2,360 research outputs found
Edith Penrose and a learning-based perspective on the MNE and OLI
No full textWe apply insights from Edith Penrose’s work to extant theory of the multinational enterprise (MNE) as enveloped by John Dunning’s Ownership, Location, Internalization (OLI) Paradigm. We suggest that Penrose’s knowledge-based approach has important implications on the nature of, and the interactions between, O, L and I. Importantly, the resource/knowledge-based perspective of Penrose helps endogenize and integrate the three elements of Dunning’s triad in the context of a dynamic, strategic and forward-looking knowledge-based perspective of the MNE.Penrose, Learning, MNE, OLI
[News Clip: Edith Deen]
No full textVideo footage from the WBAP-TV television station in Fort Worth, Texas, to accompany a news story about author, columnist, and lecturer Edith Alderman Deen receiving an honorary Doctor of Letters degree from Texas Women's University
Conversations with authors: Edith Pearlman
No full textA 2011 conversation with the author Edith Pearlman about her life and the inspiration for her work
Interview with Major Edith Vowell Part 2
No full textAnna Maria Island author included Major Edith Vowell in his book, Combat Nurses of World War II. Here she tells her story, with adventures in Brisbane, Australia, on ships and a GI troop train. She also lists her postwar nursing postings
A note on Cyert and March (1963) and Penrose (1959): A case for synergy
No full textCyert and March’s 1963 seminal book is one of the two major economics-based theories of the firm that goes inside the ‘black box’ (the firm) – the other being the contribution of Edith Penrose. The two theories have differences, but also similarities, and substantial scope for cross-fertilisation that have gone unnoticed in the literature. In this note we try to integrate important ideas in both books, paying particular attention to the issue of ‘excess resources’ /slack, and (intra-firm) conflict. We then build on the integrated framework by delving into the nature of intra-firm conflict, the degree of intra-firm rivalry and the relationship between firm performance and ‘productive opportunity’. We derive propositions common to the two theories, but also new ones, of import to our understanding of organisational growth and change.Behavioral theory of the firm, Edith Penrose, excess resources, slack, conflict, innovation
Sample Complexity Bounds for Influence Maximization
No full textInfluence maximization (IM) is the problem of finding for a given s ≥ 1 a set S of |S|=s nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set S of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a deterministic reachability function. Two well-studied special cases are the Independent Cascade (IC) and the Linear Threshold (LT) models of Kempe, Kleinberg, and Tardos [Kempe et al., 2003]. The influence function in stochastic diffusion is unbiasedly estimated by averaging reachability values over i.i.d. simulations. We study the IM sample complexity: the number of simulations needed to determine a (1-ε)-approximate maximizer with confidence 1-δ. Our main result is a surprising upper bound of O(s τ ε^{-2} ln (n/δ)) for a broad class of models that includes IC and LT models and their mixtures, where n is the number of nodes and τ is the number of diffusion steps. Generally τ ≪ n, so this significantly improves over the generic upper bound of O(s n ε^{-2} ln (n/δ)). Our sample complexity bounds are derived from novel upper bounds on the variance of the reachability that allow for small relative error for influential sets and additive error when influence is small. Moreover, we provide a data-adaptive method that can detect and utilize fewer simulations on models where it suffices. Finally, we provide an efficient greedy design that computes an (1-1/e-ε)-approximate maximizer from simulations and applies to any submodular stochastic diffusion model that satisfies the variance bounds
Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees
No full textThe average distance from a node to all other nodes in a graph, or from a query point in a metric space to a set of points, is a fundamental quantity in data analysis. The inverse of the average distance, known as the (classic) closeness centrality of a node, is a popular importance measure in the study of social networks. We develop novel structural insights on the sparsifiability of the distance relation via weighted sampling. Based on that, we present highly practical algorithms with strong statistical guarantees for fundamental problems. We show that the average distance (and hence the centrality) for all nodes in a graph can be estimated using O(epsilon^{-2}) single-source distance computations. For a set V of n points in a metric space, we show that after preprocessing which uses O(n) distance computations we can compute a weighted sample S subset of V of size O(epsilon^{-2}) such that the average distance from any query point v to V can be estimated from the distances from v to S. Finally, we show that for a set of points V in a metric space, we can estimate the average pairwise distance using O(n+epsilon^{-2}) distance computations. The estimate is based on a weighted sample of O(epsilon^{-2}) pairs of points, which is computed using O(n) distance computations. Our estimates are unbiased with normalized mean square error (NRMSE) of at most epsilon. Increasing the sample size by a O(log(n)) factor ensures that the probability that the relative error exceeds epsilon is polynomially small
- …
