digest the lactose contained in fresh milk. This capacity – as reported	in	a	recent	survey,	Gerbault	et	al.	(2011)	–	is	called	LP,	standing	for lactose-persistence. LP is not ubiquitous in humans: itP varies widely in human populations, both between and within continents (Figure 11.1). As Gerbault	et	al.	explain,	"in	lactase	non-persistent	individuals,	the	fermentation by colonic bacteria and osmotic effects of undigested lactose often cause symptoms such as abdominal pain, bloating, flatulence and diarrhoea" (Gerbault	et	al.	2011:	864).	The	exact	genetic	basis	of	the	LP	trait	is	certainly more complex than	a single gene, and is still not fully	known.2	In fact, it has been	shown	that	some	individuals	who	aren't	able	to	synthesize	lactase	can	in fact	consume	lactose-containing	products	without	any	obvious	ill	effects,	and changes in the	composition	of the	gut flora	–	a	non-genetic trait	–	may	be	as important	as the	active	enzyme.	Nonetheless, for simplicity, I	will talk	about the "lactase gene". One of the amazing features of the evolutionary genetics 2	In recent years, a number of single nucleotide polymorphisms (SNPs) have been found in association	with the LP trait in different populations. The first to be identified, -13910*T, is found	not	in	the	LCT	gene	(the	lactase	gene)	but	within	an	intron	of	a	neighbouring	gene,	MCM6. This	nucleotide	change	affects	lactase	promoter	activity,	and	the	allele	explains	only	partly	the distribution	of	LP	(its	frequency	map	does	not	completely	overlap	that	of	LP). 3 body	of	theory	is	just	its	relative	independence	from	the	genetic	details	of	the considered	trait,	up	to	traits	like	lactose	digestion	that	are	not	reducible	to	any genetic	character. Figure	11.1.	HERE. Can the lactase gene be an example of genes influencing the differential probability	of	assuming	and	keeping	cultural	habits?	Lactose	intolerants	who are able to blame milk for their morning nausea, and who lack access to chemical remedies or lactose-free alternatives (e.g., soy milk), may be reluctant to adopt the breakfast pattern from their family or colleagues. Therefore, their genotype will influence the probability distribution of different cultural alternatives.	On the	other	hand, there is the issue	of social pressure towards	having	breakfast in	a	certain	way. In Italy,	going for	coffee and	pastry is	a	very important	social	activity,	so it	might	turn	out	difficult to avoid milk. Furthermore, family habits may be highly important in conditioning individual	behavior,	making	you	feel like	you	would	never	start your day	without a cup of fresh	milk and your favorite butter biscuits from back when you were teen. Personal aversion and social pressure to milk consumption, with their respective strengths, will influence the probability distribution of consuming milk, and many factors will eventually settle individuals on a landscape of alternative behavior types. Settling on one alternative	doesn't	necessarily	mean	a life	choice.	Of	course, it is	possible	for the	individual	to	choose	a	definitive	stand	on	the	matter,	and	to	drink	always and only orange juice instead of cappuccino, but maybe the individual will choose	from	time	to	time	whether	to	have	latte	or	not,	or	she	will	switch	from time	to	time	to	stable	solutions	that	are	adequate	to	the	various	periods	of	life. The	probability	distribution	of	different	cultural	alternatives	aims	to	subsume and synthesize all those individual life solutions, making them comparable and allowing scientists (for instance, medical researchers) to make general considerations	on	breakfast	behavior. Population genetics – a field explained below – began to consider these issues	as	relevant	when	it	was	already	a	mature,	60-years-old	field,	thanks	to the	work	of important innovators	such	as	Marcus	Feldman	and	Luca	CavalliSforza.	Population	genetics is	a fundamental	part	of	evolutionary	biology.	As the	name says, it deals	with	questions about	populations and their genetics: why	does	the	lactase	gene	occur	at	particular	frequencies	across	populations of	the	world?	What	populations'	characteristics	are	relevant	to	this	issue? Population	genetics	considers	a	gene	as	something	that	spreads	and	remains in a population by means of reproduction through generations. More precisely, population genetics studies the fate of particular forms of genes, called	alleles,	with	respect	to	alternative	forms	of	the	same	genes.	In	the	case we	are	considering,	LP	(lactose-persistence)	is	an	allele,	while	its	alternative	is lactose	intolerance.	The	expansion	or	decline	of	the	LP	allele	in	the	population depends	on	how	often	and	how	luckily	the	allele	ends	up	into	the	offspring.	LP has been a lucky allele in some populations. If we analyze a particular population,	say,	Italians,	and	we	find	that	LP	is	almost	omnipresent	(while	less frequent in other populations), population genetics will guide us to some historical factors that may explain why. If lactose intolerance has been unlucky in Italy, for example, the reason	might	be that for	millennia Italians had	been	farming	and	consuming	milk	as	a	primary	element	in	their	diet.	In	a milk-consuming	population,	the	lactose	intolerance	allele	is	unlucky	because	it finds itself in individuals that, on average, are slightly sicker than the 4 population mean. Therefore, lactose intolerance gets transmitted less frequently	due	to	health	problems	of	its	bearers.	In	technical	terms,	in	a	milkconsuming population, lactose intolerance has a lower fitness than LP. Population genetics calculates, for example, how long must a population sustain	milk	consumption	in	order	to	erase	the	influence	of	initial	frequencies of	milk	tolerance	and	intolerance	on	the	current	amount	of	lactose	tolerance; as time goes by, in fact, the influence of those initial frequencies	will decay. Other dynamics such as rates of emigration and immigration may be very important,	and	are	considered	and	quantified	in	population	genetics. In	the	1970s,	mathematicians	such	as	Cavalli-Sforza	and	Feldman	found	out and	understood	that	to	explain	or	predict	the	frequency	of	an	allele	such	as	LP it is	absolutely insufficient to	characterize the	population	as	milk-consuming or not. Rather, the individual bearer's behavior	will be crucial to determine how	lucky	the	gene	is.	If	a	lactose	intolerant	person	is,	on	average,	also	milkaversive,	then	lactose	intolerance	will	be	pretty	as	much	fit	as	milk	tolerance: the	allele	will	be	lucky	to	end	up	in	a	person	that,	being	intolerant,	decides	to refrain from	consuming	milk. In	other	words,	while the fate	of the	allele	still depends on how frequently the allele ends up in a combination rather than another, it is the gene-behavior combination that has a fitness. And, as we have	seen	in	talking	about	breakfast,	when	you	know	whether	a	person	has	a certain gene, you still don't know what her behavior will be. Social and cultural pressures will play a role. In a milk consuming population, for example,	for	various	reasons	and	through	all	kinds	of	particular	cases	that	are not	even	visible	at	the	population	level, the	social	pressure	towards	drinking cappuccino for breakfast might be very low. This would allow for relative well-being	of	lactose	intolerance:	people	will	choose	only	with	their	bellyache. Cavalli-Sforza and Feldman demonstrated in this prototypic case that the strength of cultural transmission in a population is a crucial measure to establish sound relationships between genes (lactose persistence and intolerance),	behavior	(milk	consumption),	their	frequencies	and	their	change through time.	They	also	demonstrated that social and cultural	pressures	are needed	to	explain	the	observed	frequencies	of	LP. We will get back to the issue of lactose absorption later, to show how powerful a body of mathematical theory can be in resolving historical scenarios.	But	first	we	are	going	to	see	how	the	body	of	theory	of	evolutionary genetics	came	about	in	the	first	place,	to	appreciate	that	the	value	of	a	body	of theory	lays	not	only	in	its	modeling	power,	but	also	in	the	amount	of	work	that has	been	necessary	to	build	it	and	accrue	knowledge	around	it. 11.2 The	Historical	Growth	of	a	Body	of	Theory: Evolutionary	Genetics Charles Darwin's idea of a character was foundational to evolutionary biology. In Darwin's Origin of Species (1859), the character got early and tightly related to inheritance.3	Darwin	made the strong statement that "Any variation	which is not inherited is unimportant for us" (ivi: 12), and	wrote: "Perhaps	the	correct	way	of	viewing	the	whole	subject	[of	characters],	would be,	to	look	at	the	inheritance	of	every	character	whatever	as	the	rule,	and	non- 3	Connection:	Darwin's	thoughts,	as	expressed	in	his	writings,	are	protagonist	also	of	Sections 13.3,	16.2,	18.2,	20.3	and	20.4. 5 inheritance as the anomaly" (ivi: 13). Due to the "law" of natural selection, characters	are	so	important	that	they	become	almost	autonomous	phenomena in	the	evolutionary	process: Although natural selection can act only through and for the good of each being, yet characters	and	structures,	which	we	are	apt	to	consider	as	of	very	trifling	importance, may thus be acted on. When we see leaf-eating insects green, and bark-feeders mottled-grey; the alpine ptarmigan white in winter, the red-grouse the colour of heather,	and	the	black-grouse	that	of	peaty	earth,	we	must	believe	that	these	tints	are of	service	to	these	birds	and	insects	in	preserving	them	from	danger	(Darwin	1859:	84, emphasis	added). Natural	selection	'acts	upon'	characters	only	in	an	utterly	metaphorical	way, but characters, inheritance, and natural selection, along with individual organisms, are inseparable faces of the same phenomenon. Color is a real thing:	a	certain	shade	of	green	protects	a	species	of	leaf-eating	animals,	and	in this	way	it	prevails	on	other	shades	of	green	and	on	other	hues.	