Balancing Inertia, Innovation, and Imitation in Complex Environments

By Hodgson, Geoffrey M.; Knudsen, Thorbjorn | Journal of Economic Issues, June 2006 | Go to article overview
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Balancing Inertia, Innovation, and Imitation in Complex Environments


Hodgson, Geoffrey M., Knudsen, Thorbjorn, Journal of Economic Issues


Since Thorstein Veblen, perennial themes in institutionalist writings have included the role of imitation (or emulation) and the tension between inertia (or conservatism) and innovation in individual and organizational behavior. Prior models of organizational behavior have examined two search processes that represent this tension. One is local search, in which an organization restricts experimentation to a single attribute at a time. In contrast, distant search is associated with changing all of the organization's attributes, in other words, extreme innovativeness. In both cases, the organization adopts the new form if its fitness is thereby improved.

Previous research has established that high levels of complexity favor extreme innovativeness (distant search) over a modest level of inertia (local search). However, it is unclear if organizations balancing inertia and innovativeness at intermediate levels may have an advantage over these extremes (Sorenson 2002). In order to address this gap in our knowledge, we are here concerned with balancing inertia and innovativeness in task environments of intermediate complexity, in other words, when organizational attributes are more or less interdependent. The present work is related to literature which has developed agent-based models of interacting innovators and imitators. Peter Allen and J. M. McGlade (1986) described two distinct search strategies among fishing vessels: "stochasts" who randomly seek out new areas, and "cartesians" who watch where stochasts go and then fish in the most promising areas. The fisheries model is a topical variation on the well-known exploitation-exploration problem (March 1991), with stochasts representing the exploration pole and cartesians representing the exploitation pole. (1) In social organizations, innovation and imitation is usually a mixture, however, rather than distinct modes of behavior in different kinds of organization. Also, the complexity of the task environment may influence the viable proportion of innovation and imitation in a social organization. To address these issues, we characterize organizations with mixtures of innovation and imitation and examine how the viable mixture is influenced by the complexity of the task environment.

The next section develops a modeling structure on the basis of Stuart Kauffman's (1993) NK model. This is followed by sections providing results and a conclusion.

The Model

Organizational Forms and Fitness

Kauffman's (1993) NK model has been widely employed in the study of organizations (Ethiraj and Levinthal 2004; Gavetti and Levinthal 2000; Levinthal 1997; McKelvey 1999; Rivkin and Siggelkow 2003; Sorenson 2002). We use a variant of this model and specify a set of possible organizational forms as consisting of N attributes. Each attribute can take on two states, so there are [2.sup.N] different organizational forms. The fitness landscape created by the NK model is a mapping of the set of attributes onto fitness values. The fitness values of each of the N attributes are determined by random draws from a uniform distribution over the unit interval. The fitness of the organizational form is the average of the values assigned to each of its N attributes.

Organizational attributes can be more or less interdependent; the value of each of the N individual attributes is affected by both the state of that attribute itself and the states of K other attributes. If K = 0, there are no interdependencies among the attributes of an organization's form. As K increases, more and more attributes become interdependent. With K = N - 1, all attributes of an organization's form are interdependent. The number of interdependencies given by K determines the surface of the fitness landscape. With K = 0, the fitness surface is smooth. As K increases, the fitness surface becomes more rugged. That is, higher K leads to a loss of order in the correspondence between organizational forms and fitness values.

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