Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Trading off learning and performance: Exploration and exploitation at work

Employees are increasingly given control over how they learn, and their choices for training are diverse and varied, yet employees must balance competing demands. On one hand, they are expected to be increasingly efficient in their current job duties – on the other hand, they are expected to develop new skills and competencies that enable them to adapt and respond to changing job demands. Drawing from the organizational learning literature, we propose a model of worker and work characteristics that inform choices between two mindsets related to learning at work. The first mindset is exploration, or the pursuit of learning outside one’s current knowledge domain; the second mindset is exploitation, the refinement/deepening of one’s existing knowledge stock focusing on the task at hand. We further propose that these strategic choices, or trade-offs, influence employee learning and performance in unique ways, with different implications for both routine and adaptive performance. Finally, we incorporate the notions of feedback loops and risk assessments that influence ongoing decisions between exploration and exploitation mindsets. Recommendations for future research and extensions of the theoretical model are also proposed.     

双元视角下调节聚焦对国防研发人员创新行为的影响机制

调节聚焦作为个体实现目标的动机调节系统，对个体创新行为具有重要作用，是组织行为学领域关注的热点。基于调节聚焦等相关理论，从二维视角深入考察个体调节聚焦对其双元创新的过程机理与边界机制。对527名国防科技研发人员进行问卷调研分析，结果发现：①促进聚焦和防御聚焦均正向预测双元创新及其平衡性与互补性，促进聚焦的预测作用更强；②探索式失败学习和利用式失败学习同时中介上述关系；③正向组织差错管理氛围正向调节促进聚焦与双元创新及其互补性的关系，负向组织差错管理氛围正向调节防御聚焦与双元创新及其互补性的关系。     

创业失败经验对连续创业意愿的影响——创业失败学习与市场动荡性的作用

基于经验学习理论，探究创业失败经验如何通过创业失败学习影响创业者连续创业意愿，以及市场动荡性的调节作用。对176位有失败经历的创业者进行问卷调查，结果表明：创业失败经验对创业失败学习和连续创业意愿均有显著正向影响；创业失败学习在创业失败经验与连续创业意愿关系间起中介作用；市场动荡性负向调节创业失败学习与连续创业意愿的关系，即市场动荡性越高，创业失败学习与连续创业意愿间的关系越弱。研究结论对于拓展创业失败经验与连续创业意愿研究、帮助创业者从失败中复原，以及激励连续创业具有重要理论价值和实践意义。     

创业网络结构属性如何影响创业学习方式的选择——基于印记理论的单案例研究

创业网络对具有新进入缺陷的新创企业以及创业者而言有着至关重要的作用，不仅表现为"知识源"作用，也表现为"印记源"作用。印记理论指出个体或组织在其容易受到影响的时期，外部环境的显著特征将对其产生重大影响，个体和组织将打上环境特征的烙印，并且这种烙印将持续影响个体和组织的行为。基于此，本研究基于印记理论，利用对一家创业企业的归纳式纵向单案例分析探讨创业网络不同的结构属性经由何种印记过程，继而影响创业者学习方式的选择。结果表明：当创业网络表现为封闭、半开放以及开放的不同网络结构时，经由不同结构属性导致的结构印记和认知印记过程差异，影响着创业者实验式学习、观察式学习以及协同式学习的不同选择。     

On the existence of the ex post symmetric random entry model

This paper studies symmetry among countably infinitely many agents who randomly enter into a stochastic process, one for each period. Upon entry, they observe only the current period signal and try to draw inference about the underlying state governing the stochastic process. We show that there exist random entry models under which agents are ex post symmetric. That is, all agents have identical posterior belief about the underlying states, although they are not ex ante symmetric. The form of the posterior belief is uniquely pinned down by ex post symmetry and a stationarity condition. Our results provide a common prior foundation for the model studied in Liu and Skrzypacz (2014).     

创业企业何以提高创新绩效——基于创业学习与商业模式创新协同联动视角的QCA方法

以150个创业企业为样本，基于创业学习和商业模式创新理论，运用模糊集定性比较分析方法（fsQCA），探究经验学习、认知学习、新颖型商业模式创新和效率型商业模式创新对创新绩效的影响路径及机制。研究发现，存在新颖型商业模式创新等4条创新绩效产出关键路径；相较于效率型商业模式创新，新颖型商业模式创新在关键路径中更容易提高创新绩效；利用QCA方法证实了影响创业企业创新绩效的组态内部各要素之间的替代性。结论丰富了创新绩效产出内部机制，可为创业企业提高创新绩效提供指导和借鉴。     

军民融合社会关系网络对军民融合新创企业成长的影响

军民融合新创企业是推动我国国防建设和社会经济融合发展的重要力量，军民融合社会关系网络具有丰富的异质性资源，可以有效促进军民融合新创企业成长。借鉴社会网络理论，构建以关系深度、关系广度、关系高度为代表的军民融合社会关系网络、耦合共生与军民融合新创企业成长理论模型，通过对收集的304份军民融合新创企业问卷数据进行实证分析，结果发现：军民融合社会关系网络对军民融合新创企业成长具有正向影响；耦合共生在军民融合社会关系网络与军民融合新创企业成长间发挥中介作用；创业导向显著调节耦合共生与军民融合新创企业成长的关系。     

Performance and learning in an ambiguous environment: A study of cryptocurrency traders

We investigate the performance and learning ability of traders in an environment governed by ambiguity, such as the cryptocurrency market. Using a profit decomposition methodology, we find significant cross-sectional and temporal heterogeneity in performance. Traders do not learn to progressively increase the magnitude of returns; however, they are able to improve on their ability to realise profits as a mechanism of adaptation to survive through ambiguity. This adaptation increases as traders progress through their career. Moreover, we find evidence in support of the gambler’s fallacy. We argue that learning in ambiguous environments has limitations, allowing traders primarily to survive.