A	character	in Darwin	has	the	crucial	features	of	being	inherited	and	variable.	We	see	also	the foreshadow of the population genetics idea that a character has a fitness. Darwin	wrote: I	can	see	no	reason to	doubt that	an	accidental	deviation in the	size	and form	of the body, or in the curvature and length of the proboscis, &c., far too slight to be appreciated by us, might profit a bee or other insect, so that an individual so characterised would be able to obtain its food more quickly, and so have a better chance	of	living	and	leaving	descendants	(Darwin	1859:	94). We see here that	Darwin had a	more	mechanistic view	of fitness than the more	probabilistic	one	that	would	emerge later.	Yet, the idea that	one	of the crucial dimensions of a character in evolution is 'inter-generational transmission' was already clear. Work on inheritance and evolution was carried	out	after	Darwin,	way	into	the	twentieth	century,	and	resulted in	the construction	of	a	powerful	mathematical,	statistical	body	of	theory	that	is	still with	us	today,	centered	on	a	mathematical	object	called	Mendelian	population. 11.2.1	Galton William Provine in his classic book The Origins of Theoretical Population Genetics	(1971)	tells	the	story	of	how	the	issue	of	inheritance	was	addressed after Darwin. The story begins with Francis Galton, who was not at all convinced	of	Darwin's	theory	of	inheritance,	i.e.	'pangenesis'	(1869).	Galton,	in trying to demonstrate his own theory of inheritance, invented regression analysis, a mathematical method that became perhaps the first tool in the evolutionary genetics body of theory. Galton's methods are still used today (Hartl	&	Clark	2007),	although	interestingly	disconnected	by	most	of	his	ideas. For	example,	Galton's	saw	his	own	methods	as	endorsing	his	anti-Darwinian idea of evolution 'by jumps'; moreover, his hypothesis that "genius was a hereditary trait" (1869) fell squarely in the context of the foundation of human eugenics, an extremely controversial idea (and later, a practice) entangled with ideologies and tragedies in history.4	Still, as Provine says, if some	of	Galton's	derivations	and	positions	were	questionable	on	empirical	as well as on philosophical grounds, "he nevertheless opened the door to a 4	Connection:	see	Chapter	4	for	some	more	hints	on	eugenics. 6 statistical	analysis	of	correlations	of	characters,	an	analysis	which	was	to	have immense	influence	upon	evolutionary	thought"	(Provine	1971:	22-23). Galton's problem was to model the transgenerational dynamics of traits such	as	size	in	sweet	peas,	and	stature,	eye	color,	temper,	artistic	faculty,	and disease in humans. These	were 'quantitative', as opposed to discrete, traits. Stature is inheritable – tall parents generally have tall children – but is not copied identically from	one	parent, nor is it simply the parental	mean.	How are parents' and offspring's stature correlated? Galton measured that the stature of the adult offspring is, on the whole, "more mediocre" than the stature of their parents, that is to say, nearer to the general population's median (Galton 1869: 95; cit. in Provine 1971: 20; see Figure 11.2). This phenomenon,	valid for	all	continuous	characters	studied	by	Galton, is	named regression,	and	implies,	for	example,	that	offspring	of	extreme	individuals	are quickly brought back towards the median of the population, more so the larger the parental deviance. For Galton this implied that natural selection acting	on	variation	can	"be	of	no	permanent	value	for	evolution,	because	there is	a	constant	tendency	in	the	offspring	to	'regress'	towards	the	parental	type" (Galton 1869: 34; cit. in Provine 1971: 23). No cumulative change through selection was possible for Galton, who thought that new characters in evolution	only	emerge	as	as	new	complexes	in	equilibrium	–	called 'sports'	– not	subject	to	regression.	"Types	of	intelligence",	for	example,	were	explained in	this	way	(Galton	1869). Figure	11.2.	HERE. 11.2.2	Statistics,	Biometrics,	and	Mendelism Provine's	famous	reconstruction	proceeds,	after	Darwin	and	Galton,	with	the rediscovery of Mendel's work in 1900, and with the consequent, all-British intellectual	battle	between	so-called	Mendelians	and	Biometricians, featuring scientists such as Karl Pearson, W.F.R. Weldon and William Bateson. The parties were linked by "the study of variation as the key to unresolved problems	in	evolution"	(Provine	1971:	38).	From	several	aspects	of	the	story, one is tempted to frame the conflict as one between scientists who knew mathematics	and	others	who	didn't	know	or	value	it.	Whether	this	is	the	case or	not,	views	and	methods	provided	by	biometricians	were	to	survive,	suitably incorporated	into	the	later	mathematized	theory	of	evolutionary	genetics. Weldon,	a	prominent	biometrician,	wrote	this	telling	aphorism:	"It	cannot	be too strongly urged that the problem of animal evolution is essentially a statistical	problem" (Weldon	1893:	329; cit. in	Provine	1971:	31).	And later, he	wrote that "the questions raised by the	Darwinian	hypothesis are purely statistical, and the statistical method is the only one at present obvious by which	that	hypothesis	can	be	experimentally	checked"	(Weldon	1895:	380-1, cit. in Provine 1971: 49). Weldon, working directly with Galton, developed Galton's	methods to	address the	amount	of interrelation	between	characters within an individual, thus introducing the correlation coefficient, a fundamental feature of evolution. Weldon also had Pearson working on a problem of characters distribution in the shore crab. In such animal, all characters	had	normal	distributions	except	one: the relative frontal	breadth. With the	method	of	moments for fitting a theorerical curve to observational data, Pearson uncovered dimorphism of frontal breadth, signaling the presence of two distinct races in the crab. The statistical methods used by Pearson	and	Weldon,	although	developed	from	Galton's,	countered	his	view	of 7 evolution:	they	were	particularly	suited	for	the	study	of	continuous	variation and its evolution, whereas Galton had emphasized sports. According to Provine, for	Pearson	and	Weldon	Galton	had	"simply	misinterpreted	his	own valid methods" (Provine 1971: 34). This story illustrates several epistemological peculiarities of mathematized science. For example, sometimes a law can get to be interpreted in a radically new way, while remaining	mathematically the same. This happened in 1898, when Pearson corrected and revised Galton's "law of ancestral heredity" (Galton 1897) quantifying the contribution of each past generation to the heritage of the current	one.	Pearson	made the law	consistent	with	Darwinian	selection,	and was	even	praised	by	Galton	for	doing	that.	These	statisticians	and	statisticallyinclined biologists like Weldon and Pearson were "Darwinists" as well as continuists, for their	belief in the	effectiveness	of	natural selection.	Opposite positions	were	held	and	strongly	defended	by	the	Mendelian,	William	Bateson, who	was	convinced	of	Galton's	dichotomous	view	of	variation,	combined	with the	discrete	nature	of	the	characters	studied	by	Mendel:	although	continuous variation	was	actually	there,	the	major	source	of	novelties	in	evolution	had	to be something else. For Bateson, the source of innovation	was hybridization between distinct variants. Bateson provided biological arguments, and it is true that the issues brought out by this controversy have persisted under different forms and still are important in evolutionary biology. However, as Provine notices, "Bateson never became competent in	mathematics – a sore point in his later controversy with the biometricians" (Provine 1971: 36). Mendelians	worked	under	Mendel's idea that	characters	depend	on	"factors" that are somehow transmitted from parents to offspring. A pea plant has white flowers because it carries a certain two-factors combination.	Wilhelm Johanssen	coined	the	distinction	between	phenotype	(the	type	of	flowers,	e.g., "white") and genotype (the particular combination of factors). Factors from the parents will "segregate" in the offspring and their recombinations will result in the observed variability among siblings. We see that by the time Mendel's	work	on	heredity	was	rediscovered	by	Hugo	De	Vries,	Carl	Correns and Erich von Tschermak, "the situation	was already tense" (Provine 1971: 55).	The	theoretical	dispute	on	whether	evolution	is	continuous	and	driven	by natural selection, or discontinuous and realized through sports or macromutations, surrounded the subsequent period of intense experimental researches	on	heredity. As	Provine	says,	"by	1918,	primarily	as	a	result	of	the	analysis	of	successful selection	experiments,	many	geneticists	had	realized	that	Mendelian	heredity and	Darwinian	selection	were	complementary"	(Provine	1971:	130)	and	that Mendelian	characters,	mostly	very	small	and	preserved	throughout	crossings, provided the variability for selection.	While experimental data and findings piled	up,	along	with	interpretations	thereof,	mathematical	methods	were	laid down into the toolbox that was taking shape and that was to become evolutionary genetics.5	Udny Yule (1903) and Pearson (1904) had outlined mathematically	the	possibility	already	grasped	by	Mendel	that	if	two	or	more pairs	of	factors	are	involved	in	a	phenotypic	character,	the	result	might	be	an apparently continuous array of variations. Natural selection operating through continuous variation might thus provoke sorting of the involved 5	Case studies surely played a role as well, as exemplars, in the toolbox, used by scientists through the epistemological strategy of abduction. But my focus here is on mathematical generalizations	rather	than	on	case	studies. 8 alleles. Assuming random breeding populations, Yule, Godfrey Hardy and German Wilhelm Weinberg had independently studied the equilibrium frequencies,	i.e.	those	frequencies	that,	once	reached,	are	conserved	due	to	the mechanism	of	segregation	alone.	