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2011 42: 447 originally published online 31 May 2011Management LearningMax Boisot

CERNGenerating knowledge in a connected world: The case of the ATLAS experiment at

  

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Management Learning42(4) 447 –457

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Generating knowledge in a connected world: The case of the ATLAS experiment at CERN

Max BoisotESADE, University of Ramon Llull, Spain and The I-Space Institute, USA

AbstractThe spatial challenges posed by the dynamics of globalization together with the availability of new information and communication technologies (ICTs) have fostered the development of virtual collaboration. Driven by organizational authority systems, however, much of this activity remains of a top-down, hierarchical nature. Although the proportion of bottom-up activity has increased, it has not displaced the top-down bias in the governance structures of firms and the formal processes that give them effect. Yet recent developments are challenging the organizational assumptions that underpin such structures and processes. In what follows, we first offer a theoretical perspective on the above questions and then illustrate it with a look at the way that the ATLAS experiment at CERN—one of the four experiments that are using the Large Hadron Collider (LHC)—is organized and managed. The ATLAS Collaboration—the team of physicists responsible for the experiment—consists of a culturally heterogeneous and loosely coupled population of agents, each operating in a different institutional setting. We shall use our theoretical perspective to interpret some of the issues raised by this kind of ‘big science’ experiment and discuss their implications for a broader class of organizations.

Keywordscommunication technologies, knowledge flow, organizational coordination

Introduction

The spatial challenges posed by the dynamics of globalization together with the availability of new information and communication technologies (ICTs) have fostered the development of virtual col-laboration. In many industries—automobile, aerospace, pharmaceuticals, heavy engineering, etc.—scientific and technological teams now collaborate transnationally. In companies like Ford, Boeing, etc., the design and development of new products is carried out through international net-works. Face-to-face interaction can be maintained in the absence of physical co-presence through video-conferencing or Skype. Furthermore the ease of communicating through email actually reduces the need for frequent face-to-face interaction.

Corresponding author:Max Boisot, ESADE, University of Ramon Llull, PO Box 144, 08870 Stiges Barcelona, Spain. Email: max.boisot@gmail.com

408676 MLQXXX10.1177/1350507611408676BoisotManagement Learning

Article

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Driven by organizational authority systems, however, much of this activity remains of a top-down, hierarchical nature. Although the proportion of bottom-up activity has increased, it has not displaced the top-down bias in the governance structures of firms and the formal processes that give them effect. Yet recent developments are challenging the organizational assumptions that underpin such structures and processes. How, for example, do people ever get to collaborate in distributed, non-hierarchical networks such as Linux (Raymond, 1999)? How is network coordina-tion actually achieved? How does trust evolve to the point where it can substitute for hierarchical control? Finally, what level of task complexity are such networks capable of managing in this dis-tributed fashion?

Such questions invite a deeper look at the nature of organizational coordination, that is, at the different ways that knowledge flows and gets integrated in space and time across formal and informal organizational and national boundaries. In what follows, we first offer a theoreti-cal perspective on the above questions and then illustrate it with a look at the way that the ATLAS experiment at CERN—one of the four experiments that are using the Large Hadron Collider (LHC)—is organized and managed. The ATLAS Collaboration—the team of physi-cists responsible for the experiment—consists of a culturally heterogeneous and loosely cou-pled population of agents, each operating in a different institutional setting. We shall use our theoretical perspective to interpret some of the issues raised by this kind of ‘big science’ experiment and discuss their implications for a broader class of organizations. We shall then offer a brief conclusion.

A conceptual framework: The I-Space

Managerial coordination requires that information-bearing data flows through communication channels between centres of authority and centres of task execution. Information is then extracted from the data to construct representations of situations that either match or fail to match prior expectations. If the essence of coordination is to bring situations, intentions and behaviours into alignment, the essence of managerial coordination is to do so through the agency of others (Barnard, 1938). This requires communication—information flows—between human agents, the effectiveness of which depends in part on the way that information is structured. We explore this dependency by drawing on a conceptual framework, the Information-Space or I-Space, that relates the speed and extent of information flows within a population of agents to the possibilities for information structuring (Boisot, 1995, 1998). The structuring of information consists of two inter-related activities:

1. Codification—the creation of categories to which different phenomena can be assigned. The clarity with which categories can be created varies, and to that extent codification is a matter of degree. Also, discernible differences between phenomena can be simple, involv-ing simple attributes such as colour, weight, smell, size, etc., or they can be complex and involved multiple correlated attributes—i.e. does this person qualify for unemployment benefits? Does the candidate meet the requirements of our job description? Can this patient be considered cured? Etc.

