Paper to be presented at the DRUID 2012

on

June 19 to June 21

at

CBS, Copenhagen, Denmark,

Interlaced Knowledge in the Emergence of a New Architecture at ATLAS,

CERNPhilipp Tuertscher

Vienne University of Economics and Business AdministrationE&I Institute for Entrepreneurship and Innovation

philipp.tuertscher@wu.ac.at

Raghu GarudPennsylvania State UniversityManagement and Organization

rgarud@psu.edu

Arun KumaraswamyTemple University

Strategic Managementakumaras@temple.edu

AbstractWe report on a longitudinal study of the emergence of the ATLAS detector, a complex technological system developedat CERN, Geneva. Our data show that justification is an important mechanism for coordination, and that it results in thecreation of interlaced knowledge, i.e., a partial overlapping of knowledge across actors and groups. Neither ?modular?nor ?common,? such interlaced knowledge confers generative properties on the enterprise by enabling actors withdiverse backgrounds to make informed choices on technological alternatives, anticipate and address latentinterdependencies, and harmonize their contributions leading to the emergence of a system architecture over time.Jelcodes:O32,O33

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INTERLACED KNOWLEDGE IN THE EMERGENCE OF A NEW ARCHITECTURE AT ATLAS, CERN

ABSTRACT

We report on a longitudinal study of the emergence of the ATLAS detector, a complex technological system developed at CERN, Geneva. Our data show that justification is an important mechanism for coordination, and that it results in the creation of interlaced knowledge, i.e., a partial overlapping of knowledge across actors and groups. Neither “modular” nor “common,” such interlaced knowledge confers generative properties on the enterprise by enabling actors with diverse backgrounds to make informed choices on technological alternatives, anticipate and address latent interdependencies, and harmonize their contributions leading to the emergence of a system architecture over time.

Keywords:

Modularity; complex system; knowledge structure

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Many complex technological systems are organized modularly to benefit from the contributions of

multiple organizations.2 Existing literature suggests “decomposability” (Simon, 1962) as a principle for

the division of labor among organizations to reduce transaction costs and facilitate distributed

development (Baldwin, 2008). These distributed efforts must be integrated eventually for the system to

perform as a whole, a task that requires coordination across boundaries (Clark & Fujimoto, 1990; Ulrich,

2003). To the extent that the technological and social architectures “mirror” each other, coordination is

enhanced (Baldwin, 2008; Sanchez & Mahoney, 1996).

However, such upfront decomposition and mirrored coordination may not be possible in the

development of new complex systems. This is because system requirements and features are difficult to

identify initially and subsystem and component technologies are unclear while they are still emerging.3 To

complicate matters, engaged actors responsible for developing specific subsystems may attempt to

actively shape system architecture to their advantages based on their own technological frames (Garud,

Jain, & Kumaraswamy, 2002). The process of architecture emergence, therefore, is likely to be fraught

with potential controversies and conflict over and above the complications introduced by indeterminacy in

the technology.

These observations motivate our question: What are the mechanisms that can engender

coordination among actors under these circumstances? To address this question, we conducted a study of

the emergence of ATLAS, a complex technological system that was developed at CERN, the European

Organization for Nuclear Research, over a period of 18 years. Our in-depth probe of the ATLAS

collaboration highlights how participants coordinated their activities by creating multiple forums for

engagement in which proponents of different technological options offered justifications for their

proposed alternatives. The justification processes that unfolded resulted in the creation of interlaced

knowledge – a partial overlap of knowledge across participants – that was neither “modular” (Baldwin,

2008; Sanchez & Mahoney, 1996) nor “common” (Grant, 1996b). In turn, such interlaced knowledge

                                                                                                               2 We use the term “complex” to suggest that overall system performance is based not only on the performance of its subsystems and components, but also on the interactions among them. 3 For consistency, in the remainder of the paper, we use the term ‘subsystems’ to denote what are typically referred to as subsystems, modules or components in the modularity literature.

3

conferred generative properties on the ATLAS collaboration enabling participants to anticipate and

address problems even as the system architecture emerged over time.

Our paper is organized as follows. First, we provide a theoretical discussion and framework for

understanding the coordination challenges involved in the emergence of a new system architecture. Next,

we discuss our research site, methods, data and analytical techniques, and present a longitudinal case on

the emergence of a revolutionary architecture at ATLAS. Then, we discuss and interpret our findings and

conclude by presenting the implications of our findings.

EMERGENCE OF ARCHITECTURES IN COMPLEX SYSTEMS

The literature on innovation highlights the role of modularity in the emergence of architectures in

complex technological systems (Baldwin & Clark, 2000; Henderson & Clark, 1990). A modular

architecture consists of self-contained building blocks or subsystems that interact with one another

through standardized interfaces (Garud & Kumaraswamy, 1995). In systems with modular architectures,

various subsystems can be developed autonomously and in parallel at low cost (Garud & Kotha, 1994).

This is because coordination of the development process occurs through standardized interface

specifications embedded in the architecture, obviating the need for coordination through managerial

authority (Sanchez & Mahoney, 1996). Modularity also reduces system complexity because the scope of

interaction between subsystems is limited by the standardized interface specifications (Baldwin & Clark,

2000). Moreover, modular architectures enable the mixing and matching of subsystems to rapidly create a

variety of system configurations in response to market and technological changes (Garud &

Kumaraswamy, 1995; Sanchez & Mahoney, 1996; Schilling, 2000; Schilling & Steensma, 2001; Ulrich &

Eppinger, 2000).

What is the basis for the creation of a system’s architecture? Current research suggests that

technological systems have “natural transfer bottlenecks” that represent optimal locations for

decomposition (Baldwin, 2008). Consistent with this line of thinking, recent contributions offer tools and

heuristics, such as the “design structure matrix” (Baldwin & Clark, 2005; Eppinger, Whitney, Smith, &

Gebala, 1994), to identify natural boundaries for partitioning complex systems. The intent of such tools is

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to offer “design rules” (Baldwin & Clark, 2000; Brusoni & Prencipe, 2006) that are useful for organizing

and improving existing system architectures. Similarly, work on task decomposition by Ethiraj and

Levinthal (2004) suggests that the problem of designing a complex system can be conceived as a search

for the optimal modular architecture that, in turn, facilitates the allocation and governance of subtasks (i.e.,

the development of individual subsystems) to different contributors (Baldwin, 2008; Langlois, 2006;

Simon, 1996). Indeed, this forms the basis for a “mirroring hypothesis” (Baldwin & Clark, 2000; Sanchez

& Mahoney, 1996) wherein technical and social architectures mirror one another.

In the case of novel systems, however, such a natural partitioning of the technological system and

mirroring of the social system may not be a straightforward task. Before a dominant design emerges

(Abernathy & Utterback, 1978; Anderson & Tushman, 1990), many potential options may exist for each

subsystem and a number of combinations may exist across subsystems that render it difficult to identify

the “one best” (i.e., optimal) solution. Moreover, it may not be feasible to identify natural boundaries at

the outset because many interdependencies among subsystems of an emerging technological system are

latent and become visible only upon development (Alexander, 1964). Even if emerging architectures build

partially upon existing technologies, novel ways of linking existing technologies or combining them with

newly designed technologies may cause interactions that may not have been observed in prior systems

(Barry & Rerup, 2006; Garud & Munir, 2008). Ironically, all these difficulties in predicting

interdependencies may result in situations where subsystem designers have to contend with unproductive

interdependencies post hoc because of efforts to decompose ex ante (Staudenmayer, Tripsas, & Tucci,

2005).

Research on technology evolution suggests that we must also consider social factors to understand

the emergence of complex technological systems. As Tushman and Rosenkopf (1992) noted, the choice

between alternative architectures involves not only technical decisions, but also issues that revolve around

self-interests of involved parties. Optimizing the performance of individual subsystems may lead to

conflicting claims and controversies impacting system emergence. Moreover, controversies may arise on

5

the very notion of system performance and the various dimensions of merit that are critical to system

performance (Anderson & Tushman, 1990; Garud et al., 2002; Tushman & Rosenkopf, 1992).

