In recent years, western democracies have experienced a damaging erosion of faith in scientific expertise. While online conspiracy theories and the alternative facts promoted by charismatic demagogues are partly responsible, some blame attaches to the naïve way in which governments apply scientific data to policy questions, writes Richard Bronk. Science can neither substitute for political choices between competing goals nor replace the need for nuanced judgment of the multifaceted nature of specific problems

Bronk, Richard

English

LSE Research Online

The	art	of	following	the	scienceIn	recent	years,	western	democracies	have	experienced	a	damaging	erosion	of	faith	in	scientific	expertise.	Whileonline	conspiracy	theories	and	the	alternative	facts	promoted	by	charismatic	demagogues	are	partly	responsible,some	blame	attaches	to	the	naïve	way	in	which	governments	apply	scientific	data	to	policy	questions,	writesRichard	Bronk.	Science	can	neither	substitute	for	political	choices	between	competing	goals	nor	replace	the	needfor	nuanced	judgment	of	the	multifaceted	nature	of	specific	problems.The	daily	press	conferences	from	Downing	Street	since	March	2020	underline	the	prominence	given	toepidemiologists,	behavioural	scientists	and	the	medical	profession	in	driving	policy	reaction	to	the	Covid-19	crisis.This	may	be	evidence	of	a	welcome	return	of	scientific	expertise	to	the	heart	of	government	after	a	period	whenmuch	of	the	population	and	elements	of	the	government	had,	in	the	words	of	Michael	Gove,	‘had	enough	ofexperts.’	But,	despite	the	obvious	glories	of	vaccine	research,	there	is	a	danger	that	continual	reference	by	electedgovernments	to	scientific	modelling	to	justify	contentious	policy	choices	may	further	undermine	scientific	expertiseand	evidence-based	policy	in	the	eyes	of	the	electorate.Popular	distrust	of	social-science	expertise	has	been	growing	for	some	time.	Economics,	in	particular,	suffered	anear-fatal	blow	to	its	credibility	in	the	court	of	public	opinion	after	the	2008	financial	crisis,	thanks	in	part	to	thewidespread	misuse	of	economic	models	to	make	predictions	of	unwarranted	precision	as	a	result	of	a	basicconfusion	between	calculable	risks	and	radical	uncertainty.	Distrust	was	intensified	by	the	tendency	forpolicymakers	to	justify	controversial	decisions	by	delegating	them	to	the	outputs	of	‘black	box’	(cost-benefit,	risk-measurement	or	macroeconomic)	models	promising	to	solve	the	equations	of	life.Faith	in	the	medical	and	natural	sciences	has	generally	remained	stronger.	But	the	persistence	of	climate-changedenial	and	large	pockets	of	Covid-19	scepticism	suggest	that,	if	natural	scientists	get	blamed	for	governmentpolicies	that	are	unpopular	with	sections	of	the	electorate,	their	findings	are	also	likely	to	become	increasinglypoliticised.	This	is	particularly	true	if	governments	hide	key	value	choices	and	distributional	decisions	behind	theneed	to	react	to	scientific	findings,	or	if	scientists	allow	themselves	to	become	proselytisers	for	particular	politicalvalues	or	goals.	Much	damage	is	done	to	the	independent	and	non-partisan	status	of	science	by	illegitimatelyconflating	value	choices	with	empirical	analysis.One	unhelpful	myth	promoted	by	many	economists	and	other	social	scientists	under	the	philosophical	influence	ofutilitarianism	is	that	all	values	can	be	rendered	commensurable	in	a	single	unit	of	account,	so	that	there	is	alwaysone	right	answer	as	to	the	most	rational	trade-off	between	them.	This	feeds	into	a	technocratic	view	that	analysisand	data	(such	as	‘willingness	to	pay’	for	non-market	goods)	can	on	their	own	provide	the	correct	solution	to	policyquestions	–	often	with	the	help	of	state-of-the-art	algorithms.By	contrast,	voters	intuitively	understand	that	the	trade-offs	between	different	values	or	goals	–	such	as	medicalhealth	and	economic	growth,	economic	efficiency	and	equality,	or	freedom	and	security	–	are	highly	contested.They	want	to	see	the	different	possible	value	weightings	for	each	goal	relative	to	others	articulated	by	theirpoliticians	and	debated	since	there	is	no	one	rational	answer.	Indeed,	the	choice	of	trade-offs	made	will	define	thevery	identity	of	the	body	politic.	This	makes	it	dangerous	for	governments	to	claim	that	any	particular	decision,	suchas	closing	schools	or	parts	of	the	economy	to	reduce	Covid-19	transmission,	is	driven	purely	by	the	science	ratherthan	by	a	political	choice	–	in	this	case	to	privilege	the	health	service	over	economic	growth	and	the	lives	of	highlyat-risk	(mostly	elderly)	groups	over	the	life-chances	of	younger	generations.Science	has	an	important	role	in	informing	voters	and	politicians	about	the	practical	implications	of	choosing	anyparticular	trade-offs	between	conflicting	goals,	and	it	may	at	times	show	analytically	that	some	apparent	trade-offsbetween	goals	are	illusory.	