Clarifying the Dominant Logic Construct by Disentangling and Reassembling its Dimensions

Since its introduction, Prahalad and Bettis's concept of dominant logic has informed a variety of scholarly conversations in management and strategy research. However, scholars have interpreted dominant logic in different ways, emphasizing different aspects, such as managerial mindsets, administrative tools and management functions, as defining elements. Similarly, empirical studies have captured various aspects, such as meanings of entrepreneurs, observable strategic decisions and business model similarity, as indicators of dominant logic. Consequently, the concept lacks analytical clarity, and it is difficult to compare or generalize findings from this diverse set of studies. The aim of this review is to improve conceptual clarity by analysing, comparing and evaluating the existing interpretations and assessments of dominant logic in 94 studies. In the first part of the review, by disentangling the interpretations of the concept, we show that dominant logic consists of four defining dimensions: (i) shared mental models; (ii) values and premises; (iii) organizational practices; and (iv) organizing structures. In the second part, we reassemble dominant logic into an integrative model and theorize about how these dimensions operate in concert to produce a firm's dominant logic. Thus, our main contribution is a clarification and synthesis of the literature, which comes with implications on how future research can conceptualize and operationalize dominant logic more consistently

Distributed Optimization with Application to Power Systems and Control

In many engineering domains, systems are composed of partially independent subsystems—power systems are composed of distribution and transmission systems, teams of robots are composed of individual robots, and chemical process systems are composed of vessels, heat exchangers and reactors. Often, these subsystems should reach a common goal such as satisfying a power demand with minimum cost, flying in a formation, or reaching an optimal set-point. At the same time, limited information exchange is desirable—for confidentiality reasons but also due to communication constraints. Moreover, a fast and reliable decision process is key as applications might be safety-critical. Mathematical optimization techniques are among the most successful tools for controlling systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization control the subsystems in a distributed or decentralized fashion, reducing or avoiding central coordination. These methods have a long and successful history. Classical distributed optimization algorithms, however, are typically designed for convex problems. Hence, they are only partially applicable in the above domains since many of them lead to optimization problems with non-convex constraints. This thesis develops one of the first frameworks for distributed and decentralized optimization with non-convex constraints. Based on the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm, a bi-level distributed ALADIN framework is presented, solving the coordination step of ALADIN in a decentralized fashion. This framework can handle various decentralized inner algorithms, two of which we develop here: a decentralized variant of the Alternating Direction Method of Multipliers (ADMM) and a novel decentralized Conjugate Gradient algorithm. Decentralized conjugate gradient is to the best of our knowledge the first decentralized algorithm with a guarantee of convergence to the exact solution in a finite number of iterates. Sufficient conditions for fast local convergence of bi-level ALADIN are derived. Bi-level ALADIN strongly reduces the communication and coordination effort of ALADIN and preserves its fast convergence guarantees. We illustrate these properties on challenging problems from power systems and control, and compare performance to the widely used ADMM. The developed methods are implemented in the open-source MATLAB toolbox ALADIN-—one of the first toolboxes for decentralized non-convex optimization. ALADIN- comes with a rich set of application examples from different domains showing its broad applicability. As an additional contribution, this thesis provides new insights why state-of-the-art distributed algorithms might encounter issues for constrained problems

Distributed Optimization with Application to Power Systems and Control

Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization

Distributed Optimization with Application to Power Systems and Control

Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization

Preferences and Beliefs in a Sequential Social Dilemma: A Within-Subjects Analysis

Within-subject data from sequential social dilemma experiments reveal a correlation of first-and second-mover decisions for which two channels may be responsible, that our experiment allows to separate: i) a direct, preference-based channel that influences both first- and second-mover decisions; ii) an indirect channel, where second-mover decisions influence beliefs via a consensus effect, and the first-mover decision is a best response to these beliefs. We find strong evidence for the indirect channel: beliefs about second-mover cooperation are biased toward own second-mover behavior, and most subjects best respond to stated beliefs. But when first movers know the true probability of second-mover cooperation, subjects' own second moves still have predictive power regarding their first moves, suggesting that the direct channel also plays a role.experimental economics, consensus effect, social dilemmas

An essentially decentralized interior point method for control

Distributed and decentralized optimization are key for the control of networked systems. Application examples include distributed model predictive control and distributed sensing or estimation. Non-linear systems, however, lead to problems with non-convex constraints for which classical decentralized optimization algorithms lack convergence guarantees. Moreover, classical decentralized algorithms usually exhibit only linear convergence. This paper presents an essentially decentralized primal-dual interior point method with convergence guarantees for non-convex problems at a {super}linear rate. We show that the proposed method works reliably on a numerical example from power systems. Our results indicate that the proposed method outperforms ADMM in terms of computation time and computational complexity of the subproblems