In	the	U.S.,	H.S.	Jennings	and	Raymond	Pearl tried	to	derive	formulas	to	calculate	the	genetic	composition	of	a	population. H.T.J.	Norton	and	Howard	C.	Warren	had	quantified	the	effect	of	selection	on	a gene under different conditions. With the celebrated systematic work by Ronald Alymer Fisher, Sewall Wright, and J.B.S. Haldane, along with the progressive demonstration that Mendelian inheritance could account for observed correlations (parent-offspring, fraternal...) and for observed responses to artificial selection, we see the gradual emergence of a logicalmathematical object – the Mendelian space – to which all quantitative considerations	would	apply.	Evolutionary	genetics	was	born. 11.2.3	The	Mendelian	Combination	Space The	Mendelian	space	is	probably	the	most	important	object	of	evolutionary genetics.6	It is	a	mathematical	object. It is the	space	of	all	possible individual combinations	given	a	number	of	loci	with	their	correspondent	sets	of	alleles.	It is	thus	a	combination	space:	an	ordered	collection	of	individuals,	where	each individual	consists in	a	combination, i.e.	an	array	of	alleles, in the	number	of two	for	each	genetic	locus.	Genetic	loci	are	the	same,	in	number	and	identity, all	over	the	space:	all	individuals	share	the	very	same	loci,	but	each	individual is	a	unique	combination	of coupled	alleles	occupying	each locus.	Conversely, for every genetic locus a number of different alleles are available, and any combination of them is allowed in the space, including the homozigous combination	(i.e.,	two	copies	of	the	same	allele	in	a	locus).	At	any	time,	only	a number of individuals – conventionally designated with the letter N – are considered existent, and	N is exceedingly small compared to the number of possible	combinations	in	the	space.	These	N	individuals	are	one	population	at a	particular	moment,	a	subset	of	the	Mendelian	space.	Individuals	are	sexually reproducing,	and	reproduction	consists in the	production	of	novel individual 6	This	statement	is	a	simplification	and	is	exposed	to	several	criticisms.	For	example,	it	could	be argued that the Mendelian space is central to only one tradition of population genetics. For Lewontin (1980), population genetics was actually split into two fundamental "research traditions",	each	of	which	based	on	a	"theoretical	structure"	or	"scheme"	with	deep	roots	in	the history	we	have	told	so	far.	Lewontin	viewed	the	two	traditions	as	dating	back	to,	respectively, Sewall	Wright	and	Ronald	A.	Fisher.	In	the	latter	–	a	continuation	of	"biometrical	genetics"	(see 11.2.2)	–	everything is	dealt	with in terms	of	phenotype,	while	genes "get lost in the	shuffle", (Lewontin	1980:	63). It	was	Fisher	(1918)	who	showed	compatibility	–	or	even	mathematical entailment	–	between	the	kind	of	continuous	variation	which	is	found	in	phenotypic	traits	and the distribution of discrete Mendelian genetic variation with a number of independent loci (Hartl	&	Clark	2007:	12).	In	this	way,	however,	Fisher	legitimated	the	two	traditions	in	pursuing autonomous research strategies, each through equations that handled the continuity of variation	and	change	in	different	ways.	Today,	one	of	the	most	used	handbooks	of	evolutionary genetics,	by	Hartl	and	Clark	(2007),	avails	Lewontin's idea	of the two	traditions,	and	shows	a flourishing development of the part Lewontin called Mendelian genetics (chps. 1-7). Only chapter 8 deals with "evolutionary quantitative genetics". In this theoretical structure, the variance of a quantitative trait is partitioned into various components representing different causes of variation. Reminiscent of Galton's work, quantitative genetics describes systematic relationships	between	traits,	across	parents	and	offspring	or	also	within	an	organism.	However, the	most	promising	results	come	from	merging	the	two	theoretical	traditions.	For	example,	the response of a trait to selection is necessarily tied to genetic variation affecting the trait (ivi: 397).	Therefore,	while, e.g., heritability can	be interpreted in	purely statistical terms,	with	no genetic contents, "if	we	postulate that there are	Mendelian genes	underlying the	phenotypes, then	the	genetic	underpinning	allows	us	to	do	more"	(ivi:	403). 9 combinations	in	the	space	by	random	recombination	between	parents.	Every new	generation	replaces the	previous	one,	as	generations	are	conventionally non-overlapping in the Mendelian space, but the composition of the new generation	depends	on	the	previous.	How	does	a	population	of	N individuals explore this space over generations? This became the central question – definitely, a non-trivial question – of the new evolutionary genetics body of theory. The establishment of a research community and tradition around the Mendelian	space	allowed,	on	the	one	hand,	for	the	accumulation	of	a	large	and precious knowledge base. Modifications to the space were introduced to reflect both advancements in computational tools and empirical discoveries (e.g., linkage disequilibrium, namely the non-complete independence of segregation	between	physically contiguous loci).	On the	other	hand,	one can safely	say	that	the	Mendelian	space	was	nwvwr	known	in	full,	as	all	methods are partial and require several layers of theorization and statistical summarization. As Sewall Wright had noted already in 1932, a Mendelian space	with	a	realistic	number	of	loci	contains	a	number	of	combinations	that	is greater	than	the	number	of	protons in	the	universe.	How	can	we	know	what happens	in	such	an	astronomically	huge	space? For comparison, take an extremely simple abstract system described by Cavalli-Sforza	and	Feldman	in	a	book	we	will	encounter	again	(1981:	78-84). The	system	is	composed	by	some	agents,	each	of	which	can	be	in	one	of	only two	states	H or	h.	The	agents	mate in couples.	Each	couple	necessarily finds itself	in	either	one	of	four	states:	HH,	hh,	Hh,	or	hH.	The	couples	give	rise	to	the new generation of agents. How does this system behave over few or many generations? Don't try to answer with your intuition: even with only two agent states,	H and	h, the system's dynamical behavior can vary very	much according	to	different	assumptions.	The	graph	in	Figure	11.3	shows	how	fast or slowly H may disappear (leaving room to h), or otherwise conquer the whole population. We see that each trait has a trajectory in evolutionary genetics,	which is the temporal dynamics of its frequency in the	population, possibily in relation with the trajectories of other traits, and certainly in dependence	from	many	features	of	the	population.	The	graph	gives	a	sense	of how	many	things	you	should	know	(or	assume)	about	the	system	to	be	able	to predict	its	dynamics:	the	initial	frequency	of	H,	how	often	the	alternative	types of	couples	are	formed,	as	well	as	each	kind	of	couple's	probability	to	have	H	or h offsprings. See the	Figure's caption for some	details.	Deposited	knowledge about this abstract system will thus include at least: (1) a whole list of parameters	that	are,	or	can	be,	relevant	to	the	dynamics	of	systems	of	this	kind (for example, p0, p1, p2, p3, i.e. the respective probabilities of the different couple types to be formed at each generation;	b0,	b1,	b2,	b3, explained in the Figure caption); (2) ideas on the effects of different configurations of the parameters onto the system dynamics, with relative weights for their importance (e.g., population size has great effect); and (3) mathematical methods,	some	numerical	and	some	probabilistic,	to	statistically	analyze	what goes	on	in	the	system. Figure	11.3.	HERE. We	are	starting	to	get	a	sense	of	how	much	knowledge	is	promoted	and,	at the	same	time,	required	by	a	single	simple	model.	Let's	add	that	evolutionary genetics	has to	derive, from	studies	of these	simple-but-complex	models, the dynamics across the hundreds or thousands of loci in a realistic population 10 exploring	the	Mendelian	space.	In	light	of	this	complexity,	the	body	of	theory accumulated around the Mendelian space acquires a huge importance: the body of theory is a fragile and complex ensemble, full of assumptions and theorems, but it is the unescapable way of accessing what happens in determinate	kinds	of	systems. History teaches that one good reason to be conservative and to go with familiar	mathematical systems is that knowing the behavior of even simple mathematical	systems,	despite	the	fact	that	they	are	built	by	us,	is	so	laborious that	it	requires	the	establishment	of	a	community	of	people	that	improve	upon each	others'	work	for	years	and	decades,	developing	a	knowledge	tradition. 11.3 The	Application	of	Evolutionary	Genetics	to	Culture Evolutionary genetics is a mathematical body of theory with a centennial history. In the	1970s, the	body	began to	be	modified to accommodate other transmission	patterns	than	the	Mendelian	one.	The	occasion	was	provided	by lactose	absorption,	which	was	playfully	introduced	earlier	in	this	chapter,	and by	other	empirical	case	studies. 11.3.1	Cavalli-Sforza,	Feldman,	and	Gene-Culture	Coevolutionary Theory Luca	Cavalli-Sforza	and	Marcus	Feldman	kicked	off	a	collaborative	stream	of work	in	1973,	taking	cue	from	a	debate	over	the	genetic	basis	of	Intelligence Quotient (IQ). They built mathematical models of a new kind, where "the phenotype	of	a	child is	determined	by	the	phenotypes	of	his	parents	and the child's	genotype"	(ivi:	619).	Studying	the	mathematical	systems	in	which	these two	transmission	mechanisms	work	together	–	ranging	from	one	extreme,	i.e., pure cultural transmission, to the other extreme, i.e., pure biological transmission – the authors performed one of the	most typical services of a mathematical	body	of theory: they	had	spelled	out	all the	relevant	statistical measures	that	necessarily	influence	the	outcome,	presenting	them	as	guides	to empirical investigations of problems such as the IQ heritability. They concluded	that	"formalization	of	the	contribution	of	cultural	transmission	to	a trait	is	possible"	(Cavalli-Sforza	&	Feldman	1973:	636,	emphasis	added).	