The	social	in	the	platform	trap:	Why	a	microscopic	system focus	limits	the	prospect	of	social	machines Markus	Luczak-Roesch School	of	Information	Management Victoria	University	of	Wellington,	NZ mail@markus-luczak.de http://markus-luczak.de Ramine	Tinati Web	and	Internet	Science University	of	Southampton,	UK rt506@ecs.soton.ac.uk http://www.ecs.soton.ac.uk/people/rt1r13 This	is	the	self-archived	pre-print	version	of	an	article	published	in Discover	Society	under	CC	BY-NC-ND	3.0.	Please	refer	to http://discoversociety.org/2017/01/03/the-social-in-the-platformtrap-why-a-microscopic-system-focus-limits-the-prospect-of-socialmachines/	and	cite	this	as: Luczak-Roesch,	M.,	and	Tinati,	R.,	2017.	The	social	in	the	platform trap:	Why	a	microscopic	system	focus	limits	the	prospect	of	social machines.	Discover	Society,	34. "Filter	bubble", "echo	chambers", "information	diet"	– the	metaphors to	describe today's information dynamics on social	media platforms are fairly diverse (Tufekci, 2016). People use	them	to	describe	the	impact	of	the	viral	spread	of	fake,	biased	or	purposeless	content online,	as	witnessed	during	the	recent	race	for	the	US	presidency	or	the	latest	outbreak	of the	Ebola	virus	(in	the	latter	case	a	tasteless	racist	meme	was	drowning	out	any	meaningful content).	This	unravels	the	potential	envisioned	to	arise	from	emergent	activities	of	human collectives	on	the	World	Wide	Web,	as	exemplified	by	the	Arab	Spring	mass	movements	or digital	disaster	response	supported	by	the	Ushahidi	tool	suite. Social	machines:	The	story	so	far Under the label social machines, researchers investigate this kind of socio-technical phenomena in	order to	understand the	general characteristics that	make	up	a	purposeful and	successful	orchestration	of	humans	and	machines in	a	variety	of	application	contexts. The	term	social	machines	stems	from	a	vision	articulated	by	the	inventor	of	the	World	Wide Web, Sir Tim Berners-Lee, who stated in his book Weaving the Web (Berners-Lee and Fischetti, 2000): "Real life is and must be full of all kinds of social constraint – the very processes	from	which	society	arises.	Computers	can	help if	we	use	them	to	create	abstract social	machines on the	Web: processes in	which the people do the creative	work and the machine	does	the	administration." Early	work	on	social	machines	put	the	individual	systems	such	as	Twitter,	facebook,	reddit, Zooniverse	or	Mechanical	Turk	at	the	center	of	the	consideration.	By	classifying	the	sociotechnical properties of those systems (e.g. incentive mechanisms, information sharing capabilities	or	general	high-level	system	goals)	researchers	devised	frameworks	that	provide developers	with	system	design	patterns that	can	be imitated	or	adapted in	order to	build new	participatory	Web-based	systems	successfully. An alternative to this is the strongly qualitative work on narrative structures about purposeful	collective	processes	(Tarte	et	al.,	2015).	The	goal	of	this	line	of	work	is	to	account for sociality	as	an inherent	property	of social	machines	and to consider	purposeful	action that	can	range	across	the	boundaries	of	individual	platforms. A third line of work is concerned with technologies to spin up autonomous agents to support humans in achieving goals collectively (Ahmad & Kamvar, 2013; Robertson & Giunchiglia, 2013; Chopra & Singh, 2016). This angle is sometimes also referred to as human-agent	collectives	(Jennings	et	al.,	2015)	and	adds	a	constructive	dimension	to	social machines	research	while	the	former	two	work	areas	were	highly	retrospective. Engineering	complex	social	systems	or	social	engineering	of	complex	systems? Returning to our earlier examples, let us suggest that this social machines research is currently in a retrospective platform trap. The study of existing applications and past activities	(work	on	classification	and	archetypes)	carries	the	danger	that	we	get	locked	in	a state	where	we	seek	to	understand	complex	social	phenomena	with	data	that	is	blurred	by the	particulars	of	the	platform.	