Complexity in organizations: Consequences for climate policy analysis

Algorithmic models specifying the kinds of computations carried out by economic organizations have the potential to account for the serious discrepancies between the real-world behavior of firms and the predictions of conventional maximization models. The algorithmic approach uncovers a surprising degree of complexity in organizational structure and performance. The fact that firms are composed of networks of individual agents drastically raises the complexity of the firm's optimization problem. Even in very simple network models, a large number of organizational characteristics, including some for which no polynomial time computational algorithm is known, appear to influence economic performance. We explore these effects using regression analysis, and through application of standard search heuristics. The calculations show that discovering optimal network structures can be extraordinarily difficult, even when a single clear organizational objective exists and the agents belonging to the firms are homogeneous. One implication is that firms are likely to operate at local rather than global optima. In addition, if organizational fitness is a function of the ability to solve multiple problems, the structure that evolves may not solve any of the individual problems optimally. These results raise the possibility that externally-driven objectives, such as energy efficiency or pollution control, may shift the firm to a new structural compromise that advances other objectives of the firm also, rather than necessarily imposing economic losses.