TensionThe ability to see like a data structure afforded us the technology we have today. But it was built for and within a set of societal systems—and stories—that can’t cope with nebulosity. Worse still is the transitional era we’ve entered, in which overwhelming complexity leads more and more people to believe in nothing. That way lies madness. Seeing is a choice, and we need to reclaim that choice. However, we need to see things and do things differently, and build sociotechnical systems that embody this difference.This is best seen through a small example. In our jobs, many of us deal with interpersonal dynamics that sometimes overwhelm the rules. The rules are still there—those that the company operates by and laws that it follows—meaning there are limits to how those interpersonal dynamics can play out. But those rules are rigid and bureaucratic, and most of the time they are irrelevant to what you’re dealing with. People learn to work with and around the rules rather than follow them to the letter. Some of these might be deliberate hacks, ones that are known, and passed down, by an organization’s workers. A work-to-rule strike, or quiet quitting for that matter, is effective at slowing a company to a halt because work is never as routine as schedules, processes, leadership principles, or any other codified rules might allow management to believe.The tension we face is that on an everyday basis, we want things to be simple and certain. But that means ignoring the messiness of reality. And when we delegate that simplicity and certainty to systems—either to institutions or increasingly to software—they feel impersonal and oppressive. People used to say that they felt like large institutions were treating them like a number. For decades, we have literally been numbers in government and corporate data structures. BreakdownAs historian Jill Lepore wrote, we used to be in a world of mystery. Then we began to understand those mysteries and use science to turn them into facts. And then we quantified and operationalized those facts through numbers. We’re currently in a world of data—overwhelming, human-incomprehensible amounts of data—that we use to make predictions even though that data isn’t enough to fully grapple with the complexity of reality.How do we move past this era of breakdown? It’s not by eschewing technology. We need our complex socio-technical systems. We need mental models to make sense of the complexities of our world. But we also need to understand and accept their inherent imperfections. We need to make sure we’re avoiding static and biased patterns—of the sort that a state functionary or a rigid algorithm might produce—while leaving room for the messiness inherent in human interactions. Chapman calls this balance “fluidity,” where society (and really, the tech we use every day) gives us the disparate things we need to be happy while also enabling the complex global society we have today.

Vice versa, many researchers and practitioners who are mainly interested in human-centered social constructs choose to ignore the to them often alienating world of technical systems design.

human-centered aspects that predominate in community informatics, like ethics, legitimacy, empowerment, and socio-technical design

socio-technical

Our under-standing of the gap is driven by technological exploration through artifact cre-ation and deployment, but HCI and CSCW systems need to have at their corea fundamental understanding of how people really work and live in groups, or-ganizations, communities, and other forms of collective life. Otherwise, wewill produce unusable systems, badly mechanizing and distorting collabora-tion and other social activity.

The gap is also CSCW’s unique contribution. CSCW exists intellectually atthe boundary and interaction of technology and social settings. Its unique intel-lectual importance is at the confluence of technology and the social, and its

Nonetheless, it has been argued here that theunique problem of CSCW is the social–technical gap. There is a fundamentalmismatch between what is required socially and what we can do technically.Human activity is highly nuanced and contextualized. However, we lack thetechnical mechanisms to fully support the social world uncovered by the socialfindings of CSCW. This social–technical gap is unlikely to go away, although itcertainly can be better understood and perhaps approached.

Nonetheless, several guiding questions are required based on thesocial–technical gap and its role in any CSCW science of the artificial:• When can a computational system successfully ignore the need fornuance and context?• When can a computational system augment human activity withcomputer technologies suitably to make up for the loss in nuance andcontext, as argued in the approximation section earlier?• Can these benefits be systematized so that we know when we are add-ing benefit rather than creating loss?• What types of future research will solve some of the gaps betweentechnical capabilities and what people expect in their full range of so-cial and collaborative activities?

The final first-order approximation is the creation of technical architecturesthat do not invoke the social–technical gap; these architectures neither requireaction nor delegate it. Instead, these architectures provide supportive oraugmentative facilities, such as advice, to users.

Another approximation incorporates new computational mechanisms tosubstitute adequately for social mechanisms or to provide for new social issues(Hollan & Stornetta, 1992).

First-order approximations, to adopt a metaphor from fluid dynamics, aretractable solutions that partially solve specific problems with knowntrade-offs.

Similarly, an educational perspective would argue that programmers andusers should understand the fundamental nature of the social requirements.

CSCW’s science, however, must centralize the necessary gap between whatwe would prefer to construct and what we can construct. To do this as a practi-cal program of action requires several steps—palliatives to ameliorate the cur-rent social conditions, first-order approximations to explore the design space,and fundamental lines of inquiry to create the science. These steps should de-velop into a new science of the artificial. In any case, the steps are necessary tomove forward intellectually within CSCW, given the nature of the social–tech-nical gap.

Ideological initiatives include those that prioritize the needs of the peopleusing the systems.

