What Big Science Teaches About Collaboration, Complexity, and Innovation

Physicists recently confirmed discovery of the Higgs boson by scientists using the Large Hadron Collider (LHC) at CERN, the European Organization for Nuclear Research. For innovation business leaders, however, this is more than one of the longest, most expensive and most complex searches in science history. CIMS Academic Fellow Mariann Jelinek is confident it holds a number of lessons for any manager of a complex, uncertain, technically demanding, and politically sensitive project where success hinges on creating new shared knowledge.

Prof. Jelinek introduces those lessons with some necessary background:

The LHC is a megaproject, the epitome of “big science.” Its equipment is complex; the amount of data required and thus the computer infrastructure to capture, analyze and share it across the globe is complex.

And, not surprisingly, managing an international effort costing billions of euros and engaging some 3,000 scientists and engineers affiliated with 178 research institutions across 38 countries, collaborating over decades, is a hugely complex challenge—which underlines its value and interest for managers of other seemingly intractable, uncertain and complexly political endeavors. How did they do it?

Collisions and Collaboration: The Organization of Learning in the ATLAS

Experiment at the LHC, edited by Max Boisot, Markus Nordberg, Said Yami, and Bertrant Nicquevert (Oxford University Press, 2011, 335 pp.) offers a ringside seat and an excellent explanation by participants. I have drawn on their account for much of what follows.

Managerial Uncertainties

This leap into the future project began with a theoretical prediction in 1964 that required decades to test. Necessarily, then, the theoretical idea, the project’s methods and aims, and its uncertainty had to be persuasively explained to non-technical politicians whose backing was required.

As with other big commercial ideas, there were always appealing lesser projects of greater certainty being urged by others. So, too, with the LHC’s elaborately collaborative staffing— how might it be meaningfully “organized” in any sense, given that no one knew at the outset just exactly what would be required, or how to design the experiments (and thus the equipment to capture and analyze the data).

Because the project pushed “the edge of the state of the art,” and because both that state and the technology available changed over time, how could project financing be sensibly overseen? How could the enormous amounts of money be faithfully stewarded over decades and across borders, particularly when so many of those involved were not CERN employees?

How could initially vague and distant aims be sufficiently clarified to specify actual equipment to be built and operated (much less explained to politicians who decide on funding)?

What relationship should be crafted between the LHC ATLAS experiment and CERN, the host institute?

How should a desire to create the future by extending the state of the art balance against the sheer economics of currently available equipment (which can be reliably cost estimated, explained, purchased, and installed)?

These are profoundly managerial questions, with echoes for many contemporary managers.

When Contracts Cannot Be Specified

The more uncertain the methods and desired outcomes that collaborators intend across boundaries — like relational partners in an innovation network, for example — the less suitable traditional managerial and economic thinking about contractual obligations will be.

ATLAS and the LHC are operated under a Memorandum of Understanding — not a contract — because the desired outcomes could not be specified in advance: it was not known whether the Higgs boson existed or could be detected, nor what it might take to find out. For commercial projects, if technical uncertainty is sometimes less formidable, market uncertainties are greater when the product is genuinely new: precisely where potential gains and strategic advantages are greatest.

When Essential Workers Are Free Agents

Where contemporary supply chains rely on outside sources for key technology, for designs, or for manufacturing expertise, many of the essential personnel ¾ like those comprising virtually all of the ATLAS and LHC personnel ¾ are not company employees. Managing them, motivating them, and calling forth from them the engagement and creativity, conviction and commitment needed for innovation will not be achieved by top-down, hierarchical, bureaucratic approaches.

Instead, a culture of collaboration must be created to foster essential information sharing and evidence-based collaborative decision-making near the decision point; that is to say, by those with the relevant expertise (versus those in positions of authority who will almost certainly lack expertise).

Giving orders is impractical, because only the experts possess the requisite specialist knowledge (and no one of them possesses all of it). Thus “Persuasion and reputation are the only ‘control devices’ effectively available, and the whole enterprise operates mostly in a ‘bottom-up’ fashion” (Boisot et al., p.55).