All these sociotechnical factors give rise to what we label a ‘coordination of coordination’

problem. By this, we mean that the technical architecture cannot serve as the basis for embedded

coordination (Sanchez & Mahoney, 1996), as it has yet to emerge. Therefore, actors (with their different

perspectives) must find ways to coordinate their activities to develop a technical architecture that, only

once it has emerged, can become the basis for embedded coordination. Although this is a significant

challenge, current literature has not yet addressed this question systematically. We do so by exploring the

emergence of one complex system that was driven neither by embedded coordination nor by social

hierarchy: the ATLAS particle detector at CERN, in Geneva.

RESEARCH SITE AND METHODS

ATLAS, the high energy particle detector located in Geneva4, is a large-scale scientific project;

similar endeavors can be found in domains ranging from astronomy to life sciences (Galison & Stump,

1996). The notional cost of the detector has been estimated at one billion dollars and is the culmination of

a project that started in 1992 when physicists and engineers began designing a detector to identify the sub-

atomic particles created by particle beam collisions in the Large Hadron Collider (LHC). So far, the

project has proceeded through three phases: design (1992-1998), construction and installation (1998-

2007), and calibration and ‘data–taking’ (since 2007). The focus of our study is on the first two phases

when the architecture of the ATLAS detector emerged.

The detector (Figure 1), a complex technological system that is 45 meters long, 25 meters in

diameter and weighs about 7,000 tons, is housed in an eight-story underground cavern. It consists of

subsystems, each of which is a complex system. Like a giant microscope, the detector is capable of

identifying high-energy particles that are invisible to the naked eye and are created when proton beams

collide. The different subsystems identify different types of particles based on energy and momentum

measurements. The data generated during collisions can easily fill 100,000 CDs every second. However,

                                                                                                               4 http://atlas.ch/

6

ATLAS singles out only a small fraction of potentially interesting events for later analysis, still generating

a volume of data equivalent to 27 CDs per minute.

-- Figure 1 here --

With the first collisions of subatomic particles in 2009, the ATLAS project entered its “data-

taking” phase. But, to reach this outcome, a tremendous collaborative effort was required over a period of

18 years involving more than 2,800 scientists and engineers with specific expertise in the areas of physics,

engineering and computer science. Because of the unprecedented size and scope of the project, previous

designs could not be re-used for the ATLAS detector. Groups of scientists and engineers, affiliated with

170 institutions distributed across 38 different countries (and bound together by a non-enforceable

Memorandum of Understandings rather than by a traditional hierarchy) collaboratively designed and built

the various ATLAS subsystems before shipping them to Geneva. Eventually, all these subsystems had to

be lowered into a cavern 100 meters underground and installed in the right sequence akin to building a

ship in a bottle. Once installed, replacing a subsystem was virtually impossible.

Data Collection

For this study, we gathered 20 years of archival data (1991-2010) and six years of contemporary

data (2005-2010) (please see Table 1 for a summary). For our analysis, we relied primarily on archival

data from CERN’s archives because they were generated in real time and represent “unobtrusive measures”

(Webb & Weick, 1979). The archival materials included hundreds of documents such as transparencies

presented at meetings, meeting minutes, and internal documents pertaining to central issues and decisions.

The materials also included presentations made by scientists to the ATLAS collaboration as a whole and

to other scientists at professional gatherings.

-- Table 1 here --

In addition to data from the CERN archives, we gained access to personal notes written by

participating scientists. These personal notes offered insights into the micro details of emergence that were

not readily apparent in the meeting minutes and other archival sources from CERN. Electronic mailing list

archives that captured real-time conversations amongst ATLAS scientists complemented this rich data.

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Besides this wealth of data, we also carried out 108 extensive, in-depth interviews with a range of

participants from different subsystem communities and groups within the collaboration. Following

Lincoln and Guba’s (1985) guidelines for “purposeful sampling,” we interviewed scientists involved in

deliberations around key decisions during the ATLAS project. We began by interviewing scientists whose

names were listed in the meeting minutes and then employed a snowballing technique to identify and

interview others. These informants helped us make sense of the many technical details and to identify

critical points during the emergence of the ATLAS architecture.

Many of these critical points turned out to be controversies related to choices between subsystems

and their designs. The presence of controversies is not surprising given the complexities involved and the

uncertainties surrounding the technologies themselves. Indeed, others too have noted the presence of

controversies in such settings and have advocated following controversies to understand the core

constitutive dynamics of collaborations (Latour, 1987; Pickering, 1993). Our informants had no difficulty

remembering these controversies (such as around the “air core toroid decision” or the “inner detector

cooling review,” discussed later in this paper).

The interview process itself was iterative. For instance, we gathered and analyzed archival

material pertaining to new events identified by our informants. Then, we sought additional informants on

the basis of information found in archival records. We continued this process until we reached data and

theoretical saturation (Strauss & Corbin, 1998).

In addition, during 12 field visits, each typically lasting a week, we had the opportunity to observe

conferences and participate in meetings, where we took detailed notes. We also took advantage of chance

encounters and engaged in many informal conversations with participants in the ATLAS collaboration.

Both during and after these interactions, we also took detailed notes. These field visits gave us a deep

appreciation of the culture of collaboration at ATLAS that emerged over many years. The data analysis

and results of our study are based on this overall corpus of data and our in-depth understanding of the

research site.

Data Analysis

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Following suggestions made by scholars who have studied large-scale projects and organizations

similar to ATLAS (Latour, 1987; Pickering, 1993), we examined the controversies and challenges that

participants encountered and how they were addressed. We recorded critical events, including differences

in opinion, choices made, and conflicts that arose regarding the ATLAS architecture. The archival

documents we examined revealed that these controversies had been discussed in several different venues

such as in working groups, review panels, or plenary meetings. Moreover, our informants confirmed that

these controversies and their resolutions were critical moments in the emergence of the architecture.

Finding such mutually confirming evidence increased our confidence in the quality, depth and validity of

our data and analysis.

We also studied documents such as the ATLAS Letter of Intent, ATLAS Technical Proposal and

the Technical Design Reports for individual ATLAS subsystems. These detailed descriptions of

technological concepts and design considerations represented snapshots of the ATLAS architecture in the

years 1992, 1994, and 1997. These snapshots made it possible for us to trace how the ATLAS architecture

had emerged over time.

Triangulating across the data sources, we generated a chronology of events as a way to trace and

understand the mechanisms that participants used to coordinate their activities over time. Consistent with a

process perspective, we considered each event as an important occurrence within a larger flow (Van de

Ven, 1992), an approach that led to a deeper understanding of the unfolding processes. Following

recommendations offered by Langley (1999), we also drew diagrams to get a holistic understanding of the

flow of events.

We coded the data for thematic content (Miles & Huberman, 1984) by abstracting raw quotations

and text segments from our interviews and from archival materials such as meeting minutes. In particular,

we studied how controversies arose, were addressed and resolved as the advocates of different

technological solutions confronted one another during meetings and review panels. We performed pattern

coding (Miles & Huberman, 1984) to identify emergent themes and explanations. Tentative insights

emerged as we engaged with the data – e.g., that justification processes, which enabled coordination, also

9

generated overlaps in knowledge among participants. We decided to provisionally test this hypothesis by

using an embedded design approach (Yin, 1994) that enabled us to study the processes unfolding within

different ATLAS subsystem communities and to identify patterns across these communities (Trochim,

1989). Specifically, we compared justifications and overlaps in knowledge over time within and across

different ATLAS subsystem communities.5 To do so, we used “latent semantic analysis” (Deerwester,

Dumais, Furnas, Landauer, & Harshman, 1990), a natural language processing technology, to explore the

importance of justifications from the documents produced by ATLAS scientists within each community

during the development process. Moreover, we used a combination of co-word analysis (Callon, Law, &

Rip, 1986) and co-authorship analysis (Palla, Barabási, & Vicsek, 2007) to depict and compare the

structure of the distributed yet collective knowledge that emerged over time within the communities that

were working on different ATLAS subsystems.

At various stages of our research process, we had the opportunity to present our findings and

interpretations to the ATLAS members who validated the results of our analysis and our interpretations.

They also offered us useful feedback that we incorporated into our analysis. Our interactions with these

scientists and engineers increased our confidence in the data we had gathered, the analysis we had

conducted, and the inferences we had induced. We share these in the remainder of the paper.

                                                                                                               5 We analyzed justifications and knowledge structures of communities working on all three ATLAS subsystems; however, given space limitations here, we report our findings for the calorimeter and muon spectrometer communities only. These were the two detector subsystem communities that were the most different with respect to their justification patterns. The results obtained for the inner detector subsystem community are consistent with our reported findings and are available upon request.

