On	the	wisdom	of	algorithmic	markets: governance	by	algorithmic	price Pip	Thornton1	and	John	Danaher2 Abstract Leading	digital	platform	providers	such	as	Google	and	Uber	construct marketplaces	in	which	algorithms	set	prices.	The	efficiency-maximising	free market	credentials	of	this	approach	are	touted	by	the	companies	involved	and	by legislators,	policy	makers	and	marketers.	They	have	also	taken	root	in	the	public imagination.	In	this	article	we	challenge	this	understanding	of	algorithmically constructed	marketplaces.	We	do	so	by	returning	to	Hayek's	(1945)	classic defence	of	the	price	mechanism,	and	by	arguing	that	algorithmically-mediated price	mechanisms	do	not,	and	probably	cannot,	accumulate	and	signal	that	same kinds	of	knowledge	that	Hayek	felt	were	essential	to	effective	market governance.	Indeed,	we	argue	that	algorithmically-constructed	marketplaces	are closer	to,	though	distinct	from,	the	central	planning	model	that	Hayek	critiqued. Regardless	of	how	you	feel	about	Hayek's	argument,	this	has	important consequences	for	how	we	respond	to	the	rise	of	algorithmic	governance	tools	in both	markets	and	elsewhere. 1	Pip	Thornton	is	a	PhD	candidate	in	Geopolitics	&	Cybersecurity	at	Royal Holloway,	University	of	London. Email:	pip.thornton.2013@live.rhul.ac.uk 2	John	Danaher	is	a	lecturer	in	Law	at	National	University	of	Ireland,	Galway. Email:	john.danaher@nuigalway.ie Electronic copy available at: https://ssrn.com/abstract=3314078 2 Introduction In	September	1945,	Friedrich	von	Hayek	published	his	most	famous	paper	'The Use	of	Knowledge	in	Society'.	In	it,	he	made	a	bold	claim	about	the	information processing	powers	of	the	free	market.	He	argued	that	the	market's	price mechanism	collated	and	communicated	information	from	diverse	sources, thereby	enabling	people	to	better	coordinate	their	actions	to	mutually	beneficial ends.	By	this	logic,	the	price	mechanism	was	a	governance	tool	par	excellence, one	that	could	be	used	to	accumulate	and	communicate	important	knowledge across	society.	Hayek	used	this	'Knowledge	Argument'	to	critique	centrally controlled	governance	tools,	arguing	that	they	could	never	perform	the	same kind	of	information-processing	feats	at	could	the	market (Hayek	1945). We	live	in	an	era	characterised	by	the	use	of	another	information processing	tool	in	governance:	the	computer-coded	algorithm	(Danaher	2016; Pasquale	2015;	Rouvroy	2013).	In	our	networked	and	digitised	age	–	an	age	in which	nearly	every	move	we	make	and	breath	we	take	is	recorded	and	logged	in a	digital	repository	–	we	have	come	to	expect,	and	possibly	even	demand, automated	algorithmic	assistance.	We	look	to	the	information	processing	powers of	big	data	algorithms	to	help	us	make	sense	of	the	complexity	we	have	created. These	algorithms	feed	us	information,	often	packaged	into	simple	metrics	like scores	and	ratings,	that	we	use	to	coordinate	and	cooperate	with	one	another, and	to	facilitate	the	smooth	running	of	our	everyday	needs	and	consumption.	As Karen	Yeung	(2017)	notes,	these	algorithmic	decision-systems: Electronic copy available at: https://ssrn.com/abstract=3314078 3 manage	risk	or	alter	behaviour	through	continual	computational	generation	of knowledge	from	data	emitted	and	directly	collected	(typically	in	real	time	on	a continuous	basis)	from	numerous	dynamic	components	pertaining	to	the regulated	environment	in	order	to	identify	and,	if	necessary,	automatically	refine (or	prompt	refinement	of)	the	system's	operations	to	attain	a	pre-specified	goal. There	are	many	ways	in	which	modern	marketplaces	are	algorithmically mediated.	Consumer	information	is	collected	by	many	suppliers	and	is	often mined	for	useful	patterns.	Suppliers	then	push	particular	goods	and	services	to consumers,	or	particular	deals	on	goods	and	services,	based	on	those	patterns. This,	in	essence,	is	what	lies	at	the	heart	of	variable	price	algorithms.	They	try	to use	consumer	data	to	segment	the	marketplace	into	different	groups	who	are willing	to	pay	different	amounts	for	the	same	good	or	service.	Any	business owner	can	download	a	software	program	that	will	automatically	perform	this kind	of	variable	pricing	for	them.	Likewise,	there	are	many	markets	in	which automated,	algorithmically	guided	systems	do	the	buying	and	selling,	and thereby	affect	the	market-clearing	price	of	a	good	or	service.	The	automated trading	algorithms	used	on	the	stock	market	are	the	most	obvious	examples	of this. All	of	these	markets	involve	a	form	of	algorithmically-mediated	pricing insofar	as	algorithms	have	some	effect	on	market	price,	but	they	are	not	the	most interesting	or	significant	kind,	nor	the	kind	that	we	focus	on	here. The	most interesting	and	significant	kind	are	what	we	call	'algorithmically	constructed market	places'.	These	marketplaces	are	constructed	by	large	digital	platform Electronic copy available at: https://ssrn.com/abstract=3314078 4 providers,	such	as	Google,	Uber,	Facebook	and	Amazon, and	are	used	to	match consumers	to	suppliers	(where	the	suppliers	are	sometimes	the	platform providers	themselves)	via	a	shared	platform.	Algorithms	construct	this	type	of marketplace	because	they	are	used	to	set	the	prices	that	suppliers	can	charge and	consumers	can	pay	on	that	shared	platform,	and	they	also	determine	what kinds	of	information	are	available	and	communicable	to	those	participants. These	constructed	marketplaces	often	include	other	non-price	based	algorithmic scoring	systems	(e.g.	rating	systems)	that	match	consumers	to	suppliers.	As	such, the	entire	experience	of	being	a	consumer	or	supplier	on	this	marketplace	is mediated	through	an	automated	algorithmic	governance	structure.	This structure	tries	to	automate	the	knowledge	accumulation	and	signaling	needed for	the	marketplace,	rather	than	relying	on	traditional	feedback	loops	and methods	of	algorithmic	calculation. What	is	particularly	interesting	about	some	of	these	new	forms	of algorithmic	market	governance	is	that	they	attempt	to	recreate,	in	automated form,	the	price	mechanism	beloved	by	Hayek,	or	to	preempt	our	position,	they attempt	to	recreate	a	semblance	of	the	Hayekian	model.	While	markets	are	not, and	never	have	been,	perfectly	'free'	in	a	Hayekian	sense,	algorithmically mediated	auctions	and	bidding	processes,	as	well	as	algorithmically-controlled price-setting	tools,	are	often	touted	and	defended	in	Hayekian	terms.	This suggests	that	the	creators	of	these	algorithmically	constructed	marketplaces believe	that	there	is	no	tension	between	this	automated	algorithmic	governance and	Hayek's	preferred	free	market	governance. Electronic copy available at: https://ssrn.com/abstract=3314078 5 In	this	paper,	we	dispute	this	perspective.	Contrary	to	the	claims	of	these digital	platform	providers,	we	argue	that	there	are	considerable	tensions between	what	happens	on	their	algorithmically-constructed	marketplaces	and what	Hayek	said	happened	on	a	free	market.	To	highlight	this	tension,	we	go back	to	Hayek's	Knowledge	Argument	and	argue	that	it	can	be	turned	against these	platform	providers.	Our	claim	is	that	when	you	appreciate	the	logic	and content	of	Hayek's	original	argument,	you	realize	that	algorithmic	price-setting mechanisms	do	not	(and	probably	cannot)	share	the	benefits	that	Hayek	claimed for	the	price	mechanism.	On	the	contrary,	algorithmically-constructed	price mechanisms	suffer	from	many	of	the	same	limitations	and	distortions	that	Hayek lamented	in	centrally	planned	forms	of	governance. In	arguing	this,	we	do	not	claim	that	digital	platform	providers	are	the exact	equivalent	of	centrally-planned	governments	(though	they	may	have aspirations	in	that	direction),1	but	rather	that	Hayek's	Knowledge	Argument	can be	applied	to	different	targets,	something	he	himself	recognized	and	has	been pointed	out	by	others	(Bowles,	Kirman	and	Sethi	2017,	216).	In	his	day,	the biggest	threat	to	market	governance	came	from	central	planning;	in	our	day,	we argue	that	it	may	well	come	from	digital	platform	providers.	