The futility of decision making research

We have each spent more than 50 years doing research that has had little impact. Even more lamentable is that our field, judgment and decision making (JDM), has on the whole had little impact during that span. We attribute that failure to the use of methodologies that emphasize testing models rather than looking for differences in behavior. The “cognitive revolution” led the field astray, toward the goal of studying model fit rather than comparing observable results. With modeling as the goal, experimentation was stultified. Simple tasks became dominant. Although a poor metaphor for real decision making, the gambling paradigm has lasted forever because the inputs to the decision are known to the researcher and thus easily modeled.     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.     

现代西欧对术语学传统思想的质疑和新的思想突破

维斯特创立的普通术语学思想在20世纪70年代基本形成后,在30年中稳步前进。自21世纪以来,欧洲对术语学的讨论忽然变得热烈,产生了一些"颠覆性"的质疑。文章介绍了欧洲术语学传统思想的承继者们如何应对这些质疑,以及"现代术语学革新派"突破性的理论思想贡献,并浅析未来的术语学理论发展的趋势和远景。     

结合时间序列分解和神经网络的河流溶解氧预测

为克服小流域数据资料少,河流溶解氧的非平稳特性及动态变化造成的预测困难,提出结合具有自适应噪声的完整集成经验模态分解(CEEMDAN)和Elman动态神经网络的预测方法.使用CEEMDAN方法对原始溶解氧时序数据进行平稳化处理及降噪,提取溶解氧随时间变化的波动特征、周期特征,以及长期趋势,通过计算样本熵(SE)值,将相似的特征序列合并,以减小误差累积,对合并后的新序列分别采用布谷鸟搜索(CS)算法优化的Elman模型进行预测,将各预测值叠加,得到最终预测结果.实验结果表明:CEEMDAN-SE-CS-Elman方法平均绝对误差(E_MA)为0.14;平均绝对百分误差(E_MPA)为2.07%;均方根误差(E_RMS)为0.24;可决系数(R²)达到0.951 6,精度较其他时间序列预测模型有所提高.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

Existence of macroscopic spatial superpositions in collapse theories

It is shown that the collapse dynamics in the CSL model will entangle two independent systems under certain conditions, and their state after collapse may be an entangled superposition of spatially separated states. However, since the conditions can hardly be satisfied in reality, the occurrence of such superpositions is very improbable, and thus collapse theories still provide a promising solution to the measurement problem.     

基于车车通信合流影响区外侧车辆决策模型

在入口匝道合流区,驾驶员通过观察运行环境及估计周边车辆行驶特征,做出的相应的驾驶决策,常会使车流紊乱.针对该问题,从车车通信环境下获得的周边车辆运行全信息入手,建立上匝道合流影响区车辆决策机制模型,并更新了新决策机制情况下运行规则.数值试验分析车辆在两种不同环境下的时空轨迹图、合流区及上游换道情况、平均行程速度.结果表明:在车车通信环境及决策机制模型条件下,合流区只有很低水平的换道;平均行程速度最大提高了32.4％,在不同车辆输入情况下速度波动降低了76.7％,通行能力提高了13.3％.     

黄渤海悬浮颗粒物散射特性研究

【目的】研究悬浮颗粒物的散射特性,用以描述水体固有光学特性。【方法】利用2014年11月黄渤海海区航测数据,分别选取555nm和532nm作为参考波长,建立悬浮颗粒物散射及后向散射的光谱模型,并对模型拟合效果进行检验;此外,还建立了悬浮颗粒物散射参数与水体组分浓度的关系模型。【结果】模型验证结果表明:光谱模型的平均相对误差绝对值(MAPE)分别在60%和35%以内;散射参数与悬浮颗粒物质量浓度模型MAPE的最小值仅为15.0%。【结论】黄渤海颗粒物散射特性主要由非藻类颗粒物主导。     

基于改进隶属云模型蚁群算法的喷涂机器人喷枪轨迹组合优化

摘要： 针对复杂曲面分片后的喷枪轨迹组合优化问题,利用哈密尔顿图将其转化为广义开环旅行商问题(OTSP),采用“问题无关的进化算法与问题相关的局部搜索相结合”的策略,首先引入隶属云模型来自适应调节蚁群算法中控制的随机性,然后引入K-opt局部搜索策略的基于改进隶属云模型蚁群算法(MCMACA)对喷枪轨迹组合优化的OTSP问题进行求解.仿真结果表明,改进隶属云模型蚁群算法的全局搜索性和局部收敛性更佳,在复杂曲面上对喷涂机器人喷枪轨迹进行优化具有明显的优越性.
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