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EMERGENCE OF ARCHITECTURE AT ATLAS, CERN

The origins of ATLAS can be traced to two successful CERN High Energy Physics (HEP)

experiments (UA1 and UA2) in the 1980s that led to a Nobel Prize in 1984. Even as these experiments

were unfolding, scientists from both experiments had initiated R&D projects to explore new concepts for

future particle detectors.6 Each R&D project was of limited scope, focusing on a specific technology that

potentially could be used for detecting one or more types of particles in a future HEP experiment. When

CERN decided to build the Large Hadron Collider (LHC), these scientists started “proto-collaborations”

to integrate their disparate efforts into a joint proposal that included all components required for a new

particle detector (Knorr-Cetina, 1995). By 1992, several proto-collaborations had emerged, each interested

in developing and building a detector for the LHC.

In response, CERN sought a merger among the various proto-collaborations to rationalize the use

of limited resources. Two groups, The Experiment for Accurate Gamma, Lepton and Energy

Measurement (EAGLE) and Apparatus with SuperCOnducting Toroids (ASCOT), came together to form

ATLAS, an acronym for A Toroidal LHC ApparatuS. The immediate charge to the newly formed group

was to come up with a single new architecture. Ex ante, there was no inevitability in the emergence of a

specific architecture for the ATLAS detector system.7 Indeed, both EAGLE and ASCOT had opted for

different approaches that were manifest in the form of different designs.

Specifying a suitable architecture was a challenging task for various reasons. Prime amongst them

was the unprecedented levels of energy, radiation and collision rates required to discover sub-atomic

particles such as the Higgs Boson (Stapnes, 2007). Operating at such a level of performance implied

pushing the frontiers of science and technology. Consequently, prior detector designs, or even subsystems

thereof, could not be reused. Indeed, the new design would be based partly on technologies that had yet to

                                                                                                               6 This parallel development of upgrades and future generations of experiments is typical of HEP experiments because the development and construction of a new detector system requires 10 – 15 years of work. While ATLAS was being developed, several groups within the collaboration were working already on upgrades for 2015 and beyond. 7 Currently, there are three other collaborations at the LHC, each with its own architecture. In fact, one of the collaborations, CMS, also shares a common objective with the ATLAS collaboration (i.e., to detect the Higgs Boson particle), but uses a detector architecture that is fundamentally different from that used by the ATLAS collaboration (ATLAS Collaboration, 1994; CMS Collaboration, 1994).

11

materialize, and scientists could only estimate the performance of these technologies using sophisticated

simulation studies.

Complicating matters, the two proto-collaborations, EAGLE and ASCOT, had already begun

working on different designs. Each design was comprised of somewhat equivalent but competing

alternatives. Consequently, each group wanted its own technology to be used for the new ATLAS detector

system. Many different configurations were possible depending upon the specific choices made. The

possibility of competing options in the presence of technological uncertainty generated controversies.

Given the distributed and voluntary nature of the ATLAS collaboration, these controversies could not be

resolved by a centralized decision making body as in a traditional hierarchy. Moreover, no one group had

the necessary knowledge to make such decisions, and a top-down choice against a particular technology

could easily alienate the group of scientists associated with that technology. If not handled delicately, this

group could exit the collaboration and, in the process, take with them critical knowledge, manpower and

financial resources.

Overall, then, the ATLAS collaboration confronted a situation where there was neither a technical

architecture nor a social hierarchy in place to shape the development of this complex system. The lack of a

technical architecture precluded the possibility of embedded coordination, in which participants need only

know the functioning of their specific subsystem while leaving the coordination of activities across

subsystems to already agreed-upon interfaces (Sanchez & Mahoney, 1996). The lack of a social hierarchy

precluded systems integration by a few as the basis for coordination among disparate groups with different

solutions (Brusoni, Prencipe, & Pavitt, 2001). In short, the ATLAS collaboration confronted what we

labeled earlier as a coordination of coordination problem.

Coordination through justification

To address this problem, the senior scientists at ATLAS instituted a number of forums to foster

coordination. Matters pertinent to specific subsystems were addressed in subsystem working groups.

Issues relevant to more than one subsystem were presented at plenary sessions of the collaboration to then

12

be discussed, scrutinized, and eventually voted on by members of a collaboration board.8 For particularly

challenging problems, ATLAS set up dedicated forums involving experts from various subsystems and,

sometimes, even experts from outside the ATLAS collaboration.

The most important of these dedicated forums were review panels at which proponents of

competing technologies presented their alternative designs for each subsystem. Underlying the functioning

of these panels was a core principle that guides ATLAS even today – that competing technological options

“…can and should be analyzed in a scientific (and not emotional) way” (Scientist F., 1992). Each panel

was charged with the task of recommending to the members of the collaboration at large “which one of

the competing technologies to choose and why” (Scientist D., 2011). The rationale behind this principle

was to reduce the role of politics by allowing “obvious” decisions to emerge rather than being imposed. A

physicist, who had participated in many of these review panels, described the processes that unfolded:

The panelists chosen by the management were the judges and there were two parties against each other. In our case, for the hadronic part [of the calorimeter], we were against the liquid argon people. We were looking for problems with their approach and they were looking for problems with our approach. The whole thing was relatively formal. We would present our results and our calculations and they would present their results and their calculations. And, then, we would ask them nasty questions in writing and they would ask us nasty questions in writing. There would be answers to these questions at the next meeting. (Scientist C., 2006) As this observation underscores, review panels were intended to enable an interpenetration of

knowledge between members of different groups working on a specific subsystem. In particular, the

dialectical processes that unfolded at these review panels required each group to develop a deep

understanding of competing technologies so as to identify their strengths and shortcomings. These were all

articulated in the form of a critique to which proponents of a technology had to respond. Proponents of a

technology had to then justify their technological choice by offering evidence from simulation studies,

tests from prototypes or support from external experts. These justifications were explicated in the form of

presentation handouts and reports to be scrutinized by competing groups and members of the collaboration

at large.

                                                                                                               8 Each participating institute has one vote on the collaboration board to decide on issues relevant to the overall collaboration. It is the role of the plenary to raise and discuss these issues beforehand.

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We found many examples of such dialectical processes in all the review panels. For instance, an

important issue had to do with the tolerance of some sensors to radiation. Members of the review panel

sought details from the scientists working on this technology as to how they had estimated expected

radiation damage. In response, the group explained that they had used a “scaling law” based on prior HEP

experiments. The review panel, in turn, scrutinized the reply and questioned the appropriateness of the

group’s assumptions and rejected their arguments. As is recorded in the minutes of a meeting, members of

the review panel noted: “It is not well accepted by everybody that a simple scaling law, predicting the

lifetime in years as being given by tau = (R[cm]/10)**2, can be applied also at small radii” (Meeting

minutes, 1993-09-28). Because of this feedback, the group had to reconsider their assumptions and

provide new estimates for radiation damage based on more appropriate scaling laws.

Even scientists who were not involved in the review engaged with this dialectical process For

instance, a scientist who was not formally a participant of the review process sent the following email

correspondence (1993-11-22) to the muon spectrometer review panel:

I see the following line of reasoning which might have led to this conclusion: (A) For ANY muon trigger scheme, the partitioning in the second coordinate must be of the order of a few centimeters in order for the trigger not to be swamped by background. The trigger-detector will necessarily measure the second coordinate with a resolution of a few centimeters. (B) It seems virtually impossible to derive a proper muon trigger from any of the ATLAS precision detector options. A separate, stand-alone trigger detector is required! (C) Combination of (A) and (B) then seems to lead to the conclusion that the stand-alone trigger device has to measure the second coordinate anyway and hence that no second coordinate is needed from the precision detector. I fully support the conclusions under (A) and (B). I do however argue that the apparently very logical conclusion (C) is not well considered and unjustified. It has a number of less favorable consequences which I would like to point out to the panel. [Emphasis added.] The muon spectrometer review panel had not considered this argument until this scientist brought

it to their attention. Reflecting on the process, a senior scientist who had participated in the review panels

explained the importance of the overall dialectic process and of exposing arguments to scrutiny, both to

members of the review panel and to the ATLAS collaboration at large:

Very sensible questions were asked, and the more this process rolled on, the more facets were discovered which initially had not even been taken into account... Many things came up which had not been thought of before. This process was scientifically very valuable, but also very costly. But if you ask me whether I would endorse such a situation, I do it

14

absolutely. Because the alternative is that you just knock on the table and take a decision. But then you have not thought about everything, not to the same extent to which such a detailed, very painful scientific evaluation process leads you. This is a big advantage. (Scientist D., 2011) On occasion, unexpected facets of a specific technology were identified only when interactions

with interdependent subsystems were taken into consideration. On these occasions, the dialectical process

involved knowledge not only of specific subsystems under review, but also of interdependent ATLAS

subsystems. A scientist involved in the calorimeter review panel (Scientist O., 2006), for example,

explained how the choice of a specific technology for the inner detector subsystem resulted in the use of

material that was more than what had been planned, thereby potentially introducing errors into the

calorimeter measurements. Specifically, electron and proton showers caused by this excess material would

result in biased energy measurements in the calorimeter. To account for this error, one group from the

calorimeter subsystem community proposed a modification to the calorimeter design that enabled the

detection of electron and photon showers before they entered the calorimeter. This accurate measurement

of the shower positions allowed this group to correct the biased energy measurement.