A	dry and	useful review	of	what happened thereafter is provided	by Feldman and Laland (1996). A new branch of evolutionary genetics was born that, "in addition to modeling the differential transmission of genes from one generation	to	the	next,	incorporates	cultural	traits	in	the	analysis"	(Feldman	& Laland 1996: 453), where cultural traits are those traits for which, in the mathematical	model,	a	different	transmission	mechanism	with	a	whole	set	of parameters	is	set	up.	Cultural	alleles	are	like	H	and	h in	the	model	presented above (Figure 11.3): they do not combine in pairs because there is no genotype-phenotype distinction, and they get transmitted accordingly.	With respect to purely Mendelian systems, systems with the addition of one "culturally transmitted" trait are more complicated. The possible combinations	(sometimes	called	"phenogenotypes")	are	at	least	doubled,	and the	transmission	mechanisms intersect.	Therefore,	very	different trajectories and	stable	equilibria	are	possible for the system.	To	predict the	outcomes, a remarkable	extension	of	the	evolutionary	genetics	body	of	theory	is	required. 11 I like to	describe this	expansion	as	an	application	of the	body	of theory to	a new	domain.7 In the "body of theory" perspective presented here, cultural traits were added into the known mathematical systems because this was elicited by some	situations	the	body	of	theory	ran	into,	and	was	not	prepared	to.	Lactose absorption	was	studied	thanks	to	the	addition	of	cultural	traits	in	the	models (Aoki	1983;	Feldman	&	Cavalli-Sforza	1989).	The	formalization	assumed	three possible genotypes for the genetic trait (AA, As, and aa) and two possible states for the	cultural trait:	milk	user	and	non-user.	Despite the fact that the genetic	bases	of	lactose	absorption	were	even	less	clear	than	today	(Gerbault et	al.	2011),	the	studies	were	able	to	explore	–	variable	by	variable,	parameter by	parameter	–	the	conditions	under	which	the	'absorption	allele'	does	spread or does not. From the lactose absorption frequencies actually observed in populations across the world, the models were built to work backwards, evaluating the conditions for those frequencies to come about. Another interesting	domain	of	analysis	was	the	spread	of	agriculture	(Aoki	1987;	Aoki et al. 1996), where the researchers set up mathematical models to aid determination of the demographic composition of the "expanding wave" of farmers	in	the	Neolithic	(around	12,000	years	ago):	how	many	of	them	were converted	hunter-gatherers,	and	how	many	were	descendants	of farmers?	In other	words:	was	the	spread	of	agriculture	more	a	demographic	expansion	of farmers	or	more	a conversion	of	hunter-gatherers to farming?	Several other case studies are reviewed in Feldman and Laland (1996), and more were carried	out	later.	As	a	consequence	of	all	this	work,	the	evolutionary	genetics body	of	theory	became	adjusted	to	a	whole	set	of	new	systems. A	key	moment	in	the	development	of	the	"gene-culture"	branch	of	the	body of theory	was	Cavalli-Sforza	and	Feldman's	1981	book,	Cultural	transmission and	evolution:	a	quantitative	approach.8	The subject	of the	book is said to	be "the	dynamics	of	the	changes	within	a	population	of	the	relative	frequencies	in the	forms	of	a	cultural	trait"	(ivi:	5).	After	a	long	and	thoughtful	Introduction that justifies the approach, the book presents an impressive and perhaps unsurpassed body of	mathematical	methods and results. The authors adapt techniques from all over population genetics and combine them to study problems in the framework of multiple transmission mechanisms. For example,	they	gather	methods	for	both	discrete	and	continuous	characters.	As Panebianco	(this	volume)	demonstrates,	Cavalli-Sforza	and	Feldman's	adapted framework would soon be noticed by modelers in economics, who would contribute	and	expand it	with their	mathematical skills.	Within	evolutionary genetics, the introduction of cultural transmission allowed, for example, for long-awaited new models of group selection for the origin of human cooperation. Explaining unselfish and altruistic behaviors	without infringing natural selection once seemed to require group selection, i.e., selection of 7	Many	philosophers	of	science	have	reflected	on	this	problem.	I	only	cite	one	stimulating	work by Ankeny and Leonelli (2011), who talk about the changing "representational scope" of a model.	The	representational	scope is	distinct from	the	"representational target", i.e. the initial domain that inspired the construction of the model. The scope can stretch in unpredictable ways	as	science	proceeds. If	we	take	the	Mendelian	space	as	a	model in	the	sense	of	a	"stable target	of	explanation"	(Keller	2002:	115),	then	culture	will	constitute	an	extension	of	its	original representational	scope. 8	Connection:	Several	Chapters	and	Sections	of	this	book	rely	on	the	way	of	thinking	developed from Cavalli-Sforza and Feldman's formal approach, often mediated by some informal and inspirational	books	written	by	Cavalli-Sforza.	See	7.2,	7.3;	12.4;	13.3,	13.5;	15.1;	16.1,	16.2,	16.3; 18.1,	18.3. 12 traits because they are good for the group. But models based on genetic inheritance	go	against the	efficacy	of	group	selection.	Robert	Boyd	and	Peter Richerson (1982; 1985; 1989) became famous for building models with genetic and "conformist" cultural transmission, in which group selection became strong enough to explain the evolution of unselfish behaviors. By doing so, they also indicated a way for evolutionary theory to consider behavior	as	culturally	transmitted,	as	opposed	to	genetically	determined	as	in sociobiology (Wilson 1975).9	Further mathematical innovations were linked to	the	work	on	cultural	transmission.	An	example	is	niche	construction,	with	its proposal	of	modifying	the	models	of	evolutionary	genetics	by	adding	selection feedbacks mediated by environmental impact: frequencies in a locus may influence	the	amount	of	a	resource	in	the	environment,	affecting	back	genetic frequencies in other loci (Odling-Smee et al. 2003). Niche construction selective loops	are	particularly	stimulating	when	they	are	applied	to	cultural traits	that	have	a	deep	impact	on	the	environment,	such	as	farming	(Kendal	et al.	2011). 11.3.2	Tinkering	and	Justification A	half	of	the	process	of	mutual	adjustment	between	a	body	of	theory	and	a new	domain	of	application	is	thus	the	mathematical	tinkering	performed	upon the	body	of	theory	to	make	it	more	suitable	to	the	new	domain.	This	tinkering will likely go through a phase of experimentation and low control, because new	systems	are	born	in	the	body	and	new	knowledge	needs	to	be	built	upon them.	The	evolutionary	genetics	body	of	theory	is	scientific	wealth	that	can	be invested in studying every situation that happens to turn out suitable to be modeled	as	a	Mendelian	population,	or	as	a	modified	Mendelian	population,	or as a population of continuously varying individuals with an underlying Mendelian	basis.	But the	application	of a	body	of theory to	a	new	domain is not	automatically	appropriate. It	requires	ad	hoc	definition	of	the	domain. In gene-culture	coevolutionary	theory,	as	Feldman	and	Laland	(1995)	point	out, "culture is treated as shared ideational phenomena (ideas, beliefs, values, knowledge) that are learned and socially transmitted between individuals" (Feldman & Laland 1995: 453, emphasis added). Treating "milk use" as a cultural	trait	may	sound	strange,	but	here	comes	the	second	half	of	the	mutual adjustment between body of theory and domain: justification. A strong justification	for	the	use	of	a	body	of	theory	is	the	identification	of	fundamental questions	that	will	thereby	have	more	chances	to	be	answered.	In	the	case	of lactose	persistence, the	body	of theory	may	be	employed to collect	data	and eventually constrain the possible courses of population history, aiding historical reconstruction. Analogously, considering "farming" as a cultural trait	allows for the	body	of theory to	provide	evidence for	either	one	of two possible scenarios of the spread of agriculture (conversion of huntergatherers	vs.	demographic	expansion	of	farmers). Since	Darwin,	a	candidate	trait	in	evolutionary	biology	is	anything	of	which multiple instances	exist	and	get	multiplied,	and	which	is	therefore	able	to	be shared	among	an indefinite	number	of	units. If I	mean to treat "milk	use"	or "farming" as cultural traits, I need to make a case that this is not absurd according	to	what	we	know	about	culture. 9 Boyd and Richerson's gene-culture co-evolutionary theory was hailed as a welcome alternative	to	sociobiology.	For	an	overview	of	criticisms	to	sociobiology,	see	Driscoll	(2013). 13 Sometimes justification can be carried out by collecting existing studies where suitable definitions of the new domain may already be available. As seen	in	some	essays	of	this	volume,	culture	is	a	case	in	point.	Culture	scientists are not completely alien to the idea of "cultural traits".10	Philosophers and 'evolutionary epistemologists', primum Donald Campbell (1960), must be credited for early intuition and conceptual chopping, with their idea that cultural	variants	undergo	processes	of selection	and	drift that	are	analogous to, though	distinct from,	those	that	operate	among	organic forms	and	animal behaviors.	Case	studies	from	social	psychology	or	archaeology	are	frequently brought	to	support	the	existence	of	cultural	traits	and	their	trajectories.11 Besides general justifications, there are contextand scale-sensitive justifications: while treating "milk use" as a transmitted trait might be meaningful and useful at the scale of population history – where it appropriately	summarizes	some	aggregate	behavior	of	the	population	–	"milk use"	might be an inconsistent unit at the scale of everyday life or over few generations. 