Paul S. Rosenbloom is a professor of computer science at the USC Viterbi School of Engineering and a project leader at the USC Institute for Creative Technologies (ICT). His work at ICT focuses on a new approach to cognitive and virtual human architectures based on graphical models. Rosenbloom was a member of the USC Information Sciences Institute (ISI) for two decades, leading the institute’s new directions activities over the second decade, and finishing up as deputy director in 2007. Inspired by his activities at ISI, Rosenbloom is writing a book that is tentatively entitled What is Computing? The Architecture of the Fourth Great Scientific Domain. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and has served as a AAAI councilor and as chair of the Association for Computing Machinery Special Interest Group on Artificial Intelligence. Before arriving at USC in 1987, he spent a year as research faculty at Carnegie Mellon University (CMU) and three years as an assistant professor of computer science and psychology at Stanford University. He received a B.S. degree from Stanford in mathematical sciences in 1976 (with distinction) and M.S. and Ph.D. degrees in computer science from CMU in 1978 and 1983, respectively.
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The concept of a great scientific domain broadens what is normally considered to be within the purview of science while identifying four such domains – the physical, life, social and computing sciences – and suggesting that the humanities naturally fit within the sciences as part of an expanded social domain. The relational architecture that has been developed to aid in understanding disciplinary combinations across great scientific domains then guides an exploration of the structure and content of the digital humanities in terms of a space of relationships between computing and the humanities.
The digital humanities analyzed in terms of a space of relationships across two great scientific domains.
For roughly a decade (1998–2007) I led new directions activities at the University of
Southern California’s Information Sciences Institute across the domain of computing
and its interactions with engineering, medicine, business, and the arts &
sciences. Reflections on this extended multidisciplinary experience have led to the
articulation of a new perspective on the nature and structure of computing as a
scientific discipline
The purpose of this article is to further explore these notions with respect to the
emerging area of the
As I have been reflecting on computing, and following the resulting implications
where they lead, I have come to accept the notion that any enterprise that
On the flip side, it is important that a scientific enterprise also not tend to
increase our misunderstanding over time. Fortune telling and religious prophecy can
lead to correct predictions, particularly when ambiguity is combined with generous
post-hoc interpretations and rationalizations, but neither has demonstrated any
ability to predict better than random guessing. On the whole, they thus can
contribute much more to misunderstanding than to understanding despite occasional
hits. Such activities are clearly not scientific. But, while the difference is clear
here, there can be grey areas where it is difficult to determine whether some
activity tends to increase our understanding. Work on a normative
When I read a scientific article, what I care about is learning something new and
important that I can be convinced is true (or at least plausible enough to be worth
considering further). I am agnostic, at least in principle, concerning what methods
were used to invent or discover the new thing, or what methods were used to convince
me of their reality, as long as they achieve the desired ends. Great science requires
all three of these attributes: novelty, importance and veracity. Good science makes
some compromises. To many researchers, all that is important for good science is
veracity, and work may be publishable with miniscule quantities of the other two
attributes as long as there is sufficient evidence of — or methodology for
establishing — truth. In contrast, I often learn more from sufficiently novel and
important conjectures — even before there is a great deal of evidence or methodology
in their favor — leaving me more comfortable in labeling such papers as good science
than traditional small-but-validated results. I don’t learn of necessary truth from
such articles, but they may still revolutionize my way of thinking about a topic,
opening up new possibilities and plausibilities never previously considered. This is
a form of increase in understanding more akin to that emphasized by Kuhn in his work
on scientific revolutions, and that is absolutely crucial to long-term progress in
science
This distinction is actually reminiscent of an interchange in Austen’s (1818)
the company of clever, well-informed people, who have a great deal of conversation,while the latter states that
Good company requires only birth, education, and manners, and with regard to education is not very nice.Mr. Elliot then goes on to state of Anne’s notion that
that is not good company; that is the best.What is
bestin both company and science is that which improves understanding, whether based on novel facts or new ways of thinking. The forms are of secondary importance at best.
