“Propaganda is called upon to solve problems created by technology, to play on maladjustments and to integrate the individual into a technological world” (Ellul xvii).

How might future research into digital culture approach a purported “post-digital” age? How might this be understood?

1.

A problem comes from the discourse of ‘the digital’ itself: a moniker which points towards units of Base-2 arbitrary configuration, impersonal architectures of code, massive extensions of modern communication and ruptures in post-modern identity. Terms are messy, and it has never been easy to establish a ‘post’ from something, when pre-discourse definitions continue to hang in the air. As Florian Cramer has articulated so well, ‘post-digital’ is something of a loose, ‘hedge your bets’ term, denoting a general tendency to criticise the digital revolution as a modern innovation (Cramer).

Perhaps it might be aligned with what some have dubbed “solutionism” (Morozov) or “computationalism” (Berry 129; Golumbia 8): the former critiquing a Silicon Valley-led ideology oriented towards solving liberalised problems through efficient computerised means. The latter establishing the notion (and critique thereof) that the mind is inherently computable, and everything associated with it. In both cases, digital technology is no longer just a business that privatises information, but the business of extending efficient, innovative logic to all corners of society and human knowledge, condemning everything else through a cultural logic of efficiency.

In fact, there is a good reason why ‘digital’ might as well be an synonym for ‘efficiency’. Before any consideration is assigned to digital media objects (i.e. platforms, operating systems, networks), consider the inception of ‘the digital’ inception as such: that is information theory. If information was a loose, shabby, inefficient method of vagueness specific to various mediums of communication, Claude Shannon compressed all forms of communication into a universal system with absolute mathematical precision (Shannon). Once information became digital, the conceptual leap of determined symbolic logic was set into motion, and with it, the ‘digital’ became synonymous with an ideology of effectivity. No longer would miscommunication be subject to human finitude, nor be subject to matters of distance and time, but only the limits of entropy and the matter of automating messages through the support of alternating ‘true’ or ‘false’ relay systems.

However, it would be quite difficult to envisage any ‘post-computational’ break from such discourses – and with good reason: Shannon’s breakthrough was only systematically effective through the logic of computation. So the old missed encounter goes: Shannon presupposed Alan Turing’s mathematical idea of computation to transmit digital information, and Turing presupposed Shannon’s information theory to understand what his Universal Turing Machines were actually transmitting. The basic theories of both have not changed, but the materials affording greater processing power, extensive server infrastructure and larger storage space have simply increased the means for these ideas to proliferate, irrespective of what Turing and Shannon actually thought of them (some historians even speculate that Turing may have made the link between information and entropy two years before Bell Labs did) (Good).

Thus a ‘post-digital’ reference point might encompass the historical acknowledgment of Shannon’s digital efficiency, and Turing’s logic but by the same measure, open up a space for critical reflection, and how such efficiencies have transformed not only work, life and culture but also artistic praxis and aesthetics. This is not to say that digital culture is reducibly predicated on efforts made in computer science, but instead fully acknowledges these structures and accounts for how ideologies propagate reactionary attitudes and beliefs within them, whilst restricting other alternatives which do not fit their ‘vision’. Hence, the post-digital ‘task’ set for us nowadays might consist in critiquing digital efficiency and how it has come to work against commonality, despite transforming the majority of Western infrastructure in its wake.

The purpose of these notes is to outline how computation has imparted an unwarranted effect of totalised efficiency, and to label this effect the type of description it deserves: propaganda. The fact that Shannon and Turing had multiple lunches together at Bell labs in 1943,  held conversations and exchanged ideas, but not detailed methods of cryptanalysis  (Price & Shannon) provides a nice contextual allegory for how digital informatics strategies fail to be transparent.