Furthermore,	it	is important	to	realise	that	our	argument	critiques	a	particular	rhetorical	defence	of algorithmically	constructed	markets	and	not	necessarily	the	reality.	We	do	not presume	that	markets	are	or	can	be	perfectly	Hayekian,	nor	do	we	posit	that	the algorithmic	model	is	identical	to	the	Hayekian	model.	Instead	we	question	the quasi-Hayekian	rhetoric	that	has	grown	up	around	these	systems.	We	argue	that this	Hayekian	rhetoric	is	often	a	smokescreen,	an	attempt	to	abdicate Electronic copy available at: https://ssrn.com/abstract=3314078 6 responsibility	or	accountability	for	what	happens	on	an	algorithmicallyconstructed	marketplace.	We	consequently	argue	that	this	is	problematic	given the	power	that	such	perceptions	might	engender	when	touted,	encouraged	or facilitated	by	companies	such	as	Google	and	Uber. We	defend	this	argument	in	three	stages.	First,	we	set	out	the	context	for interpreting	the	argument,	explaining	Hayek's	knowledge	argument	in	favour	of free	markets. Second,	we	defend	our	argument,	using	two	examples	to	highlight the	shortcomings	of	algorithmically	constructed	price	mechanisms:	Google's AdWords	auction	and	Uber's	surge	pricing.	And	third,	we	consider	the consequences	of	our	argument,	focusing	in	particular	on	why	algorithmically constructed	price	mechanisms	fail	to	meet	the	Hayekian	criteria	and	what	this means	for	political	accountability	and	transparency	in	the	age	of	algorithmic governance. 2.	Understanding	Hayek's	Knowledge	Argument The	argument	we	defend	in	this	paper	must	be	interpreted	correctly.	Our	goal	is to	contribute	to	the	understanding	of	the	emerging	phenomenon	of	algorithmic governance,	not	to	further	or	denigrate	Hayekian	theory.	We	do	this	by	looking at	the	logical	and	historical	intersections,	tensions,	and	overlaps	between	this contemporary	phenomenon	of	algorithmic	governance	and	longer-standing forms	of	market	governance	(Aneesh	2006	&	2009).	We	argue	that contemporary	algorithmic	governance	mechanisms	cannot	replicate	the	features of	market	governance	that	were	touted	by	Hayek.	We	make	this	case	by appealing	in	particular	to	Hayek's	knowledge	argument.	Hayek's	argument	was Electronic copy available at: https://ssrn.com/abstract=3314078 7 originally	used	to	critique	centrally	planned	economies,	but	we	argue	that	it	can be	applied	to	other	targets,	particularly	to	what	happens	on	algorithmically constructed	marketplaces.	There	is	some	irony	to	this	because	the	use	of	digital surveillance	and	computational	power	was,	historically,	perceived	as	a	way	to overcome	the	knowledge	problem	identified	by	Hayek	and	seems	to	be perceived	in	that	way	by	modern	digital	platform	providers.	It	is,	consequently, important	to	understand	the	key	propositions	and	premises	of	Hayek's	argument as	well	as	some	of	the	historical	uses	of	that	argument. How	Hayek's	knowledge	argument	works Hayek's	knowledge	argument	has	a	simple	structure.	It	starts	with	the observation	that	'planning'	is	essential	to	any	economy.	Decisions	(plans)	have	to be	made	about	what	goods	and	services	to	produce,	how	to	produce	them	and who	to	produce	them	for.	The	question	is	who	should	do	the	planning	(Hayek 1945,	520).	When	Hayek	was	writing	there	seemed	to	be	two	answers	to	that question.	There	were	those	who	thought	governments	should	do	the	planning (Lange	1936	&	1937)	and	those	who	thought	that	individual	actors,	free	from government	interference,	should	do	the	planning	(Von	Mises	1920	&	1922). Hayek	sided	with	the	latter.	He	argued	that	the	planning	problem	could	be distilled	down	into	a	knowledge	problem	(Hayek	1945,	519-520).	The	challenge for	any	putative	planner	was	that	there	was	lots	of	data	(information)	available to	them,	but	not	all	of	that	data	was	interpretable	or	meaningful	to	economic decision-making	(i.e.	counted	as	'knowledge').2	The	critical	question,	therefore, was	not	who	was	best	placed	to	make	use	of	existing	data	on	resources, Electronic copy available at: https://ssrn.com/abstract=3314078 8 productive	processes	and	consumer	preferences,	but	rather	who	was	best	placed to	discover	and	translate	data	into	knowledge,	and	communicate	it	to	others. The	problem,	according	to	Hayek,	was	that	the	knowledge	required	to plan	effectively	was	dispersed	throughout	a	network	of	actors	and	could	only	be discovered	and	rendered	meaningful	through	market-based	transactions: The	economic	problem	of	society	is...a	problem	of	how	to	secure	the	best	use	of resources	known	to	any	of	the	members	of	society,	for	ends	whose	relative importance	only	these	individuals	know.	Or,	to	put	it	briefly,	it	is	a	problem	of	the utilisation	of	knowledge	which	is	not	given	to	anyone	in	its	totality	(Hayek	1945, 519-521). To	be	more	precise,	Hayek	argued	that	the	knowledge	required	to	solve	the planning	problem	had	three	distinct	properties	(Bronk	2013;	Hayek	1945): Discreteness:	It	did	not	come	in	a	single	package;	it	was	dispersed	among many	different	actors,	who	had	partial	access	to	the	totality. Tacitness:	It	could	not	always	be	easily	articulated	or	codified;	it	rested	on practical	know-how	and	subconscious,	tacit	understandings	of	how productive	processes	worked	and	how	consumers	behaved	(cf.	Polanyi 1961	and	Autor	2014). Electronic copy available at: https://ssrn.com/abstract=3314078 9 Subjectivity:	The	values	and	preferences	of	consumers	and	producers were	subjective	–	known	ultimately	only	to	themselves. In	addition	to	this,	Hayek	argued	that	much	of	the	knowledge	had	yet	to	be discovered:	you	needed	the	competitive	ethose	of	the	market	to	get	people	to exert	the	effort	needed	to	discover	it. Once	these	properties	are	appreciated,	Hayek's	knowledge	argument	in favour	of	free	markets	is	easy	to	make.	Hayek	claimed	that	central	planners	were ill-placed	to	discover,	collate,	and	communicate	the	forms	of	knowledge	needed to	solve	the	planning	problem.	They	could	not	hope	to	amass	the	discrete perspectives	of	many	individuals	into	a	single	coherent	plan	for	the	market;	they could	not	codify	and	articulate	the	tacit	knowledge	that	underlay	many	market processes;	and	they	could	not	hope	to	know	the	minds	of	the	market	actors.	Free markets	could	do	a	better	job.3	Free	markets	had	a	wonderful	knowledge accumulation	and	communication	device	at	their	heart:	the	price	mechanism. The	discrete,	tacit	and	subjective	knowledge	of	market	actors	could	be	translated into	prices.	These	prices	would	tell	people	which	goods	and	services	were	worth producing	and	supplying,	and	which	were	worth	buying.	This	would	enable market	actors	to	coordinate	towards	mutually	beneficial	ends.	Any	interference from	a	central	planner	with	the	price	mechanism	would	necessarily	disrupt	and distort	the	knowledge	accumulation	and	communication	functions	that	the	price mechanism	performs,	sending	people	off	in	the	wrong	direction,	leading	to	the overor	under-production	of	vital	goods	and	services.	On	top	of	this,	Hayek	felt that	free	markets	incentivised	innovation	and	creative	destruction,	which Electronic copy available at: https://ssrn.com/abstract=3314078 10 enabled	new	knowledge	to	be	discovered	and	to	be	entered	into	the	market.	This was	something	that	centrally	planned	economies	could	never	adequately recreate. Hayek's	argument	can	certainly	be	criticised.	In	its	initial	formulation,	it was	used	to	critique	centrally	planned	economies,	but	it	clearly	has	broader implications:	it	can	be	used	to	challenge	any	system	that	distorts	the	knowledge discovery,	accumulation	and	communication	functions	needed	for	effective economic	decision-making. Indeed,	as	Bronk	(2013)	and	Bowles,	Kirman	and Sethi	(2017)	point	out,	the	price	mechanism	can	be	distorted	by	market	forces	as well	as	by	government	forces.	In	particular,	they	suggest	that	information cascades	and	price	speculation	can	lead	to	distortions	of	the	knowledge	provided by	the	price	mechanism,	which	may	point	to	a	role	for	government	interference in	maintaining	the	knowledge	accumulation	and	communication	powers	of	the market.	