11.3.3	The	Balance	between	Tinkering	and	Justification	in	CavalliSforza	and	Feldman,	1981 According	to	the	"body	of	theory"	perspective	presented	here,	evolutionary genetics is	an	extremely	precious	deposit	of theoretical	knowledge	revolving around some mathematical systems, such as the Mendelian space. Researchers	working	on	a	body	of theory	are	always,	by	essence,	at	work to modify the body. Often, they are inspired to do so and fueled by empirical findings	and	problems.	Evolutionary	genetics	is	certainly	a	case	in	point,	with its constant dialogue with the variety of living beings, their reproductive systems	and	their	ecological	situations	(Hartl	&	Clark	2007).	At	the	same	time, any	situation	where	populations	of	combinations	of	traits	can	be	identified	is potentially	liable	to	be	treated	by	the	body	of	theory's	mathematical	methods and solutions about relevant parameters and predicted outcomes. History says why, since the 1970s, workers on the body of theory felt the need to incorporate	cultural	elements	into	the	body	of	theory,	yielding	the	creation	of a specific branch named "gene-culture coevolutionary theory". Subsequent developments	exemplify	that	a	body	of	theory,	when	applied	to	a	new	domain, must be fixed and adjusted, more than the other way around. Also, when evolutionary genetics started to deal	with	matters such as dietary habits, it certainly met with domains that had long been conceptualized by other approaches: culture studies, anthropology, philosophy etc. When a body of theory	reaches	a	new	domain,	it	has	to	be	accepted	therein,	and	this	requires justifications. Some justifications may argue for the epistemic value of the body of theory. Other justifications may leverage on the presence of unanswered questions and unresolved competitions among scenarios in the domain:	does	the	frequency	of	the	lactose	absorption	allele	reflect	the	farming history	of	a	population?	And	how?	Was	the	"wave	of	advance"	of	the	spread	of agriculture in the	Neolithic	made	of expanding farmer families or converted hunter-gatherer	groups?	The	tools	from	the	body	of	theory	can	be	proposed	as solutions to long-standing debates and to open problems that cannot be 10	Connection:	See	Chapters	2,	3	and	13,	and	several	other	Chapters, to look	for familiarity	of social	sciences	with	notions	that	resemble	'cultural	traits'. 11	Connection: In this book, see Chapter 7 for psychology, and Chapters 15, 16 and 17 for archaeology. 14 solved by other means. Another way to justification is the adoption of a working	definition	of	the	domain	that	makes	it	suitable	for	the	application	of the	body	of	theory.	If	the	working	definition	is	a	pre-existing	definition,	all	the better. In any case, general definitions of the domain are hardly sufficient justifications:	more	specific	ones	are	often	required	relative	to	the	particular questions and scenarios at hand. For example, concepts of various social sciences that resemble "cultural traits" can contribute to justify the employment of the body of theory, but why can "milk consumption" be considered a cultural traits at a particular scale? The possibility of talking about	cultural	phenomena	as	"traits"	is,	in	fact,	contextand	scale-sensitive. A masterful model of theoretical justification is the long Introduction of Cavalli-Sforza and Feldman's landmark book on evolution and cultural transmission (1981: 3-76), in which the authors work hard to define the domain and circumscribe it so that the body of theory will be acceptably applicable.	Cavalli-Sforza	and	Feldman	"accept	as	the	cultural	unit,	or	trait,	the result	of	any	cultural	action	(by	transmission	from	other	individuals)	that	can be	clearly	observed	or	measured	on	a	discontinuous	or	continuous	scale"	(p.	73, emphasis in original). Traits can be aspects of "thought, speech, action [meaning	behavior],	and	artifacts"	(ivi:	10),	and	their	general	definition	is	the following: We	will use the term "cultural" to apply to traits that are learned	by any	process	of nongenetic	transmission,	whether	by	imprinting,	conditioning,	observation,	imitation, or	as	a	result	of	direct	teaching	(Cavalli-Sforza	and	Feldman	1981:	7). Among	learned	traits,	the	applicability	of	the	models	is	further	restricted	to those	which	are	irreversibly	learned	by	individuals: Cultural transmission – the acquisition by one individual of a trait from another individual	–	may	involve	long	and	complex	learning	processes.	These	processes	may	in practice be wholly or partially reversible. Our models deal with traits that do not change	after	the	process	of	learning	is	complete.	This	can	be	accomplished	by	studying the	population	at	the	same	age	in	every	generation	–	an	age	at	which	all	individuals	are mature	for	the	trait	under	study	(Cavalli-Sforza	and	Feldman	1981:	62). The fidelity of transmission is also important, further restricting the candidate traits to customs. A custom is "any behavioral trait that is transmitted	with little individual	variation [...]	custom is	always the	absence of	novelty,	which	is	avoided	because	it	is	disruptive	and	costly"	(ivi:	64-65). Cavalli-Sforza and Feldman also proceed by showing general, perhaps unfamiliar	properties	of	the	domain	that	justify	employing	the	body	of	theory. An	example	is	the	issue	of	randomness	of	innovation.	Since	genetic	innovation is the periodic origin of new alleles by random mutation, defending the plausibility	of	the	application	of	genetic	models	to	culture	implies	a	defense	of the randomness of cultural innovation, which is counterintuitive: cultural innovation	is	evidently	intentional	to	an	important	extent.	Here	Cavalli-Sforza and Feldman appeal not only to the frequency of literally chance cultural innovations,	but	also	to	the	limited	foresight	of	intentional	ones: the	chance	that	the	innovations	will	prove	truly	adaptive	in	the	long	run	is	not	100% [...]. Because of this, and because some cultural mutation is simply copy error, a significant	proportion	of	new	cultural	mutations	might	be truly random	without	any resemblance	of	adaptiveness	(ivi:	66). 15 Another issue in the application	of the	body	of theory to culture is that of conceptualizing free choice in the domain. Individuals cannot choose what genes	they	inherit,	but	they	can	make	cultural	choices.	Here	Cavalli-Sforza	and Feldman introduce the fundamental concept of cultural fitness, already recognized	by	Campbell. In the	body	of theory,	a cultural trait	has	a cultural fitness that	measures its	probability	of	acceptance, that is the	probability for each individual to choose to get the trait. Only with 100% probability of acceptance it might be said that individuals have no choice. With lower probability, each individual does choose, while the overall system remains predictable	in	a	statistical	sense.12	What	is	lacking,	as	admitted	by	the	authors, is	variation	of	acceptance	probability	across	individuals:	"as	a	first	conceptual approximation	we prefer to think of cases in	which it is relatively constant across individuals" (p. 15). Individual differences of yet another kind – learning abilities – were deferred to another whole book which apparently was	never	written: Another volume will take account of individual, inherited differences in learning abilities.	The introduction	of individual	differences, for instance in capacity to learn, requires a quantification of some classical genetic concepts, such as "norm of reaction", and allows us to make predictions about that elusive entity, genotypeenvironment	covariance	(ivi:	vii). Many	other	considerations	are	made	in	the	Introduction	and	in	the	Epilogue of Cavalli-Sforza and Feldman's book. We may say that other justificatory works that would follow in the subsequent years would retrace its encompassing blueprint.13	On the other hand, justification	was not at all the main	part	of	Cavalli-Sforza	and	Feldman's	book.	The	authors,	firmly	centered into the	body	of theory,	pointed	out the	modifications that	were	needed	and the	constraints	that	were	in	place	therein.	A	fundamental	idea	to	translate	into the body of theory was that cultural traits undergo not only "vertical" transmission, i.e., parents to offspring,	which is typical of genetic traits, but also "horizontal" and "oblique" transmission, involving peers and all people beyond	genealogical	bonds.	Further	distinctions	produced	11	distinct	modes of transmission (Cavalli-Sforza and Feldman 1981: 55-60). The internal structure	of	"nongenetic" transmission	mechanisms	was	crucial,	not	as	much to	justify	the	use	of	mathematical	models	as	to	figure	out	how	to	build	them. There were 260+ pages of mathematical studies (ivi: 77-339), divided into "Vertical Transmission", "Oblique and Horizontal Transmission", "Multiple State Traits" (i.e., traits with more than two alternatives available in the population),	and	"Cultural	Transmission	for	a	Continuous	Trait",	with	results that	showed	how,	for	example,	sometimes	"cultural	transmission	can	simulate genetic transmission,	making it	difficult to	separate	them	in	careful	analysis" (ivi:	9),	or	that	vertical	transmission	is	"more	important	than	anticipated"	(ivi: 76). Cavalli-Sforza and Feldman remarked the importance of matching the right level of analysis to the right mathematical method (ivi: 71), while maintaining tractability (ivi: 73). Different parts of the body of theory	were dragged into play – for example, discrete traits vs. continuous traits – according	to	appropriateness	and	tractability. 12	Connection:	This concept	of choice is fundamental to	models in	economics, the "science	of choice",	as	explained	in	Chapter	12. 13	In	fact,	many	major	works	mostly	retrace	Cavalli-Sforza	and	Feldman's	(1981)	Introduction, and	pile	up	more	and	more	examples	from	the	social	sciences	on	the	same	blueprint. 