Firm Resources and Sustained Competitive Advantage

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

https://arxiv.org/pdf/cond-mat/9910332.pdf

Emergence of Scaling in Random Networks

This article synthesizes the large but diverse literature on organizational legitimacy, highlighting similarities and disparities among the leading strategic and institutional approaches. The analysis identifies three primary forms of legitimacy: pragmatic, based on audience self-interest; moral, based on normative approval: and cognitive, based on comprehensibility and taken-for-grantedness. The article then examines strategies for gaining, maintaining, and repairing legitimacy of each type, suggesting both the promises and the pitfalls of such instrumental manipulations.

https://www.jstor.org/stable/pdf/258788.pdf

Managing Legitimacy: Strategic and Institutional Approaches

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

This research was supported by grants provided to the first author by the Social and Behavioral Sciences Research Institute, University of Arizona, and the Aspen Institute Nonprofit Sector Research Fund and by grants to the second author by the College of Business and Public Administration, University of Arizona. We have benefited from productive exchanges with numerous audiences to whom portions of this paper have been presented: a session at the 1994 Academy of Management meetings, the Social Organization workshop at the University of Arizona, the Work, Organizations, and Markets workshop at the Harvard Sociology Department, the 1994 SCOR Winter Conference at Stanford University, and colloquia at the business schools at the University of Alberta, UC-Berkeley, Duke, and Emory, and the JFK School at Harvard. For detailed comments on an earlier draft, we are extremely grateful to Victoria Alexander, Ashish Arora, Maryellen Kelley, Peter Marsden, Charles Kadushin, Dick Nelson, Christine Oliver, Lori Rosenkopf, Michael Sobel, Bill Starbuck, Art Stinchcombe, and anonymous reviewers at ASQ. We thank Dina Okamoto for research assistance and Linda Pike for editorial guidance. Address correspondence to Walter W. Powell, Department of Sociology, University of Arizona, Tucson, AZ 85721. We argue in this paper that when the knowledge base of an industry is both complex and expanding and the sources of expertise are widely dispersed, the locus of innovation will be found in networks of learning, rather than in individual firms. The large-scale reliance on interorganizational collaborations in the biotechnology industry reflects a fundamental and pervasive concern with access to knowledge. We develop a network approach to organizational learning and derive firm-level, longitudinal hypotheses that link research and development alliances, experience with managing interfirm relationships, network position, rates of growth, and portfolios of collaborative activities. We test these hypotheses on a sample of dedicated biotechnology firms in the years 1990-1994. Results from pooled, within-firm, time series analyses support a learning view and have broad implications for future theoretical and empirical research on organizational networks and strategic alliances.*

https://www.jstor.org/stable/pdfplus/2393988.pdf

Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.

This paper explores the dynamics of allocation under increasing returns in a context where increasing returns arise naturally: agents choosing between technologies competing for adoption. Modern, complex technologies often display increasing returns to adoption in that the more they are adopted, the more experience is gained with them, and the more they are improved.1 When two or more increasing-return technologies 'compete' then, for a 'market' of potential adopters, insignificant events may by chance give one of them an initial advantage in adoptions. This technology may then improve more than the others, so it may appeal to a wider proportion of potential adopters. It may therefore become further adopted and further improved. Thus a technology that by chance gains an early lead in adoption may eventually 'corner the market' of potential adopters, with the other technologies becoming locked out. Of course, under different 'insignificant events' - unexpected successes in the performance of prototypes, whims of early developers, political circumstances - a different technology might achieve sufficient adoption and improvement to come to dominate. Competitions between technologies may have, multiple potential outcomes. It is well known that allocation problems with increasing returns tend to exhibit multiple equilibria, and so it is not surprising that multiple outcomes should appear here. Static analysis can typically locate these multiple equilibria, but usually it cannot tell us which one will be 'selected'. A dynamic approach might be able to say more. By allowing the possibility of 'random events' occurring during adoption, it might examine how these influence ' selection' of the outcome - how some sets of random 'historical events' might cumulate to drive the process towards one market-share outcome, others to drive it towards another. It might also reveal how the two familiar increasingreturns properties of non-predictability and potential inefficiency come about: how increasing returns act to magnify chance events as adoptions take place, so that

/pdf/competing-technologies-increasing-returns-and-lock-in-by-3xfy5hl1w5.pdf

Competing technologies, increasing returns, and lock-in by historical events*

Increasing Returns and Path Dependence in the Economy

* Introduction W.B. Arthur, S.N., Durlauf, and D. Lane * Asset Pricing Under Endogenous Expectations in an Artificial Stock Market W.B. Arthur, J.H. Holland, B. LeBaron, R. Palmer, and P. Tayler * Natural Rationality V.M. Darley and S.A. Kauffman * Statistical Mechanics Approaches to Socioeconomic Behavior S.N. Durlauf * Is What Is Good for Each Best for All? Learning From Others in the Information Contagion Model D. Lane * Evolution of Trading Structures Y.M. Ioannides * Foresight, Complexity, and Strategy D. Lane and R. Maxfield * The Emergence of Simple Ecologies of Skill J.F. Padgett * Some Fundamental Puzzles in Economic History/Development D.C. North * How the Economy Organizes Itself in Space: A Survey of the New Economic Geography P. Krugman * Time and Money P. Shubik * Promises Promises J. Geanakoplos * Macroeconomics and Complexity: Inflation Theory A. Leijonhufvud * Evolutionary Dynamics in Game-Theoretic Models K. Lindgren * Identification of Anonymous Endogenous Interactions C.F. Manski * Asset Price Behavior in Complex Environments W.A. Brock * Population Games L.E. Blume * Computational Political Economy K. Kollman, J. H. Miller, and S. Page * The Economy as an Interactive System A.P. Kirman * How Economists Can Get A Life L. Tesfatsion * Some Thoughts About Distribution in Economics P.W. Anderson

/pdf/the-economy-as-an-evolving-complex-system-ii-3ihjmnyrcq.pdf

The economy as an evolving complex system II

The type of rationality we assume in economics--perfect, logical, deductive rationality--is extremely useful in generating solutions to theoretical problems. But it demands much of human behavior--much more in fact than it can usually deliver. If we were to imagine the vast collection of decision problems economic agents might conceivably deal with as a sea or an ocean, with the easier problems on top and more complicated ones at increasing depth, then deductive rationality would describe human behavior accurately only within a few feet of the surface. For example, the game Tic-Tac-Toe is simple, and we can readily find a perfectly rational, minimax solution to it. But we do not find rational "solutions" at the depth of Checkers; and certainly not at the still modest depths of Chess and Go.

/pdf/inductive-reasoning-and-bounded-rationality-ujh1ajw522.pdf

Inductive Reasoning and Bounded Rationality

In "The Nature of Technology", ground-breaking economist W. Brian Arthur explores the extraordinary way in which the technology that surrounds us and allows us to live our modern lives has actually been developed. Rather than coming from a series of one-off inventions, almost all the technology we use today comes from previous developments: these technologies are not being created, but are instead evolving. With fascinating examples, from laser printers to powerplants, Arthur reveals how our own problem-solving skills and creative vision can evolve alongside these technologies, and how this understanding can even improve our understanding of the wider world.

W. Brian Arthur

Papers

Competing technologies, increasing returns, and lock-in by historical events*

Increasing Returns and Path Dependence in the Economy

The economy as an evolving complex system II

Inductive Reasoning and Bounded Rationality

The Nature of Technology: What it Is and How it Evolves