In this case and as with the others we studied, through the constant unraveling of different

technological features of competing designs, specific groups were able to convince others in the

collaboration that the design they were championing was the most appropriate, given the other choices

being made for the ATLAS detector. Criteria such as performance, cost, and risk were all taken into

consideration. Reflecting on the decision processes that unfolded, a senior scientist noted: “When decision

making was an item on the agenda, this often meant that something which was already agreed upon and

clear for everyone in the collaboration was made plausible and formally approved” (Scientist F., 2006).

In other words, the process minimized the dysfunctional aspects of politics in the decision making

process. This was made possible only because of transparency in the review panel proceedings. Even

those who were not physically present during reviews could easily access the knowledge exchanged

within these panels as they were codified into documents that were circulated through electronic mailing

15

lists and made available on several cross-linked websites that were accessible to anybody in the

collaboration.9

As a result, scientists and engineers who already possessed deep knowledge of their own

technologies were also able to develop some knowledge of alternative technologies and other

interdependent subsystems. Such partial overlapping of knowledge occurred not only across participants

associated with specific interdependent subsystems, but also across the entire collaboration. Conversations

unfolded as different groups presented the progress they had made and the challenges they confronted and

anticipated at periodically held plenary meetings. These conversations played an important role in

focusing the attention of all participants and in helping groups working on interdependent subsystems

synchronize their work by keeping track of changes taking place across the ATLAS system.

Emergent coordination

Over a period of two years, the resolution of early controversies in the ATLAS review panels

resulted in choices from competing technological alternatives for each subsystem. In late 1994, the

scientists and engineers then agreed on a workable architecture and a provisional “decomposition” (Simon,

1962) of the architecture into subsystems through the use of preliminary interface specifications. This

measure was taken to enable different communities to work separately and in parallel on their respective

subsystems. All participants knew that they were agreeing upon these provisional specifications for

pragmatic reasons and that they would encounter controversies when different subsystems were eventually

integrated and deployed.

For example, a controversy emerged around the large superconducting magnet required for the

muon spectrometer subsystem. The muon spectrometer, initially designed with 12 coils and an inner

radius of 5 meters, set the spatial parameters for the other subsystems. However, over time, the

collaboration realized that the costs involved and the risks associated with the deployment of the 12-coil

magnet system were far greater than anticipated. Consequently, a “Magnet Working Group” was formed

to examine this issue. After some deliberation, the group recommended a superconducting magnet with

                                                                                                               9 CERN had pioneered the use of digital technologies for disseminating documents to non-collocated scientists and invented the World Wide Web specifically for this purpose.

16

eight coils and an inner radius of only 4.3 meters, which would reduce the costs and risks associated with

the muon spectrometer.

However, these new specifications impacted other subsystems. Specifically, the reduced space

now available inside the magnet became a problem. For instance, the space originally kept aside for inner

detector electronics was now no longer available. Generating space for these electronics implied

compromising the space available for the components of the calorimeter and the muon spectrometer

subsystems.

Given the systemic nature of this problem, ATLAS formed a “Descoping Task Force” to find a

solution. All three major subsystem communities were represented in this task force whose members were

charged “with taking a global perspective rather than acting as a representative for their subsystem” (task

force report, 1995-11-24). Details of the task force meetings were distributed to the collaboration at large

to solicit comments. The comments that were generated were also distributed to all members of the

collaboration. Eventually, after in-depth investigations and intense negotiations, the task force arrived with

an acceptable compromise. Specifically, gaps would be introduced between the calorimeter and the muon

spectrometer subsystems to generate space for additional cables and cooling pipes required for the inner

detector subsystem. In the process, the muon spectrometer subsystem gave up some space to host the inner

detector electronics.

However, this was not the end of the episode. The resolution of the first controversy around

physical space generated additional controversy from the point of view of electromagnetic fields. A small

digression is in order to understand the nature of such interdependencies. In many discussions of

technological systems, interdependencies are conceptualized in a unidimensional manner – for instance, a

design that optimizes the use of space or a design that optimizes performance. But, in fact, any system can

and ought to be conceptualized using multiple dimensions. For instance, the ATLAS detector is not just a

system of interconnected subsystems within a physical space, but also a system whose subsystems are

interconnected because of the electromagnetic fields that are generated. Reflecting this interdependence,

the cables installed to connect the electronics with the inner detector subsystem began picking up signal

17

noise due to the magnetic fields generated when they passed close to power supplies. Once again, the task

force addressed this issue and came up with effective shielding to protect these cables from picking up

signal noise. The shielding, in turn, introduced additional materials, generating particle interactions that

had to be minimized to reduce disturbance to the calorimeter.

Varying justification processes across subsystems

As these examples illustrate, the ATLAS collaboration encountered problems that had not been

anticipated when the system was provisionally decomposed. However, each time the collaboration

encountered such unanticipated problems, a fresh round of negotiations ensued within task forces, with

representatives of different subsystem communities providing scientific arguments to justify and

eventually resolve conflicting claims. In doing so, they not only were concerned with the functioning of

their own subsystems, but also with the functioning of the overall ATLAS detector. In other words,

engagement around emergent controversies resulted in solutions that were both local (at the subsystem

level) and global (at the ATLAS system level).

Although we observed such justification processes within communities designing each ATLAS

subsystem, our detailed analysis of justification processes within review panels also revealed differences

among the different subsystem communities (please see Table 2 for a summary of these differences). For

instance, the calorimeter community placed a strong emphasis on justifications right from the very

beginning. Proponents of each technological option were expected to make available all relevant

documentation beforehand to allow for informed discussions throughout the panel review. Competing

groups were able to prepare questions in advance to be addressed during the panel meetings. Groups

offered justifications for their options that were scrutinized intensely by opponents. In addition, members

of the calorimeter review panel were highly reflective about the justification criteria. For example, they

discussed whether the selected criteria were appropriate for evaluating the technologies and concluded that

the results of simulation studies offered by the two competing groups were not meaningful, as they were

based on one too many assumptions. Rather than rush to a premature conclusion based on simulation data,

the panel decided to delay a choice to take into account results from prototypes.

18

-- Table 2 here --

The justification processes that unfolded within the muon spectrometer review panel were not as

critical or transparent in comparison to the processes that unfolded within the calorimeter panel. The data

suggest that the muon spectrometer panel seemed almost disconnected from the rest of the collaboration.

After the first few review meetings, the spokesperson for the entire ATLAS collaboration expressed

concern about the lack of discourse within this panel:

A Panel should not work in isolation but rather in close contact with the collaboration. As most of the people do not know what happens in the muon spectrometer panel they have a right to know. Otherwise we will have panic about recommendations being suddenly made and thrown onto the collaboration, without them having a chance of following and appreciating the deliberation process. (Email correspondence 1993) Despite these sentiments, review processes did not change until the group that had “lost out”

responded by leading a revolt. One member of this group wrote a formal complaint to the muon

spectrometer review panel noting that the “way of justifying – or rather not justifying – was unacceptable”

(Email correspondence to panel chair, 1993). Another influential person wrote:

I firmly believe that after many man-years of hard and dedicated work the proponents of all technologies, but especially the losing ones, are entitled to at least one line of comment as to what are, in the eyes of the panel members, their flaws or weaknesses in comparison to the competitors. (Email correspondence 1993) These protests led to significant changes in the processes employed by the muon spectrometer

review panel. Workshops were organized to discuss the different arguments. The outcome was a proposal

that was acceptable to all. The intensity with which the muon spectrometer community engaged in

justification changed substantially after the revolt, eventually becoming comparable to that within the

calorimeter community (as we show in our subsequent analysis).