But in saying this, I do not mean that companies only use digital networks for propagative means (although that happens), but that the very means of computing a real concrete function is constitutively propagative. In this sense, propaganda resembles a post-digital understanding of what it means to be integrated into an ecology of efficiency, and how technical artefacts are literally enacted as propagative decisions. Digital information often deceives us into accepting its transparency, and of holding it to that account: yet in reality it does the complete opposite, with no given range of judgements available to detect manipulation from education, or persuasion from smear. It is the procedural act of interacting with someone else’s automated conceptual principles, embedding pre-determined decisions which not only generate but pre-determine ones ability to make choices about such decisions, like propaganda.

This might consist in distancing ideological definitions of false consciousness as an epistemological limit to knowing alternatives within thought, to engaging with a real programmable systems which embeds such limits concretely, withholding the means to transform them. In other words, propaganda incorporates how ‘decisional structures’ structure other decisions, either conceptually or systematically.

2.

Two years before Shannon’s famous Masters thesis, Turing published what would be a theoretical basis for computation in his 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem.” The focus of the paper was to establish the idea of computation within a formal system of logic, which when automated would solve particular mathematical problems put into function (Turing, An Application). What is not necessarily taken into account is the mathematical context to that idea: for the foundations of mathematics were already precarious, way before Turing outlined anything in 1936. Contra the efficiency of the digital, this is a precariousness built-in to computation from its very inception: the precariousness of solving all problems in mathematics.

The key word of that paper, its key focus, was on the Entscheidungsproblem, or decision problem. Originating from David Hilbert’s mathematical school of formalism, ‘decision’ means something more rigorous than the sorts of decisions in daily life. It really means a ‘proof theory’, or how analytic problems in number theory and geometry could be formalised, and thus efficiently solved (Hilbert 3). Solving a theorem is simply finding a provable ‘winning position’ in a game. Similar to Shannon, ‘decision’ is what happens when an automated system of function is constructed in such a sufficiently complex way, that an algorithm can always ‘decide’ a binary, yes or no answer to a mathematical problem, when given an arbitrary input, in a sufficient amount of time. It does not require ingenuity, intuition or heuristic gambles, just a combination of simple consistent formal rules and a careful avoidance of contradiction.

The two key words there are ‘always’ and ‘decide’. The progressive end-game of twentieth century mathematicians who, like Hilbert, sought after a simple totalising conceptual system to decide every mathematical problem and work towards absolute knowledge. All Turing had to do was make explicit Hilbert’s implicit computational treatment of formal rules, manipulate symbol strings and automate them using an ’effective’ or “systematic method” (Turing, Solvable and Unsolvable Problems  584) encoded into a machine. This is what Turing’s thesis meant (discovered independently to Alonzo Church’s equivalent thesis (Church)): any systematic algorithm solved by a mathematical theorem can be computed by a Turing machine (Turing, An Application), or in Robin Gandy’s words, “[e]very effectively calculable function is a computable function” (Gandy).

Thus effective procedures decide problems, and they resolve puzzles providing winning positions (like theorems) in the game of functional rules and formal symbols. In Turing’s words, “a systematic procedure is just a puzzle in which there is never more than one possible move in any of the positions which arise and in which some significance is attached to the final result” (Turing, Solvable and Unsolvable Problems  590). The significance, or the winning position, becomes the crux of the matter for the decision: what puzzles or problems are to be decided? This is what formalism attempted to do: encode everything through the regime of formalised efficiency, so that all of mathematically inefficient problems are, in principle, ready to be solved. Programs are simply proofs: if it could be demonstrated mathematically, it could be automated.

In 1936, Turing had showed some complex mathematical concepts of effective procedures could simulate the functional decisions of all the other effective procedures (such as the Universal Turing Machine). Ten years later, Turing and John von Neumann would independently show how physical general purpose computers, offered the same thing and from that moment on, efficient digital decisions manifested themselves in the cultural application of physical materials. Before Shannon’s information theory offered the precision of transmitting information, Hilbert and Turing developed the structure of its transmission in the underlying regime of formal decision.