And	the	attempt	to	attach	agent-based	technology	to	those containers	in	order	to	support	emergent	social	processes	is	challenged	by	the	fact	that	the self-organization	principles	that	govern	"how	the	agents'	actions	translate	into	an	outcome" (Dash et al., 2003) suffers from manipulation and deception by the economic goals of platform	providers. We	recently	confirmed	these	issues	during	the	testing	of	a	novel	crowdsourcing	system.	Our prototype	reacts	upon	bursts	of	activity	occurring	on	different	social	media	platforms	and autonomously engages with human participants to support coordinated problem solving across	the	boundaries	of	a	single	system	(Luczak-Roesch	et	al.,	2016).	The	tool	is	intended	to be applied in scenarios that are inherently broadcasting orientated and do not feature a pre-defined	online	community	to	engage	with,	such	as	disaster	response	using	social	media as	well as citizen science.	However, tests in	which	we linked	our system to facebook	and Twitter showed that the identified bursts only reflected the biased exhausted of the platforms	and	may	even	amplify those.	The	expected	socio-technical filtering function	got stuck due to a lack of reputation of the bot account, leaving the autonomous system repeating	the	messages	it	was	trying	to	collect	feedback	for. This	reputational	underachievement	happened	because	we	deliberately	did	not	invest	into building	a	reputation	through	strategies	that	exploit	the	filtering	and	ranking	algorithms	of the	platforms	(e.g.	by	buying	followers	or	by	building	an	artificial	follower	network	for	the bot	account	upfront).	Hence	we	had	to	observe	that	both	platforms,	Twitter	and	Facebook, hindered	the	system	from	getting	promoted	or	at	least	listed	in	public	feeds.	We	conclude from	this	failure	that	an	autonomous	agent	has	hardly	any	chance	to	gain	visibility	if	it	does not	aim	to	deceive	the	platform	and	consequently	also	other	users	on	it. The	social	machines	dilemma	and	a	call	for	non-positivistic	engines	of	social	action The	example	shows	our	agent	would	have	to	rely	on	economic	principles for	coordination and adaptation. But this would limit its sociality to at most instrumental rationality according	to	Weber's theory	of	social	action	(Weber,	1978)	and	creates	a	critical	dilemma for	the	social	machines	vision.	If	social	machines	are	meant	to	cover	the	full	non-positivistic spectrum of social action, system developers have to make sure that the technical components preserve this spectrum and do not overwrite it with a	model dominated by economics. Such an enriched view to computer and system design ethics responds well to the one presented by Spiekermann (2011). It calls for a general practice of an open design of intelligent and ethical systems, or, as Shadbolt et al. (2016) put it "exploits the power of open	–	open	source,	open	standards,	open	data,	open	licenses".	Both,	the	content	and	the infrastructure, are built by humans,	which calls for similar ethics for the applications and systems	that	the	World	Wide	Web	brought	to	data:	open,	transparent, linked,	owned	and controlled	by	the	creator. All	this	gives	rise	to	a	grand	challenge	for	social	machines	research	that	has	the	chance	to ultimately	demarcate the important	and	distinct	positioning	of this	young	area	within the stress field of computer science, social science, psychology and cognitive science. This challenge is about	winning the incentivisation	game,	which	means	not to try to	mask the artificiality	of	technology	–	as	in	the	famous	task	of	Turing's	imitation	game	(Turing,	1950)	– but to	develop intelligent	and	ethical technology that is resilient	against	continuous	spam and	deception	by	other	human	and	machine	peers interacting	with it.	This involves	smart technology to separate irrelevant,	misleading	and	harmful content,	but	also	–	and	maybe even	more	importantly	–	strategies	to	incentivise	human	and	machine	peers	on	the	Web,	so that	these	decrease	or	even	give	up	any	potentially	ill-intentioned	action. References Ahmad,	S.	and	Kamvar,	S.,	2013,	October.	The	dog	programming	language.	In	Proceedings	of the	26th	annual	ACM	symposium	on	User	interface	software	and	technology	(pp.	463-472). ACM. Berners-Lee, T., Fischetti, M., 2000. Weaving the	Web: The original design and ultimate destiny	of	the	World	Wide	Web	by	its	inventor.	Harper	Information. 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