We examine how firms search, or solve problems, to create new products. According to organizational learning research, firms position themselves in a unidimensional search space that spans a spectrum from local to distant search. Our findings in the global robotics industry suggest that firms' search efforts actually vary across two distinct dimensions: search depth, or how frequently the firm reuses its existing knowledge, and search scope, or how widely the firm explores new knowledge.

http://web.stanford.edu/~rkatila/new/pdf/KatilaSomethingold.pdf

Something Old, Something New: A Longitudinal Study of Search Behavior and New Product Introduction

A simple model is developed to explore the interrelationship between processes of organizational level change and population selection forces. A critical property of the model is that the effect on organizational fitness of the various attributes that constitute an organization's form is interactive. As a result of these interaction effects, the fitness landscape is "rugged." An organization's form at founding has a persistent effect on its future form when there are multiple peaks in the fitness landscape, since the particular peak that an organization discovers is influenced by its starting position in the space of alternative organizational forms. Selection pressures influence the distribution of the organizational forms that emerge from the process of local adaptation. The ability of established organizations to respond to changing environments is importantly conditioned by the extent to which elements of organizational form interact in their effect on organizational fitness. Tightly coupled organizations are subject to high rates of failure in changing environments. Furthermore, successful "reorientations" are strongly associated with survival for tightly coupled organizations, but not for more loosely coupled organizations that are able to engage in effective local adaptation.

Adaptation on rugged landscapes

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Complex organizations exhibit surprising, nonlinear behavior. Although organization scientists have studied complex organizations for many years, a developing set of conceptual and computational tools makes possible new approaches to modeling nonlinear interactions within and between organizations. Complex adaptive system models represent a genuinely new way of simplifying the complex. They are characterized by four key elements: agents with schemata, self-organizing networks sustained by importing energy, coevolution to the edge of chaos, and system evolution based on recombination. New types of models that incorporate these elements will push organization science forward by merging empirical observation with computational agent-based simulation. Applying complex adaptive systems models to strategic management leads to an emphasis on building systems that can rapidly evolve effective adaptive solutions. Strategic direction of complex organizations consists of establishing and modifying environments within which effective, improvised, self-organized solutions can evolve. Managers influence strategic behavior by altering the fitness landscape for local agents and reconfiguring the organizational architecture within which agents adapt.

Complexity Theory and Organization Science

Despite tendencies toward convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior. An agent-based adaptive model reveals the effects of a mechanism of convergent social influence. The actors are placed at fixed sites. The basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits. Unlike previous models of social influence or cultural change that treat features one at a time, the proposed model takes into account the interaction between different features. The model illustrates how local convergence can generate global polarization. Simulations show that the number of stable homogeneous regions decreases with the number of features, increases with the number of alternative traits per feature, decreases with the range of interaction, and (most surprisingly) decreases when the geographic territory grows beyond a certain size. MAINTENANCE OF DIFFERENCES If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear? Social scientists have proposed many mechanisms to answer this question. The purpose of this article is to explore one more mechanism. The mechanism proposed here deals with how people do indeed become more similar as they interact, but also provides an explanation of why the tendency to converge stops before it reaches completion. It therefore provides a new type of explanation of why we do not all become alike. Because the proposed mechanism can exist alongside other mechanisms, it can be regarded as complementary with older explanations rather than necessarily competing with them. Unfortunately, no good term describes the range of things about which people can influence each other. Although beliefs, attitudes, and behavior cover a wide range indeed, there are still more things over which interpersonal influence extends, such as language, art, technical standards, and social norms. The most generic term for the

/pdf/the-dissemination-of-culture-a-model-with-local-convergence-2dvtnze8b0.pdf

The Dissemination of Culture: A Model with Local Convergence and Global Polarization

We present a theory for predicting how business firms form alliances to develop and sponsor technical standards. Our basic assumptions are that the utility of a firm for joining a particular standard-setting alliance increases with the size of the alliance and decreases with the presence of rivals in the alliance, especially close rivals. The predicted alliance configurations are simply the Nash equilibria, i.e., those sets of alliances for which no single firm has an incentive to switch to another alliance. We illustrate our theory by estimating the choices of nine computer companies to join one of two alliances sponsoring competing Unix operating system standards in 1988.

/pdf/coalition-formation-in-standard-setting-alliances-1l1s0p0zbq.pdf

Coalition Formation in Standard-Setting Alliances

This study proposes a relatively simple computational model of organizational evolution based on the genetic algorithm. The model includes three organizational processes: variation, adaptation, and...

Organizational Evolution, Learning, and Selection: A Genetic-Algorithm-Based Model

In this paper we outline a specific and practical agenda to move computer simulation from the margin into the mainstream of social science research.

From the Margin to the Mainstream: An Agenda for Computer Simulations in the Social Sciences

I propose that both individuals and organizations can learn strategies in complex or ill-defined situations more effectively, using the process of inductive versus deductive reasoning.
To support this proposition, a theory of strategy learning is developed, where a strategy consists of a set of moves which can be combined many ways to respond to business situations. Strategy learning is captured by three basic processes: variation, adaptation, and selection. My theory is implemented using a computer-simulated game among artificial agents, with each agent represented by a computer program capable of intelligent learning. This game can make more significant predictions for a reputation game than traditional game theory. Computer simulation results are then compared with results from corresponding economic experiments. They indicate that sophisticated strategies such as reputation building can be learned inductively.
Using computer simulation of the genetic algorithm, Part II explores different hierarchical levels of organizational evolution. In the case presented, selection at the population level of analysis influenced learning capabilities at the organizational level, while the learning capabilities influenced the selection process. Thus, it is crucial to understand how strategy learning at these different organizational levels interact with each other.
Part III discusses the importance of long chains of strategy moves. A mathematical model demonstrates that specific strategic instances can be identified effectively with a "bottom-up" hierarchical search, which first looks for the smallest, stable parts of an action chain and then combines them into larger and larger sub-strategies until the final strategy is identified. This model can predict how humans learn specific strategic instances when provided with intermediate feedback. Empirical evidence from a pilot experiment indicates that humans use hierarchical search to discover action chains.
This work provides a theoretical foundation for the evolution of intelligent organizations. If individuals can learn sophisticated strategies inductively, then so can organizations. Organizations should be able to function like large, parallel-distributed information systems, capable of learning strategies more effectively and able to leverage their strategic decision-making capabilities through the contribution of many intelligent individuals.

How strategies are learned

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Papers

Coalition Formation in Standard-Setting Alliances

Organizational Evolution, Learning, and Selection: A Genetic-Algorithm-Based Model

From the Margin to the Mainstream: An Agenda for Computer Simulations in the Social Sciences

How strategies are learned