The scientific method provides a validated approach for developing insights, but it
is not the only method, nor necessarily always the most appropriate method. For any
particular domain, and any particular problem within that domain, there may be zero,
one, or more methods applicable to them. If we define the
The potential diversity of appropriate methods, both within and across domains, does
suggest a form of
Domains can be ranked by the strength of the methods they are able to effectively use, with the physical sciences traditionally able to use stronger methods than the life sciences and the life sciences stronger methods than the social sciences. But this should not be confused with a claim that this hierarchy correlates with the quality of the science pursued within the domain. We have the need to understand all of these domains, and good science is equally possible within each, based on the strongest methods available for them. The methods used within the humanities, although generally even weaker than those standard in the social sciences, can be applied to increase our understanding within their domain, while stronger methods have so far not proven so successful. They thus can potentially serve as the basis for good science. Still, it is worth noting that even good science can be relatively unproductive if the best available methods are insufficient to increase our understanding of it to any significant extent.
The notion that the term science
is appropriate for all human intellectual
endeavors that meet the criterion of tending to increase our understanding over time
can, to some extent, be viewed as a return to the original notion of philosophy, or
The notion of a great scientific domain actually goes beyond even this broad notion
of understanding, to also include
Interestingly, the humanities are already very much like computing along this
dimension, in that they primarily study human-created artifacts. In Simon’s terms,
both computing and the humanities are sciences of the artificial
Broadly, a great scientific domain concerns the
It is the dynamic richness and vitality of the interactions among an extensive body
of structures and processes that leads to great scientific domains. It is also what
drives the need for experimentation across much of the sciences. It isn’t that
science itself inherently requires experimentation, but that complex interactions
among structures and processes can severely limit the effectiveness of the analytical
methods that can be so useful in less dynamic domains. Mathematics, for example,
focuses almost exclusively on structures — equations, theorems, proofs, etc. — and is
thus able to make great strides without resorting to experiments. However, its
resulting lack of processes and their interactions with structures make it a
The story for the humanities is analogous to that for mathematics. The humanities are full of structures — books, paintings, statues, etc. — and analyses of such structures, but there is, in general, little process to interact with these structures. There are some limited exceptions to this, such as: disciplines like history for which there is significant ambiguity as to whether they belong to the humanities or the social sciences; and linguistics, whose informational aspect implies an overlap with the domain of computing. However, aside from these multidisciplinary outliers, the predominant lack of processes in the humanities deprives it of the dynamic richness that demands experimentation and enables a great scientific domain.
This essentially static essence of the humanities has been noted before, such as in
The political problem with such a merger is, of course, that stronger methods tend to drive out weaker ones that strive to coexist in the same environment. Even in subdomains where the stronger methods are not applicable, their presence in the same intellectual environment can sap the credibility of the weaker ones. Computer science grew out of mathematics in a number of universities, but had to separate itself, at least in part, to have the freedom to perform experiments, a method that although weaker than proof is essential in studying much of computing. The analytical and critical methods of the humanities are weaker than those used in the more traditional sciences, or even than those used in the rest of the social sciences, but they are presumably particularly attuned to their subdomain of the social sciences, and can thus still be valuable to the extent that they remain among the strongest methods available for increasing our understanding of important aspects of people and their culture. Even acknowledging this political problem, though, shouldn’t keep us from an awareness of the true conceptual connection that exists between the humanities and the social sciences, or of the understanding that this connection is possible without diminishing either participant.
The relational architecture provides a means of analyzing scientific topics and
disciplines in terms of the great scientific domains they involve and the
relationships among these domains that are implicated. It also provides a vehicle for
systematically investigating the space of interdisciplinary overlaps that can occur
among domains. In this article, the focus is on analyzing the digital humanities in
terms of the potential space of overlaps the architecture identifies between
computing and the humanities. In computing, architectures often induce languages, and
the relational architecture is no exception. The
At the top level of the relational architecture, the four great scientific domains are denoted by their initial letters: P(hysical), L(ife), S(ocial), and C(omputing). The discipline of digital humanities then concerns the relationships between two of these domains: the social sciences (S) and the computing sciences (C). If the addition symbol (+) is used to denote that there is some form of relationship between two domains, we can express the digital humanities as S+C. However, we can also introduce a new initial for the H(umanities) — with H understood to be a subdomain of S (H ⊂ S) — to specialize the overall expression more particularly for the digital humanities to H+C.
The relational architecture further partitions the generic notion of across-domain
relationships (+) into two general types:
An implementation relationship (/) exists between two domains when multiple
structures and processes in one domain combine to bring into being elementary
structures and processes in the other. The physical domain implements the life domain
(L/P) when molecules and their forces combine to yield cells and their processes.
