Luigi Marengo

Perspective---On the Evolutionary and Behavioral Theories of Organizations: A Tentative Roadmap

Cyert and Marchs A Behavioral Theory of the Firm has been acknowledged as one of the most fundamental pillars on which evolutionary theorizing in economics is built. Nelson and Winters 1982 book is pervaded by the philosophy and concepts previously developed by Cyert, March, and Simon. Behavioral notions, such as bounded rationality are also at the heart of economic theories of institutions such as transaction costs economics. In this paper, after briefly reviewing the basic concepts of evolutionary economics, we discuss its implications for the theory of organizations (and business firms in particular), and we suggest that evolutionary theory should coherently embrace an “embeddedness” view of organizations, whereby the latter are not simply efficient solutions to informational problems arising from contract incompleteness and uncertainty, but also shape the “visions of the world,” interaction networks, behavioral patterns, and the identity of the agents. After outlining the basic features of this perspective, we analyze its consequences and empirical relevance.

Coordination and Organizational Learning in the Firm

This paper discusses the role of the organizational structure in shaping the organizational learning process. Learning is modelled by means of a computational model in which search takes place in the space of problem representations and cannot be reduced to mere probability updating within a given and constant representation.When the assumption of a unique and given representation of the problem is dropped, organizational learning emerges from the coordination of individual learning processes. Some simulations analyze the performance, in different environmental conditions, of centralized and decentralized coordination modes.

Norms as emergent properties of adaptive learning: The case of economic routines

Interaction among autonomous decision-makers is usually modelled in economics in game-theoretic terms or within the framework of General Equilibrium. Game-theoretic and General Equilibrium models deal almost exclusively with the existence of equilibria and do not analyse the processes which might lead to them. Even when existence proofs can be given, two questions are still open. The first concerns the possibility of multiple equilibria, which game theory has shown to be the case even in very simple models and which makes the outcome of interaction unpredictable. The second relates to the computability and complexity of the decision procedures which agents should adopt and questions the possibility of reaching an equilibrium by means of an algorithmically implementable strategy. Some theorems have recently proved that in many economically relevant problems equilibria are not computable. A different approach to the problem of strategic interaction is a “constructivist” one. Such a perspective, instead of being based upon an axiomatic view of human behaviour grounded on the principle of optimisation, focuses on algorithmically implementable “satisfycing” decision procedures. Once the axiomatic approach has been abandoned, decision procedures cannot be deduced from rationality assumptions, but must be the evolving outcome of a process of learning and adaptation to the particular environment in which the decision must be made. This paper considers one of the most recently proposed adaptive learning models: Genetic Programming and applies it to one the mostly studied and still controversial economic interaction environment, that of oligopolistic markets. Genetic Programming evolves decision procedures, represented by elements in the space of functions, balancing the exploitation of knowledge previously obtained with the search of more productive procedures. The results obtained are consistent with the evidence from the observation of the behaviour of real economic agents.

Interdependencies, nearly-decomposability and adaptation

In this paper we discuss some limitations that selection mechanisms face when the entities subject to selection are complex systems of interdependent elements. We briefly present Kauffman’s NK model which addresses this problem in biological systems. It is argued that, contrary to the myopic search behaviour, underlying biological fitness landscapes, social organisations are not bound in their search dynamics. This amounts to say that the problem of finding optima on a fitness landscape can be decomposed in many different ways. Following work by Page (1996), we present some measures of the complexity of a fitness landscape in terms of the complexity (size) of the algorithm that decomposes the problem most accurately, while still being able to locate the global optima with full certainty. We then extend this measures to allow for nearly-decomposability in a sense close to Simon (1969). Finally we study some evolutionary properties of populations of agents characterised by different decompositions of the same given problem.