2. Abstraction – Minimizing the number of categories to which a given phenomenon need be assigned. People’s eligibility for unemployment benefits, for example, might be determined by a single variable: their income. A concrete representation of phenomena draws on a large number of categories; an abstract representation draws on a few.

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Taken together, codification and abstraction minimize the amount of data processing required to categorize and respond to phenomena, and speed up data transmission. Thus, as illustrated in Figure 1, codified and abstract information will diffuse through a population of agents faster and more extensively per unit of time than information that is uncodified and concrete.

Managerial coordination requires that a diversity of information flows be integrated to make available unified representations of relevant phenomena to given agents. To achieve integration, the network of communication channels between different agents must be struc-tured so as to ensure that information flows as intended. The structures of these networks will reflect the possibilities offered by the information environment in which coordination takes place. An information environment characterized by high degrees of codification, abstraction, and diffusibility, for example, will deliver impersonal networks where agents do not have to know each other in order to interact: markets, where the diffusion of information is uncon-trolled; bureaucracies, where it is subject to some degree of central control. On the other hand, an information environment characterized by low degrees of codification, abstraction—and hence diffusibility—will deliver highly personalized networks in which trust and shared val-ues are essential: clans, where the information is diffused face-to-face within a group of lim-ited size; fiefs, where undiffused information remains confined within a single head to become a source of personal power.

Figure 1. The Information-Space (I-Space)

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Over time, where interactions are recurrent, the four types of interaction network identified above—others are, of course, possible—become sources of cultural values and practices and may even get institutionalized. We locate them in the different information environments of the I-Space as shown in Figure 2 and briefly outline their key attributes in Table 1.

The information environments that characterize the different regions of the I-Space vary in their complexity. We distinguish between two types of complexity:

1. Cognitive complexity—associated with the amount of data processing that a given catego-rization task requires. Since more data processing implies more complexity, cognitive com-plexity increases as one moves into the lower-front regions of the I-Space where information is both uncodified and concrete.

2. Relational complexity—associated with the number of agents participating in a given inter-action. The further to the right along the diffusion dimension of the I-Space social interac-tions take place, the more agents they will involve so that relational complexity increases as one moves to the right in the I-Space.

The interplay of cognitive and relational complexity delivers three regimes in the I-Space—the ordered, the complex, and the chaotic—located as indicated in Figure 3. As can be seen, bureaucra-cies are order-generating structures, whereas clans sit close to what complexity theorists call ‘the edge of chaos’ (Bak, 1996; Kauffman, 1993).

Finally, new information and communication technologies (ICTs) increase both data processing and data transmission capacities. How might they affect the development of interaction networks? We identify two effects:

Figure 2. Institutions and cultures in the I-Space

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Figure 3. Ordered, complex, and chaotic regimes in the I-Space

Table 1. Cultures in the I-Space.

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1. The diffusion effect—at all levels of codification and abstraction, the new ICTs can process and transmit more data to more people per unit of time than hitherto. We can describe this as a shift to the right in the diffusion curve. In Figure 4, the effect is indicated by the right-pointing arrow that is parallel to the diffusion dimension of the I-Space.

2. The bandwidth effect—a given target population in the I-Space can now be reached at a lower level of codification and abstraction than hitherto. Interactions at a distance that thirty years ago used to take place by telex now take place through videoconferencing. We depict this with the downward-pointing arrow in Figure 4.

As indicated by Figure 4, if by dint of the size of the population that can now be reached with the new ICTs, the diffusion effect favours market processes, the bandwidth effect, by re-personal-izing communications, favours clan-like processes. How might these developments affect the chal-lenge of coordination? In the next two sections we address this issue by briefly examining a complex ‘big science’ project: the ATLAS experiment at CERN.

The case of the ATLAS experiment at CERN

ATLAS is one of four high-energy physics (HEP) experiments being conducted at CERN, the host laboratory, using the Large Hadron Collider (LHC). The LHC is designed to collide two counter-rotating beams of protons or heavy ions at an energy of 7 Teravolts (TeV) per beam. Protons are accelerated to within a tiny fraction of the speed of light and then made to collide with each other. Physicists then use detectors to get information about short-lived particles—the product of proton-to-proton collisions—whose paths are too short to detect. To do this they look at the particles’ decay products which exist long enough to be detected. The proton beams move around the LHC ring inside a continuous vacuum guided by magnets.