In	other	words,	both	sets	of	authors	think	the	Knowledge	Argument	is separable	from	the	free	market	ideology	defended	by	Hayek,	and	can	be	used	to challenge	what	happens	among	private	actors	on	the	market	as	well	as	what happens	when	governments	interfere.	We	take	a	similar	position	here.	We	think that	the	Knowledge	Argument	can	be	used	to	critique	more	than	just	centrally planned	economies.	In	particular,	we	think	it	can	be	used	to	critique	the algorithmically	constructed	marketplaces	created	by	large	digital	platform providers.	This	obviously	involves	a	shift	in	focus	from	what	concerned	Hayek. Hayek's	argument	was	focused	on	the	economy	as	a	whole,	not	on	the	markets for	particular	goods	and	services.	The	examples	we	discuss	below	will	be	focused on	particular	markets	(e.g.	the	market	for	online	advertising).	Nevertheless,	we Electronic copy available at: https://ssrn.com/abstract=3314078 11 think	that	Hayek's	argument,	because	it	can	be	separated	from	its	original application,	is	a	useful	model	for	understanding	and	critiquing	the	claims	made on	behalf	of	these	specific	algorithmically-constructed	marketplaces. In	addition	to	this	general	point	about	separability,	there	are	two	additional reasons	for	thinking	that	the	Knowledge	Argument	is	an	appropriate	tool	for critiquing	the	practices	of	these	digital	platform	providers.	First,	the	companies in	question	are	large	and	provide	platforms	that	capture	quasi-monopolistic shares	of	relevant	markets	and	so	the	analogy	between	what	they	do	and	what happens	in	a	centrally-planned	economy	is	not	inapt.4	Second,	the	companies, and	their	boosters,	often	defend	their	algorithmically-constructed	marketplaces on	quasi-Hayekian	terms	and	are	encouraged	to	do	so	by	regulators	and legislators.	It	seems	important	then	to	critically	probe	the	basis	for	this rhetorical	defence.	Doing	so,	we	believe,	shows	that	the	rhetorical	defence	is flawed:mthe	Knowledge	Argument	actually	tells	against	what	these	companies are	doing. To	make	this	point,	we	need	to	highlight	three	features	of	the	knowledge argument.	The	first	is	the	coordinating	power	that	the	argument	ascribes	to	the price	mechanism.	It	claims	that	prices	work	as	signals,	telling	people	what	they should	do,	and	encouraging	them	to	do	so	in	an	efficient	fashion.	The	second	is	to highlight	the	knowledge	accumulation	power	of	the	price	mechanism.	By observing	fluctuations	in	prices,	Hayek	is	claiming	that	we	learn	something important	about	the	discrete,	tacit	and	subjective	forms	of	knowledge	that	are often	neglected,	ignored	or	distorted	by	central	planning.	The	third	is	to	highlight Electronic copy available at: https://ssrn.com/abstract=3314078 12 the	knowledge	creation	power	of	price	competition	on	the	market.	By incentivizing	people	to	compete	with	one	another	to	make	profits,	the	market incentivizes	people	to	discover	knowledge	that	previously	did	not	exist.	This	was important	for	Hayek	because	the	pre-existence	of	such	knowledge	is	something that	is	often	assumed	by	central	planning	models,	as	well	as	mainstream equilibrium	theory	(Bowles,	Kirman	and	Sethi	2017,	218).	Our	claim	in	what follows	is	that	algorithmically	constructed	marketplaces,	of	the	sort	used	by Google	and	Uber	(in	particular)	fail	to	adequately	perform	these	functions. 3.	The	unwisdom	of	algorithmic	prices:	the	case	of	Google	and Uber Before	we	get	to	the	specific	critique	of	digital	platforms,	it	will	help	if	we	take	a brief	detour	into	the	nature	of	the	algorithmic	governance	mechanisms	they	use to	construct	their	marketplaces,	and	the	historical	association	between	these mechanisms	and	centrally	planned	economies.	Doing	so	will	highlight	some	of the	ironic	historical	development	of	the	rhetoric	around	the	use	of	algorithmic governance	mechanisms	in	markets.	This	irony	is	something	we	wish	to accentuate	by	turning	the	Knowledge	Argument	against	purveyors	of algorithmically-constructed	marketplaces. Terminology	is	important.	We	use	the	term	'algorithmic	governance'	in	a narrow	way.	'Governance'	is	a	catch-all	term	for	the	techniques	and	practices whereby	human	behaviour	is	nudged,	incentivized,	manipulated	and	otherwise controlled.	In	a	trivially	broad	sense,	most	governance-related	decision-making Electronic copy available at: https://ssrn.com/abstract=3314078 13 is	algorithmic:	it	takes	inputs	and	produces	outputs	by	following	a	series	of decision	rules.	Sometimes	these	rules	are	explicit,	in	which	case	the	'algorithmic' nature	of	the	decision-making	is	transparent;	sometimes	these	rules	are	tacit	or unexpressed,	in	which	case	the	algorithmic	nature	of	the	decision	is	masked	or hidden,	though	it	may	be	rendered	explicit	by	careful	observation	and	study.	This is	true	both	for	bureaucratic	decision-making	for	the	price-setting	decisions made	by	economic	actors	every	day.	A	person	running	a	bakery,	for	example, must	use	some	sort	of	algorithm	to	determine	how	much	they	will	charge customers	for	bread	on	a	daily	basis	(e.g.	they	may	have	a	minimum	profit margin	target	and	so	they	compute	price	based	on	production	costs	and	this minimum	target). If	our	discussion	and	critique	were	about	algorithmic	governance	of	this trivially	broad	sort	it	would	neither	be	interesting	nor	insightful.	So	it	should come	as	no	surprise	that	we	do	not	use	the	term	in	this	trivially	broad	sense. When	we	talk	about	'algorithmic	governance'	mechanisms	we	are	talking	about	a specific,	relatively	recent,	technological	infrastructure	for	governance	that	relies on	computer-coded	algorithms	(Danaher	2016;	Levy	2017).	To	be	more	precise, we	are	talking	about	a	mode	of	governance	that	is	made	possible	by	modern information	communications	technology,	specifically	Big	Data	systems,	which work	via	a	combination	of	mass	surveillance	and	collation	of	data	from networked	technologies	(computers,	phones,	smart	devices	etc.)	and	datamining	(descriptive	and	predictive	analytics).	The	algorithms	that	power	such systems	rely	on	statistical	analysis	and	statistical	learning	rules.	These	are 'smart'	and	adaptable,	often	adjusting	and	tweaking	their	own	operations Electronic copy available at: https://ssrn.com/abstract=3314078 14 without	immediate	input	from	their	original	creators.	Such	systems	are increasingly	familiar,	with	virtually	every	internet	company	or	service	provider taking	advantage	of	them	in	marketing	and	selling	to	consumers.	Governments are	also	taking	advantage	of	them	in	making	risk-related	decisions,	such	as	who to	audit	for	tax	purposes,	who	poses	a	terrorist	threat,	who	should	be	released from	jail	and	so	on	(Ferguson	2017;	Eubanks	2018;	O'Neil	2016). In	his	study	of	the	rise	of	these	contemporary	algorithmic	governance systems	–	which	he	terms	'algocratic'	systems	–	A.	Aneesh	(2006	&	2009) contrasts	them	with	pre-existing	governance	structures.	In	particular,	he contrasts	them	with	market-based	governance	structures	and	legal-bureaucratic governance	structures.	The	former	are	characterised	by	their	use	of	the	price mechanism	to	'govern'	human	behaviour;	the	latter	by	their	use	of	rules	and laws.	He	argues	that	algocratic	governance	constitutes	something	new:	the	use	of computer-coded	architectures	to	govern	human	behaviour.	These	new	systems then	work	alongside	the	old	governance	structures,	sometimes	being	grafted	on top	of	them,	and	sometimes	complementing	them	(Danaher	2016),	creating multiple	interlocking	and	overlapping	layers	of	governance	in	human	life. The	critical	question	for	us	is	this:	are	these	newer	modes	of	algorithmic governance	compatible	with	Hayekian-style	market-based	governance	–	as purveyors	of	digital	platforms	seem	to	be	believe	–	or	are	they	in	tension	with them?	Can	you	simply	recreate	the	virtues	of	the	market	through	an	automated, algorithmically	constructed	price	mechanism?	Or	is	this	project	doomed	to failure?	The	answer	might	seem	obvious	if	we	consult	the	historical	record.	