But	ultimately	science	cannot	determine	what	is	the	right	value	choice	to	make.	Suchchoices	are	the	stuff	of	politics	and	the	responsibility	of	political	leaders	accountable	to	the	electorate.	They	shouldnever	simply	be	buried	in	the	assumptions	of	technocratic	models	and	algorithms.LSE European Politics and Policy (EUROPP) Blog: The art of following the science Page 1 of 3		Date originally posted: 2021-01-18Permalink: https://blogs.lse.ac.uk/europpblog/2021/01/18/the-art-of-following-the-science/Blog homepage: https://blogs.lse.ac.uk/europpblog/Professor	Jonathan	Van	Tam,	Deputy	Chief	Medical	Officer	for	England,	at	a	media	briefing	in	November	2020,	Credit:	Pippa	Fowles	/	No	10	Downing	Street	(CC	BY-NC-ND	2.0)Confusion	between	value	choices	and	analytical	findings	is	one	threat	to	the	legitimacy	of	science	as	a	key	supportfor	policy.	Another	is	the	naïve	empiricism	of	many	policymakers	when	they	talk	of	‘data-driven	policies’	andpromote	‘big	data’	as	the	solution	to	our	dilemmas,	as	if	data	are	some	entirely	objective	touchstone.	In	practice,scientists	never	have	direct	access	to	underlying	reality.	Rather,	their	way	of	seeing	the	world	is	mediated	by	theparticular	languages,	conceptual	grids	and	scientific	instruments	they	use.	The	data	they	analyse	–	and	the	factsupon	which	governments	base	their	assumptions	–	are	partly	constructed	by	the	theories	and	concepts	scientistsinternalise	and	by	the	data	collection	methods	used.	In	this	sense,	while	data	can	help	stress-test	policy	ideas,	theyare	never	entirely	neutral	or	incontrovertible	readings	of	reality.	Data	need	to	be	evaluated	in	the	light	of	alternativescientific	frames	and	their	pertinence	carefully	judged.All	scientific	theories	(and	related	data	collection	methods)	are	selective	and	focus	only	on	certain	aspects	of	multi-faceted	reality.	Indeed,	the	main	value	of	most	scientific	methods	is	to	abstract	from	complexity	and	contingentcomplications	and	isolate	certain	systematic	tendencies	in	the	natural	or	social	world:	conclusions	are	presentedwith	the	caveat	that	‘other	aspects’	or	factors	are	assumed	to	‘remain	equal’	(ceteris	paribus)	and	can	therefore	besafely	ignored.	This	means	that	whenever	scientific	findings	are	applied	to	particular	policy	problems,	the	firstchallenge	is	to	assess	whether	the	ceteris	paribus	clause	in	the	relevant	model	remains	a	safe	assumption.	And	forthis	the	policymaker	needs	to	assess	the	potential	relevance	of	a	range	of	scientific	perspectives	that	mayilluminate	other	–	often	incommensurable	–	aspects	of	the	problem	at	hand.The	challenges	with	data-driven	policy	are	greater	still	when	dealing	with	the	data	needed	to	model	socialbehaviour.	For	a	start,	social	scientists	are	seeking	to	explain	a	pre-interpreted	world	–	that	is,	they	have	to	takeaccount	of	the	context-specific	interpretations	or	constructions	of	reality	that	are	actually	motivating	people’sdecisions.	This	was	one	of	the	reasons	that	Friedrich	Hayek	criticised	the	use	of	aggregate	statistics	by	economistsand	socialist	planners:	as	he	put	it,	the	data	that	matter	for	explaining	economic	behaviour	are	the	hard-to-collatesubjective	assessments,	opinions	and	tacit	knowledge	of	local	actors	–	‘the	things	known	to	the	persons	whosebehaviour	we	try	to	explain.’	Similar	reasoning	accounts	for	some	of	the	emphasis	placed	on	focus	groups	inhelping	design	policy.	It	also	shines	a	light	on	the	Bank	of	England’s	increasing	use	(noted	by	David	Tuckett	et	al)of	regional	agencies	and	citizen	reference	panels	to	glean	the	narratives	and	stories	shaping	different	actors’interpretations	of	current	events	and	prospects.	Such	qualitative	data	can	help	the	Bank	decipher	new	trends	ineconomic	behaviour.LSE European Politics and Policy (EUROPP) Blog: The art of following the science Page 2 of 3		Date originally posted: 2021-01-18Permalink: https://blogs.lse.ac.uk/europpblog/2021/01/18/the-art-of-following-the-science/Blog homepage: https://blogs.lse.ac.uk/europpblog/In	policy	and	business,	much	of	the	faith	in	‘big	data’	is	predicated	on	the	assumption	that	patterns	in	past	data	area	good	predictor	of	the	future	behaviour	of	economic	actors	and	the	socio-economic	system	as	a	whole.	But,	asJens	Beckert	and	I	argue,	this	assumption	ignores	the	extent	to	which	the	innovation	and	constant	novelty	endemicin	late	capitalist	systems	introduces	a	basic	indeterminacy	that	ensures	that	the	future	is	rarely	a	statistical	shadowof	the	past.	