16 Cavalli-Sforza and Feldman are the best example of the complex, bidirectional epistemological dynamics of dialogue between tradition and innovation, linking	older	and	more	recent literature	in	evolutionary	genetics. This	dynamics,	according	to	the	"body	of	theory"	perspective	presented	here, is the right context in which to understand the mathematical studies of evolutionary	genetics	applied	to	culture. 11.4 Conclusion:	Minding	the	Balance	and	Recalling	the Body	of	Theory The	mutual	adjustment	process	between	a	body	of	theory	and	a	new	domain is	twofold:	there	is	mathematical	tinkering	upon	the	body	of	theory	to	make	it more	suitable	to	the	new	domain,	and	there	is	justification,	explaining	why	and to	what	extent	the	body	of	theory	can	be	useful in	the	new	domain.	We	have seen	the	balance	between	mathematical	innovation	and	verbal	justification	in the seminal book by Cavalli-Sforza and Feldman (1981). There, justification consists in refining an acceptable definition of culture so that the body of theory is applicable. At the same time, justification highlights those features that must be taken into account for the production of new mathematical studies,	and	draws	the	boundaries	of	the	sub-domain	where	the	mathematics can	be	of	any	help. The relative proportion between mathematical tinkering and justification may	be	a	criterion	to	look	at	current	"cultural	evolution"	works,	too.14	In	some works,	we	may	find	significant	imbalance	in	favor	of	justification.	These	works miss the challenge of modifying the body of theory to make it suitable to answer	an	open,	pressing	research	question,	and	they	just	reiterate	arguments for using evolutionary genetics to analyze culture. I am not claiming that mathematical innovation is necessary per se in each and every "cultural evolution" work, but, with	mathematical tinkering, there goes awareness of the	existence	and	nature	of the	mathematical	body	of theory, its	growth	and dialogue	with	domains.	In	fact,	where	justification	is	abundant	and	elaboration lacks, justification has an utterly new rhethoric role with respect to the function it played in Cavalli-Sforza and Feldman: it delivers a very different idea of theory, namely the idea of a theory as the faithful and exhaustive description of a domain. In fact, tools of biology are advocated as the right ones,	and	culture	is	presented	as	having	"key	Darwinian	properties";	a	certain definition of culture is justified, reiterated and enriched by	more and	more examples,	against	other	ways	of	understanding	culture	that	are	presented	as inexact and less productive (e.g., Mesoudi et al. 2006). There is a radical difference	between	justifying	the	applicability	of	a	body	of	theory	as	a	possible tool to	answer	open	questions, and	defending	some "new theory	of culture". The "body of theory" perspective can be a	way to describe the relationship between evolutionary genetics and culture that more properly understands 14	I	am	referring	here	to	books	and	papers	such	as	Boyd	&	Richerson	(1985),	Richerson	&	Boyd (2005), Mesoudi (2007, 2011), Mesoudi et al. (2004, 2006). A flourishing literature in philosophy of biology builds arguments or "dual inheritance theories" to hit forms of sociobiology	and	evolutionary	psychology that	don't take cultural transmission into sufficient account.	A	careful	analysis	is	well	beyond	the	scope	of	this	chapter.	Here,	in	light	of	the	"body	of theory"	perspective,	I	just	offer	one	possible	criterion	for	analyzing	these	texts:	the	criterion	of the	proportion	between	justification	and	mathematical	innovation. 17 the	epistemological	mechanism	at	work,	and	brings	evolutionary	genetics	into healthier	dialogue	with	other	ways	of	studying	culture. References Ankeny,	R.,	&	Leonelli,	S.	(2011).	What's	so	special	about	model	organisms?	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Sunderland,	Mass.:	Sinauer	Associates. Keller, E.F. (2002). Making sense of life: explaining biological development with models, metaphors, and machines. Cambridge-London: Harvard University Press. 18 Kendal, J.R.,	Tehrani, J.J.,	&	Odling-Smee, J. (Eds.) (2011).	Theme issue 'Human	niche construction'. Philosophical Transactions of the Royal Society of London. Series	B,	Biological	Sciences,	366(1566). Lewontin,	R.C. (1980).	Theoretical	population	genetics in the	evolutionary	synthesis. In E. Mayr, W.B. Provine (Eds.), The Evolutionary Synthesis (pp. 58-68). Cambridge	&	London:	Harvard	University	Press. Mayr.	E.	(1980).	Prologue:	some	thoughts	on	the	history	of	the	evolutionary	synthesis. In E. Mayr, W.B. Provine (Eds.), The Evolutionary Synthesis (pp. 1-48). Cambridge	&	London:	Harvard	University	Press. Mesoudi, A. (2007). A Darwinian theory of cultural evolution can promote an evolutionary synthesis for the social sciences. Biological Theory, 2(3), 263275. Mesoudi, A. (2011). 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(2005). Not by genes alone: How culture transformed human	evolution.	Chicago:	University	of	Chicago	Press. Sarkar, S. (2004). Evolutionary theory in the 1920s: the nature of the "synthesis." Philosophy	of	Science,	71(5),	1215-1226. Weldon, W.F.R. (1893). On certain correlated variations in Carcinus moenas. Proceedings	of	the	Royal	Society,	54,	318-329. Weldon, W.F.R. (1895). Attempt to measure the death-rate due to the selective destruction of Carcinus moenas with respect to a particular dimension. Proceedings	of	the	Royal	Society,	58,	360-379. Wilson,	E.O.	(1975).	Sociobiology,	Cambridge,	MA:	Belknap/Harvard	University	Press. Yule,	G.U.	(1903).	Professor	Johannsen's	experiments	in	heredity.	New	Phytologist,	2, 235-242. 19 Concluding	Remarks Removing	Barriers	in	Scientific	Research: Concepts,	Synthesis	and	Catalysis Emanuele	Serrelli So what? At the end of a book on 'cultural traits' which includes 20 contributions	from	the	most	diverse	disciplines,	from	cultural	anthropology	to archaeology, from psychology to history of science, from economics to musicology, from	philosophy to linguistics and evolutionary genetics...	what have	we	learned	about	cultural	traits?	Well,	much.	But	perhaps	many	readers will feel unsatisfied by an incomplete vast interdisciplinary venture	without some sort of synthetic conceptual framework, some new understanding of cultural	traits,	or	at	least	some	good	taxonomy	of	what	is	meant	by	a	cultural trait,	or	minimally	a	list	of	conceptual	problems.	I	will	devote	a	few	lines	here to an analysis of 'cultural traits' as a putative scientific category emerging from our collection. But I need to make clear in advance that a precise conceptual	synthesis	is	not	here.	We	have	been	as	thoughtful	as	we	could,	and we have tried to follow all the connections, but there is no such thing as a 'magic paragraph' able to capture such interconnectedness. Indeed, this concluding	note	is	actually	a	critical	comment	against	what	I	and	Panebianco see	as	a	hurry	to	close	the	discourse	in	a	given	logical	scheme. In	Chapter	1,	we	summarized	in	our	words	the	contents	of	all	Chapters,	with particular focus on what the authors mean by cultural trait, and on some conceptual	problems	that,	however,	are	much	better	explained	in	the	Chapters themselves. We provided three ways of navigating the book: spontaneous interdisciplinary links, guided Connections that help the reader 'jump' between	different points to	deepen concepts and issues, and the criteria	we embedded	in	the	Chapters	sequence.	Let	us	review	these	latter	criteria.	A	first 'block' of Chapters presents two takes in cultural anthropology on cultural traits:	the	first	inviting	generalizing	attempts,	the	second	showing	the	reasons for locality. Then two essays show how societies and nations educate their people, explicitly and implicitly, to their own typical cultural traits and by means	of cultural traits (typical institutions and objects). Two contributions address the psychology of the trait, emphasizing its nature of 'evocator' of something deeper or larger. Two Chapters show the importance of geographical meeting points and melting pots, and a third Chapter problematizes	the	ideas	of	trait,	of	culture,	of	geographical	map,	and	of	maps and diagrams in general, exposing our obstinate hunt for 'origins'. Two authors	demonstrate	the	importance	of	mathematical	models,	the	plurality	of modeling strategies, and the influence of modeling strategies on ways of thinking, and two more essays show how these ways of thinking can be applied to understand concrete situations of cultural contact, between countries and between generations. Three contributions, revolving around archaeology and material culture, show how lineages of artifacts can be 20 traced,	and	what	mechanisms	govern	their	change.	Another	essay	introduces the important repository and historical record of cultural traits: language. Three more Chapters formulate theories of 'why' traits such as visual art, aesthetic preferences, and the passion for literature exist in the first place, being, in the second place, domains of circulation of cultural traits such as styles,	preferences,	genres. 'Synthesis', 'integration', 'unification' and similar phenomena have long gained important place in philosophy of science. Philosophers actively seek examples of interdisciplinary and inter-field 'coordination', and ask: how	do different areas of science combine	or integrate their divergent	methods and conceptual resources?	What are the limits and facilitating conditions?	There was	a	time	when	philosophers	reasoned	in	terms	of	"theory	reduction",	using conceptual analysis to identify the most fundamental theory other theories would get reduced to. Now philosophers talk about theories pluralism and mechanisms heterogeneity (Mitchell 2003, Craver 2009). Yet, they typically continue to see conceptual analysis as the	main tool and clarification as the ultimate	goal.	