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.

Fifty years ago our technologies, our organizations and our lives were less complicated than today. Things were simpler. Most of us prize this plainness, this simplicity. Yet we are fascinated by complexity. Lately I've been wondering why the simple becomes complex. Is there a general principle causing things to get more complicated as time passes? Is complexity useful? One good place to look for answers to these questions is the history of technology. The original turbojet engine, designed by Frank Whittle in the early 1930s, was beautifully simple. The idea was to propel aircraft by a jet of highspeed air. To do this, the engine took in air, pumped up its pressure by a compressor and ignited fuel in it. It passed the exploding mixture through a turbine to drive the compressor, releasing it through an exhaust nozzle at high speed to provide thrust. The original prototype worked well with just one moving part the compressor-turbine combination. Yet over the years, jet engines steadily became more complicated. Why? Commercial and military interests exert constant pressure to overcome limits imposed by extreme stresses and temperatures and to handle exceptional situations. Sometimes these improvements are achieved by using better materials more often by adding a subsystem. And so, over time, jet designers achieve higher air pressures by using not one but an assembly of many compressors. They increase efficiency by a guide-vane control system that admits more air at higher altitudes and velocities and prevents engine stalling. They increase combustion temperatures, then cool the white-hot turbine blades by a system that circulates air inside them. They add bleed valve systems, afterburner assemblies fire-detection systems, fuel-control systems, deicing assemblies. But all these additions require subsystems to monitor and control them and to enhance their performance when they run into limitations. These subsystems in turn require subsubsystems to enhance their performance. All this indeed improves performance--today's jet engine is 30 to 50 times more powerful than Whittle's. But it ends up encrusting the original simple system with subsystem upon subsystem and subassembly upon subassembly in a vastly complicated array of interconnected modules and parts. Modern engines have upwards of 22,000 parts. There's nothing wrong with this increase in complexity. We can admire it. On the outside, jet engines are sleek and lean; on the inside, complex and sophisticated. In nature, higher organisms are this way, too. On the outside, a cheetah is powerful and fast; on the inside, even more complicated than a jet engine. A cheetah, too, has temperature-regulating systems, sensing systems, control functions, maintenance functions-all embodied in a complex s assembly of organs, cells and organelles, modulated not by machinery and electronics but by interconnected networks of chemical and neurological pathways. The steady pressure of competition causes evolution to \"discover\" new functions occasionally that push out performance limits. There's something wonderful about this--about how, over eons, a cheetah forms from its simple multicellular ancestors.

Why Do Things Become More Complex

This paper draws on modern psychology to argue that as humans, in economic decision contexts that are complicated or ill-defined, we use not deductive, but inductive reasoning. That is, in such contexts we induce a variety of working hypotheses or mental models, act upon the most credible, and replace hypotheses with new ones if they cease to work. Inductive reasoning leads to a rich psychological world in which an agent's hypotheses or mental models compete for survival against each other, in an environment formed by other agents' hypotheses or mental models---a world that is both evolutionary and complex. Inductive reasoning can be modeled in a variety of ways. The main body of the paper introduces and models a coordination problem---``the bar problem''---in which agents' expectations are forced to be subjective and to differ. It shows that while agents' beliefs never settle down, collectively they form and ``ecology" that does converge to an equilibrium pattern.

Inductive Reasoning, Bounded Rationality and the Bar Problem

Large-scale simulation modeling is a highly visible arrival on the population and development scene-one that commands increasing resources year by year. In 1967 only a few modestsized "economic-demographic models" existed, and only one, the General Electric Company's TEMPO-I, was in wide use. By 1975 at least a dozen large models were in various stages of construction and operation; more were in the planning stage. Each of these models consists of a detailed, recursive computer program for the simulation of long-term development. The focus is on population, and the idea is to give developing country planners a means to test alternative policy choices and to deepen their insight into the future impact of present decisions. This paper examines the thinking behind the current economic-demographic models, and presents a critical assessment of their potential use to development planners. Although in its present form, systems-based and computer-intensive, large-scale simulation modeling in population and development has been in existence less than ten years, the origins go further back-at least to the 1958 Coale-Hoover model for evaluating the impact of a hypothetical fertility reduction in India.' That this model became the ancestor of so many computer-based analyses was somewhat unintentional: Coale and Hoover argued the case for fertility reduction verbally, through careful, nuanced analysis, in the main body of their book; the formal model is added almost as an afterthought in the back pages-in 1958, systems thinking did not dominate economic analysis. This early study spawned a number of similar formulations during the 1960s, these efforts gradually becoming less

http://www.jstor.org/stable/pdfplus/1972223.pdf

Large-Scale Simulation Models in Population and Development: What Use to Planners?

Strong laws for a class of path-dependent stochastic processes with applications

It is often taken for granted that as systems evolve over time they tend to become more complex But little is understood about the mechanisms that might cause evolution to favor increases in complexity over time This paper proposes three means by which complexity tends to grow as systems evolve In co-evolutionary systems it may grow by increases in ``species'' diversity: under certain circumstances new species may provide further niches that call forth further new species in a steady upward spiral In single systems it may grow by increases in structural sophistication: the system steadily cumulates increasing numbers of subsystems or sub-functions or sub-parts to break through performance limitations, or enhance its range of operation, or handle exceptional circumstances Or, it may suddenly increase by ``capturing software'': the system captures simpler elements and learns to ``program'' these as ``software'' to be used to its own ends Growth in complexity in all three mechanisms is intermittent and epochal And in the first two is reversible, so that collapses in complexity may occur randomly from time to time Ilustrative examples are drawn from biology, but from economics, adaptive computation, artificial life, and evolutionary game theory

W. Brian Arthur

Papers

Why Do Things Become More Complex

Inductive Reasoning, Bounded Rationality and the Bar Problem

Large-Scale Simulation Models in Population and Development: What Use to Planners?

Strong laws for a class of path-dependent stochastic processes with applications

On the evolution of complexity