It	follows	that	the	focus	of	economics	should	be	on	providing	models	that	can	act	as	diagnostic	tools	forspotting	new	patterns	in	real-time	data	rather	than	on	trawling	huge	existing	data	sets	in	the	hope	of	being	able	toextrapolate	the	future	from	patterns	in	the	past.More	broadly,	all	scientific	results	are	provisional	–	and	should	be	open	to	audit	by	other	scientists	–	and	nonecapture	more	than	a	subset	of	reality.	When	dealing	with	real-world	problems,	this	means	that	following	the	sciencemust	involve	treating	its	findings	as	provisional	and	partial	guideposts,	while	making	careful	judgments	about	therelevance	of	specialist	models	in	elucidating	any	particular	problem.	Furthermore,	when	coping	with	multi-facetedissues	–	involving,	for	example,	economic,	social,	psychological	and	physiological	factors	–	the	policymaker	mustwork	out	the	feasibility	of	synthesising	the	findings	of	the	different	relevant	scientific	disciplines	to	form	a	roundedpicture	of	the	contingent	particular.Such	a	‘holistic’	assessment	may	be	loosely	based	on	science	but	involves	consideration	of	a	range	of	factorsbeyond	the	scope	of	any	individual	science.	In	other	words,	the	art	of	integrating	science	into	governmentaldecision-making	involves	reversing	the	abstraction	from	the	complex	interaction	of	different	factors	that	any	sciencemust	engage	in	and	using	the	findings	of	heterogeneous	scientific	methods	to	illuminate	the	messy	particular.John	Neville	Keynes	made	a	similar	point	more	than	a	century	ago,	when	he	argued	that	the	findings	of	abstracteconomics	are	‘conditional’	and	must	only	be	applied	to	make	practical	recommendations	after	carefulconsideration	of	the	particularities	of	real-world	problems	and	relevant	non-economic	factors	and	ethical	questions.Such	applied	economics	was,	he	stressed,	an	‘art’	not	a	science	–	a	view	recently	revived	by	David	Colander.In	the	opening	chapters	of	The	Sense	of	Reality,	Isaiah	Berlin	also	concluded	that	the	‘art	of	governing’	issomething	quite	different	from	scientific	knowledge.	It	involves	the	insight	and	judgment	required	to	understand	theinterplay	between	the	different	relevant	aspects	and	levels	of	reality	captured	by	different	sciences.	Like	science,governing	requires	epistemological	humility,	constant	improvisation	and	trial	and	error.	But	unlike	science,	it	cannever	afford	to	abstract	from	the	contingent	particularity	of	life.	As	Berlin	put	it,	good	government	involves	a	specialsensitivity	to	the	small	changes	that	may	in	fact	be	critical	to	outcomes	and	an	appreciation	of	the	‘dark	mass	offactors	whose	general	drift	we	perceive	but	whose	precise	interrelations	we	cannot	formulate.’	The	art	of	goodgovernance	is	to	avoid	taking	scientific	illumination	of	a	single	aspect	of	reality	as	the	whole	truth.	It	involvescombining	the	generalisable	and	testable	findings	of	science	with	an	understanding	of	‘a	particular	situation	in	its	fulluniqueness’.This	is	not	to	underestimate	the	political	challenge	of	such	a	nuanced	use	of	science.	Voters	may	respond	betterinitially	to	politicians	using	simple	messages	to	express	unwarranted	certainty	than	to	those	who	acknowledge	thatthe	science	on	which	their	decisions	are	based	is	partial	and	provisional.	As	Berlin	noted	laconically	in	a	letter	toKay	Graham	quoted	by	Aurelian	Craiutu,	‘nuances	are	merely	a	nuisance’;	they	are	difficult	to	articulate,	and	theyare	‘treated	as	confusion	or	evasion’.Here	central	bankers	can	perhaps	show	politicians	the	way.	In	recent	years,	they	have	developed	the	art	offashioning	nuanced	narratives	based	on	the	output	of	scientific	models	supplemented	with	qualitative	assessmentof	broader	factors.	Crucially,	these	narratives	guide	our	expectations	while	being	couched	in	provisional	terms	thatensure	their	credibility	is	not	shredded	when	reality	springs	a	surprise.Note:	This	article	gives	the	views	of	the	author,	not	the	position	of	EUROPP	–	European	Politics	and	Policy	or	theLondon	School	of	Economics.	Featured	image	credit:	Pippa	Fowles	/	No	10	Downing	Street	(CC	BY-NC-ND	2.0)LSE European Politics and Policy (EUROPP) Blog: The art of following the science Page 3 of 3		Date originally posted: 2021-01-18Permalink: https://blogs.lse.ac.uk/europpblog/2021/01/18/the-art-of-following-the-science/Blog homepage: https://blogs.lse.ac.uk/europpblog/

The art of following the science

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The art of following the science

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