Ginzburg II: Complexity, clues and the emergence of conjectural computing

Ginzburg discusses the gulf between natural sciences and their increasing abstraction and the concreted and detailed nature of the human sciences, almost always engaged in the individual case, about which natural science almost always remains silent. The “individuum est ineffabile”-imperative will simply not work in history, for example, where the individual case remains a node in a network of clues used to understand and think about history. History, and the social sciences, Ginzburg seems to suggest, need to work from what he calls a conjectural paradigm – and we need to understand the merits and de-merits of that system rather than try for the mathematization of all disciplines.

This particular discussion has developed even further in our time, where the computational turn in the humanities to some represent the logical end point for sciences suffering from physics envy, but now consigning themselves to uninteresting discoveries of patterns where deep insights were once to be had. Ginzburg writes:

“Galileo has posed an awkward dilemma for human sciences. Should they achieve significant results from a scientifically weak position, or should they put themselves in a strong scientific position but get meager results?”(p 110)

But Ginzburg’s warning should not, I think, be read as a warning against the use of computer models and methods in the human sciences, but rather as a reminder that what we need are models that allow for the messiness of the individual case. The computational turn can, in fact, mean that we can explore Morellian space for individual cases much more effectively, finding signs and symptoms that we otherwise would not detect as easily. Computer science can augment the search for semiotic patterns even in the individual case. And perhaps this is the way we have to move overall if we want to say something about society at all. Ginzburg notes the relationship between the need for a use of clues and the increasing complexity of the phenomena under study:

“The same conjectural paradigm, in this case used to develop still more sophisticated controls over the individual in society, also holds the potential for understanding society. In a social structure of ever-increasing complexity like that of advanced capitalism, befogged by ideological murk, any claim to systematic knowledge appears as a flight of foolish fancy. To acknowledge this is not to abandon the idea of totality. On the contrary the existence of a deep connection which explains superficial phenomena can be confirmed when it is acknowledged that direct knowledge of such a connection is impossible. Reality is opaque, but there are certain points – clues, symptoms – which allow us to decipher it.” (p 109)

The exploration of Morellian space, the idea of clues and indirect access to deep relationships seem to suggest the need for a computational extension of semiotics, perhaps a kind of conjectural computing. Which, of course, is what we have seen the past 20 years or so with machine learning, probabilitistic AI and more. But what it also suggests is that these methods of computer science actually are, or should be, methods used in the human and social sciences as well. Computer science, then, is not just a branch of, say, mathematics, but a fundamental shift in the way we think about and model an “ever-increasing complexity” and maybe, just maybe, the way that the two cultures ultimately will merge.

Ginzburg here points towards an observation that has, by now, been suggested many times: that computational methods and technologies actually present a different way to think about what constitutes scientific method – from theory to model, from experiment to simulation, from proof to search.

Such a shift encourages new technologies and inventions. The notion of “conjectural computing” as a way of describing it is not new, but helps us think about the nature and philosophy of the conjecture in a way that is rich and thought-provoking. Finally, Ginzburg in discussing this development also points out the rising importance of the aphorism. He writes:

“Side by side with the decline of the systematic approach, the aphoristic one gathers strength – from Nietzsche to Adorno. Even the word aphoristic is revealing. (It is an indication, a symptom, a clue: there is no getting away from our paradigm.). Aphorisms was the title of a famous work by Hippocrates. In the seventeenth-century collections of “Political Aphorisms” began to appear. Aphoristic literature is by definition an attempt to formulate opinions about man and society on the basis of symptoms, clues; a humanity and a society that are diseased, in crisis.” (p 109)

The rise of aphoristic thinking, the possibility and reality of aphoristic algorithms, algorithms that predict on the basis of clues – all of that is in evidence around us.

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