Making Decisions

Preliminary and rather vague schemas for ATLAS functionalities (and thus for the devices needed) were agreed to early on. But the agreement carried a range of options, and with rather different understandings by different participants. These were not rapidly pruned to hasten decisions — as is often advised in business — since new knowledge was required, and active discussion of how different knowledge might interact was the only reasonable foundation for deciding.

ATLAS decisions were made when enough evidence arose from discussions of the points of disagreement to make the best decision clear: every participant had a stake in the success of the overall project, not in any one particular approach.

Boisot and colleagues term this “interlaced knowledge:” a kind of “distributed knowledge, some of which is shared, some of which is not,” that evolves into “a deeper understanding and appreciation of each other’s context and requirements” as the parties “heedfully interrelate with one another when unforeseen changes occurred” in service of their joint endeavor (Boisot et al., p.92). Cross-boundary innovation projects could learn from this practice! These include not only cross-departmental or cross-divisional projects, but collaborations beyond the company walls as well.

Because only the researchers possess the expertise to make appropriate decisions they, themselves, must create the needed coordination:

Our analysis of the different collective strategies deployed by the ATLAS Collaboration has implications for the conduct of science in general and for the pursuit of knowledge creation in the knowledge economy. The spread of ‘network organizations,’ of alliances, and of the Internet all seem to favor the institutionalization of trust-based collaborations that are clan-like. In the commercial sector, however, the seductive concept of ‘soft power’ butts up against anti-trust legislation and competition policies designed to forestall the emergence of its dark side—namely, collusion and monopoly. (Boisot et al., p. 114).

Knowledge-Based Leadership

“Leadership” amidst the uncertainty of a project like ATLAS depends upon what’s relevant when new physical facts like these are discovered—whether or not they had been predicted. The question of the moment will reveal which insight is relevant, what connections and capabilities are necessary:

Who could have anticipated, for example, that the beam strength of the LEP, the LHC’s predecessor, would be modified by the moon’s gravitational tug, or by the departure of high-speed trains (TGVs) as they left Geneva’s main railway station? (Boisot et al., p. 241, n.3)

This kind of dynamic, distributed, knowledge-based leadership is a far cry from the purely fiduciary responsibility so often vested in a manager by virtue of position, with omniscience assumed. Instead, Boisot and colleagues identify three different sorts of leadership: intellectual leadership, project leadership and institutional leadership, needed at different times by complex projects, and exercised by different people over time, as circumstances demand.

The uncertainty, futurity, expense, and collaborative nature of major undertakings— whether these are public scientific ventures like the LHC and ATLAS, major infrastructure projects, or major commercial development efforts — typically renders them both technically difficult and politically sensitive.

Project champions are well known in project management and innovation lore, but intellectual, project and institutional leadership ideas point to forms of innovation leadership in developing shared knowledge that are far less well known.

Creating a Learning Culture 

Learning is a critical task for complex, collaborative undertakings like ATLAS, as for similar innovation efforts in business. Organizing interactions and information flow to achieving that learning is the heart of the matter. “Learning” and “information flow organization” are somewhat fractal ideas insofar as patterned dynamics replicate at interpersonal, group, intergroup, organizational, and inter-organizational levels.

At the largest-scale (designing, building and operating the LHC) collaboration across many boundaries depends on different and only partly shared learning. But such learning will not occur without effective organizing to create an appropriate learning culture: negotiating workable interfaces between individuals, groups, organizational entities, disciplines, buyers and suppliers, researchers and funding entities, and more.

All this goes well beyond “planning,” or “project management,” “leadership” or mainstream management as these are usually understood—so the traditional control-based theories on these topics are essentially useless. Instead, complex undertakings like ATLAS and the LHC require managers to foster and maintain trust-oriented, reputationally-focused and evidence-based management styles that are worlds apart from the “command and control” approaches of the past.

So what can business leaders learn from ATLAS? A lot about the management of complex, uncertain, technically demanding, and politically sensitive projects where success hinges on creating new shared knowledge!

Mariann Jelinek, The Richard C. Kraemer Professor of Strategy, Emerita

College of William and Mary and CIMS Academic Fellow; Mariann.Jelinek@business.wm.edu

 

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