Yet, there was also a non-computational importance here, for Turing was also fascinated by what decisions couldn’t compute. His thesis was quite precise, so as to elucidate that if no mathematical problem could be proved, a computer was not of any use. In fact, the entire focus of his 1936 paper, often neglected by Silicon Valley cohorts, was to show that Hilbert’s particular decision problem could not be solved. Unlike Hilbert, Turing was not interested in using computation to solve every problem, but as a curious endeavour for surprising intuitive behaviour. The most important of all, Turing’s halting, or printing problem was influential, precisely as it was undecidable; a decision problem which couldn’t be decided.

We can all picture the halting problem, even obliquely. Picture the frustrated programmer or mathematician starting at their screen, waiting to know when an algorithm will either halt and spit out a result, or provide no answer. The computer itself has already determined the answer for us, the programmer just has to know when to give up. But this is a myth, inherited with a bias towards human knowledge, and a demented understanding of machines as infinite calculating engines, rather than concrete entities of decision. For reasons that escape word space, Turing didn’t understand the halting problem in this way: instead he understood it as a contradictory example of computational decisions failing to decide on each other, on the account that there could never be one totalising decision or effective procedure. There is no guaranteed effective procedure to decide on all the others, and any attempt to build one (or invest in a view which might help build one), either has too much investment in absolute formal reason, or it ends up with ineffective procedures.

Undecidable computation might be looked at as a dystopian counterpart against the efficiency of Shannon’s ‘digital information’ theory. A base 2 binary system of information resembling one of two possible states, whereby a system can communicate with one digit, only in virtue of the fact that there is one other digit alternative to it. Yet the perfect transmission of that information, is only subject to a system which can ‘decide’ on the digits in question, and establish a proof to calculate a success rate. If there is no mathematical proof to decide a problem, then transmitting information becomes problematic for establishing a solution.

3.

What has become clear is that our world is no longer simply accountable to human decision alone. Decisions are no longer limited to the borders of human decisions and ‘culture’ is no longer simply guided by a collective whole of social human decisions. Nor is it reducible to one harmonious ‘natural’ collective decision which prompts and pre-empts everything else. Instead we seem to exist in an ecology of decisions: or better yet decisional ecologies. Before there was ever the networked protocol (Galloway), there was the computational decision. Decision ecologies are already set up before we enter the world, implicitly coterminous with our lives: explicitly determining a quantified or bureaucratic landscape upon which an individual has limited manoeuvrability.

Decisions are not just digital, they are continuous as computers can be: yet decisions are at their most efficient when digitally transferred. Decisions are everywhere and in everything. Look around. We are constantly told by governments and states that are they making tough decisions in the face of austerity. CEOs and Directors make tough decisions for the future of their companies and ‘great’ leaders are revered for being ‘great decisive leaders’: not just making decisions quickly and effectively, but also settling issues and producing definite results.

Even the word ‘decide’, comes from the Latin origin of ‘decidere’, which means to determine something and ‘to cut off.’ Algorithms in financial trading know not of value, but of decision: whether something is marked by profit or loss. Drones know not of human ambiguity, but can only decide between kill and ignore, cutting off anything in-between. Constructing a system which decides between one of two digital values, even repeatedly, means cutting off and excluding all other possible variables, leaving a final result at the end of the encoded message. Making a decision, or building a system to decide a particular ideal or judgement must force other alternatives outside of it. Decisions are always-already embedded into the framework of digital action, always already deciding what is to be done, how it can be done or what is threatening to be done. It would make little sense to suggest that these entities ‘make decisions’ or ‘have decisions’, it would be better to say that they are decisions and ecologies are constitutively constructed by them.

The importance of neo-liberal digital transmissions are not that they become innovative, or worthy of a zeitgeist break: but that they demonstrably decide problems whose predominant significance is beneficial for self-individual efficiency and accumulation of capital. Digital efficiency is simply about the expansion of automating decisions and what sort of formalised significances must be propagated to solve social and economic problems, which creates new problems in a vicious circle.

The question can no longer simply be ‘who decides’, but now, ‘what decides?’ Is it the cafe menu board, the dinner party etiquette, the NASDAQ share price, Google Pagerank, railway network delays, unmanned combat drones, the newspaper crossword, the javascript regular expression or the differential calculus? It’s not quite right to say that algorithms rule the world, whether in algo-trading or in data capture, but the uncomfortable realisation that real entities are built to determine provable outcomes time and time again: most notably ones for cumulating profit and extracting revenue from multiple resources.

