Incentives for Quality through Endogenous Routing

We study how rework routing together with wage and piece rate compensation can strengthen incentives for quality. Traditionally, rework is assigned back to the agent who generates the defect (in a self routing scheme) or to another agent dedicated to rework (in a dedicated routing scheme). In contrast, a novel cross routing scheme allocates rework to a parallel agent performing both new jobs and rework. The agent who passes quality inspection or completes rework receives the piece rate paid per job. We compare the incentives of these rework allocation schemes in a principal-agent model with embedded quality control and routing in a multi-class queueing network. We show that conventional self routing of rework can never induce first-best effort. Dedicated routing and cross routing, however, strengthen incentives for quality by imposing an implicit punishment for quality failure. In addition, cross routing leads to workload allocation externalities and a prisoner’s dilemma, thereby creating highest incentives for quality. Firm profitability depends on capacity levels, revenues, and quality costs. With ample capacity, dedicated routing and cross routing both achieve first-best profit rate, while self routing does not. With limited capacity, cross routing generates the highest profit rate when appraisal, internal failure, or external failure costs are high, while self routing performs best when gross margins are high. When the number of agents increases, the incentive power of cross routing reduces monotonically and approaches that of dedicated routing.queueing networks; routing; Nash equilibrium; quality control; piece rate; epsilon equilibrium.

Network connectivity during mergers and growth: optimizing the addition of a module

The principal eigenvalue $\lambda$ of a network's adjacency matrix often determines dynamics on the network (e.g., in synchronization and spreading processes) and some of its structural properties (e.g., robustness against failure or attack) and is therefore a good indicator for how ``strongly'' a network is connected. We study how $\lambda$ is modified by the addition of a module, or community, which has broad applications, ranging from those involving a single modification (e.g., introduction of a drug into a biological process) to those involving repeated additions (e.g., power-grid and transit development). We describe how to optimally connect the module to the network to either maximize or minimize the shift in $\lambda$, noting several applications of directing dynamics on networks.Comment: 7 pages, 5 figure

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio