Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE Transactions on Signal Processing, Sept. 201

Team Learning: A Theoretical Integration and Review

With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

Learning in Strategic Alliances

{Excerpt} Strategic alliances that bring organizations together promise unique opportunities for partners. The reality is often otherwise. Successful strategic alliances manage the partnership, not just the agreement,for collaborative advantage. Above all, they also pay attentionto learning priorities in alliance evolution. The resource-based view of the firm that gained currency in the mid-1980s considered that the competitive advantage of an organization rests on the application of the strategic resources at its disposal. These days, orthodoxy recognizes the merits of the dynamic, knowledge-based capabilities underpinning the positions organizations occupy in a sector or market. Strategic alliances—meaning cooperative agreements between two or more organizations—are a means to enhance strategic resources: self-sufficiency is becoming increasingly difficult in a complex, uncertain, and discontinuous external environment that calls for focus and flexibility in equal measure. Everywhere, organizations are discovering that they cannot “go” it alone and must now often turn to others to survive

Learning spillover and analogy-based expectations: a multi-game experiment

We consider a multi-game interactive learning environment and ask ourselves whether long run behaviors in one game are a¤ected by behaviors in the other, i.e whether there are learning spillovers. Our main �nding is that learning spillovers arise whenever the feedback provided to subjects about past play is not easily accessible game by game and thus subjects get a more immediate impression about aggregate distributions. In such a case, long run behaviors stabilize to an analogy-based expectation equilibrium (Jehiel 2005), thereby suggesting how one should broaden the notion of equilibrium to cope with learning spillovers

Social norms and human normative psychology

Our primary aim in this paper is to sketch a cognitive evolutionary approach for developing explanations of social change that is anchored on the psychological mechanisms underlying normative cognition and the transmission of social norms. We throw the relevant features of this approach into relief by comparing it with the self-fulfilling social expectations account developed by Bicchieri and colleagues. After describing both accounts, we argue that the two approaches are largely compatible, but that the cognitive evolutionary approach is well- suited to encompass much of the social expectations view, whose focus on a narrow range of norms comes at the expense of the breadth the cognitive evolutionary approach can provide