Their	urge	for	conceptual	grip	can,	I	claim,	divert	their	attention away	from	their	own	goal,	and	perhaps	from	the	meaning	of	the	experiences they	are	studying. In 2010, philosophers Brigandt and Love (2010) urged philosophers to study	"integration	in	action"	in	order	to	understand	the	nature	and	dynamics of interdisciplinarity.	To	set	a	good	example, they	organized	a	meeting	of	20 experts in different fields of biology to work on the non-trivial concept of 'evolutionary novelty', with the perspective of a time-extended research on the	same	topic.	In	biology,	'evolutionary	novelty'	is	an	elusive	concept	just	like 'cultural	trait':	it	is	a	'false	friend',	full	of	conceptual	pitfalls.	It	means	different things to different specialists (geneticists, morphologists, phylogeneticists etc.),	and	nothing	to	many	others.	How	do	you	delimit	a	novelty?	Is	a	new	gene a novelty, even though it	makes its carrier organism only slightly different? Sometimes	an	unchanged	gene	in	a	new	context	can	be	much	more	innovative than	a	new	gene	in	a	familiar	context.	Are	birds'	wings	innovative	even	though they	are	just	modified	limbs?	Probably	they	are,	but	exactly	at	what	degree	of modification do they become a novelty? 'Evolutionary novelty' can be a framework	for	interesting	questions,	or	a	source	of	confusions	and	dead	ends. During the 2010 meeting, definitions were elicited, and participants were stumbling and striving for "single, unambiguous" definitions, under the assumption	that	conceptual	clarification	would	have	brought	about	some	kind of "coordination" among them and their fields. Brigandt and Love's brief account illustrates their firm focus on concepts: philosophers hunt for conceptual clarification as a priority, as a primary and	ultimate goal, loaded with bright scientific expectations. The 'evolutionary novelty' concept was considered interesting just in it being the focal point of opposite conceptual tensions,	that	were	expected	to	be	solved	in	some	way.	The	concept	was	thus both the assembling criterion of a 20-people research group and the transactional object in the group.	To the	philosopher's eye, concepts are the domain-delimiting factor, and, within the domain, they are the positive or negative measure of coordination. But this unconditional confidence in concepts is insensitive to evidence that concepts are often followers in scientific	community	processes.	For	example, they	are	subject to fashion	and to economic pressures: Brigandt and Love themselves cite workshop participants	reporting	that	they	used	to	mention	'evolutionary	innovation'	just as "a rhethorical device in the process of grant writing", despite their 21 conviction of its biological unimportance. Scientists use concepts they don't find	useful	in	order	to	set	up	a	research	project	and	get	some	funds,	and	later they are forced to maintain that same troublesome word, incoherent with their	research	assumptions	–	a "semantic	curse".	Concepts	are	also	cruxes	of identity fights, so that putting conceptual convergence as a goal may be exactly	the	move	that	shuts	up	the	dialogue	(see	the	Mesoudi	et	al.	vs.	Ingold case	in	Chapter	1).	In	sum,	my	impression	is	that	concepts	are	often	a	way	to close	rather than	a	way	to	open	a	discourse.	The	philosophers' insistence	on concepts can eventually do harm or be just tangential to the directions of science. In	search	for insights	about 'synthesis' in	science, I	recently	visited	the	U.S. National Evolutionary Synthesis Center (NESCent) in Durham, North Carolina.15	I	will	say	something	on	the	more	general ideas,	and	then	describe NESCent	catalysis	meetings.	In	fact,	if	our	book	cannot	absolutely	be	compared to a working group or a synthesis project, it may bear some similarities to NESCent	catalysis	meetings. At	NESCent,	synthesis is	not	at	all	considered	a	conceptual	phenomenon in the first place. Synthesis is, first of all, the combination	of	data collected by many people working in different ways, fields, approaches, regions of the world, in order to answer new, bigger, and pressing research questions. Genomes can be an easy example. Whole genome sequences that are periodically	announced	in	the	news	–	the	human	genome,	the	platyus	genome, the carrot genome – are assembled	by dozens of labs in the	world. Genome data	can	then	be	combined,	for	example,	with	medical	data	which	are	collected in	a	completely	different	–	but	systematic	–	way.	Further	on,	these	syntheses can	be	compared	cross-species,	and	so	on.	As	for	cultural	data,	an	example	can be	seen	in	the	combination	of	linguistic,	cultural,	geographic	world	databases like those	we cited in Chapter 1. Now, for synthesis to happen, researchers have	to	be	willing	to	share	and	to	combine	their	data,	seeing	the	advantage	of doing that, and they need to know how to gather, store, and share data for them to be useful to others. In other words, they need to have a certain 'culture	of	data' (notice the importance	of	databases	and informatics).	To	do all	this,	researchers	may	need	common	concepts,	but	concepts	are	secondary and instrumental to answer their communal, new, pressing research questions. In 2009, NESCent published a paper formalizing the 'synthesis' idea (Sidlauskas et al. 2009). Question-driven data synthesis is the fundamental idea,	but	there	are	actually	four	modes	of	synthesis,	depending	on	the	kind	of elements getting combined. Multiple modes can coexist in any particular synthetic	study.	As the	genomes	example	shows,	synthesis	does	not	coincide with interdisciplinarity:16	intra-disciplinary	syntheses,	which	are	surprisingly rare	and	most	needed,	combine	elements	from	multiple	studies.	There	are	also barriers	to	synthesis,	and	ways	to	overcome	them. The first mode of synthesis, Data aggregation, "reinterprets raw data underlying prior investigations to answer questions at new and typically larger	scales"	(Sidlauskas	et	al.	2009:	873).	While	this	may	look	simple,	in	fact 15 The National Evolutionary Synthesis Center (NESCent) is an American NSF-funded collaborative	research	center	operated	by	Duke	University,	the	University	of	North	Carolina	at Chapel	Hill,	and	North	Carolina	State	University	(see	Cunningham	2005,	Sidlauskas	et	al.	2009, Rodrigo	et	al.	2013). 16	Interdisciplinary	syntheses	exist,	and	they	may	eventually	"erode	disciplinary	boundaries	or create	new	fields	of	study". 22 "the	vast	majority	of	data supporting	previous studies	are	unavailable,	often because the	data are lost or	preserved in inaccessible forms" (ivi: 876).	The second	mode,	Reuse	of	results,	focuses,	indeed,	on	results	of	other	studies	used as data in a new context. But results, too, are scarcely available in standardized	and	compatible forms.	These two first	modes	of	synthesis	both undergo publication problems, since "many	mainstream journals emphasize primary	data collection over synthesis" (ivi: 875), and	publication of results over publication of datasets (in spite of the fact that datasets demand a remarkable number and degree of technical skills). For Sidlauskas et al., to promote	data	aggregation	and	reuse	of	results solutions are reasonably straightforward. Journal editors should encourage the publication of synthetic datasets and analyses and should make an effort to invite objective	reviews	from	scientists	who	are	also	engaged	in	synthesis. The	third	mode	of	synthesis,	Methodological	integration,	"links	two	or	more methods to create a new analytical pathway" (Sidlauskas et al. 2009: 873). Finally,	Conceptual	synthesis	does	not	resume	elements	from	previous	studies, rather "bridges the theories or paradigms of thoughts that underlie and motivate	prior	studies"	(ivi:	873). Obstacles to synthetic research lie, for Sidlauskas et al., in training, job search	and	tenure	evaluation,	grant	review,	publication	policies,	and	language barriers.	Not	only	should	there	be	a	more	interdisciplinary	early	training,	but it should "become easier for scientists to develop skills in aggregating and reusing information at every career stage" (ivi: 874). Synthesis training	will address	not	only	specific	skills	such	as	software	programming,	but	also	basic skills: "information literacy (the ability to locate relevant information and assemble	a	knowledge	base),	statistical	literacy	(understanding	how	statistical manipulation	affects	data	and	inference),	and	data	literacy	(the	skills	required to	manipulate	and	present	data)"	(Sidlauskas	et	al.	2009:	874). The	current	lack	of	training	reverberates	upwards	to	the	level	of	panels	who evaluate	grant	proposals,	where	a	few	"synthetically	minded	scientists",	if	any, are present. Notice how the "synthetic scientist" is presented as a specific profile, corroborated by published official guidelines 17 that still lack incorporation	into	the	hiring	criteria	of	research	institutions.	But	many	points made by Sidlauskas et al. concern another, very important issue: that synthesis can never ever be an exclusive concern of synthetic scientists. On the contrary, for synthesis to happen, all the scientific community must be involved	in	it.	Journals	and	funding	agencies	have	their	part	to	do,	by	requiring "all	data to	be	shared	at the time	of	publication".	Data	repositories	and	their hosting	organizations should standardize formats,	develop	vocabularies, and establish minimal reusability requirements. Software developers should ensure the longevity of softwares (for example, by using software repositories)	because,	even	when	data	endure,	analyses	cannot	be	repeated	if tools are not stable. In fact, "technology can also hinder synthesis if a proliferation of methods, data standards, languages, and protocols hampers communication and interoperability".	While synthetic scientists, experts and managers	work	at	all	these	levels,	at	the	same	time	"a	culture	of	data	sharing" has	to	prevail,	along	with	a	different	culture	of	"science	as	a	collective,	rather 17	Also in consideration of these documents and official declarations, Sidlauskas et al. are however	optimistic	that	a	new	cultural	shift	is	leading	synthetic	science	to	receive	more	support than	ever	(Sidlauskas	et	al.	2009:	874). 