The Value and Costs of Modularity: A Problem‐Solving Perspective

This paper discusses the issue of modularity from a problem-solving perspective. Modularity is in fact a decomposition heuristic, through which a complex problem is decomposed into independent or quasi-independent sub-problems. By means of a model of problem decomposition, this paper studies the trade-offs of modularity: on the one hand finer modules increase the speed of search, but on the other hand they usually determine lock-in into sub-optimal solutions. How to balance effectively this trade-off depends upon the problem environment and in particular on its complexity and volatility: we show that in stationary and complex environments there exists an evolutionary advantage to over-modularization, while in highly volatile environments, contrary to usual wisdom, modular search is inefficient in the long run. The empirical relevance of our findings is discussed especially with reference to the literature on systems integration.

Structure, Competence and Learning in an Adaptive Model of the Firm

Organizational learning [cf. for instance the pioneering work of Cyert and March (1963) and, for a broad outline of its main economic implications, Nelson and Winter (1982) is an issue which deserves primary attention when studying the dynamic performance of economic systems. But organizational learning — it will be argued in this chapter — cannot be adequately handled within the existing dominant analytical framework of economic theory. Recent attempts to accommodate organizational issues within the neoclassical theory of the firm have impressively broadened the scope of the latter and tackled fundamental questions which used to lie outside the concern of economic theory; but they have not been able to deal in a satisfactory way with the problem of learning because neoclassical theory, in these most recent developments, is concerned with information, whereas learning is about knowledge.

Competence, innovative activities and economic performance in Italian high–technology firms

This paper presents an empirical, questionnaire–based enquiry into the main types of competencies which characterize Italian high–technology firms, their main sources, and the relationship between such different types of competencies and the innovative and economic performance of firms.

A learning-to-forecast experiment on the foreign exchange market with a classifier system

Abstract This paper reports on an experiment of learning and forecasting on the foreign exchange market by means of an Artificial Intelligence methodology (a ‘Classifier System’) which simulates learning and adaptation in complex and changing environments. The experiment has been run for two different exchange rates, the US dollar-D mark rate and the US dollar-yen rate, representative of two possibly different market environments. A fictitious “artificial agent” is first trained on a monthly data base from 1973 to 1990, and then tested out-of-sample from 1990 to 1992. Its forecasting performance is then compared with the performance of decision rules which follow the prescription of various economic theories on exchange rate behaviour, and the performance of forecasts given by VAR estimations of the exchange-rates determinants.

How to Get What You Want When You Do Not Know What You Want: A Model of Incentives, Organizational Structure, and Learning

In this paper we present a model of the interplay between learning, incentives and the allocation of decision rights in the context of a generalized agency problem. Within this context, not only actors face conflicting interests but diverging cognitive ?visions? of the right course of action as well. We show that a principal may obtain the implementation of desired organizational policies by means of appropriate incentives or by means of appropriate design of the allocation of decisions, when the latter is cheaper but more complex. We also show that when the principal is uncertain about which course of action is more appropriate and wants to learn it from the environment, organizational structure and incentives interact in non-trivial ways and must be carefully tuned. When learning is not at stake, incentives and organizational structure are substitutes. When instead learning is at stake, organizational structure and incentives may complement each other and have to be fine tuned according to the complexity of the learning process and the competitive pressure which is put on fast or slow learning.

Firm size, managerial practices and innovativeness: some evidence from Finnish manufacturing

In this study, we use a survey data on 398 Finnish manufacturing firms for the years 2002 and 2005 to empirically explore whether and which organisational factors explain why certain firms produce larger innovative research output than others, and whether the incentives to innovate that certain organisational practices generate differ between small and large firms, and between those firms that are operating in low-tech and high-tech industries. Our study indicates that there are vast differences in the organisational practices leading to more innovation both between small and large firms, and between the firms that operate in high- and low-tech industries. While innovation in small firms benefits from the practices that enhance employee participation in decision-making, large firms that have more decentralised decision-making patterns do not seem to innovate more than those with a more bureaucratic decision-making structure. The most efficient incentive for innovation among the sampled companies seems to be the ownership of a firm’s stocks by employees and/or managers. Performance-based wages also relates positively to innovation, but only when it is combined with a systematic monitoring of the firm’s performance.