The Electronic copy available at: https://ssrn.com/abstract=3314078 15 early	use	of	automated	systems	of	algorithmic	governance	seemed	to	be	very closely-aligned	with	centrally-planned,	bureaucratic	modes	of	governance.	In many	ways,	the	quantitative,	statistical	models	used	in	the	early	iterations	of algorithmic	governance	structures	were	the	bread	and	butter	of	the	central planner,	as	Hayek	himself	pointed	out	(1945,	524;	1989). Furthermore,	if	we	look	into	the	history	of	socialist	governance	we	see some	obvious	attempts	to	use	information	communications	technology	to	solve the	knowledge	problem	that	Hayek	identified	and	to	enable	socialism	to	flourish. The	clearest	example	of	this	is	in	the	Cybersyn	project	run	by	the	Allende government	in	Chile	in	the	early	1970s	(Medina	2011;	Morozov	2014),	which was	designed	to	be	'a	real-time	control	system	capable	of	collecting	economic data	throughout	the	nation,	transmitting	it	to	the	government,	and	combining	it in	ways	that	could	assist	government	decision	making'	(Medina	2011,	3). All	of	this	suggests	that	the	history	of	algorithmic	governance	is	positively un-Hayekian,	missing	the	benefits	of	the	free	market's	price	mechanism	that	he endorsed.	But	the	historical	tide	has	now	started	to	shift.	In	recent	years,	several algorithmically	constructed	marketplaces	have	come	into	operation,	and	while market	actors	have	been	using	algorithmic	systems	of	calculation	for	years,	what is	important	is	that	these	newer	systems	are	trying	to	replicate	the	benefits	of the	free	market	within	the	new	digital	infrastructures	made	possible	by algorithmic	governance	technologies.	This	practice	is	defended	on	the	grounds that	it	finds	the	most	efficient	'market-clearing'	price	for	a	good	or	service.5 Google,	for	example,	sets	up	algorithmically	mediated	auctions	to	sell	words	to Electronic copy available at: https://ssrn.com/abstract=3314078 16 potential	advertisers.	These	auctions	include	a	price-setting	mechanism	that tries	to	replicate	the	bidding	process	that	might	be	expected	to	happen	on	a	free market.	Indeed,	and	despite	Sergei	Brin	and	Larry	Page's	original	intentions	to keep	their	search	engine	untainted	by	market	forces	(Brin	&	Page	2012,	3832), Google	has	defended	the	use	of	this	algorithmically	constructed	auction	in explicitly	free	market	terms,	claiming	that	a	competitive	auction	leads	to	efficient pricing	(Kordestani	2008). Also,	Uber	(the	ride-sharing	company)	has	gained notoriety	for	its	'surge'	pricing	algorithm	that	automatically	increases	and decreases	the	prices	it	charges	for	rides	when	there	is	over	or	under-supply.	This practice	is	defended	on	essentially	Hayekian	grounds	because	it	provides	more information-rich	signals	to	potential	suppliers	and	consumers	of	taxi	rides (Cohen	et	al.	2016).	For	example,	a	2015	report	authored	by	Uber-employed academics	stated	that	'economic	theory	tells	us	that	using	prices	to	signal	to riders	that	rides	are	scarce	and	inducing	driver-partners	to	forgo	other	activities will	close	the	gap	between	supply	and	demand	and	lead	to	improved	outcomes for	both	riders	(as	a	whole)	and	driver-partners'	(Hall,	Kendrick	&	Nosko	2015). Hayek	would	certainly	concur	with	the	logic	of	this	statement. These	are	just	some	of	the	many	examples	out	there.	The	rise	of	such algorithmically-constructed	pricing	mechanisms	suggests	that	Hayek's	original suspicions	about	quantitative,	statistical	models,	and	the	historical	association between	cybernetic	control	systems	and	socialist	forms	of	governance,	are misplaced:	if	we	are	to	follow	the	logic	of	these	more	recent	efforts	at	algorithmic market	governance,	it	would	seem	that	the	use	of	automated	price-setting mechanisms,	powered	by	big	data	algorithms,	can	actually	help	to	perfect	the Electronic copy available at: https://ssrn.com/abstract=3314078 17 knowledge	accumulation	and	communication	functions	identified	by	Hayek,	at least	within	discrete	markets.	We	would	therefore	be	on	the	cusp	of	creating	an ideal	form	of	the	Hayekian	market	in	particular	internet	platforms,	one	stitched together	through	a	Big	Data	infrastructure. But	is	this	really	the	case?	Can	we	really	perfect	the	information processing	powers	of	the	market	through	our	modern	technological infrastructure?	We	argue	that	this	is	unlikely.	Algorithmic	price	mechanisms cannot	accumulate	and	signal	the	kinds	of	knowledge	that	Hayek	felt	were essential	to	the	success	of	free	markets.	Indeed,	a	proper	understanding	of Hayek's	argument	provides	the	tools	for	dismantling	the	claims	made	on	behalf of	such	systems.	We	defend	this	position	in	three	stages.	First,	we	provide	two examples	of	algorithmic	price	mechanisms	in	operation:	(i)	Google's	AdWords auction;	and	(ii)	Uber's	surge	pricing	system.	We	highlight	the	ways	in	which both	marketplaces	involve	distortions	to	the	price	mechanism	that	are	prevent	it from	realizing	the	coordination,	discovery	and	communication	functions	that Hayek	highlighted.	Then	we	defend	a	more	'in	principle'	critique	of	such algorithmically	constructed	price	mechanisms,	arguing	that	they	will	necessarily involve	compromises	that	block	the	knowledge-generating	virtues	touted	by Hayek. Example	one:	Google's	AdWords	market Let	us	start	by	considering	the	case	of	Google's	AdWords	market,	the	system used	to	sell	advertising	space	on	Google	search	results	which	Frederic	Kaplan (2014)	called	'the	first	global,	real-time,	and	multilingual	linguistic	market'. Electronic copy available at: https://ssrn.com/abstract=3314078 18 Google	AdWords	operates	on	an	auction	model,	whereby	advertisers	bid	on	the keywords	most	likely	to	attract	customers	to	their	adverts.	Each	time	someone searches	for	a	keyword	on	Google,	a	mini-auction	takes	place,	and	the	advertiser who	wins	the	auction	has	their	advert	displayed	in	one	of	a	number	of	ranked spots	at	the	top	or	bottom	of	the	search	page,	made	visibly	separate	from	the non-paid	organic	results	by	a	small	Ad	box	next	to	the	paid	result.	The	winning advertiser	pays	Google	one	cent	more	than	the	second	highest	bidder	every	time someone	clicks	on	the	advert.	In	this	way,	the	AdWords	system	supposes	that 'every	word	of	every	language	has	a	price,	that	fluctuates	according	to	market laws'	(Bruno	2012,	144). Google	claim	that	the	AdWords	auction	provides	a	platform	for	free market	competition	which	is	'by	far	the	most	efficient	way	to	price	search advertising'	(Kordestani	2008).	Distancing	themselves	from	potential accusations	of	anti-trust	and	price-fixing	over	a	potential	ad-sharing	deal	with Yahoo	in	2008,	senior	business	officer	Omid	Kordestani	confirmed	that	'Google does	not	set	the	prices	manually	for	ads;	rather,	advertisers	themselves determine	prices	through	an	ongoing	competitive	auction'	(Kordestani	2008). AdWords	can	therefore	appear	to	be	set	up	as	the	most	efficient	producer	of 'commercial	information'	(Kordestani	quoted	in	Battelle	2005),	gathering	signals from	a	distributed	marketplace	in	order,	as	Google's	then	CEO	Eric	Schmidt	put	it 'to	provide	a	platform	that	mediates	supply	and	demand	for	pretty	much	the entire	world	economy'	(in	Battelle	2005,	248). The	AdWords	auction	can therefore	by	seen	as	a	clear	attempt	to	instantiate	Hayek's	vision	of	the marketplace	in	an	automated,	algorithmically-constructed	form	(Mirowski	2009, Electronic copy available at: https://ssrn.com/abstract=3314078 19 11). Although	there	are	of	course	alternative	search	engines	for	advertisers	and consumers	to	use,	a	recent	US	court	case	illustrates	just	how	ubiquitous	and normalised	Google's	algorithmic	price-setting	systems	have	become,	and	how their	complete	control	over	the	market	is	increasingly	being	held	by	legislators and	governments	as	providing	conditions	positive	for	fair	competition.	In	the	1800-Contacts	case,	an	'army'	of	Federal	Trade	Commission	lawyers	argued	that Google	was	being	unfairly	harmed	by	the	contact	lens	company	1-800-Contacts 'fixing'	AdWords	keyword	search	results	by	collaborating	with	other	companies.6 As	the	FTC's	pre-trial	brief	states: [I]n	addition	to	not	being	able	to	serve	up	a	large	volume	of	potentially	relevant advertising,	these	artificially-imposed	restraints	hamper	the	search	engines' ability	to	learn	by	analyzing	what	users	are	choosing	to	click	on	(or	not	to	click on)....	(FTC	2017,	22). So	not	only	did	the	FTC	conclude	that	this	was	financially	damaging	to	Google, but	crucially,	that	by	1-800-Contacts	meddling	in	the	market,	Google	was	being denied	access	to	the	information	needed	to	provide	consumers	and	advertisers with	the	correct	information.	This	is	revealing	as	it	shows	an	implicit	assumption on	behalf	of	the	FTC	regulators	that	it	is	Google's	role	and	indeed	responsibility –	to	maximize	the	analytic	capacity	of	the	search	market,	and	that	competition would	'hamper'	this	perceived	right.	Google	is,	as	we	have	already	mentioned, not	the	only	search	engine,	but	this	case	suggests	that	it	is	increasingly	seen	as such	by	a	variety	of	actors,	including	regulators. Electronic copy available at: https://ssrn.com/abstract=3314078 20 But	the	reality	is	that,	even	without	competition,	AdWords	is	full	of 'artificially-imposed	restraints'	on	how	knowledge	is	generated,	accumulated, and	communicated	to	buyers	of	advertising	space.	It	is	a	distorted	marketplace where	Google's	software	engineers	interpose	their	own	knowledge	and	ideology between	the	advertisers	and	the	consumers.	The	bid	prices	and	auction-winning prices	are	not	reflections	of	discrete,	tacit	or	subjective	knowledge	about	the value	of	certain	words.	The	success	of	the	platform	lies	not	purely	in	a	'highest bidder	wins'	formula.	The	potential	for	adverts	to	win	auctions	(and	therefore the	top	ad	slots),	or	indeed	to	appear	on	the	results	page	at	all,	will	depend	on	a prior	ranking	of	quality	and	effectiveness.	As	well	as	the	bid	price,	adverts	are given	algorithmically-generated	quality	scores	to	determine	what	is	known	as their	Ad	Rank.	A	poorly	performing	advert	will	not	necessarily	be	shown,	even	if its	keyword	bid	won	the	auction.	If	it	fails	to	attract	enough	clicks,	it	is	not	cost effective	to	host	the	advert,	and	more	importantly,	in	free	market	rhetoric,	it	has not	provided	a	good	enough	service;	it	has	failed	to	read	the	signals	from consumers.	This	sensitivity	to	distributed	consumer	feedback	might	seem	like	a self-regulating	mechanism	reacting	to	the	knowledge	produced	by	the	market, but	before	the	decision	on	whether	to	click	on	an	advert	or	not	reaches	the consumer,	the	process	has	already	been	heavily	mediated	by	the	quality	ranking algorithms,	and	by	Google's	own	internal	policies.	The	price	is,	consequently,	not a	pure	reflection	of	distributed	knowledge.	This	is	far	from	the	Hayekian	ideal. In	addition	to	this,	Google	is	constantly	changing	the	goalposts	in	its constructed	marketplace.	Trying	to	keep	on	top	of	Google's	evolving	advertising systems	has	become	an	industry	in	itself.	Search	Engine	Optimisation	(SEO) Electronic copy available at: https://ssrn.com/abstract=3314078 21 experts	are	in	a	constant	struggle	between	penalisation	for	attempting	to	'game' the	system,	and	remaining	visible	amongst	the	search	results.	The	rules	of	the SEO	game	are	often	modified	or	tweaked	with	no	warning,	and	at	great	expense to	advertisers,	whose	revenue	streams	can	be	drastically	reduced	when	their adverts	effectively	disappear	overnight.	Many	SEO	forums	contain	discussions expressing	outrage	at	any	suspected	artificial	distortions	of	this	perceived market,	including	accusations	of	cost	per	click	(CPC)	inflation	caused	by	Google raising	lowest	bid	prices	in	the	face	of	increasing	competition	for	finite	linguistic resources.7	Due	to	the	opacity	of	the	AdWords	algorithms,	it	is	of	course	almost impossible	to	empirically	prove	the	anecdotes	of	frustrated	SEO	professionals, but	they	serve	to	provide	an	important	insight	into	how	'narratives'	can	take hold	in	allegedly	free	markets,	and	how	these	narratives	about	the	worth	of certain	keywords,	and	the	functioning	of	the	algorithm,	can	distort	the knowledge	generation,	accumulation	and	communications	powers	of	the	price mechanism	on	the	AdWords	markets.	The	parallels	with	Bronk's	claims	about the	role	of	narratives	in	distorting	the	knowledge	communication	function	of stock	market	prices	are	striking	(Bronk	2013). Apart	from	potential	internal	manipulation	of	bid	prices,	certain	keywords are	removed	from	the	marketplace	entirely	through	Google's	ethical	policies	on censorship.	Google's	current	AdWords	guidelines	state	that: We	value	diversity	and	respect	for	others,	and	we	strive	to	avoid	offending	users with	ads,	websites	or	apps	that	are	inappropriate	for	our	ad	network.	For	this reason,	we	don't	allow	the	promotion	of	any	of	the	following: Electronic copy available at: https://ssrn.com/abstract=3314078 22 • hatred;	violence;	harassment;	racism;	sexual,	religious,	or	political intolerance	or	organisations	with	such	views • content	that's	likely	to	shock	or	disgust • content	that's	exploitative	or	appears	to	unfairly	capitalise	at	the	expense	of others	[authors'	emphasis]	(Google	2016). Of	all	the	above	opaquely	and	artificially-imposed	restraints,	perhaps	the	most pertinent	is	the	banning	of	content	which	'appears	to	unfairly	capitalise	at	the expense	of	others'	–	the	precise	meaning	of	which	is	unclear,	but	certainly	does not	indicate	that	this	algorithmic	price	mechanism	would	be	able	to	collate	the information	necessary	for	the	creation	of	true	distributed	knowledge. So	far	from	a	'free	for	all'	marketplace,	the	mechanisms	of	Google's advertising	are	closely	controlled	and	monitored.	Google's	2015	year	end	US Securities	and	Exchange	Commission	report	details	how	the	company	has	been 'removing	hundreds	of	millions	of	bad	ads	from	[its]	systems	every	year	[and] closely	monitoring	the	sites	and	apps	that	show	our	ads	and	blacklisting	them when	necessary	to	ensure	that	our	ads	do	not	fund	bad	content'	(US	SEC	2015). The	list	of	prohibited	keywords	is	perhaps	understandably	confidential	in	order to	avoid	manipulation	of	the	system	by	advertisers,	but	this	restriction	is certainly	not	conducive	to	the	creation	of	knowledge	based	on	all	available information. Electronic copy available at: https://ssrn.com/abstract=3314078 23 Another	apparent	distortion	of	the	linguistic	market	can	be	seen	in	Google Ad	Grants,	a	scheme	that	allows	non-profit	and	charitable	organisations	to	use up	to	10k	dollars	a	month	worth	of	AdWords	for	free.	In	effect,	the	scheme enables	small	groups	and	charities	to	enter	into	the	paid	ads	market,	but	a	$2	cap on	these	gifted	keywords	means	that	charities	can	lose	out	to	big	businesses	–	or even	political	partiescompeting	for	the	same	word.	Before	the	UK	General Election	in	2017,	for	example,	a	leading	Alzheimers	charity	lost	its	AdGrants enabled	spot	at	the	top	of	the	search	results	page	when	the	three	main	political parties	began	bidding	on	the	phrase	'dementia	tax',	thereby	pushing	the dementia	charity	to	the	second	page	of	results	(Thornton	2017).	While	Google's apparently	philanthropic	AdGrants	system	might	be	seen	as	a	harsh,	but competition-based	marketplace,	its	distortion	of	the	AdWords	market,	and	its influence	on	the	production	of	knowledge	and	information	is	potentially	huge.	In 2013	Google's	UK	Head	of	Public	Policy	Sarah	Hunter	(HAC	2013)	told	UK parliament	that	'[b]y	the	end	of	2012	we	had	donated	over	$33	million	to	over 11,000	UK	charities	through	giving	them	this	free	advertising'.3 But	as	we	have	seen	with	the	'dementia	tax'	example,	Google	AdWords can	be	used	explicitly	for	political,	rather	than	commercial	advertising	purposes, and	indeed	some	Google	AdGrants	users are	also	actively	encouraged	to	use their	free	keywords	to	fund	and	promote	overtly	political	agendas	such	as	antiextremism	campaigns.	Developed	in	response	to	increasing	government pressure	to	take	more	responsibility	for	potentially	spreading	radicalising 3	The	wording	is	misleading	here.	There	is	no	actual	upfront	donation	of	cash from	Google.	