One pertinent example: consider George Dantzig’s simplex algorithm: this effective procedure (whose origins began in multidimensional geometry) can always decide solutions for large scale optimisation problems which continually affect multi-national corporations. The simplex algorithm’s proliferation and effectiveness has been critical since its first commercial application in 1952, when Abraham Charnes and William Cooper used it to decide how best to optimally blend four different petroleum products at the Gulf Oil Company (Elwes 35; Gass & Assad 79). Since then the simplex algorithm has had years of successful commercial use, deciding almost everything from bus timetables and work shift patterns to trade shares and Amazon warehouse configurations. According to the optimisation specialist Jacek Gondzio, the simplex algorithm runs at “tens, probably hundreds of thousands of calls every minute” (35), always deciding the most efficient method of extracting optimisation.

In contemporary times, nearly all decision ecologies work in this way, accompanying and facilitating neo-liberal methods of self-regulation and processing all resources through a standardised efficiency: from bureaucratic methods of formal standardisation, banal forms ready to be analysed one central system, to big-data initiatives and simple procedural methods of measurement and calculation. The technique of decision is a propagative method of embedding knowledge, optimisation and standardisation techniques in order to solve problems and an urge to solve the most unsolvable ones, including us.

Google do not build into their services an option to pay for the privilege of protecting privacy: the entire point of providing a free service which purports to improve daily life, is that it primarily benefits the interests of shareholders and extend commercial agendas. James Grimmelmann gave a heavily detailed exposition on Google’s own ‘net neutrality’ algorithms and how biased they happen to be. In short, PageRank does not simply decide relevant results, it decides visitor numbers and he concluded on this note.

With disturbing frequency, though, websites are not users’ friends. Sometimes they are, but often, the websites want visitors, and will be willing to do what it takes to grab them (Grimmelmann 458).

If the post-digital stands for the self-criticality of digitalisation already underpinning contemporary regimes of digital consumption and production, then its saliency lies in understanding the logic of decision inherent to such regimes. The reality of the post-digital, shows that machines remain curiously efficient whether we relish in cynicism or not. Such regimes of standardisation and determined results, were already ‘mistakenly built in’ to the theories which developed digital methods and means, irrespective of what computers can or cannot compute.

4.

Why then should such post-digital actors be understood as instantiations of propaganda? The familiarity of propaganda is manifestly evident in religious and political acts of ideological persuasion: brainwashing, war activity, political spin, mind control techniques, subliminal messages, political campaigns, cartoons, belief indoctrination, media bias, advertising or news reports. A definition of propaganda might follow from all of these examples: namely, the systematic social indoctrination of biased information that persuades the masses to take action on something which is neither beneficial to them, nor in their best interests: or as Peter Kenez writes, propaganda is “the attempt to transmit social and political values in the hope of affecting people’s thinking, emotions, and thereby behaviour” (Kenez 4)  Following Stanley B. Cunningham’s watered down definition, propaganda might also denote a helpful and pragmatic “shorthand statement about the quality of information transmitted and received in the twentieth century” (Cunningham 3).

But propaganda isn’t as clear as this general definition makes out: in fact what makes propaganda studies such a provoking topic is that nearly every scholar agrees that no stable definition exists. Propaganda moves beyond simple ‘manipulation’ and ‘lies’ or derogatory, jingoistic representation of an unsubtle mood – propaganda is as much about the paradox of constructing truth, and the irrational spread of emotional pleas, as well as endorsing rational reason. As the master propagandist William J. Daugherty wrote;

It is a complete delusion to think of the brilliant propagandist as being a professional liar. The brilliant propagandist […] tells the truth, or that selection of the truth which is requisite for his purpose, and tells it in such a way that the recipient does not think that he is receiving any propaganda…. (Daugherty 39).