19

Justification patterns and emergent knowledge structures

We conducted additional analysis to investigate justification differences among ATLAS

subsystem communities and to explore the emergent knowledge structures that these differences generated.

To do so, we first identified the level of justification over time for the calorimeter and muon spectrometer

subsystem communities using computer linguistic analysis. Second, we analyzed the knowledge structures

that emerged for each of these subsystem communities by generating bipartite networks consisting of the

individuals involved (several hundred physicists and engineers spread across different research institutes

and countries) and their areas of expertise. We then examined the relationship between justification and

the knowledge structures over time, and between the different subsystem communities by comparing the

results of the prior two steps. Finally, we analyzed data on the progress made by these different subsystem

communities to see whether or not the differences in knowledge structures were related to differences in

justification processes. Next, we describe these steps and the outcomes of our analysis.

Justifications. Our approach to identifying different justification levels among subsystem

communities involved conducting a computer linguistic analysis of the ATLAS documents generated in

the review panels and the task forces pertaining to each subsystem. ATLAS operated by documenting all

review panel and task force proceedings. These texts were important as they: (a) provided transparency to

the process, (b) could easily circulate among participants of the collaboration so that even those who were

not present at the meetings could scrutinize the rationale for a decision, and (c) generated a memory of

why a certain decision was taken. Thus, analyzing the considerable available data offered a way to probe

the justifications that had unfolded at these meetings.

Altogether, we had access to 2,419 documents that ATLAS scientists had generated between 1992

and 2007. Each document was time-stamped and attributable to specific ATLAS subsystems, enabling us

to explore differences in justification patterns over time within each subsystem community. As a proxy,

we identified terms such as “because,” “since,” “therefore,” and “due to” in the text as indicators of

justifications offered by authors. We used latent semantic analysis (Deerwester et al., 1990), a natural

language processing approach, to measure the importance of these words in the documents pertaining to

20

each subsystem community. An advantage of latent semantic analysis is its ability to overcome the

problems that emerge because of synonymy and polysemy10, a feature that made it possible for us to

capture the semantic network of concepts around justification within a document rather than conduct a

simple word count (Landauer, Foltz, & Laham, 1998).

Figure 2 shows the justification factors for the calorimeter and muon spectrometer communities

over time. The justification factor for a specific subsystem community was the mean value of cosine

similarity (Deerwester et al., 1990) of the search vector for justification related terms (“because,” “since,”

“therefore,” and “due to”), and all documents generated by that specific subsystem community, indicating

the extent to which that community engaged in justification. As this figure indicates, the level of

justification was high within the calorimeter community during the early design stage and even increased

slightly during detailed development of the subsystem. The level of justification in the muon spectrometer

community was low initially but increased after the revolt and reached the highest level during detailed

development between 1996 and 1998. Overall, the pattern in this figure is consistent with our earlier

qualitative description of the justification processes across these subsystem communities.

-- Figure 2 here --

Figure 2 also shows that justifications within both subsystem communities decreased once

construction and installation of the detector had begun. This pattern is consistent with Green’s (2004)

work which suggests that the level of justification within organizations is likely to decrease as “taken-for-

grantedness” sets in. In the beginning of the design process, the architecture of the ATLAS system was

still unclear, and the many choices that had to be made across competing technologies required

participants to engage in justifications. Once the architecture had emerged, many details became taken-

for-granted across the collaboration, and the extent to which participants questioned specifications and

engaged in justification decreased.

Knowledge structures. We also analyzed the bibliographic records of the 2,419 documents to

understand and depict the knowledge structures that had emerged within the calorimeter and muon

                                                                                                               10 Synonymy is a situation where different words describe the same idea, whereas polysemy is a situation where the same words describe different ideas.

21

spectrometer communities. Specifically, we identified each member’s areas of expertise by analyzing the

topics appearing in the titles, keywords and abstracts of the documents they had co-authored. We used

latent semantic analysis to account for terms that were used synonymously to depict an area of expertise.

Based on these bibliographic records, we generated a bipartite network of all scientists belonging to a

specific subsystem community (several hundred people in each case) and their respective areas of

expertise relevant to any ATLAS subsystem.11

From this data, we analyzed the knowledge structures of the different subsystem communities

using density12 as a network measure (Figure 3). Density represents the ratio of connections between all

nodes of a network to the number of connections that are theoretically possible (Mitchell, 1969). Networks

with many connections between diverse areas of expertise will have a higher density score than networks

with fewer such connections.

-- Figure 3 here --

A visual comparison of the knowledge structures during the early design period in Figure 3 (left

panel) shows that the knowledge structure associated with the calorimeter community had a higher density

than the knowledge structure associated with the muon spectrometer community. Figure 3 also shows that

the density of the knowledge structures increased for both communities (right panel). This is also clear

from Figure 4, which shows a plot of density of the knowledge structures of the two subsystem

communities for the different stages of development (i.e., early design, development, and installation). As

Figure 4 shows, the knowledge structures increased in density for both subsystem communities during

early design. That is, the number of connections between diverse areas of expertise increased, as did

connection strength. The density of knowledge structures for both subsystem communities reached their

peaks around 1998 when the development of the ATLAS system was largely completed and the

architecture had stabilized. That is, as the design settled down and the ATLAS collaboration began system

                                                                                                               11 In the case of documents co-authored by several scientists, we discounted the indicated areas of expertise for each scientist by the number of co-authors, on the assumption that each scientist contributed only a fraction of the total expertise represented by that document (see Palla et al., 2007). 12 We also calculated closeness centrality (Freeman, 1979), an alternative network measure that indicates how easily information located in a network can be accessed by any node in the network, either directly or indirectly. Our analysis using closeness centrality is consistent with our findings and is available on request.

22

installation, the number of connections between diverse areas of expertise decreased relative to earlier

stages.

-- Figure 4 here --

A juxtaposition of Figure 2 with Figure 4 suggests a relationship between justification and

knowledge structures. For each subsystem, the density of the knowledge structure (Figure 2) parallels the

extent of justification involved (Figure 4). Moreover, across the two subsystems, knowledge structure

density is commensurate with the justifications (or lack thereof) that were made. While we cannot ascribe

causality from such a super-imposition, our qualitative and contextualized analysis strongly suggests such

an inference. Specifically, as we described earlier, the design for the calorimeter was heavily debated and

the choice of a specific option required a high level of justification during the early design stage, thereby

generating a densely connected knowledge structure within the calorimeter community (please see Table

2). In contrast, the muon spectrometer community did not undergo such intense scrutiny during the early

design stage, and justification played only a minor role in the process. Consequently, the knowledge

structure that emerged within the muon community was less dense when compared to the calorimeter

community. However, by the end of development stage, the muon spectrometer community also had

developed a justification pattern similar to that within the calorimeter community and, therefore, its

knowledge structure also became densely connected.

Knowledge overlaps and anticipatory coordination

From this analysis, we inferred that justifications (disseminated widely and transparently) had

played an important role in the emergence of the ATLAS architecture by generating a knowledge structure

with essential points of overlap within and across communities working on different subsystems. But what

role did this knowledge structure play in the emergence of the ATLAS architecture? An answer can be

found in the comments offered by a senior scientist who participated in many review panels. He pointed

out that, at each point in the journey, the reviews generated knowledge that turned out to be “answers to

some other problem encountered later in some other context” (Scientist S., 2006). In other words,

scientists working on interdependent subsystems were able to use the knowledge that had emerged at a

23

given time to come up with robust solutions to unforeseen problems at a later time. In some instances,

without such knowledge, some problems may not have been identified until it was too late.

An example will help illustrate this point. A task force investigating the cooling system for the

inner detector subsystem identified potential risks in using binary ice as a coolant.13 Inner detector

subsystem engineers had not perceived this risk earlier as they had focused only on the superior cooling

performance of binary ice to help extract heat from the densely packed inner detector subsystem. However,

because of knowledge overlaps that had emerged, other scientists drew attention to the negative impact of

binary ice on the performance of their own subsystems. This concern sensitized the task force to the risks

associated with binary ice (such as water leakages) and prompted it to propose an evaporative cooling

system instead. The resolution of this controversy resulted in a design that not only used less material, but

also minimized the risk of water leakage within the inner detector subsystem.