Figure 4. The impact of ICTs in the I-Space

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The LHC is located in a circular tunnel, 27 km in circumference, that straddles the Swiss and French borders on the outskirts of Geneva and is buried between 50 and 175 m underground. The detector/accelerator system acts like a giant microscope, so powerful that it can make fundamen-tal particle activity visible to us within the tiny atomic nucleus. Four detectors—ATLAS, CMS, ALICE, and LHCb—each with its own distinctive design and each conceived to carry out differ-ent experiments, are positioned at different points on the circumference of the LHC. The ATLAS experiment uses a multi-component detector, housed in an eight-storey underground cavern, to test different aspects of an event. Each of its components identifies different particle types and then measure their energies and momenta. When an event is detected, individual particles can be singled out from the multitudes for analysis. After each detected event, thousands of computers collect and interpret the vast quantity of data generated by the detector—the volume of data gen-erated would fill up 100,000 CD per second—and present the information extracted from these data to the physicist.

The performance of an accelerator/detector system is set by the rate at which collisions can be engineered, the sensitivity with which collisions can be detected, and recorded, and the capacity to process collision data. We can represent these three performance requirements on a spidergraph as shown in Figure 5. At the centre of the spidergraph, performance requirements are minimal—anyone, so to speak, can achieve them. As one moves toward the tip of any one performance dimension however, one enters unexplored territory where no one has yet ventured. Here much of the relevant knowledge has not yet been codified and, being embedded in the concrete, idiosyncratic behaviour of specific pieces of equipment, measuring instruments and machinery, it resists summary abstract representations. Furthermore, as one approaches the tip of different performance dimensions, they begin to interact in unpredictable ways. An increase in the collision rate for example—the beam’s lumi-nosity—necessarily entails a requirement for an improvement in detection abilities. Improved detection, in turn, calls for greater data-processing capacities.

The ATLAS experiment, along with the other three, will explore the basic forces that have shaped our universe since its creation and that will determine its fate. It aims to understand the origins of mass—thought to be imparted to other particles by the elusive Higgs boson—the dimen-sionality of space, and microscopic black holes. It also seeks evidence for dark matter candidates

Figure 5. The ATLAS performance spidergraph

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in the universe. The detector is one of the most complex scientific instruments ever built, and the ATLAS Collaboration—the multinational team of scientists and engineers that developed the detector and will run the experiments—is one of the largest collaborative efforts ever attempted in the physical sciences. It involves the coordination of over 3000 physicists, working in 174 univer-sities and laboratories, and spread across over 38 countries.

As indicated in Figure 6, the ATLAS Collaboration is organized around both the components and the need for integrating these. Each component is the responsibility of a team of physicists and engineers. The team will typically have several hundred members, spread across the research insti-tutes of many countries. The interactions between different—and sometime conflicting—perform-ance requirements call for trade-offs and often negotiations between the ATLAS teams responsible for the performance of the different components that make up the detector. Although the Collaboration has a project management team, it manages with a light touch with little formal managerial authority to draw upon. The glue that binds participating institutions together is not contracts but Memoranda of Understanding. Team members are paid for by their respective participating institutions and do not readily ‘take orders’ from other members of the Collaboration. Indeed, the Collaboration’s project leader is called a ‘spokesperson’ and is considered a primus inter pares (a first among equals). Coordination is therefore mostly a bottom-up, consensus-driven affair, achieved numerous by face-to face meetings within and between the different teams, with many participants who cannot be physically present taking part virtually.

The ATLAS Collaboration in the I-Space

The ATLAS Collaboration is a large global network undertaking what is perhaps the most com-plex and sophisticated ‘big science’ project ever conceived. How might we characterize the

Figure 6. The organization of the ATLAS Collaboration

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network in I-Space terms? Where in the Space should we locate it? It draws on plenty of codified, abstract knowledge, but it does so in the context of tasks and performance requirements that have never been encountered before. Operating with highly intricate physical equipment at the scientific frontier, the critical knowledge it draws on is both uncodified and concrete, thus placing it in the lower regions of the I-Space. Yet being too loosely coupled to qualify as a clan, it is best thought of as what Mintzberg, following Alvin Toffler (1970) labels an adhocracy, an organizational con-figuration that enables sophisticated innovation and that is able to fuse networks of experts drawn from different disciplines into smoothly functioning ad hoc project teams (Mintzberg, 1979). According to Mintzberg, no one in an adhocracy is in a position to monopolize the power to inno-vate. As he puts it: ‘Decision-making power is distributed among managers and nonmanagers at all the levels of the hierarchy, according to the nature of the different decisions to be made( Mintzberg, 1979: 436).

