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.     

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.
关键词： 组中图分类号：文献标志码： A     

采动影响型地下煤火诱发地表裂隙率的时空分布模型

基于随机介质移动理论,构建了采动影响型地下煤火诱发地表裂隙率的时空统一分布模型,并实例分析了矩形火区引发地表线(张)裂隙率、面裂隙率以及剪裂隙率的分布及动态变化规律。地表线(张)裂隙率、面裂隙率和剪裂隙率的极大值分别分布在采(燃)空区边界内侧约20 m (约为煤层厚度的3~4倍)的位置、四周边界线的四个中点位置和四个边角端点所对应的地表区域。随着煤层燃烧,垂直于煤火发展方向上的线裂隙率以及空区边界处对应的地表面裂隙率均呈半正态曲线形式变化并最终稳定于最大值;而剪裂隙率、煤火发展方向上的线裂隙率及空区内部对应的地表面裂隙率均呈正态曲线形式变化。     

小方坯结晶器水温水速对传热过程影响

通过建立结晶器内钢液和水的二维对流-传热耦合模型过程,研究了小方坯结晶器冷却水入口温度和流速对铜管温度和结晶器内平均热流的影响.该模型使用Fluent进行求解,模拟了钢液和冷却水的流动和传热,凝固坯壳的生长,以及热量以辐射和导热两种通过保护渣和气隙.通过将坯壳厚度和铜管温度与其他研究的结果进行对比来验证模型准确性.研究结果表明,结晶器冷却水的温度显著影响铜管的冷面温度,水温超过313K会导致铜管冷面最高温度超过水的沸点.水流速升高0.49 m·s-1能够消除水温升高4K带来的不利影响.