In sum, the emerging knowledge structure was not only the outcome of the justification processes

involved, but also served as a medium for anticipatory coordination, enabling interdependent groups to

identify emerging problems and develop solutions as the project unfolded. From this perspective, it was

not surprising to see a contrast between the communities working on the calorimeter and the muon

spectrometer. An analysis of the ATLAS Project Progress Tracking (PPT) database showed that the

calorimeter community had performed the task of developing and constructing their subsystem more

effectively. Whereas 9% of all design related tasks of the calorimeter community were delayed because of

unexpected technical problems, the muon spectrometer community was challenged by unanticipated

problems in 14% of all design related tasks. This difference is even more striking when the mean delay of

design tasks undertaken by either subsystem community are compared. The average delay of muon

spectrometer related design tasks (6.7 months) was more than double the average delay of calorimeter

related design tasks (2.7 months).

DISCUSSION

                                                                                                               13 A coolant consisting of ice crystals in a cooling liquid pumped through a complex system of pipes.

24

We began the paper by asking how a complex system’s architecture emerges in situations where

traditional mechanisms for coordination are not possible or available. This was the case with the ATLAS

collaboration where the development of a fundamentally new design precluded the possibility of

embedded coordination typically offered by an existing technical architecture (Sanchez & Mahoney,

1996). The voluntary nature of the collaboration and the lack of a formal organizational hierarchy

precluded systems integration by a few central actors as the basis for coordination (Brusoni et al., 2001).

In sum, ATLAS confronted a “coordination of coordination” challenge.

Our data illustrate how ATLAS scientists and engineers were able to coordinate their activities

despite the absence of these traditional mechanisms. First, they instituted multiple forums for mutual

engagement at various levels of the collaboration for participants to justify their alternative technological

solutions. The justification processes that unfolded in early forums resulted in the emergence of “obvious”

technological choices. These processes, being transparent to the collaboration at large, led to partial

overlaps of knowledge across participants, a knowledge structure that facilitated the provisional

decomposition of the system into various subsystems so that participants could work on their respective

subsystems even as they kept themselves and others informed of potential interdependencies, conflicts and

overall progress. Finally, these very forums for engagement and the knowledge overlaps they gave rise to

enabled participants to anticipate and resolve latent interdependencies without compromising the overall

integrity of the system. We now discuss the theoretical significance of these points.

Forums for mutual engagement and interlaced knowledge

A striking feature of the ATLAS collaboration was the presence of forums for mutual engagement.

Within these forums (e.g., the review panels at the early stages), different groups could confront one

another with their perspectives on alternative technological choices for various detector subsystems, and

thus, for the overall ATLAS architecture. Many different points of view and subsystem-specific

perspectives were articulated in these forums in an overall process characterized by others as “contests of

unfolding” (Knorr-Cetina, 1999) and “trials of strength” (Latour, 1987). To engage in such contests,

25

participants had to develop a deep understanding of their own and others’ technologies so as to challenge

competing proposals. Groups, when challenged, had to present arguments to justify their technologies.

According to Toulmin (1983), the central characteristic of justification (argumentation, in his

words) is that claims must be substantiated by reason to be accepted as valid. Indeed, competing groups at

ATLAS constantly offered justifications to convince others that their solutions were the best choice for

ATLAS. This process resulted in some technologies emerging as being better suited than others given the

other concurrent choices across the ATLAS collaboration. It also resulted in opening up the technology

black boxes, as more and more data and knowledge about competing technologies began surfacing

because of the dialectic processes within these review panels.

Research on organizational epistemology suggests justification as a key mechanism in the creation

of organizational knowledge (Boltanski & Thévenot, 2006; Nonaka, 1994). As individuals and groups

justify their choices to convince others, their claims become a part of an overall fabric of knowledge that

emerges over time. Indeed, as our analysis highlights, partial overlaps in participants’ knowledge about

various ATLAS subsystems was a defining characteristic of this fabric of knowledge that emerged at

ATLAS. We label this “interlaced knowledge.”

A way to understand the significance of interlaced knowledge is to contrast it against “common”

knowledge in technological systems (Grant, 1996b). Common knowledge “comprises those elements of

knowledge common to all organizational members: the intersection of their individual knowledge sets”

(Grant, 1996b: 115). While common knowledge may be one way to coordinate activities, it is clearly not

the most efficient approach as the size of a group increases. As Grant (1996a) noted, the level of common

knowledge tends to decrease as organizational size and the scope of knowledge being integrated increases.

For an organization such as ATLAS with membership running into the thousands, common knowledge as

a basis for coordination would simply have overwhelmed participants.

Interlaced knowledge is also different from modular knowledge that requires participants to know

only specific facets of the technological system (Baldwin, 2008; Sanchez & Mahoney, 1996). Modular

knowledge results from “information hiding” (Parnas, 1972), a design principle that extols the virtues of

26

“black boxing” technological components such that designers of a subsystem need not have any

knowledge about other subsystems, as long as all subsystems conform to already agreed upon interface

specifications. Coordination among subsystem designers occurs just by conforming to these pre-specified

interface specifications within an overall architecture that has more or less stabilized.

In this regard, rather than information hiding and black boxing, justification processes open up

technology black boxes. These processes are essential for coordination, especially when the system

architecture has not yet emerged and there is uncertainty about the performance of each subsystem and

how they will interact and perform when integrated together as a system. Interlaced knowledge that

emerges due to these justification processes serves as both the medium and outcome of efforts to

coordinate activities in real time. Indeed, it is because of the presence of such interlaced knowledge that

participants can provisionally decompose the system in a way that reduces unproductive

interdependencies when the outcomes of parallel and distributed efforts are re-integrated. We discuss this

aspect in greater detail next.

Provisional decomposition

Accounts of the emergence of technological systems highlight a progressive process wherein

components at the apex of a system hierarchy first crystallize and then set the parameters for the

emergence of other subsystems and components lower in the system hierarchy (e.g., Clark, 1985;

Murmann & Frenken, 2006; Tushman & Murmann, 1998). But, in cases involving new systems where one

cannot start with an existing architecture, there is no pre-existing hierarchy of subsystems and components

to establish interdependencies and guide system development. For instance, in the ATLAS case, each

subsystem of the detector was as important as the others, and choices made in the case of any subsystem

significantly impacted others. Indeed, it was clear to all participants right from the very beginning that it

was only through the interactions between the different subsystems that the detector as a system could

function effectively.

Such interdependencies, many of them latent, make it impossible to pre-specify the system

architecture or its subsystems with any level of certainty. One solution, as adopted by ATLAS, is to

27

provisionally decompose the emerging system to allow parallel and distributed efforts to unfold. However,

it should be noted that the purpose of such provisional decomposition is not to pre-determine the specifics

of the system architecture or any one of its subsystems ex ante. Rather, the purpose is to generate a

“boundary infrastructure” that connects heterogeneous subsystems and actors (Bowker & Star, 1999). As a

boundary infrastructure, the provisionally decomposed architecture can be interpreted from different

perspectives; yet many connection points across interdependent communities enable participants to co-

orient (Taylor & Van Every, 2000) their conversations while negotiating the design. Different participants

and communities can use the provisional architecture as a “means of representing, learning about, and

transforming knowledge to resolve the consequences that exist at a given boundary” (Carlile, 2002: 442).

Provisional decomposition as boundary infrastructure and the heedful interrelating (Weick &

Roberts, 1993) that follows allows participants to anticipate some of the controversies that are likely to

emerge when the subsystems are integrated together to work as a system. Anticipating such controversies,

however, requires continued interaction and engaged discussion among participants to ensure that

interdependent others and the collaboration as a whole can stay abreast of developments even as they

continue working on their specific subsystems. It is because of such anticipatory coordination that

interlaced knowledge is maintained and embellished over time, allowing participants to productively

address controversies that emerge during system integration. As these controversies are resolved, the

architecture itself evolves as adjustments are made to accommodate the resistances and affordances of the

various components to facilitate the optimal functioning of the system as a whole (see also Pickering,

1993).14

All of this provides a far more dynamic view on the emergence of architectures than has been

typically chronicled in the modularity literature. The extant literature on modularity conceptualizes a                                                                                                                14 In his studies of other detector projects within the HEP community, Pickering (1993) observed that interdependent actors are bound to encounter controversies, which they then try to address. He describes the processes that unfold as a “mangle of practice.” His examples focus on the activities of individual scientists as they generate “accommodations” to “resistances” they encounter. For instance, he described how a physicist encountered “resistance” from a bubble chamber, an instrument to detect particles, when it failed to produce expected results. The physicist had to engage in “engineering jiggling” to accommodate the emergent design to these resistances, which took the design in unanticipated directions. With the ATLAS detector too, we saw collective accommodations unfold as actors with different frames of reference and different levels of inclusion engaged with one another to address problems that were simultaneously local and global.