We view adhocracies as loosely coupled networks operating to the right of clans in the I-Space. If clans sit close to the ‘edge of chaos’, however, adhocracies sit even closer. Chaos, in this context does not refer so much to disorganization as to the absence of codified and abstract structures that can serve a priori as a basis for organized action. Whatever structures guide action, emerge gradu-ally from the interactions of the players themselves and remain provisional and subject to change—the outcome of what Mintzberg and Waters (1985) describe as emergent strategies. Yet if the ATLAS Collaboration is an adhocracy operating close to or in the chaotic regime in the I-Space, how has it managed to deliver one of the most complex pieces of machinery ever built? What is the nature of the coordination that can achieve this? The matrix structure of a typical NASA project leads to much more tightly coupled operations than those of the ATLAS Collaboration. How, then, does the latter manage to deliver?

We hypothesize that the detector itself, acting as a boundary object (Carlile Paul, 2002), pro-vides the loosely coupled network that is ATLAS with much of raw materials required for effective coordination. A boundary object acts as a common reference point that allows different actors to coordinate their actions without interacting directly with each other. One can best understand the role of a boundary object by briefly examining two options for the regulation of traffic intersec-tions. One option is to use an authority-based system like traffic lights. A red light transmits an order to stop, a green light, an order to proceed. An alternative option is to use a roundabout in which the physical configuration of the roads themselves allows individual drivers to proceed at their own pace, making their own decision. In effect, a roundabout constitutes a boundary object that helps a multitude of individuals, each with a different destination, to coordinate their actions in a way that keeps traffic flowing. A set of traffic lights works most efficiently for a simple inter-section of two roads. The large roundabout at the Place de l’Etoile in Paris, by contrast, can handle six intersecting roads and could probably handle more. Both the traffic lights and roundabouts can be thought of as boundary object; but not only can roundabouts handle more complexity in a decentralized fashion than can traffic lights, their ability to coordinate is entirely self-contained whereas traffic lights depend on an external authority structure for their proper functioning.

As a boundary object, the ATLAS detector gradually moved up the I-Space toward higher levels of codification as it gained in definition. Through ever more detailed simulations and empirical tests, it also moved from being a highly abstract entity to being a concrete physical reality. Its coordinating role as a boundary object would today place it in the vicinity of the region labelled ‘bureaucracies’ in the I-Space, but, as indicated in Figure 7, closer to the concrete end of the abstraction dimension. There it acts as a generator of order. No boundary object, however, could coordinate an adhocracy of the size and complexity of the ATLAS Collaboration unaided. We therefore hypothesize that two further conditions must be met:

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1. The maintenance of clan values—The coherence that the ATLAS detector has been able to achieve in the course of its evolution draws upon deeply held values, motivations and beliefs, tacitly shared by all members of the collaboration throughout the project. These ensure that the orientation of the different participants in the collaboration towards the detector as a boundary object will be compatible.

2. The potential of the new ICTs must be fully exploited—If shared values and beliefs provide the motivation to bind the adhocracy together, the new ICTs provide the connectivity that makes it possible to do so and to achieve organizational coherence across a wide variety of cultures and institutions that are geographically dispersed. The shift in the diffusion curve brought about by the new ICTs and depicted in Figure 4 allows clan values to extend to, and be maintained in larger populations than physical presence can achieve on its own. Face-to-face interactions can now be sustained at a distance through what we have called the band-width effect. Thus whereas in earlier times an adhocracy as large and geographically dispersed as the ATLAS Collaboration would have quickly degenerated into chaos—as implied by Figure 3—the global connectivity made achievable with the new ICTs allow this complex heterogeneous network to hang together in a coherent and productive fashion and to do so over decades.

To summarize, we believe that the adhocracy that is the ATLAS Collaboration is held together first by a common focus on the ATLAS detector acting as a coordinator of the collaboration’s mem-bers; second, by the shared values and beliefs characteristic of clans that maintain the focus; and finally, by the enabling role played by the new ICTs in maintaining the necessary global connectiv-ity between the collaboration’s members.

Figure 7. Of adhocracies and boundary objects

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Conclusion

Does big science, as practiced by the ATLAS Collaboration have something to teach commercial organizations? Could a commercial firm ever manage a project of such complexity and duration? We cannot answer this question. At the frontiers of science, many of the managerial principles by which commercial organizations operate lose their purchase. But the idea that, under the right cul-tural and technological conditions—ones in which values, beliefs and motivations are deeply shared, and global connectivity can be maintained—a large, complex, physical object could take over some of the more challenging tasks of coordinating a global network merits deep reflection and further research. Three avenues for such research suggest themselves: (1) What might be a minimal specification for a boundary object? (2) What might be the maximum size and geographi-cal spread of the adhocracies that different types of boundary object could help coordinate? How complex are the tasks that different boundary objects could help to coordinate?

Funding

The reference number for this ESRC funded research project is: ES/G041555/1.

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