1,721,123 research outputs found

    The Language of Innovation

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    Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, making the usual approach of learning patterns from the past a useless exercise. Here we propose a strategy to address the problem in the context of innovative patents, by defining innovation as never-seen-before associations of technologies. We think of technological codes present in patents as a vocabulary and the whole technological corpus as written in a specific, evolving language. We leverage such structure with techniques borrowed from Natural Language Processing by embedding technologies in a high dimensional euclidean space where relative positions are representative of learned semantics. Dynamics on this space predicts specific innovation events, that are tested against null models. These methods provide a completely new way of understanding and forecasting innovation, by tackling it from a revealing perspective and opening interesting scenarios for a number of applications and further analytical approaches

    Emergence and resilience of social networks: a general theoretical framework

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    ABSTRACT. – The paper first introduces a general dynamic setup that embodies abstract formulations of the following two forces: (a) homophily, i.e. the idea that networking is favored by similarity in behavior; (b) conformity, i.e. the pressure towards similar behavior induced by interaction. This general framework is then specialized into three alternative directions, corresponding to different specific manifestations of those two forces. For each such particularization of the general model, we find that (i) sharp transitions, (ii) hysteresis, and (iii) equilibrium multiplicity are salient characteristics of the long-run social dynamics. Since features (i)-(iii) are often reported for a variety of social (network) phenomena where (a)-(b) play an important role, we sugget that the former may indeed be the result of some common mechanism at work that relies on the interplay of the latter

    Observed choices and underlying opportunities

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    Our societies are heterogeneous in many dimensions such as census, education, religion, ethnic and cultural composition. The links between individuals – e.g. by friendship, marriage or collaboration – are not evenly distributed, but rather tend to be concentrated within the same group. This phenomenon, called imbreeding homophily, has been related to either (social) preference for links with own–type individuals (choice–based homophily) or to the prevalence of individuals of her same type in the choice set of an individual (opportunity–based homophily). Choices determine the network of relations we observe whereas opportunities pertain to the composition of the (unobservable) social network individuals are embedded in and out of which their network of relations is drawn. In this view, we propose a method that, in the presence of multiple data, allows one to distinguish between opportunity and choice based homophily. The main intuition is that, with unbiased opportunities, the effect of choice–based homophily gets weaker and weaker as the size of the minority shrinks, because individuals of the minority rarely meet and have the chance to establish links together. The occurrence of homophily in the limit of very small minorities is therefore an indicator of opportunity bias. We test this idea across the dimensions of race and education on data on US marriages, and across race on friendships in US schools

    Who do currency transaction taxes harm more: short-term speculators or long-term investors?

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    We propose a new model of chartist-fundamentalist-interaction in which both groups of traders are allowed to select endogenously between different forecasting models and different investment horizons. Stochastic interest rates in both countries and different behavioral assumptions for trend-extrapolating and fundamental based forecasts determine the agents’ market orders which drive the exchange rate. A numerical analysis of the model shows that it is able to replicate stylized facts of observed financial return time series like excess kurtosis and volatility clustering. Within this framework we study the effects of transaction taxes on exchange rate volatility and traders’ behavior measured by their population fractions. Simulations yield the result that on the macroscopic level these taxes reduce the variance of exchange rate returns, but also increase their kurtosis. Moreover, on the microscopic level the tax harms short-term speculation in favor of long-term investment, while it also harms trading rules based on economic fundamentals in favor to trend extrapolating trading rules

    A heterogenous agents model usable for the analysis of currency transaction taxes

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    We extend the model by DeGrauwe and Grimaldi (2006, EER) by currency transaction taxes. This model explains the exchange rate behavior by the interaction of heterogeneous traders who display either trend chasing behavior or rely on a return of the exchange rate back to its arbitrage free fundamental value. Within this model framework we can show analytically that the steady-state of the original model is unaffected by the transaction tax rate. We inferred from numerical simulations that the transaction tax is able to reduce the number of speculative equilibria to zero. Moreover, we show that the tax will lead to a faster convergence of the system back to its fundamental steady state

    Estimation of a microfounded herding model on German survey expectations

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    The paper considers the dynamic adjustments of an average opinion index that can be derived from a microfounded framework where the individual agents switch between two kinds of sentiment with certain transition probabilities. The index can thus represent a general business climate, i.e., expectations about the future course of the economy. This approach is empirically tested with the survey expectations published by the ZEW and ifo institute. The estimated coefficients make economic sense and are highly significant. In particular, besides effects from fundamental data like the output gap in the recent past, one can identify a strong herding mechanism within both panels, such that metaphorically speaking the agents do not just join the crowd but follow each single motion of it. In addition, the transition probabilities of the ZEW agents are found to be influenced by the ifo climate but not the other way round

    A prototype model of speculative dynamics with position-based trading

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    To avoid the indeterminate and generally unbounded positions of the agents in financial market models with order-based trading, the paper considers the alternative of position-based strategies. To this end it extracts a prototype model from the literature, with fundamentalists, chartists, and a risk-averse market maker. The deterministic formulation of the model leads to a neutral delay differential equation of the price, whose mathematical analysis is non-standard. The stability conditions are nevertheless quite analogous to the order-based Beja–Goldman model. The effects of parameter variations are also studied in a stochastic setting, where special emphasis is put on the misalignment between price and the time-varying fundamental value, and on the differential profits of fundamentalists and chartists
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