The	'donations'	are	in	kind,	and	as	such	are	artificially	constructed market	influences. Electronic copy available at: https://ssrn.com/abstract=3314078 24 information,	not-for-profit	projects	such	as	the	'Redirect	Method'	are	given	free AdGrants	keywords	in	order	to	ensure	that	'meaningful	counter-speech'	is returned	in	searches	for	potential	terrorist	or	extremist	material	(HAC	2016).	A search	for	'Join	ISIS',	for	example,	would	be	'redirected'	via	the	AdWords system	from	an	organic	result,	to	specially	curated	content	designed	to	counter the	extremist	narrative.	The	Redirect	Method	defends	criticism	of	this manipulation	of	the	market	and	potential	discrimination	against	certain	opinions with	a	curious	neoliberal	logic.	Their	method,	they	say	on	their	FAQ	page is	the	same	tactic	that	businesses	use	to	advertise	to	consumers....	[W]hile	this method	fuels	a	trillion-dollar	ecommerce	business,	it's	hardly	been	used	as	a	tool to	provide	alternative	messages	to	people	who	are	looking	for	extremist	content online	(Redirect	Method	2015). In	Google's	algorithmically	constructed	marketplace,	therefore,	it	seems	it	is acceptable	to	harness	the	power	of	the	market	to	influence	ideologies	as	well	as consumer	choice.	These	are,	in	effect	'centrally	planned'	schemes	that	actively bypass	market	mechanisms	and	prices.	Some	of	Google's	interventions	might	be laudable	from	a	policy	perspective,	but	by	presuming	they	know	better	than	the market,	we	end	up	with	a	mode	of	governance	that	is	actually	far	from	the Hayekian	model. Example	two:	Uber's	surge	pricing The	case	of	Uber	is	possibly	more	challenging.	Started	in	2009,	the	ride-sharing app	Uber	has	been	hailed	as	a	challenge	to	the	monopolies,	regulations	and Electronic copy available at: https://ssrn.com/abstract=3314078 25 power	of	traditional	cab	companies.	On	the	face	of	it,	Uber's	pricing	mechanism seems	the	epitome	of	Hayekian	ideals,	purporting	to	respond	to	changes	in information	(in	demand	and	supply	patterns)	faster	than	any	individual	human could	do	(Hall	et	al.	2015).	Uber	drivers	do	not	need	to	rent	expensive	London hackney	cabs,	pay	for	extensive	training	and	accreditation	(as	in	the	case	of	the Knowledge	test	that	London	cab	drivers	must	pass),	or	buy	the	medallions	that entitle	New	York	taxi	drivers	to	operate.	Riders	(customers)	enjoy	the	benefits	of apparently	market-regulated	cheap	pricing,	while	drivers	enjoy	flexible	working hours	according	to	their	economic	needs. To	some,	Uber	is	close	to	a	perfect	capitalist	free	market,	where	prices	are determined	not	by	price	fixing	or	artificial	regulatory	interventions,	but	by	the knowledge	produced	by	supply	and	demand.	According	to	Uber's	former	C.E.O. Travis	Kalanick '[w]e	are	not	setting	the	price.	The	market	is	setting	the	price' (Morozov	2014).	Prices	go	up	(surge)	when	demand	goes	up,	and	down	when demand	goes	down,	as	explained	by	Peter	Cohen	et	al.	in	their	recent	paper: A	critical	feature	of	Uber	is	that	it	uses	real-time	pricing	("surge"	pricing)	to equilibrate	local,	short-term	supply	and	demand.	A	consumer	wishing	to	take	a particular	trip	can	face	prices	ranging	from	the	base	price...	to	five	or	more	times higher,	depending	on	local	market	conditions	(Cohen	et	al.	2016,	3). Cohen	et	al.'s	paper	is	based	on	analysis	of	data	from	54	million	Uber	user sessions	from	Chicago,	San	Francisco,	Los	Angeles	and	New	York	in	the	first	half of	2015.	The	much-coveted	database	was	supplied	by	Uber	apparently	after Electronic copy available at: https://ssrn.com/abstract=3314078 26 many	requests	to	Freakonomics	author	Steve	Levitt	on	the	proviso	that	Uber employed	economists	would	be	involved	in	the	analysis	and	resulting	outcome. An	interview	with	Levitt	on	the	subject	reveals	his	excitement	at	gaining	access to	such	a	unique	database.	Uber	is,	says	Levitt,	'an	economist's	dream',	or 'the embodiment	of	what	the	economists	would	like	the	economy	to	look	like'	(Levitt 2016). The	scope	of	the	database	made	available	to	Cohen	et	al.	showed	not	only fares	that	were	taken,	but	the	details	of	customers	who	opened	up	the	Uber	app, saw	the	surge	price,	and	decided	not	to	buy	the	product.	This	enabled	Cohen	and his	co-authors	to	compare	Uber	customers	who	might	face	similar	market conditions	while	being	presented	with	different	prices. The	report	highlights	several	obstacles	to	the	idea	of	Uber's	surge	pricing as	an	approximation	of	the	Hayekian	ideal.	The	prices	charged	are	not	organic, emergent	properties	of	distributed	interactions.	Uber	adopts	artificially	set	base rate	prices.	This	has	an	effect	on	its	surge	pricing	system.	It	means	that	'surge prices	are	always	greater	than	or	equal	to	1.0,	i.e.	the	price	is	never	lowered below	the	base	fare,	even	when	market	conditions	suggest	it	should'	(Cohen	et	al. 2016,	7).	On	top	of	this,	Uber	employs	an	incremental	pricing	structure	which means	that	two	customers	'in	near	identical	market	conditions'	can	be	charged	at different	surge	prices	due	to	the	way	the	system	rounds	up	and	down	to	discrete surge	prices.	This	suggests	some	algorithmic	distortion	in	the	prices	consumers are	presented. Electronic copy available at: https://ssrn.com/abstract=3314078 27 The	purity	of	market	regulated	surge	pricing	is	also	currently	being	called into	question	by	a	class	action	in	New	York	that	alleges	that	it	amounts	to	price fixing	amongst	drivers.12	Furthermore,	there	is	no	transparency	in	the information	available	to	either	side,	and	no	means	of	redress	or	control	over	the opacity	of	Uber's	algorithmic	decisions.	In	the	Hayekian	market,	drivers	would be	free	to	adjust	their	prices	in	response	to	their	own	discrete,	tacit	and	subject knowledge	of	the	situations	in	which	they	act.	Uber's	pricing	algorithm	allows	for none	of	this.	The	drivers	have	to	'game'	the	system	if	they	hope	to	supply	it	with their	knowledge.	Uber's	algorithm	is	therefore	purporting	to	mimick	the knowledge-accumulation	and	signalling	of	the	marketplace	while	in	practice administering	a	centrally	planned	system.	It	grows	its	business	by	offering reward	incentives	to	new	drivers,	and	reportedly	withholding	supply	to	drive	up demand.	The	price	mechanism	is	also	distorted	by	the	scoring	system	that	runs side-by-side	with	it.	Riders	are	encouraged	to	score	drivers,	and	vice	versa,	and the	algorithmically	generated	amalgams	of	these	scores	can	then	be	used	to inform	future	market	decisions.	While	this	kind	of	market	produced	information might	seem	helpful,	and	might	be	seen	to	correct	for	lacunae	or	omissions	in	the knowledge-accumulation	powers	of	the	pricing	algorithm,	it	has	the	potential both	to	distort	the	market,	and	to	limit	the	options	of	the	actors	within	it.	One Uber	driver	describes	how	'he	began	to	perceive	the	rating	system	not	as	a mechanism	of	mutual	feedback,	but	of	unequal	power'	(quoted	in	Knight	2016). On	top	of	all	this,	perhaps	the	most	fundamental	obstacle	to	viewing Uber's	model	as	being	regulated	by	the	wisdom	of	prices,	is	a	report	leaked	in early	2016	which	revealed	that	despite	its	growth,	Uber	is	failing	to	make	a Electronic copy available at: https://ssrn.com/abstract=3314078 28 profit.	The	company	is	instead	running	on	an	estimated	$9	billion	of	venture capital	investments,	which	in	effect	makes	Uber	insensitive	to	the	price mechanism,	and	any	market	based	analysis	or	defense	of	their	operating	system a	moot	point.13 Can	algorithmic	price	mechanisms	ever	be	Hayekian? These	two	examples	suggest	that	the	practicalities	of	specific	algorithmically constructed	price	mechanisms	are	far	from	Hayekian.	But	the	case	studies	are not	decisive.	Google	and	Uber	may	intervene	in	their	marketplaces	and	distort the	prices	charged	in	the	manner	of	a	central	planner,	but	this	does	not	mean that	all	algorithmically	constructed	price	mechanisms	are	vulnerable	to	such distortion.	Perhaps	a	system	could	be	created	that	was	closer	to	the	Hayekian model? We	argue	that	this	is	unlikely.	