Similarly the life domain implements the social domain (S/L) when neurons in the
brain combine with each other to implement thoughts in the mind, and the brain joins
with the rest of the body to yield human behavior. Sometimes this general form of
relationship yields a true or full implementation and at other times only a
The implementation relationship yields multiple flavors of digital humanities. When
computing implements the humanities (H/C) we get digital cultural artifacts, such as
digital paintings, sculptures in virtual environments, immersive experiences, and
digital books. Given the dynamic nature of computing, we can expect an ever-larger
fraction of the future of H/C to involve active rather than static artifacts, whether
they are thought of as digital plays, videogames, or simply interactive experiences.
Sometimes H/C artifacts are digital reproductions (simulations) of existing
non-digital artifacts and at other times they are unique artifacts in their own
right. But even a reproduction may itself be a true cultural artifact; a copy of a
famous work of art may, for example, be a cultural artifact despite not being the
original it appears to be. In addition, all computing artifacts can themselves be
viewed as (implementing) cultural artifacts even if there was no such intention when
they were constructed. The area of
In the other direction, the largely static nature of the humanities means that it cannot generally yield a full implementation of computing (C/H) — a book or a painting simply cannot compute all by itself — although special classes of dynamic cultural artifacts, such as complex mobiles, could conceivably be made to compute, and thus to provide a full implementation. What a book or a painting can do is provide a depiction or representation of a computer, essentially yielding a limited form of static simulation. In addition, if we extend the notion of the digital humanities from the overlap between computers (i.e., hardware and software) and the humanities, to the overlap between computing (as a great scientific domain) and the humanities, then the representation of information in general by cultural artifacts — which is also denoted as C/H — could be absorbed within the digital humanities. However, a broader appeal to the dynamics of the great scientific domain of the social sciences — within which the humanities exist as a static subdomain — is necessary more generally to fully implement computing. For example, a
Interaction involves a peer relationship between two domains. For example, in human
computer interaction (S↔C), there is a bidirectional flow of information and
influence between entities from the social and computing domains. However, the
relationship can in general either be bidirectional, as in this example, or
unidirectional. Computational sensing, for example, involves flow of information from
the physical world to a computer (P→C), while robotic manufacturing involves flow of
influence from the computer to the physical world (P←C). In the digital humanities,
flow from the humanities to computing represents the automated computational analysis
of cultural artifacts (H→C); for example, determining clustering of authors based on
their literary styles
These two directions of interaction can loosely be considered as representing computational understanding of the humanities (H→C) and computational shaping of the humanities (C→H). In both cases, it is the computing domain that must be the active partner in the interaction because of the static nature of the content of the humanities. However, one way to remove this limitation is to shift the focus from the static structures of the humanities to those active scholars and scientists who study it. In the relational architecture, scientists are typically represented as members of the social domain (i.e., people) who internally represent and simulate part of their domain. For the humanities, this yields H/S. We can then represent the analysis of computing artifacts by humanities scholars, as is for example studied in critical code studies, by C→H/S. However, if the scientist is an expert in the combination of the humanities and computing — denoted as (C↔H)/S — such studies should actually be denoted as C→(C↔H)/S instead. Either way, this is a compound relationship involving both implementation and interaction. It also includes the full social domain, to represent the scientist, in addition to the humanities and computing.
Other complex variants of the digital humanities can also be represented in an analogous manner. For example, human-computer collaboration in understanding the humanities becomes H→H/(C↔S), signifying analysis of the humanities (H→) by a human-computer entity (C↔S) with expertise in the humanities: H/(C↔S). Similarly, more traditional forms of informatics within the humanities — where the computer serves as a tool for use by the scientist rather than as a full scientific partner — become H→(C↔H/S), where the humanities expertise is now limited to the human participant. These relationships can also go in the reverse direction for shaping, or be bidirectional to represent the interplay between understanding and shaping. But either way, they involve two forms of interaction between computing and the humanities: the interaction of computing with the humanities researcher and the interaction of this pair with the humanities subject matter.
Linguistics provides an interesting special case. It is a core topic within the humanities, and obviously has a tight coupling with sister disciplines such as literature, but language is inherently informational and its use is predominantly social. A natural expression for its subject matter would therefore be something like C/S↔C/S — denoting informational interactions among people. If, as discussed earlier, the purview of the digital humanities is expanded to include the full domain of computing — not just computers themselves but information and its transformation — then linguistics as a whole becomes a prime example of this broader notion of the digital humanities; or at least of computational social science, since the expression uses S rather than H.