28

“mirroring” between partitions of knowledge structures and partitions of the technological system

(Baldwin & Clark, 2000; Sanchez & Mahoney, 1996).15 In contrast, as we saw in the case of ATLAS, the

provisional technology partitions and knowledge structures did not mirror one another. Rather, knowledge

structures were interlaced, allowing designers of different subsystems to appreciate the emerging system

architecture from their own vantage points. In other words, rather than mirroring, the relationship between

the technical architecture and knowledge structure is better described as being “kaleidoscopic” in nature,

generating “interpretative flexibility” (Pinch & Bijker, 1987). That is, depending on their perspective,

participants viewed different facets of the system architecture and associated interdependencies in

different yet co-oriented ways (Taylor & Van Every, 2000), and interlaced knowledge continually

emerged as participants shared their views with one another through a grid of discourse (Knorr-Cetina,

1999).

Latent interdependencies and emergent controversies

Such interlaced knowledge was useful in dealing with the controversies that arose due to the

provisional decomposition of the system. Indeed, coordinating such interdependencies is not a trivial task.

Many interdependencies are latent and only surface as system development and integration unfolds (Barry

& Rerup, 2006; Garud & Munir, 2008; Staudenmayer et al., 2005). Furthermore, the integration of

individually optimized subsystems can result in a system with low integrity. Accordingly, in order to

arrive at a robust solution, participants must simultaneously consider local and global facets of the design,

a process that is facilitated by interpenetrating one another’s knowledge bases.

If the knowledge structure is entirely modular, the development of the system itself can be locked

into a pre-specified path and lead to a system with low integrity. Or, a change in specifications in one

subsystem could easily cause the development of the system to cycle endlessly as each local change in one

subsystem triggers changes in the others (Ethiraj & Levinthal, 2004). In contrast, interlaced knowledge

enables the anticipation and resolution of such latent interdependencies (see also Sosa et al., (2004) for

                                                                                                               15 A notable exception is recent work by Brusoni et al. (2001) that challenges mainstream scholars of modularity by suggesting that systems integrators may need to know more than they do.

29

arguments as to how prior experience can help uncover direct and indirect interface characteristics in

complex aircraft systems). As unforeseen events occur, such as changes to the very form and function of

one subsystem, participants can draw on interlaced knowledge to find workarounds for changes that must

be made in some other subsystem. In this sense, interlaced knowledge represents productive redundancy,

allowing subsystem members to mindfully respond (Weick & Roberts, 1993; Weick & Sutcliffe, 2001) to

unexpected situations by developing novel solutions. And, continued and mindful interactions among

participants further serve to deepen interlaced knowledge.

Indeed, as we observed in the case of ATLAS, multiple points of view were exchanged on areas

of agreement and disagreement as participants tried to address issues associated with their specific

subsystems as well as with the overall detector system. For each community involved, such processes

deepened knowledge about their specific subsystems as they had to accommodate their designs to the

constraints posed by other subsystems. At the same time, it deepened their knowledge of other subsystems,

as the system as a whole had to function with high integrity. The outcome was the evolution and

emergence of an architecture that was acceptable to all, one that was both global and local at the same

time.

Summary

Our study illustrates the mechanisms that enabled ATLAS collaborators to coordinate their

activities during the development of a radically new technological system despite the absence of

traditional mechanisms such as a hierarchy or embedded coordination. Specifically, the ATLAS

collaboration created multiple forums to harness controversies at various levels. At these forums,

participants offered justifications for their technological solutions as a way to choose between different

options. The justification processes not only resulted in contextualized choices being made, but also led to

the creation of interlaced knowledge – a partial overlap of knowledge across actors and groups – that was

neither modular nor common knowledge. Such interlaced knowledge facilitated the provisional

decomposition of the system so that participants could work on their respective subsystems even as they

kept themselves and others informed of potential interdependencies, conflicts and overall progress.

30

Interlaced knowledge enabled interdependent groups to be mindful of one another as the architecture

emerged over time and to anticipate and resolve latent interdependencies without compromising the

overall integrity of the system.

CONCLUSION

What can we learn from the ATLAS case? One way to answer this question is to revisit the

boundary conditions that characterize ATLAS, thereby locating our observations within a larger

theoretical mosaic of ideas. ATLAS is a case of a radically new complex system whose architecture could

not be pre-specified and decomposed. Complexity arose due to irreducible uncertainty associated with the

technology and interdependencies among its subsystems. Further complicating matters, multiple

participants and groups were involved, each with their own preferred technological approaches to

developing each subsystem. No one actor had either the authority or the knowledge to simply impose a

technical architecture that could then engender coordination.

Under these circumstances, insights from the literature on modularity (such as pre-specification of

architecture, natural partitioning, black boxing, mirroring, and embedded coordination) must be revisited.

Under conditions of uncertainty and interdependence, as the ATLAS case suggests, black boxes may need

to be opened through justification processes, which in turn generate interlaced knowledge. Such interlaced

knowledge enables the system to be decomposed provisionally into subsystems. However, contrary to the

mirroring hypothesis posited by the modularity literature, interlaced knowledge must be maintained even

after provisional decomposition to anticipate and resolve latent interdependencies that are likely to emerge.

Indeed, other organizational scholars have begun making observations that go beyond the

mirroring hypothesis (e.g., Cabigiosu & Camuffo, 2011; Colfer & Baldwin, 2010). What our study has to

add to this emerging stream of thought is the important role that interlaced knowledge can play in the

emergence of new complex architectures. Being neither modular knowledge nor common knowledge,

interlaced knowledge serves to economize on what system designers need to know while enabling them to

gain an appreciation of the emerging system from their respective vantage points. Clearly, this is of

theoretical significance in the emergence of architecture where the problem has always been a tension

31

between local and global solutions. Interlaced knowledge, by allowing different people to view the entire

system and its parts in multiple co-oriented ways, endows the system with adaptive and generative

properties to deal with controversies as and when they arise. And, in the process, interlaced knowledge

itself is re-generated.

32

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Table 1: Sources of Data for this Study

Data source Description

Plenary meeting documents

Summaries of 65 meetings of the ATLAS plenary and supplementary material (presented slides and reports). The ATLAS plenary is the main forum for all-hands discussions concerning physics objectives and results, hardware and software design, and organizational matters. Plenary meetings usually spanned five days and were held during ATLAS week. Our data covers the period from 1992 until 2010. We used these minutes to identify major events relevant to our study.

Collaboration Board meetings minutes

Minutes of 68 meetings of the Collaboration Board, the policy- and decision-making body of ATLAS. The minutes extensively list the main issues (including technical as well as social issues) discussed and decisions taken between 1992 and 2010. These data complemented data from plenary meetings with additional details on issues raised in the plenary that were further discussed in the collaboration board.

Executive Board meetings minutes

Minutes of 155 meetings of the Executive Board. This forum reviews schedules and milestones, and use of financial and human resources. These minutes cover the years 1994 until 2010. These data were used to identify controversies that emerged between various ATLAS subsystems.

Review Panel meetings documents

Minutes of 40 Review Panel meetings plus presentations and reports discussed during the meetings as well as the personal notes of a senior scientist who attended all meetings. These forums were set up for choosing from alternative designs for the 3 main detector subsystems and the magnet system. Our data covered all Review Panels that took place between 1993 and 1994. These data enabled us to study in detail the dialectical process involved in choosing among competing designs for a specific detector subsystem.

Electronic mailing list archives

The electronic mailing list archives containing 128,015 items sent between 1993 and 2005 captured real-time conversations among ATLAS scientists throughout the project. These data were used to gain additional insights on details of specific incidents and for triangulation. As these messages were sent in real-time when critical events unfolded, these data were not subject to retrospective bias and offered more details and diverse views of different groups involved than meeting minutes.