Simon’s (1969/1981) book does not address the inevitable gaps betweenthe desired outcome and the means of producing that outcome for anylarge-scale design process, but CSCW researchers see these gaps as unavoid-able. The social–technical gap should not have been ignored by Simon.Yet, CSCW is exactly the type of science Simon envisioned, and CSCW couldserve as a reconstruction and renewal of Simon’s viewpoint, suitably revised. Asmuch as was AI, CSCW is inherently a science of the artificial,

At a simple level,CSCW’s intellectual context is framed by social constructionism andethnomethodology (e.g., Berger & Luckmann, 1966; Garfinkel, 1967), systemstheories (e.g., Hutchins, 1995a), and many large-scale system experiences (e.g.,American urban renewal, nuclear power, and Vietnam). All of these pointed tothe complexities underlying any social activity, even those felt to be straightfor-ward.

Yet,The Sciences of the Artificialbecame an an-them call for artificial intelligence and computer science. In the book he ar-gued for a path between the idea for a new science (such as economics orartificial intelligence) and the construction of that new science (perhaps withsome backtracking in the creation process). This argument was both charac-teristically logical and psychologically appealing for the time.

The HCI and CSCW research communitiesneed to ask what one might do to ameliorate the effects of the gap and to fur-ther understand the gap. I believe an answer—and a future HCI challenge—is toreconceptualize CSCW as a science of the artificial. This echoes Simon (1981)but properly updates his work for CSCW’s time and intellectual task.2

As Heilbroner (1994) and other researchers have argued, technological tra-jectories are responsive to social direction. I make the case that they may alsobe responsive to intellectual direction.1Indeed, a central premise of HCI isthat we should not force users to adapt.

The coevolutionary form of this argument is that we adapt resources in theenvironment to our needs. If the resources are capable of only partial satisfac-tion, then we slowly create new technical resources to better fit the need.

A second argument against the significance of the gap is historically based.There are several variants: that we should adapt ourselves to the technology orthat we will coevolve with the technology.

A logically similar argument is that the problem is with the entire vonNeumann machine as classically developed, and new architectures will ame-liorate the gap. As Hutchins (1995a) and others (Clark, 1997) noted, the stan-dard model of the computer over the last 30 years was disembodied, separatedfrom the physical world by ill-defined (if defined) input and output devices.

First, it could be that CSCW researchers merely have not found the properkey to solve this social–technical gap, and that such a solution, using existingtechnologies, will shortly exist.

Theproblem, then, was centered by social scientists in the process of design. Cer-tainly, many studies in CSCW, HCI, information technology, and informa-tion science at least indirectly have emphasized a dichotomy betweendesigners, programmers, and implementers on one hand and the social ana-lyst on the other.

In particular, concurrency control problems arise when the software, data,and interface are distributed over several computers. Time delays when ex-changing potentially conflicting actions are especially worrisome. ... Ifconcurrency control is not established, people may invoke conflicting ac-tions. As a result, the group may become confused because displays are incon-sistent, and the groupware document corrupted due to events being handledout of order. (p. 207)

Moreover,one of the CSCW findings was that such categorization (and especially howcategories are collapsed into meta-categories) is inherently political. The pre-ferred categories and categorization will differ from individual to individual.

Because some of the idealization must be ignored to pro-vide a working solution, this trade-off provides much of the tension in anygiven implementation between “technically working” and “organizationallyworkable” systems. CSCW as a field is notable for its attention and concern tomanaging this tension.

Incentives are critical.

People not only adapt to their systems, they adapt their systems to theirneeds

There appears to be a critical mass problem for CSCW systems

The norms for using a CSCW system are often actively negotiatedamong users.

Visibility of communication exchanges and of information enableslearning and greater efficiencies

eople prefer to know who else is present in a shared space, and they usethis awareness to guide their work

Exceptions are normal in work processes.

Members of organizations sometimes have differing (and multiple)goals, and conflict may be as important as cooperation in obtaining is-sue resolutions (Kling, 1991). Groups and organizations may not haveshared goals, knowledge, meanings, and histories (Heath & Luff,1996; Star & Ruhleder, 1994).

One finding of CSCW is that it is sometimes easier and better toaugment technical mechanisms with social mechanisms to control,regulate, or encourage behavior (Sproull & Kiesler, 1991)

because people of-ten lack shared histories and meanings (especially when they are indiffering groups or organizations), information must berecontextualized to reuse experience or knowledge. Systems often as-sume a shared understanding of information.

Yet, systems often have considerable difficulty han-dling this detail and flexibility.

Social activity is fluid and nuanced, and this makes systems techni-cally difficult to construct properly and often awkward to use.

March and Simon’s (1958; Simon, 1957) limited rational actormodel underlies CSCW

I also arguelater that the challenge of the social–technical gap creates an opportunity to re-focus CSCW as a Simonian science of the artificial (where a science of the arti-ficial is suitably revised from Simon’s strictly empiricist grounds).

In summary, they argue that human activity is highly flexible,nuanced, and contextualized and that computational entities such as informa-tion sharing, roles, and social norms need to be similarly flexible, nuanced, andcontextualized.

Thesocial–technical gapis the divide between what we know we must support sociallyand what we can support technically. Exploring, understanding, and hopefullyameliorating this social–technical gap is the central challenge for CSCW as afield and one of the central problems for human–computer interaction.