Propaganda, like ideology works by being inherently implicit and social. In the same way that post-ideology apologists ignore their symptom, propaganda is also ignored. It isn’t to be taken as a shadowy fringe activity, blown apart by the democratising fairy-dust of ‘the Internet’. As many others have noted, the purported ‘decentralising’ power of online networks, offer new methods for propagative techniques, or ‘spinternet’ strategies, evident in China (Brady). Iran’s recent investment into video game technology only makes sense, only when you discover that 70% of Iran’s population are under 30 years of age, underscoring a suitable contemporary method of dissemination. Similarly in 2011, the New York City video game developer Kuma Games was mired in controversy when it was discovered that an alleged CIA agent, Amir Mirza Hekmati, had been recruited to make an episodic video game series intending to “change the public opinion’s mindset in the Middle East.” (Tehran Times). The game in question, Kuma\War (2006 – 2011) was a free-to-play First-Person Shooter series, delivered in episodic chunks, the format of which attempted to simulate biased re-enactments of real-life conflicts, shortly after they reached public consciousness.

Despite his unremarkable leanings towards Christian realism, Jacques Ellul famously updated propaganda’s definition as the end product of what he previously lamented as ‘technique’. Instead of viewing propaganda as a highly organised systematic strategy for extending the ideologues of peaceful warfare, he understood it as a general social phenomenon in contemporary society.

Ellul outlined two types: political and sociological propaganda: Political propaganda involves government, administrative techniques which intend to directly change the political beliefs of an intended audience. By contrast, sociological propaganda is the implicit unification of involuntary public behaviour which creates images, aesthetics, problems, stereotypes, the purpose of which aren’t explicitly direct, nor overtly militaristic. Ellul argues that sociological propaganda exists; “in advertising, in the movies (commercial and non-political films), in technology in general, in education, in the Reader’s Digest; and in social service, case work, and settlement houses” (Ellul 64). It is linked to what Ellul called “pre” or “sub-propaganda”: that is, an imperceptible persuasion, silently operating within ones “style of life” or permissible attitude (63). Faintly echoing Louis Althusser’s Ideological State Apparatuses (Althusser 182) nearly ten years prior, Ellul defines it as “the penetration of an ideology by means of its sociological context.” (63) Sociological propaganda is inadequate for decisive action, paving the way for political propaganda – its strengthened explicit cousin – once the former’s implicitness needs to be transformed into the latter’s explicitness.

In a post-digital world, such implicitness no longer gathers wartime spirits, but instead propagates a neo-liberal way of life that is individualistic, wealth driven and opinionated. Ellul’s most powerful assertion is that ‘facts’ and ‘education’ are part and parcel of the sociological propagative effect: nearly everyone faces a compelling need to be opinionated and we are all capable of judging for ourselves what decisions should be made, without at first considering the implicit landscape from which these judgements take place. One can only think of the implicit digital landscape of Twitter: the archetype for self-promotion and snippets of opinions and arguments – all taking place within Ellul’s sub-propaganda of data collection and concealment. Such methods, he warns, will have “solved the problem of man” (xviii).

But information is of relevance here, and propaganda is only effective within a social community when it offers the means to solve problems using the communicative purview of information:

Thus, information not only provides the basis for propaganda but gives propaganda the means to operate; for information actually generates the problems that propaganda exploits and for which it pretends to offer solutions. In fact, no propaganda can work until the moment when a set of facts has become a problem in the eyes of those who constitute public opinion (114).

Looking at Ellul’s quote sideways, the issue isn’t that strategies have simply adopted contemporary technology to propagate an impressionable demographic, but that information is simply always-already efficient and effective in its automation. And with that, we can look at the relationship between digital transmission and computational decision anew.