Full-text ATLAS Notes

2,419 ATLAS Notes, documents that had been generated in the review panels and task forces across each subsystem. ATLAS operates by documenting all the proceedings of Review Panels and Task Forces. Our data covered the years 1992 - 2005. We conducted a computer linguistic analysis of the ATLAS Notes to systematically compare differences in justifications of various subsystem groups.

Bibliographic records on CERN Document Server

The CERN Document Server (CDS), an electronic document repository, was used as source of bibliographic data for 2,419 ATLAS Notes. The data covered all ATLAS Notes generated in the years 1992 until 2005. We used this data bibliometric analysis as a means of analyzing the knowledge structures of different detector subsystem groups.

ATLAS Project Progress Tracking (PPT) database

The ATLAS Project Progress Tracking database offers detailed data on work packages, milestones, and schedules. It was used as a data source for information regarding the progress of the development process within the various subsystems and provided insights on unexpected technical problems and delays. Our data covered the period from the introduction of the database in 1997 until 2007. We used these data to analyze how the development process unfolded in different detector subsystems and ATLAS overall.

In-depth interviews We conducted 84 semi-structured interviews with scientists and engineers involved in the ATLAS collaboration at different stages. Some individuals were interviewed on multiple occasions. These interviews typically lasted for 45-90 minutes. In addition, we conducted 24 interviews that began as informal conversations during field visits but, during the conversation, turned out to be valuable sources of data on specific incidents. The content of these interviews was captured in detailed field notes. The interviews were conducted between 2005 and 2010. These interviews helped us make sense of many technical details and provided additional insights on several controversies and differences in opinions during the emergence of ATLAS.

Field visits ATLAS scientists invited us to make field visits to understand and experience firsthand the mechanisms that they have continued to use from their very inception. Accordingly, during 12 field visits, each typically lasting a week, we had the opportunity to observe conferences, participate in meetings, and engage in many informal conversations. These field visits took place between 2005 and 2010. During our research project, scientists at ATLAS had already begun working on an upgrade of the detector to be installed after 2015. For this reason, we could observe in real time several mechanisms that were similar to the ones unfolding in the early phases of the collaboration.

36

Table 2: Comparison of ATLAS Review Panels

Calorimeter Muon Spectrometer

Setup of review panel

• Panel meetings were always open. • 29 external experts invited for participation. • Panel actively sought involvement and advice of

ATLAS collaboration.

• Panel meetings were closed. • Only four external experts invited for

participation. • Panel was reluctant to interact with overall

ATLAS collaboration even when asked to do so. Nature of discourse in review panel

• All documents were circulated for preparation beforehand.

• Both neutral panel members and competing groups reviewed designs and prepared questions.

• Responses and justifications were provided directly to the panel and discussed extensively.

• Panel continued to question proponents of the two technologies until it felt that all the underlying assumptions had surfaced and been scrutinized.

• Descriptive presentations of competing technologies without any systematic questioning.

• Panel members raised questions that were addressed mostly in writing.

• Responses were often taken for granted and simply referred to written responses when questions surfaced again in subsequent meetings.

• Arguments presented by competing groups were scrutinized outside the review panel rather than by the panel.

Importance of justification

• Surfacing underlying assumptions and providing justification was a central element on panel agenda.

• Extensive discussion of justifications in all sessions; some sessions even exclusively dedicated for providing justifications of technologies.

• Justification was important not only for the claims of competing technologies, but also for the criteria the panel had chosen for evaluating competing technologies.

• Most time spent presenting technological features with hardly any justification.

• Questions, if asked, did not seek details of underlying assumptions and rationales.

• Panel made little effort at justifying its final recommendation even though others thought that important facets had been left out.

 

37

Figure 1: Overview of the ATLAS Detector

Subsystems Description

Calorimeter system

• Liquid argon calorimeter

• Tile calorimeter

• The calorimeter measures the energy of charged and neutral particles. It consists of metal plates (absorbers) and sensing elements. Interactions in the absorbers transform the incident energy into a “shower” of particles that are detected by the sensing elements.

• In the inner sections of the calorimeter, the liquid argon calorimeter, the sensing element is liquid argon. The showers in the argon liberate electrons that are collected and recorded.

• In the outer sections, the sensors are tiles of scintillating plastic, i.e., the tile calorimeter. The showers cause the plastic to emit light that is detected and recorded.

Muon spectrometer

• Precision chambers

• Trigger chambers

• Muons are particles like electrons, only 200 times heavier. They are the only detectable particles that can traverse all the calorimeters without being stopped. The muon spectrometer surrounds the calorimeter and measures muon paths to determine their momentum with high precision.

• In precision chambers, gas-filled metal tubes with wires running down their axes are used as sensors. High voltage between the wire and the tube wall allows detection of the traversing muons by the electrical pulses they produce. With careful timing of the pulses, muon positions can be measured to an accuracy of 0.1 mm. The reconstructed muon path determines its momentum and charge.

• Trigger chambers are based on a similar principle; however, high time resolution rather than precision is the key feature of trigger chambers. Using thin plates or multiple wires as sensors, trigger chambers have a time resolution better than 25 ns.

Inner detector

• Pixel detector • Semi-conductor

tracker (SCT) • Transition

radiation tracker (TRT)

• The ATLAS inner detector combines high-resolution detectors at the inner radii with continuous tracking elements at the outer radii, all contained in the central solenoid magnet. The outer radius of the inner detector is 1.15 m, and the total length is 7 m.

• The pixel detector generates a set of three high precision measurements as close to the collision as possible that determine the impact parameter resolution and make it possible for the inner detector to find short-lived particles such as B-Hadrons.

• The SCT system is designed to provide a set of eight precision measurements per track in the intermediate radial range, contributing to the measurement of momentum, impact parameter and vertex position

• At larger radii, typically 36 tracking points are provided by the TRT. The Transition radiation Tracker (TRT) is based on the use of straw detectors, which can operate at expected high rates due to their small diameters and the isolation of the sense wires within individual gas volumes. Electron identification capability is added by employing Xenon gas to detect transition radiation photons created in a radiator between the straws.

38

Figure 2: Justification Associated with two ATLAS Subsystem Groups

Note: Please see soft copy for color-coding. This figure plots ‘justification factors’ for the ATLAS calorimeter and muon spectrometer groups over time. The justification factor, generated through a latent semantic analysis comparing documents generated by each subsystem group with a search vector consisting of the terms indicating justification, indicates the extent to which a subsystem group engaged in justification. As the figure indicates, the level of justification was high for the calorimeter group during the early design period and even increased slightly during the development period. The level of justification in the muon spectrometer group was low initially but increased after a revolt in the review process resulted in a change of the justification patterns and reached its highest level during development between 1996 and 1998. The level of justification decreased for both subsystem groups as the designs of the various components emerged and as the subsystem groups began to construct components in the late 1990s.

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Figure 3: Visualization of Interlaced Knowledge of two ATLAS Subsystem Groups

Node centrality: low high Edge weight: low high

Note: Please see soft copy for color-coding. The nodes in the networks represent the diverse areas of expertise involved in the development of the ATLAS detector. The color code indicates the centrality of a particular expertise area in the subsystem group’s knowledge (red indicating highest centrality). The edges connecting the nodes indicate overlaps between expertise areas, i.e. ATLAS scientists working in that subsystem group having knowledge of two or more areas of expertise (yellow edges indicating weak and green edges indicating strong connections). As this figure shows, the knowledge structure of the calorimeter group is more interlaced than the knowledge structure of the muon spectrometer group during the early design period (around 1994). This difference between the two groups is less apparent towards the end of the development of the ATLAS detector (around 1998).

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Early Design Detailed Development

Muo

n Sp

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40

Figure 4: Density of Interlaced Knowledge in two ATLAS Subsystem Groups

Note: Please see soft copy for color-coding. This is a plot of the density of two subsystem communities’ interlaced knowledge over time. It suggests that the knowledge of the calorimeter group was more densely interlaced than that of the muon spectrometer during the early design period. Towards the end of development, which corresponds with the change in the justification pattern in the muon community, the muon group was able to generate interlaced knowledge at a level comparable to the calorimeter group. Both subsystem groups generated less interlaced knowledge during the construction and installation period, as there was less need to engage in justifications (see also Figure 2).

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sity

Calorimeter Muon

Muon revolt

Early design

Detailed development

Construction and installation