23 than individual enterprise". Only in this way the deposition of data can become	"a	normal	part	of	the	research	flow",	and	all	other	necessary	changes can	happen.	For	synthesis	to	happen,	scientific	communities	must	"embrace	a culture	in	which	sharing	is	normative,	methods	exist	to	be	combined,	and	the potential longevity and utility of data exceeds the life span of the scientists that	create	it"	(Sidlauskas	et	al.	2009:	877). Synthesis – the purposeful and innovative combination of existing data – requires specific 'working groups' and meetings among scientists who concretely develop the common project. But, in the cultural context just described, the	ground	needs	to	be	prepared	for	synthesis. In fact,	sometimes synthesis cannot even begin because suitable data haven't been collected at all, or haven't been treated in systematic	ways conducive to synthesis. This lack	of	preparation	may	affect	particular	questions: ...assembling	existing	data	is	not	enough.	For	many	questions,	it	is	too	early	to	attempt a grand synthesis, and in many cases the data has not been collected in the coordinated manner needed for a grand synthesis. A mechanism is needed for networks of scientists to form among scientists in very different disciplines to coordinate	their	primary	research	efforts	(NESCent	2004:	5). Pre-synthetic activities are those activities that potentially shape the involved people's data collection activities and data sharing practices, by influencing	their	culture	of	data.	Here	come	catalysis	meetings,	which,	as	I	put forth	above,	can	be	seen	as	a	model	for	our	cultural	traits	book. In chemistry, catalysis is the increase in the rate of a chemical reaction of one or more reactants due to the participation of an additional substance called	a catalyst. Importantly, the	presence	of the	catalyst lowers the	energy that is required to reach the transition state of the reaction. A foundational document	of	NESCent	explains	that	catalysis	meetings	"bring	together	diverse groups of scientists on a wide range of subjects, not only to inspire crossdisciplinary	collaboration,	but	to	inspire	the	large	scale	of	scientific	vision	that can	only	come	from	cooperation	and	coordination"	(NESCent	2004:	2).	Several themes are recognizable in this statement. One theme is a culture of data, which is enclosed in the broader theme of the large scale scientific vision. Another	theme	is	the	generation	of	scientific	collaborations.	Catalysis	meetings are expected to trigger "large cooperative ventures necessary to collect primary data in the coordinated fashion necessary to synthesis" (NESCent 2004:	1,	emphasis	added).	Catalysis	meetings	are	also	specifically	organized	to attract	and	engage fields that, for	example,	are	distant from	informatics	tools and related data-collection methods. As Joel Kingsolver, one of the original leaders	of	NESCent,	told	me: we	realized	that	there	are	several	important	fields,	areas,	questions	where	you	are	not quite	at	that	stage	yet.	You	know	there	is	something	interesting	to	synthesize,	but	you are not sure of	what it is, or	what different data or tools are available,	what actual focused	research	questions	can	be	addressed	(Kingsolver	2014). Catalysis	meetings are also	generators	of scientific collaborations. I remark this role	because I think it is fair to	maintain	scientific collaboration	distinct from	the	construction	of	common	data	flows.	The	two	phenomena	don't	seem to be completely overlapping, although tightly related. Indeed, according to my	reconstruction,	catalysis	meetings	at	NESCent	were	never	thought	as just functional	to	motivating	people	to	become	able	to	use	informatic	tools	and	to start	using	them.	Many	other	kinds	of	scientific	collaboration	are	possible. 24 Catalysis	meetings	are	also	seen	as	a	factor	to	change	the	scope	and	vision	of primary research.	The	NESCent foundational	document (2004) remarks that members of a pilot project, "By dramatically increasing the scale of their scientific vision to encompass the historical ecology of an entire biota [...] quickly	realized	that	this	vision	offered	them	a	chance	to	make	rapid	progress on their	own	scientific agendas	as	well as advancing their scientific careers" (NESCent 2004: 2). Along this line, catalysis meetings were aimed at this dramatic increase	of "ambition, scale and	vision	of	primary research" (ivi: 6, emphasis	added),	even	before	or	in	absence	of	direct	collaboration. In	sum,	being	in	a	catalysis	meeting	can	lead	to	synthesis,	but	also	to	other kinds of scientific collaborations, or even "only" to a widening of scientific vision in one's own everyday research. Catalysis meetings allow heterogeneous	disciplinarists	to	learn	each	other's	languages,	to	see	if	they	are talking about the same things, to understand what their data are, and theoretical frameworks	are, and to	ask	whether they	can identify some	clear questions and a research setting. If not, they	will be anyhow	more aware	of each	other,	recognize	the	others	in	their	expertise,	and	be	curious	about	other ways of knowledge. It is clear that synthesis cannot be demanded a priori from a catalysis meeting, least of all can conceptual synthesis. As current Director	Allen	Rodrigo	told	me	very	effectively: if	you	are	bringing	people from	different	disciplines,	you	can't just	Skype!	You	really need to engage, to have people there, over beer, over wine, talking about their differences, really struggling to understand	what the other person is saying.	Maybe getting	frustrated,	maybe	going	away	and	thinking	"this is	rubbish",	but	then	coming back the next day and saying "ok, now I think I understand what you are talking about".	You	need	that	kind	of	passion	and	energy	and	enthusiasm...	What	we	try	to	do in	our	catalysis	meetings	is	to	share	those	vocabularies	and	make	those	vocabularies explicit, so that	what	was previously incommensurable [...] between two	disciplines becomes	commensurable:	people	understand	what they	are talking	about.	That's the first	step	towards	at least	understanding	the	differences	in	different	disciplines.	Now that	doesn't	really	solve	the	problem	to	synthesis	because	of	course	if	we	agree	with what Kuhn was talking about there are still underlying beliefs about utility, about which	way	science	should	progress,	how	science	should	be	done,	and	those	things	may still	be	different	enough	that	you're	not	going	to	reconcile	disciplines.	But	at	least	what we	try	to	do	is	make	sure	that	people	are	talking	about	the	same	thing	(Rodrigo	2014). All of this is similar to what we have tried to do in our 'Cultural Traits' project.	As	a	final	remark,	I	want	to	get	back	to	the	possible	negative	effect	of	a philosopher's obsession for concepts, conceptual clarity, conceptual summaries,	conceptual	change	and	the	like.	As	Rodrigo	continues: I	also	feel	that	there	is	going	to	be	some	instances	where	–	because	of	the	differences in beliefs about utility and the like – we're never going to achieve and overall synthesis.	And I think as a	Center	what	we	haven't done is give enough	guidance to accept that that	might	be	an	outcome, that	at least they	get	people talking	about the same	thing	and	knowing	what	they	are	talking	about:	that	might	be	a	good	outcome	in itself,	rather	than	try	to	force	a	situation	to	a	synthesis	(Rodrigo	2014). Even	within biology, and even	within evolutionary biology, scientists	who aim for synthesis like Rodrigo agree that we shouldn't always aim for synthesis,	but	recognize	that	"discordance	and	disagreement	can	be	fruitful	as well", particularly if they lead to greater understanding down the road. The pressure to close down a catalysis meeting with some kind of conceptual outcome goes against the goal of curiosity for opening new explorations, to which	concepts	are	often	provisional	and	instrumental.	In	the	project	that	led 25 to	this	book,	and	in	the	book	itself,	we	wanted	to	maintain	'cultural	traits'	as	a key	to	open interdisciplinarity,	and	so it	worked.	We	see	no	hurry to	shut	up now.	We	showed	that	it	is	possible	to	publish interesting	things	on	this	topic. We	removed	some	barriers.	We	gathered	people	in	a	virtual	(and	actual)	room to	try	to	"make	the	incommensurable	commensurable".	The	best	continuation of this book would be someone starting scientific collaborations of various kinds.	And	you,	readers,	may	be	those	who	will. Acknowledgements I thank all NESCent leaders and staff, in particular Allen Rodrigo and Joel Kingsolver, for their great generosity in letting me interview them. My research	on	NESCent, synthesis	and	catalysis	was supported	by the	National Evolutionary	Synthesis	Center	(NESCent),	NSF	#EF-0423641. References Brigandt,	I.	&	Love,	A.C.	(2010).	Evolutionary	novelty	and	the	evo-devo	synthesis:	field notes.	Evolutionary	Biology,	37(2-3),	93-99. Craver,	C.	(2009).	Explaining	the	brain:	mechanisms	and	the	mosaic	unity	of neuroscience.	Oxford:	Oxford	University	Press. Cunningham,	C.	(2005).	The	right	time	for	synthesis	in	evolutionary	biology. BioScience,	55(2),	99. Kingsolver,	J.	(2014).	Interview	to	Emanuele	Serrelli.	University	of	North	Carolina, Chapel	Hill,	NC.	July	7,	2014. Mitchell,	S.	(2003).	Biological	complexity	and	integrative	pluralism.	Cambridge University	Press. NESCent	(2004).	A	place	for	evolutionary	synthesis	in	North	Carolina's	Research Triangle.	Original	grant	proposal	to	NSF	[not	online,	courtesy	of	Jory Weintraub	and	Cliff	Cunningham]. Rodrigo,	A.	et	al.	(2013).	Science	incubators:	synthesis	centers	and	their	role	in	the research	ecosystem.	PLoS	Biology,	11(1),	p.e1001468. Rodrigo,	A.	(2014).	Interview	to	Emanuele	Serrelli.	NESCent,	Durham,	NC,	July	3,	2014. Sidlauskas,	B.	et	al.	(2009).	Linking	big:	the	continuing	promise	of	evolutionary synthesis.	Evolution,	64(4),	871-880.