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(Zielgruppe)Allgemein --- (Zielgruppe)Fachhochschul-/Hochschulausbildung --- (Zielgruppe)Fachpublikum/ Wissenschaft --- Gott --- Gottesvorstellung --- Theologie --- Religion --- Philosophie --- Anthropologie --- Hirnforschung --- Neurobiologie
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Get ahead of emerging markets with top-performer picks for up-and-comers Frontier helps investors successfully navigate markets that are yet to "emerge," with expert advice on spotting opportunities and minimising risks. With first-hand insights into frontier markets as we travel with big-name fund managers from Mark Mobius to Morgan Stanley, this practical guide ranks countries, stocks and bonds on a five- to ten-year horizon to steer investors toward the most promising destinations. Written in a compelling and accessible travelogue narrative, each chapter covers a specific country, provid
Investments, Foreign --- Investments --- Developing countries --- Commerce.
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We seed noisy information to members of a real-world social network to study how information diffusion and information aggregation jointly shape social learning. Our environment features substantial social learning. We show that learning occurs via diffusion which is highly imperfect: signals travel only up to two steps in the conversation network and indirect signals are transmitted noisily. We then compare two theories of information aggregation: a naive model in which people double-count signals that reach them through multiple paths, and a sophisticated model in which people avoid double-counting by tagging the source of information. We show that to distinguish between these models of aggregation, it is critical to explicitly account for imperfect diffusion. When we do so, we find that our data are most consistent with the sophisticated tagged model.
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We seed noisy information to members of a real-world social network to study how information diffusion and information aggregation jointly shape social learning. Our environment features substantial social learning. We show that learning occurs via diffusion which is highly imperfect: signals travel only up to two steps in the conversation network and indirect signals are transmitted noisily. We then compare two theories of information aggregation: a naive model in which people double-count signals that reach them through multiple paths, and a sophisticated model in which people avoid double-counting by tagging the source of information. We show that to distinguish between these models of aggregation, it is critical to explicitly account for imperfect diffusion. When we do so, we find that our data are most consistent with the sophisticated tagged model.
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