Complexity management

About: Complexity management is a research topic. Over the lifetime, 1591 publications have been published within this topic receiving 35078 citations.

Imitation of Complex Strategies

Abstract: Researchers examining loosely coupled systems, knowledge management, and complementary practices in organizations have proposed, informally, that the complexity of a successful business strategy can deter imitation of the strategy. This paper explores this proposition rigorously. A simple model is developed that parametrizes the two aspects of strategic complexity: the number of elements in a strategy and the interactions among those elements. The model excludes conventional resource-based and game-theoretic barriers to imitation altogether. The model is used to show that complexity makes the search for an optimal strategy intractable in the technical sense of the word provided by the theory of NP-completeness. Consequently, would-be copycats must rely on search heuristics or on learning, not on algorithmic "solutions," to match the performance of superior firms. However, complexity also undermines heuristics and learning. In the face of complexity, firms that follow simple hill-climbing heuristics are quickly snared on low "local peaks," and firms that try to learn and mimic a high performer's entire strategy suffer large penalties from small errors. The model helps to explain why some winning strategies remain unmatched even though they are open to public scrutiny; why certain bundles of organizational practices diffuse slowly even though they lead to superior performance; and why some strategies yield superior returns even after many of their critical ingredients are adopted by competitors. The analysis also suggests roles for management science and managerial choice in a world of complex strategies.

Organizations As Adaptive Systems in Complex Environments: the Case of China

Abstract: This paper treats organizations as adaptive systems that have to match the complexity of their environments. The nature of this complexity is analyzed by linking an institutional InformationSpace (I-Space) framework to the work of complexity theorists. The I-Space framework identifies the codification, abstraction, and diffusion of information as cultural attributes. Codification involves the assignment of data to categories, thus giving them form. Abstraction involves a reduction in the number of categories to which data needs to be assigned for a phenomenon to be apprehended. Information is diffused through populations of data-processing agents, thus constituting the diffusion dimension. Complexity theorists have identified the stability and structure of algorithmic information complexity in a way that corresponds to levels of codification and abstraction. Their identification of system parts and the richness of cross-coupling draws attention to the fabric of information diffusion. We discuss two modes of adaptation to complex environments: complexity reduction and complexity absorption. Complexity reduction entails getting to understand the complexity and acting on it directly, including attempts at environmental enactment. Complexity absorption entails creating options and risk hedging strategies, often through alliances. The analysis, and its practical utility, is illustrated with reference to China, the world's largest social system. Historical factors have shaped the nature of complexity in China, giving it very different characteristics than those typical of Western industrial countries. Its organizations and other social units have correspondingly handled this complexity through a strategy of absorption rather than the reduction strategy characteristic of Western societies. Western firms operating in China therefore face a choice between maintaining their norms of complexity reduction or adopting a strategy of complexity absorption that is more consistent with Chinese culture. The specifics of these policy alternatives are explored, together with their advantages and disadvantages. The paper concludes with the outlines of a possible agenda for future research, focusing on the investigation of complexity handling modes and the contingencies which may bear upon the choice between them.