The evolution of cooperation is a hot and challenging topic in the field of evolutionary game theory. Altruistic behavior, as a particular form of cooperation, has been widely studied by the ultimatum game but not by the dictator game, which provides a more elegant way to identify the altruistic component of behaviors. In this paper, the evolutionary dictator game is applied to model the real motivations of altruism. A degree-based regime is utilized to assess the impact of the assignation of roles on evolutionary outcome in populations of heterogeneous structure with two kinds of strategic updating mechanisms, which are based on Darwin's theory of evolution and punctuated equilibrium, respectively. The results show that the evolutionary outcome is affected by the role assignation and that this impact also depends on the strategic updating mechanisms, the function used to evaluate players' success, and the structure of populations.
We study the effect of financial market frictions on managerial compensation. We embed a market microstructure model into an otherwise standard contracting framework, and analyze optimal pay-for-performance when managers use information they learn from the market in their investment decisions. In a less frictional market, the improved information content of stock prices helps guide managerial decisions and thereby necessitates lower-powered compensation. Exploiting a randomized experiment, we document evidence that pay-for-performance is lowered in response to reduced market frictions. Firm investment also becomes more sensitive to stock prices during the experiment, consistent with increased managerial learning from the market. (JEL D83, G12, G14, G32, G34, M12, M52)
In recommender systems, the cold-start problem is a critical issue. To alleviate this problem, an emerging direction adopts meta-learning frameworks and achieves success. Most existing works aim to learn globally shared prior knowledge across all users so that it can be quickly adapted to a new user with sparse interactions. However, globally shared prior knowledge may be inadequate to discern users’ complicated behaviors and causes poor generalization. Therefore, we argue that prior knowledge should be locally shared by users with similar preferences who can be recognized by social relations. To this end, in this paper, we propose a Preference-Adaptive Meta-Learning approach (PAML) to improve existing meta-learning frameworks with better generalization capacity. Specifically, to address two challenges imposed by social relations, we first identify reliable implicit friends to strengthen a user’s social relations based on our defined palindrome paths. Then, a coarse-fine preference modeling method is proposed to leverage social relations and capture the preference. Afterwards, a novel preference-specific adapter is designed to adapt the globally shared prior knowledge to the preference-specific knowledge so that users who have similar tastes share similar knowledge. We conduct extensive experiments on two publicly available datasets. Experimental results validate the power of social relations and the effectiveness of PAML.
Ambers in China have been described from the various localities of both Cretaceous (e.g., Xixia amber from Henan Province and Jalainur [Zhalainuoer] amber from northeastern Inner Mongolia) and Palaeogene (e.g., Eocene Fushun amber of Liaoning Province and Miocene Zhangpu amber of Fujian Province) ages to date (e.g., Hong, 1981, 2002; Shi et al., 2014; Wang et al., 2014; Azar et al., 2019; Wang et al., 2021). Here we report a new amber locality from the Late Oligocene of Nanning Basin, Guangxi, southern China. The first amber piece was collected by one of the authors (GCZ) on 5 June 2008. In a recent field work in early 2021, we further discovered more than 50 smaller amber pieces, which are reported here.
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