Dynamicity and Performance in Adaptive Organizations

In this dissertation, I focus on the conceptualization and empirical investigation of organizational adaptation. Specifically, I intend to study how dynamic organizations evolve and under which conditions they successfully adapt to a changing environment. In essay 1 (with D. Levinthal), we develop a simulation model to clarify and explore some of the basic conceptual issues concerning the dynamics through which business practices locally adapt within an intra-organizational ecology of organizational level skills, knowledge, and capabilities subject to processes of mutation and selection. For essay 2 (with A. Prencipe), we designed and conducted a field project by collecting qualitative data: a mix of archival data, interviews and ethnographic field notes. The main goal is to investigate how organizational adaptation plays out under the pressure of various institutional forces. Our findings illustrate that institutional forces generate selective reactions within the ecology of existing organizational routines. Conversely, non-institutional forces adapt to the existing behavioral forms following a two-way dynamic process. In essay 3, I developed an empirical research design based on a panel data analysis to investigate the role of dynamic capabilities in boosting adaptation performance. This work examines some of the fundamental contingencies that impact the relationship between dynamic capabilities and organizational performance. Specifically, although prior experience in product adaptation is considered as a key driver of superior performance, its value is found to be highly conditional on both the level of focal activity - a recent adaptation effort on specific activities - and the intensity of the environmental changes

Efficient Evolution of Neural Networks

This thesis addresses the study of evolutionary methods for the synthesis of neural network controllers. Chapter 1 introduces the research area, reviews the state of the art, discusses promising research directions, and presents the two major scientific objectives of the thesis. The first objective, which is covered in Chapter 2, is to verify the efficacy of some of the most promising neuro-evolutionary methods proposed in the literature, including two new methods that I elaborated. This has been made by designing extended version of the double-pole balancing problem, which can be used to more properly benchmark alternative algorithms, by studying the effect of critical parameters, and by conducting several series of comparative experiments. The obtained results indicate that some methods perform better with respect to all the considered criteria, i.e. performance, robustness to environmental variations and capability to scale-up to more complex problems. The second objective, which is targeted in Chapter 3, consists in the design of a new hybrid algorithm that combines evolution and learning by demonstration. The combination of these two processes is appealing since it potentially allows the adaptive agent to exploit a richer training feedback constituted by both a scalar performance objective (reinforcement signal or fitness measure) and a detailed description of a suitable behaviour (demonstration). The proposed method has been successfully evaluated on two qualitatively different robotic problems. Chapter 4 summarizes the results obtained and describes the major contributions of the thesis