481 research outputs found
Gaussian process emulation for rapid in-plane mechanical homogenization of periodic masonry
No full textWhat Do Leaders Know?
No full textThe ability of a society to make the right decisions on relevant matters relies on its capability to properly aggregate the noisy information spread across the individuals of which it is made. In this paper, we study the information aggregation performance of a stylized model of a society, whose most influential individuals—the leaders—are highly connected among themselves and uninformed. Agents update their state of knowledge in a Bayesian manner by listening to their neighbors. We find analytical and numerical evidence of a transition, as a function of the noise level in the information initially available to agents, from a regime where information is correctly aggregated, to one where the population reaches consensus on the wrong outcome with finite probability. Furthermore, information aggregation depends in a non-trivial manner on the relative size of the clique of leaders, with the limit of a vanishingly small clique being singular
Judgments in the Sharing Economy : the effect of user-generated trust and reputation information on decision-making accuracy and bias
No full textThe growing ecosystem of peer-to-peer enterprise - the Sharing Economy (SE) - has brought with it a substantial change in how we access and provide goods and services. Within the SE, individuals make decisions based mainly on user-generated trust and reputation information (TRI). Recent research indicates that the use of such information tends to produce a positivity bias in the perceived trustworthiness of fellow users. Across two experimental studies performed on an artificial SE accommodation platform, we test whether users' judgments can be accurate when presented with diagnostic information relating to the quality of the profiles they see or if these overly positive perceptions persist. In study 1, we find that users are quite accurate overall (70%) at determining the quality of a profile, both when presented with full profiles or with profiles where they selected three TRI elements they considered useful for their decision-making. However, users tended to exhibit an "upward quality bias" when making errors. In study 2, we leveraged patterns of frequently vs. infrequently selected TRI elements to understand whether users have insights into which are more diagnostic and find that presenting frequently selected TRI elements improved users' accuracy. Overall, our studies demonstrate that - positivity bias notwithstanding - users can be remarkably accurate in their online SE judgments. [Abstract copyright: Copyright © 2021 Zloteanu, Harvey, Tuckett and Livan.
Dual-Readout Calorimetry
No full textIn the past 20 years, dual-readout calorimetry has emerged as a technique for measuring the properties of high-energy hadrons and hadron jets that offers considerable advantages compared with the instruments that are currently used for this purpose in experiments at the high-energy frontier. The status of this experimental technique and the challenges faced for its further development are reviewed.In the past 20 years, dual-readout calorimetry has emerged as a technique for measuring the properties of high-energy hadrons and hadron jets that offers considerable advantages compared with the instruments that are currently used for this purpose in experiments at the high-energy frontier. In this paper, we review the status of this experimental technique and the challenges faced for its further development
A spectral perspective on excess volatility
No full textWe perform a careful spectral analysis of the correlation structures observed in real and financial returns for a large pool of long-lived US corporations, and find that financial returns are characterized by strong collective fluctuations that are absent from real returns. Once the excessive comomvement is subtracted from individual financial time series, the behavior of real and financial returns is virtually identical in both the cross-sectional and time series domains, thereby demonstrating the inherently collective nature of excessive fluctuations. Put differently, if excess volatility is to be reduced then one would do well to inhibit excess comovement first. At any rate, the excessive behavior in volatility and comovement should no longer be studied in isolation of each other
Statistical mechanics of complex economies
No full textIn the pursuit of ever increasing efficiency and growth, our economies have evolved to remarkable degrees of complexity, with nested production processes feeding each other in order to create products of greater sophistication from less sophisticated ones, down to raw materials. The engine of such an expansion have been competitive markets that, according to general equilibrium theory (GET), achieve efficient allocations under specific conditions. We study large random economies within the GET framework, as templates of complex economies, and we find that a non-trivial phase transition occurs: the economy freezes in a state where all production processes collapse when either the number of primary goods or the number of available technologies fall below a critical threshold. As in other examples of phase transitions in large random systems, this is an unintended consequence of the growth in complexity. Our findings suggest that the Industrial Revolution can be regarded as a sharp transition between different phases, but also imply that well developed economies can collapse if too many intermediate goods are introduced
Macroeconomic forecasting through news, emotions and narrative
No full textThis study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as the US. These works use mainly anglophone sources of narrative, thus not capturing the entire complexity of the multitude of emotions contained in global news articles. This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world – extracted from the Global Database of Events, Language and Tone (GDELT) – into macroeconomic forecasts. We present a thematic data filtering methodology based on a bi-directional long short term memory neural network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its effectiveness by comparing results for filtered and unfiltered data. We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework, and find that including emotions from global newspapers significantly improves forecasts compared to three autoregressive benchmark models. We complement our forecasts with an interpretability analysis on distinct groups of emotions and find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict
Digital identity: The effect of trust and reputation information on user judgement in the sharing economy
No full textThe Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer enterprise. In the SE the information available to assist individuals (users) in making decisions focuses predominantly on community-generated trust and reputation information. However, how such information impacts user judgement is still being understood. To explore such effects, we constructed an artificial SE accommodation platform where we varied the elements related to hosts’ digital identity, measuring users’ perceptions and decisions to interact. Across three studies, we find that trust and reputation information increases not only the users’ perceived trustworthiness, credibility, and sociability of hosts, but also the propensity to rent a private room in their home. This effect is seen when providing users both with complete profiles and profiles with partial user-selected information. Closer investigations reveal that three elements relating to the host’s digital identity are sufficient to produce such positive perceptions and increased rental decisions, regardless of which three elements are presented. Our findings have relevant implications for human judgment and privacy in the SE, and question its current culture of ever increasing information-sharing
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