搜索引擎用户对商品搜索结果的点击行为研究

探索消费者利用搜索引擎进行商品搜索时对于搜索出来的结果条目的点击行为。基于启发式和系统式信息处理模式理论,构建一个研究模型,即消费者对商品的熟悉程度会影响其在搜索结果中的点击位置,通过设计和实施两个实验室实验,以有上网经验的在校大学生为实验对象,对提出的模型和假说进行检验。研究结果表明,对于不同产品的搜索结果,消费者对于搜索结果的点击位置有显著差异。具体地说,对于消费者熟悉的那些产品,他们更多地点击靠近中间位置的选项,而对于消费者不太熟悉的商品,他们更多地点击搜索结果中比较靠前的选项。     

核安保事件应急决策的情景库构建与情景匹配研究

纵观国际社会形势,核扩散风险、核恐怖主义威胁对全球核电发展带来诸多挑战,加强核安保事件应急决策研究至关重要。根据突发事件的情景—应对的相关理论,文章从内因、外因以及应急决策三个维度对核安保事件进行情景切分和关键因素提取,提出了核安保事件情景库的结构和构建过程,并提出了核安保事件案例库索引和基于最近相邻法的核安保事件情景相似度计算方法对核安保事件与情景库进行情景匹配。文章运用的情景库和情景匹配方法可以简练、准确地表达核安保事件应急决策的信息和实现快速的情景匹配,可为核安保事件的应急决策提供重要的支持。     

Enterprise risk management for automation in correctional facilities with pandemic and other stressors

Real-time tracking of tool and equipment inventories is a critical function of many organizations and sectors. For prisons and correctional facilities, tracking and monitoring of assets such as cookware, hardware, keys, janitorial equipment, vocational/technical specialty tools, etc., is essential for safety, security, trust, efficiency, education, etc. The performance of automated systems for this purpose can be diminished by a variety of emergent and future sociotechnical factors alone and in combination. This article introduces a methodology for contractor evaluation and selection in acquisition of innovative asset management systems, with an emphasis on evolving system requirements under uncertainty. The methodology features a scenario-based preferences analysis of emergent and future conditions that are disruptive to the performance of the asset-control system. The conditions are across technologies, operating environments, regulations, workforce behaviors, offender behaviors, prices and markets, organizations, cyber threats, etc. The methodology addresses the influence and interaction of the conditions to disrupt system priorities. Examples include: (i) infectious disease disrupting priorities among requirements and (ii)  radio-frequency identification (RFID) and wireless-technology innovations disrupting priorities among stakeholders. The combinations of conditions that most and least matter for the system acquisition are characterized. The methodology constitutes a risk register for monitoring sources of risk to project performance, schedule, and cost throughout the system lifecycle. The results will be of interest to both practitioners and scholars engaged in systems acquisition as the pandemic interacts with other factors to affect risk, uncertainty, and resilience of organizational missions and operations.     

Empirical Limits for Time Series Econometric Models

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.