For comparison, and to help evaluate the relational approach to understanding the
structure and scope of the digital humanities, it is informative to juxtapose it with
the five major modes of engagement between computing and the humanities discussed by
Svensson: information technology as
a tool, as a study object, as an expressive medium, as an experimental
laboratory and as an activist venue
To start, it does turn out that all five modes fit naturally within the architecture, although some yield more complex expressions than others. Computing as a tool used by humanities researchers maps directly onto the informatics example above: H→(C↔H/S). In this mode, the computer helps researchers acquire, manage, and analyze data about cultural artifacts. Computing as an object of study implicitly views it as (implementing) a cultural artifact: H/C. However, if we also want to explicitly represent that such an artifact is being analyzed by a humanities researcher, this expression can be extended to H/C→H/S. Computing as an expressive medium also takes the form H/C because cultural artifacts are being implemented on computers, although here we may want to expand this to H/C←H/S to emphasize the creative shaping aspect that is central to this mode. The difference between the computer as an artifact and the computer as a medium thus reduces to whether the focus is on understanding what already exists as a cultural artifact — even if not initially intended as such an artifact — versus deliberately creating new cultural artifacts. Computing as an experimental laboratory relates back to computing as a tool, and thus to informatics: H→(C↔H/S). However, instead of using computers as one-shot analysis tools, interactive exploratory analysis is supported in this mode. In contrast with traditional experimentation in active domains though, from what I can understand of the description of this mode, it really does seem to be more exploratory analysis than experimentation. Computing as an activist venue involves a shaping activity, but here the shaping is of society at large rather than merely the scientific domain of the humanities: S←(C↔H/S).
Based on the relational architecture, the two most obvious topics missing from the list of five modes of engagement correspond to two simple relationships: C/H and C→H. With respect to the first, the earlier discussion of the static nature of the humanities implies that a full implementation of computing via the humanities is impossible, except for the limited special case of dynamic artifacts. However, cultural artifacts about computing — whether books, movies, or other forms — do fit naturally here, as would all cultural artifacts embodying information if the digital humanities were broadened to the full domain of computing. Any of these possibilities yields a sixth mode of engagement based on implementing or representing information and its transformation. The closest we have seen to C→H among the five modes discussed by Svensson is H/C→H/S, where a humanities researcher studies fragments of computing as cultural artifacts. The simpler expression can be considered as a more abstract characterization of this kind of activity, with the focus narrowed to just the relationship between the two primary domains involved. However, this simple expression can also denote computing actively shaping the humanities, harking back to automated composition — authoring, painting, etc. — by computers. As discussed earlier, this is an area still in its infancy, yet it is one that could grow to become a major component of the digital humanities.
As a final comment on the digital humanities it is worth noting that while Svensson’s list is cast in terms of engagement with information technology — i.e., the more applied tool-building aspect of computing — computing as a great scientific domain is much more than just a set of tools. It is also theoretical results about information and its transformation, algorithms for transforming information, and a wealth of interdisciplinary topics involving interactions with one or more additional domains, from artificial intelligence (S/C) and robotics (L/(P↔C)) to automated construction (C→P), brain computer interfaces (L↔C), quantum and biological computers (C/P and C/L), online social networks (S↔C)* — where the star (*) represents interactions among arbitrary numbers of human-computer pairs — and the simulation, or possibly even implementation, of everything (Δ/C, where Δ denotes all domains). The set of possibilities opened up for the digital humanities by this broader perspective on computing, and in particular by domain combinations that go beyond H and C to include more complex relationships with additional domains, has yet to be tapped. We have seen a few examples already where it has been useful to bring in the full social domain (S), but it is not hard to conceive of further such topics, such as collaborative human-computer composition: (S↔C)→H. Combination with the physical domain leads to possibilities such as the computational analysis of both the content and physical embodiment — i.e., implementation by the physical domain — of cultural artifacts such as books, paintings and sculptures, yielding the expression H/P→C, or H/P→S/C if the analysis occurs via artificial intelligence. The relational architecture cannot all by itself identify where the interesting points are in this larger space, but it does provide a systematic structure over the space, while also guiding us towards an initial population of this structure.
The focus of this article has been on using the concept of a great scientific domain to understand the humanities as a subdomain of the social sciences, without diminishing either in the process, and then exploring the nature and structure of the digital humanities via the space of possible multidisciplinary relationships afforded by the relational architecture between the humanities and computing. The result is hopefully a better understanding of both the humanities and computing, and in particular of their overlap in the context of the digital humanities.