There	are	at	least	two	reasons	for	thinking that	algorithmically	constructed	price	mechanisms	will	never	have	the	same knowledge	accumulation	and	signaling	powers	that	the	Hayekian	price mechanism	is	alleged	to	have.	The	first	reason	for	this	is	that	algorithmic	price mechanisms	always	intermediate	between	suppliers	and	consumers.	Hayek imagined	that	prices	were	emergent	functions	of	discrete	actors	working	in response	to	local	variations	in	discrete,	tacit	and	subjective	knowledge. Algorithmic	price	mechanisms	might	rely	on	mass	surveillance	technologies	that aggregate	from	such	discrete	sources	of	information,	but	the	algorithm's engineers	(or	the	algorithm	itself	in	the	case	of	a	machine-learning	system)	will necessarily	suppose	themselves	to	have	some	greater	insight	or	knowledge	than Electronic copy available at: https://ssrn.com/abstract=3314078 29 these	local	actors.	The	algorithm	will	embed	assumptions	about	the	preferences of	the	market	actors	and	make	predictions	about	their	future	behaviour	in	an effort	to	better	tailor	the	prices	to	the	market.	This	might	be	a	rational	response to	uncertainty	about	future	behaviour	(Bronk	2013),	but	it	is	contrary	to	the spirit	and	purity	of	the	Hayekian	model	of	the	price	mechanism. The	second	reason	for	doubting	the	possibility	has	to	do	with	the distinction	between	information	and	knowledge,	and	the	specific	types	of knowledge	that	Hayek's	argument	focused	on:	the	tacit	and	subjective	forms	of knowledge.	Big	data	algorithms	collect	and	organize	objective	quantifiable	data. Consequently,	they	miss	important	sources	of	knowledge	that	Hayek	held	to	be crucial	to	the	price	mechanism.	They	only	collect	and	act	upon	information whose	relevance	was	foreseen	by	the	system's	designers,	and	that	can	be	seen and	read	by	digital	technologies.	This	has	led	to	systems	that	omit	important information	and	display	systematic	biases	against	certain	populations	(Crawford 2014;	O'Neil	2016).	These	omissions	might	appear	to	be	'in	principle'	fixable.	If we	just	improve	the	data	collection	technology	and	collect	all	sources	of information	(Mayer-Schonberger	and	Cukier	2013),	we	might	be	able	to	arrive	at an	algorithmically	constructed	price	mechanism	that	truly	reflected	the distributed	knowledge	of	the	masses.	But	this	is	to	miss	the	distinctive	character of	the	knowledge	Hayek	concerned	himself	with.	Say	what	you	like	about	the Hayekian	approach,	it	has	a	surprisingly	humanistic	and	anti-objective	ethos	to it.	As	noted	earlier,	Hayek	saw	a	distinction	between	information	and	knowledge. Knowledge	was	a	qualitative	interpretation	or	organization	of	information,	and he	claimed	that	there	were	specific	types	of	knowledge	that	were	available	only Electronic copy available at: https://ssrn.com/abstract=3314078 30 to	humans	in	certain	'circumstances	of	time	and	place'	(Hayek	1945,	524).	These are	forms	of	knowledge	not	easily	articulated	or	codified	and	that	are	ultimately subjective	in	quality.	Given	that	algorithmic	structures	feed	upon	objective	and codifiable	forms	of	information,	it	is	difficult	to	see	how	an	algorithmic	price mechanism	could	accumulate	and	signal	the	types	of	knowledge	that	Hayek	felt were	essential	to	price-oriented	governance. In	sum,	we	think	there	are	considerable	tensions	between	contemporary forms	of	algorithmic	governance	and	market	governance,	at	least	when	the	latter is	understood	in	Hayekian	terms. 4.	What	does	this	mean? We	want	to	close	by	highlighting	some	important	consequences	of	the	preceding argument	for	the	future	of	algorithmic	governance.	Two	consequences,	in particular,	stand	out	from	what	we	have	said. The	first	is	that	we	should	be	wary	and	suspicious	of	any	claims	made	by proponents	of	algorithmic	price	mechanisms	that	tout	their	objective,	knowledge accumulation	and	communication	credentials.	Both	Uber's	Surge	and	Google's AdWords	represent	the	pricing	algorithms	they	employ	as	an	objective, motiveless	agents.	They	claim	that	the	algorithm	reflects	the	wisdom	of	the masses	not	the	policy	preferences	of	the	company.	But	this	is	clearly	not	true: both	companies	use	the	algorithms	to	implement	policy	preferences	and ideologies.	As	Frank	Pasquale	(2016)	notes,	it	is	easy,	and	increasingly	common, for	corporations	such	as	Google	and	Uber,	to	use	both	the	market	and	the Electronic copy available at: https://ssrn.com/abstract=3314078 31 algorithm	as	'excuses'	for	their	behaviour,	allowing	the	algorithm	to	masquerade as	an	objective	regulator	of	individual	behaviour.	This	creates	a	dangerous	and oppressive	precedent.	As	Ezrachi	and	Stucke	remind	us	in	their	recent	book Virtual	Competition,	the	power	behind	algorithmic	regulation	is	not	the	invisible hand	of	the	market,	but	something	perhaps	far	more	insidious: what	might	at	first	glance	be	seen	as	competition	is,	in	fact,	the	creation	of	a	new force	the	"digitalised	hand'.	That	hand,	controlled	by	algorithms,	determines the	market	price	in	any	given	market	through	complex	calculations.	It	is controlled	by	those	who	seek	to	maximise	their	profits	(Ezrachi	&	Stucke	2016, 209). The	question	might	then	be	not	only	whether	algorithmic	governance	structures can	ever	be	compatible	with	a	Hayekian	version	of	market-based	governance, but	that	if	we	are	to	accept	a	new	hybrid	'digitalised	hand',	what	knowledge	does this	type	of	market	produce?	And	further	to	this,	what	epistemological	power	is vested	in	whoever	controls	the	systems	producing	such	'knowledge'?	As Mirowski	and	Nik-Shah	warn	us,	the	'visible	hand	that	fashions	the	auction believes	it	can	govern	the	world'	(2017,	8). In	answering	these	questions,	we	should	also	be	wary	of	attempts	made by	digital	platform	providers	to	'perfect'	or	'complete'	the	databases	of knowledge	upon	which	they	construct	their	algorithmic	markets.	It	is	unlikely that	such	attempts	will	fill	the	knowledge	gaps	needed	for	Hayekian	efficiency. Excited	by	the	rich	source	of	data	from	Uber,	Cohen	et	al.	insist	that	'better	data Electronic copy available at: https://ssrn.com/abstract=3314078 32 are	the	key	to	deeper	insights'	(2016,	22),	but	there	is	also	an	argument	to	say that	more	data,	particularly	data	of	the	wrong	type,	produces	poorer	knowledge. More	data	also	means	infringements	on	privacy,	something	brought	to	light	in Uber's	retaining	of	location	data	from	users'	mobile	phones	for	five	minutes	after the	end	of	a	journey	or	an	interaction	with	the	app,	even	when	they	did	not accept	the	ride.	Uber	has	also	been	repeatedly	criticised	for	its	'God	View' tracking	ability	that	has	been	abused	by	employees.14	This	insistence	on	the	need to	see	or	collect	as	much	data	as	possible	in	order	for	it	to	produce	the	best knowledge,	would	have	been	an	anathema	to	Hayek,	who	rejected	the	idea	that 'scientific'	aggregate	statistics	could	equal	'the	sum	of	all	knowledge'	(1945, 521),	instead	believing	in	the	importance	of	localised,	subjective	tacit	knowledge. Indeed,	if	we	look	to	Hayek's	suspicions	of	statistics	and	scientific	ways	of	trying to	'know'	and	'see'	everything	of	the	central	planning	model,	we	can	see	not	only Uber's	God	Viewdespite	its	protestations	to	the	contrarybut	also	echoes	of Donna	Haraway's	feminist	critique	of	the	God	Trick,	or	the	problematic	claim	to objectivity.	To	Haraway objectivity	turns	out	to	be	about	particular	and	specific	embodiment	and definitely	not	about	the	false	vision	promising	transcendence	of	all	limits	and responsibility.	The	moral	is	simple:	only	partial	perspective	promises	objective vision...	Feminist	objectivity	is	about	limited	location	and	situated	knowledge (Haraway	1988,	582-3). Thus	in	both	Hayek	and	Haraway	we	can	see	a	distrust	of	the	obsession	to	see everything,	or	know	everything,	which	we	might	extend	to	critique	the Electronic copy available at: https://ssrn.com/abstract=3314078 33 algorithmic	God	View	of	both	Uber	and	AdWords	and	the	increasing	modern obsession	with	big	data.15 To	Haraway,	what	she	calls	'unlocatable'	knowledge equals	'irresponsible'	knowledge,	and	'[i]rresponsible	means	unable	to	be	called into	account'.	