967 resultados para Mind change complexity
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Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2011
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Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2012
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Magdeburg, Univ., Fak. für Informatik, Diss., 2013
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Fabienne-Agnes Baumann, Klaus Jenewein, Axel Müller
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Magdeburg, Univ., Fak. für Informatik, Diss., 2015
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Here we examine major anatomical characteristics of Corydoras aff. paleatus (Jenyns, 1842) post-hatching development, in parallel with its neurobehavioral evolution. Eleutheroembryonic phase, 4.3-8.8 days post-fertilization (dpf); 4.3-6.4 mm standard length (SL) encompasses from hatching to transition to exogenous feeding. Protopterygiolarval phase (8.9-10.9 dpf; 6.5-6.7 mm SL) goes from feeding transition to the commencement of unpaired fin differentiation, which marks the start of pterygiolarval phase (11-33 dpf; 6.8-10.7 mm SL) defined by appearance of lepidotrichia in the dorsal part of the median finfold. This phase ends with the full detachment and differentiation of unpaired fins, events signaling the commencement of the juvenile period (34-60 dpf; 10.8-18.0 mm SL). Eleutheroembryonic phase focuses on hiding and differentiation of mechanosensory, chemosensory and central neural systems, crucial for supplying the larval period with efficient escape and nutrient detection-capture neurocircuits. Protopterygiolarval priorities include visual development and respiratory, digestive and hydrodynamic efficiencies. Pterygiolarval priorities change towards higher swimming efficacy, including carangiform and vertical swimming, necessary for the high social interaction typical of this species. At the end of the protopterygiolarval phase, simple resting and foraging aggregations are seen. Resting and foraging shoals grow in complexity and participant number during pterygiolarval phase, but particularly during juvenile period.
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The primary purpose of this exploratory empirical study is to examine the structural stability of a limited number of alternative explanatory factors of strategic change. On the basis of theoretical arguments and prior empirical evidence from two traditional perspectives, we propose an original empirical framework to analyse whether these potential explanatory factors have remained stable over time in a highly turbulent environment. This original question is explored in a particular setting: the population of Spanish private banks. The firms of this industry have experienced a high level of strategic mobility as a consequence of fundamental changes undergone in their environmental conditions over the last two decades (mainly changes related to the new banking and financial regulation process). Our results consistently support that the effect of most explanatory factors of strategic mobility considered did not remain stable over the whole period of analysis. From this point of view, the study sheds new light on major debates and dilemmas in the field of strategy regarding why firms change their competitive patterns over time and, hence, to what extent the "contextdependency" of alternative views of strategic change as their relative validation can vary over time for a given population. Methodologically, this research makes two major contributions to the study of potential determinants of strategic change. First, the definition and measurement of strategic change employing a new grouping method, the Model-based Cluster Method or MCLUST. Second, in order to asses the possible effect of determinants of strategic mobility we have controlled the non-observable heterogeneity using logistic regression models for panel data.
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We say the endomorphism problem is solvable for an element W in a free group F if it can be decided effectively whether, given U in F, there is an endomorphism Φ of F sending W to U. This work analyzes an approach due to C. Edmunds and improved by C. Sims. Here we prove that the approach provides an efficient algorithm for solving the endomorphism problem when W is a two- generator word. We show that when W is a two-generator word this algorithm solves the problem in time polynomial in the length of U. This result gives a polynomial-time algorithm for solving, in free groups, two-variable equations in which all the variables occur on one side of the equality and all the constants on the other side.
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"Vegeu el resum a l'inici del document del fitxer adjunt."
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Many organizations suffer poor performance because individuals within the organization fail to coordinate on efficient patterns of behavior. Using controlled laboratory experiments, we study how financial incentives can be used to find a way out of such performance traps. Our experiments are set in a corporate environment where subjects' payoffs depend on coordinating at high effort levels; the underlying game being played repeatedly by employees is a weak-link game. In an initial phase, the benefits of coordination are low relative to the cost of increased effort. Play in this initial phase typically converges to an inefficient outcome with employees failing to coordinate at high effort levels. The experimental design then explores the effects of varying the financial incentives to coordinate at a higher effort level. We find that an increase in the benefits of coordination leads to improved coordination, but, surprisingly, large increases have no more impact than small increases. Once subj
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Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the predictions of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that a certain phenomenon never happens, or that certain conditions necessitate another. For all types of rules, there is a trade-off between the rule's accuracy and its simplicity. Thus rule selection can be viewed as a choice problem, among pairs of degree of accuracy and degree of complexity. However, one cannot in general tell what is the feasible set in the accuracy-complexity space. Formally, we show that finding out whether a point belongs to this set is computationally hard. In particular, in the context of linear regression, finding a small set of variables that obtain a certain value of R2 is computationally hard. Computational complexity may explain why a person is not always aware of rules that, if asked, she would find valid. This, in turn, may explain why one can change other people's minds (opinions, beliefs) without providing new information.