Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.     

How virtual brand community traces may increase fan engagement in brand pages

Brand pages in social media are a great way to foster consumer gathering around a brand, but it can be challenging to keep fans engaged and coming back to see updated content. Brands with millions of fans on Facebook have seen organic reach fall below 2% of their base. In this article, we describe how the creation of virtual brand communities can help brand managers increase fan engagement. We suggest the steps, conditions, advantages, and limitations involved in nurturing a brand page as an online social gathering that assumes some of the characteristics of a virtual brand community. The results of our study show that a brand page can have some of the characteristics of a virtual brand community—topical information exchange, identity communication, and establishment and internalization of cultural norms. We also show the importance of having celebrities among fans in order to foster social interactions and legitimate social practices on brand pages.     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

Learning,large deviations and rare events

We examine the role of generalized stochastic gradient constant gain (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat-tailed distribution even though the model is driven by thin-tailed exogenous stochastic processes. We characterize these large deviations, driven by sequences of consistently low or consistently high shocks and then apply our model to the canonical asset pricing framework. We demonstrate that the tails of the stationary distribution of the price–dividend ratio will follow a power law.     

行为经济学视角下的社会保障理论演进

社会保障系统通过社会与个体的风险共担，增进了个人、家庭和社会的福利水平，对就业、消费、投资均有显著影响。过去社会保障理论侧重信息约束和跨期决策下的机制设计，通过政府干预来实现有效的风险和成本共担，进而有效化解社会成员所面临的各种风险。但这些干预机制有可能会对就业等产生负面影响。近年来，行为与实验经济学开始进入社会保障理论的研究，新的理论试图从理性约束视角来理解社会保障系统的性质及其影响，并提出了全新的助推机制的设计理念与思路。迄今的研究发现，干预机制和助推机制各有千秋，通过合理的互补与替代，能够更好地实现社会保障系统的有效运行。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Social housing in France: A permanent and multifaceted challenge for public policies

This paper examines the dynamics of the overwhelmingly generous social housing in France that covers a wide spectrum of recipients. Being influenced by electoral power of rotating ruling parties and social ideals, policy formulations are often reflected by idealistic politics which tends to over-stretch the nation’s budget and restrict its scope in reaching its target groups. From the 1990s, fund availability has been further hit by economic globalization causing falling business profitability and high unemployment rates affecting notably ethnic minorities. Upcoming social crisis has been met with public response via a series of technical-led remedial acts to promote social integration in housing and by identifying social ills and polarization in sensitive and problematic urban quarters. New technical and financial measures in coping with the complexity and magnitude of social housing are expected to remain highly challenging in the French parliamentary politics.     

关于对高职学生现用教材使用情况问卷调查的分析报告

本文分别从问卷调查的基本情况论述了高职学生现用教材使用情况问卷调查的分析报告。     

探究数字化时代对师范生信息素养的要求

本文从数字化时代发展的角度出发,指出数字化学习不仅是数字时代学习的重要方式,更是课程整合的核心,同时提高教师的信息素养是数字化学习的关键。随着信息社会的发展和中小学信息技术课的开展,对师范生的信息素养提出了更高的要求。目前师范生就业形式,信息素养状况怎样,存在哪些问题,怎样去解决这些问题提高自身的竞争力?这都是我们师范院校的学生所思考的问题。针对上述问题,本文提出了自己的观点和看法。     

终身学习视角下公共图书馆的转型与发展思考

公共图书馆作为公共文化服务体系的重要组成部分,在构建学习型社会的大背景下,为促进终身学习的理念,借鉴国外图书馆的前沿探索,提升人文素质和知识内涵,实现公共图书馆在新的历史形势下自身的转型与发展。