But	this	is	perhaps	the	main	problem	with	algorithmic	governance and	its	reliance	on	quantitative	big	data	(and	as	much	of	it	as	it	can	get)	–	how can	it	be	called	into	account	–	especially	when	it	operates	under	the	guise	of market	impunity? The	second	consequence	of	our	argument	is	that	it	forces	us	to	reflect once	more	on	the	relationship	between	algorithmic	governance	and	other	modes of	governance,	and	to	question	the	traditional	ideologies	associated	with	them. We	have	framed	the	argument	in	this	article	in	terms	of	a	simple,	classic,	binary view	of	contemporary	forms	of	algorithmic	governance:	are	they	(free)	marketbased	or	(to	some	extent)	centrally-planned?	We	suggested	that	they	are	not	the former,	but	our	argument	might	also	suggest	that	they	are	not	the	latter	–	that we	must	move	beyond	the	binary	view	and	see	algorithmic	governance	as something	genuinely	new	and	different.	As	Lucas	Introna	and	Helen	Nissenbaum identified,	in	the	early	days	of	the	internet,	information	was	pulled	in	two	very different	ideological	directions.	On	the	one	hand,	a	post-modern	narrative	saw the	opportunity	for	the	dissemination	rather	than	centralisation	of	knowledge, but	on	the	other	hand,	the	private	ownership	of	technology	had	a	limiting	effect (2000,	170).	Even	before	that,	there	was	a	confused	ideology	underlying	the development	of	networked	technology,	as	demonstrated	with	the	early cybernetic	projects	such	as	Cybersyn,	and	perhaps	the	somewhat	surprising apparent	harmony	between	Hayek	and	Haraway's	views	on	situated	knowledge Electronic copy available at: https://ssrn.com/abstract=3314078 34 that	we	detailed	above.	Likewise,	the	advent	of	Web	2.0	brought	possibilities	of participation	and	inclusion,	yet	the	privatisation	of	information	by	companies such	as	Google	led	to	a	'dispossession'	of	the	means	of	communication (Jakobsson	&	Stiernstedt	2010). According	to	Philip	Mirowski,	the	reason	for	ideological	confusion	is	that computational	systems	and	neoclassical	views	of	markets	are	fundamentally incompatible.	This	is	one	of	the	reasons	why	'there	is	not	as	yet	a	credible economics	of	knowledge'	in	the	computational	age	(2009,	99). If	the	marketplace	of	ideas	is	thought	to	operate	like	a	computer,	and	then	one insists	upon	neoclassical	economic	theory	as	the	correct	and	appropriate	model of	the	market,	then	economists	are	dealing	in	delusion,	since	they	regularly endow	the	market	with	capacities	that	no	existing	computer	can	or	ever	has possessed.	Although	it	is	not	a	popular	opinion	in	the	contemporary	profession, it	seems	hard	to	escape	the	implication	that	neoclassical	economics	and computers	just	are	incompatible.	One	may	wish	(as	Hayek	did)	to	portray	the entire	market	institution	as	resembling	a	computer,	but	to	do	so,	one	must relinquish	any	commitment	to	the	neoclassical	orthodoxy	(Mirowski	2009,	1434). So	what	then,	can	we	say	of	an	economics	of	algorithmic	knowledge?	Is there	new	wisdom	in	algorithmic	markets?	Perhaps	the	same	problems	exist today	as	they	did	when	Hayek	originally	formulated	the	knowledge	problem,	for Cybersyn	style	central	planners	and	for	algorithmically	governed	markets.	Not all	information	can	(and	perhaps	needs	to	be)	codified.	Just	because	we	have	the Electronic copy available at: https://ssrn.com/abstract=3314078 35 means	of	codifying	things	in	digital,	algorithmic	systems	still	doesn't	mean	that we	can	get	at	the	discrete,	tacit	and	subjective	knowledge	needed	to	create	the perfect	market	(even	if	that	was	our	objective).	More	(or	bigger)	data	does	not necessarily	solve	this	problem,	and	such	data	will	in	any	case	always	be	affected by	the	biases	and	motives	of	the	companies	and	individuals	who	create	and automate	the	processing	algorithms. What	Google	and	Uber	have	created	with tools	such	as	AdWords	and	surge-pricing	are	not	systems	of	algorithmic governance	that	level	the	epistemological	playing	field	and	provide	efficient distribution	of	services	through	the	price	mechanism,	despite	the	quasiHayekian	rhetoric	on	which	they	rely.	Instead,	what	we	have	argued	in	this	paper is	that	any	knowledge	produced	through	such	systems	will	necessarily	carry	the bias	not	only	of	its	algorithmic	structuring,	but	is	also	unreliable	knowledge based	on	centrally	planned	constructed	marketplaces	and	price	setting interventions. 1	Certain	digital	platform	providers	seem	to	have	the	desire	to	control	the marketplace	for	nearly	all	physical	goods.	The	best	example	is,	of	course,	Amazon which	was	partly	inspired	by	Jeff	Bezos's	interest	in	creating	an	'Everything Store'. 2	As	one	of	the	reviewer's	to	this	paper	noted,	there	is	a	distinction	between 'information',	which	is	a	quantitative	concept	and	knowledge,	which	is	more subjective	and	qualitative.	Hayek's	knowledge	problem	is	about	acquiring	and communicating	the	latter,	not	the	former	(though	quantitative	information	could of	course	be	useful	in	doing	this).	This	creates	particular	problems,	as	we	note below,	for	algorithmic	price	mechanisms	because	they	rely	heavily	on	objective quantifiable	data. 3	We	do	not	claim	here,	nor	would	Hayek	have	claimed,	that	free	markets	could 'solve'	the	knowledge	problem.	Unlike	modern	proponents	of	the	efficient market	hypothesis,	Hayek	did	not	believe	that	all	knowledge	was	embedded	in market	prices.	Some	things	were	ultimately	unknowable,	and	the	reality	of Electronic copy available at: https://ssrn.com/abstract=3314078 36 innovation	and	novelty	meant	that	new	knowledge	could	enter	a	market	at	any time. 4	This	is	an	observation	that	has	been	made	by	others.	Ronald	Coase,	for example,	argued	that	one	of	the	points	of	contention	between	himself	and	Hayek arose	from	the	fact	that	Coase	viewed	large	firms	as	akin	to	miniature	centrallyplanned	economies,	but	argued	that	centralizing	some	functions	within	a	firm was	efficiency-maximising	because	it	minimized	transaction	costs	(on	this	see Coase	1937	and	Bowles,	Kirman	and	Sethi	2017,	225).	We	discuss	this	in	more detail	below. 5	The	price	of	any	good	on	a	free	market	is,	ideally	anyway,	determined	by	the interaction	of	supply	and	demand.	Efficient	prices	are	ones	that	increase consumer	surplus	(the	gap	between	what	consumers	are	willing	to	pay	and	what they	actually	pay)	and	producer	surplus	(the	gap	between	the	minimum	price they	would	be	willing	to	accept	and	what	they	actually	receive).	Variable	price algorithms	are	used	to	set	an	efficient	price	that	maximizes	producer	surplus. They	do	this	by	segmenting	the	market	into	different	groups	and	exploiting	the fact	that	some	consumers	are	willing	to	pay	more	than	others.	This	segmentation is	usually	claimed	to	be	a	net	social	benefit	because	the	consumers	who	pay more	can	often	subsidise	lower	prices	for	those	who	are	willing	to	pay	less. 6	Eric	Goldman	(2017),	describes	the	apparent	volte-face	of	the	US	Government in	their	backing	of	Google	in	this	case: http://blog.ericgoldman.org/archives/2017/04/ftc-explains-why-it-thinks-1800-contacts-keyword-ad-settlements-were-anti-competitive-ftc-v-1-800contacts.htm 7	For	example	see:	http://www.mediavisioninteractive.com/blog/paidsearch/google-adwords-cpc-inflation/	(accessed	December	15	2016). 12	For	example	see: http://www.insurancejournal.com/news/east/2016/08/29/424673.htm (accessed	December	15	2016). 13	For	example	see: http://www.forbes.com/sites/briansolomon/2016/01/12/leaked-ubersfinancials-show-huge-growth-even-bigger-losses/#405fe7b15c99	(accessed December	15	2016). 14	For	example	see: https://www.theguardian.com/technology/2016/dec/13/uber-employeesspying-ex-partners-politicians-beyonce	(accessed	December	15	2016). 15	Where	Hayek	departs	(quite	dramatically)	from	Haraway	is	in	a	complete disregard	for	the	causation	of	subjective	knowledge	or	the	correction	of	its inequalities	beyond	the	balancing	laws	of	the	market,	criticism	that	could	also	be leveled	at	the	central	planning	of	Project	Cybersyn. 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