Here’s Turing again, who in his last published essay Solvable and Unsolvable Problems (1954) articulated a passing remark to the Church-Turing thesis, already outlined in his 1936 paper;

This statement is still somewhat lacking in definiteness, and will remain so [...] The statement is moreover one which one does not attempt to prove. Propaganda is more appropriate to it than proof, for its status is something between a theorem and a definition. In so far as we know a priori what is a puzzle and what is not, the statement is a theorem. In so far as we do not know what puzzles are, the statement is a definition which tells us something about what they are (Turing, Solvable and Unsolvable Problems, 188)

The statement in question not only refers to Turing’s thesis, but also alludes to the predetermined structures for how something can be effectively calculable, (Rosser) and then automated by a machine. Turing wasn’t exactly prophetic in calling it propaganda considering his contributions to cryptanalysis and intelligence. Indeed, the historical relationship between Turing’s contribution to decoding information for the Government Code and Cypher School (the forerunner of GCHQ) using developed technologies, continue to play themselves out in the ongoing NSA mass surveillance revelations (Hopkins).

Yet, why would Turing define a mathematical idea as propaganda rather than proof? He was well aware that his statement was not an effective procedure in itself, which is to say it cannot be proved – it is certainly about proofs, or how one can prove certain things in a formal system and what computational methods can decide results, but it doesn’t give us knowledge about what computational or systematic procedures are. The statement only tells us that automated machines can decide the same winning conditions through equivalent algorithmic methods. The statement or thesis does not tell us why computation might be able to solve problems at all – moreover it can’t even tell us whether a problem can be decided, before one even attempts to find a solution. There is no effective procedure to ‘decide’ every effective procedure, as per the halting problem. Thus following Turing, there is no ‘correct’ use of using this proof for practical use. By contrast, no-one cannot dispute the resolution of a mathematical ‘proof’: for unlike science, once it is proved, by its very nature it cannot be unproved, unless an error lies at the center.

Pushing speculation to its extremes, this might be the reason why Turing understood his thesis as propaganda and not proof; formal systems certainly seem to offer effective procedures to problems, but unless a winning position is proved in advance, it can never fully justify itself in offering solutions in all cases. There is no effective procedure to guarantee a proof about what effective procedures are, and this is Turing’s propaganda: there is no guaranteed provable winning position about the reality of winning positions. There is no guaranteed calculation which calculates all other calculations. There is only propaganda.

Turing’s propaganda operates as if it can always produce idealised solutions to problems, but in its operation, must hide uncomfortable paradoxes which allow its communication to occur in the first place. In other words, there are only concrete methods of effective procedure which unavoidably propagate the view that all problems can be totally solved in advance.

For what is computation if it isn’t the technical means of enacting effective, efficient, propagated pre-determined results through societal means? What if the machine was the propagandist? Frederic Charles Bartlett argued that propaganda was primarily a decisive method of suggestion, not simply designed to control psychological behaviour, but to acquire specific, effective results through purposeful action (Bartlett). Perhaps we could add to this, the deeper realisation that propaganda is no longer limited to the limits of psychological behaviour, or the limits of societal communities, but extends to the limits of decisional machines which decide results in an infrastructure.

Perhaps a post-digital culture might address newer forms of propaganda emergent in computational culture: not posters, pamphlets, zines and broadcasts, but also, gamification, platform devices, spy-ware, apparatuses, services and subscriptions: each one only allowing certain pre-determined outcomes to be realised, each one already deciding (or propagating), a limited number of routes, which users mistake for their own ‘openness’. If there is one thing Silicon Valley would love to solve, in their self-congratulatory wallowing, it is detecting whether a certain problem always has a solution: and whenever they come up with one, it usually has a market to satisfy and a propagative strategy to make it seem beneficial.

Digital information in a post-digital ecology doesn’t seem to want to be free (Polk), or at the very least, it doesn’t want to look like it is: rather digital information simply wants to propagate itself as a watchdog for any problems that are always-already resolved, refusing its own transparency in turn. The best we can hope for is to understand information’s propagative effect, and ask not of its truth, but of what it propagates. Following Orwell, we should admit that as far as digital innovation is concerned, “[a]ll propaganda is lies, even when one is telling the truth. I don’t think this matters so long as one knows what one is doing, and why” (Orwell, Davidson & Angus 229).

 

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