Random error in additive decompositions of multiattribute utility

Decomposition is a common strategy for dealing with the complexity of multiattribute decision problems. A cognitively demanding task is broken down into tasks requiring simpler, perhaps easier, judgments which can then be aggregated. But individual judgments can be inconsistent in systematic or random fashion and when aggregated there is the possibility of propagation of this inconsistency. In this paper inconsistency in the form of random error is investigated in the context of additive decomposition of multiattribute utility. The process of aggregation of random error is studied and a comparison made with random error in holistic estimates of multiattribute utility. Conditions under which decomposition improves the consistency of the multiattribute utility estimate are presented and discussed.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.     

青少年攻击行为与友谊质量的交叉滞后回归分析

该研究采用问卷法,对湖北省一所初级中学二年级青少年进行了一个学年的纵向追踪研究,使用Bjorkquist等人编制的直接和间接攻击量表(DIA)以及Parker和Asher编制的友谊质量问卷(FQQ)测查青少年的身体攻击和友谊质量。然后采用二元交叉滞后回归分析法考察了初中二、三年级学生在一个学年中的攻击行为与友谊质量(总问卷及分问卷得分)的关系。结果表明,在控制了前测"肯定与关心"得分后,身体攻击显著地负向预测了"肯定与关心"得分。在控制了前测的"冲突解决策略"得分后,攻击行为边缘显著地预测了后测的"冲突解决策略"得分。同时发现,后测"身体攻击"和"冲突与背叛"不存在显著的相关。     

CAN SCIENCE AND RELIGION RESPOND TO CLIMATE CHANGE?

With the challenge of communicating climate science in the United States and making progress in international negotiations on climate change there is a need for other approaches. The moral issues of ecological degradation and climate justice need to be integrated into social consciousness, political legislation, and climate treaties. Both science and religion can contribute to this integration with differentiated language but shared purpose. Recognizing the limits of both science and religion is critical to finding a way forward for addressing the critical challenges of climate change. How we value nature and human–Earth relations is crucial to this. We need a broader environmental ethics in dialogue with the science of climate change.     

On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models

Basic results for conditional means and variances, as well as distributional results, are used to clarify the similarities and differences between various extensions of signal detection theory (SDT). It is shown that a previously presented motivation for the unequal variance SDT model (varying strength) actually leads to a related, yet distinct, model. The distinction has implications for other extensions of SDT, such as models with criteria that vary over trials. It is shown that a mixture extension of SDT is also consistent with unequal variances, but provides a different interpretation of the results; mixture SDT also offers a way to unify results found across several types of studies.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Personality,work, and satisfaction: evidence from the German Socio-Economic Panel

Previous studies in positive psychology have indicated that work satisfaction is an important determinant of individual well-being. Research has suggested that people are most satisfied with their work when they are doing what they are drawn to naturally. We provide further evidence on this issue from a large representative data set, the German Socio-Economic Panel (SOEP). The 2005 wave of the SOEP contains a battery of personality questions as well as detailed information on personal life and work life. We extract the Big Five personality factors and one character strength: vitality. The main results are based on regression analysis. The analysis supports the hypothesis that certain personality clusters are more predominant in some occupations than in others. Furthermore, an alignment between personal profile and occupational profile tends to be related positively to satisfaction. These results indicate that ignoring mental aspects of work has its price in terms of well-being. They also highlight the importance of studying the way we structure work and harness personality and individual strengths within positive psychology.

To find out what one is fitted to do and to secure an opportunity to do it is the key to happiness.

John Dewey

The fixed person for the fixed duties who in older societies was such a godsend, in the future will be a public danger.

Alfred North Whitehead     

The spatial representation of numerical and non-numerical ordered sequences: Insights from a random generation task

It is widely believed that numbers are spatially represented from left to right on the mental number line. Whether this spatial format of representation is specific to numbers or is shared by non-numerical ordered sequences remains controversial. When healthy participants are asked to randomly generate digits they show a systematic small-number bias that has been interpreted in terms of “pseudoneglect in number space”. Here we used a random generation task to compare numerical and non-numerical order. Participants performed the task at three different pacing rates and with three types of stimuli (numbers, letters, and months). In addition to a small-number bias for numbers, we observed a bias towards “early” items for letters and no bias for months. The spatial biases for numbers and letters were rate independent and similar in size, but they did not correlate across participants. Moreover, letter generation was qualified by a systematic forward direction along the sequence, suggesting that the ordinal dimension was more salient for letters than for numbers in a task that did not require its explicit processing. The dissociation between numerical and non-numerical orders is consistent with electrophysiological and neuroimaging studies and suggests that they rely on at least partially different mechanisms.