Preference Structure Modelling for Multi-objective Decision Making: A Goal–Programming Approach

We propose a new approach for modelling preference structures in multi-objective decision-making (MODM) problems. The basic idea of the approach is to first develop PROMETHEE-influenced objective functions and then to use these to reformulate the problem as a distance-based goal–programming (GP) model. Three basic functional forms are proposed and explicit expressions are developed for them. Among other things, the expressions allow for the straightforward development of an interactive framework while keeping the information requirements from the decision maker (DM) at a minimum. An ‘automatic’ piecewise linear approximation scheme is proposed for solving the GP model. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 150–154 (1997) No. of Figures: 0. No. of Tables: 0. No. of References: 12.     

Utility Functions: A Compromise Programming Approach to Specification and Optimization

Nowadays, utility theory and compromise programming (CP) are considered very different paradigms and methodologies to measure preferences as well as to determine decision maker's optima on an efficient frontier. In this paper, however, we show that a utility function with separate variables (presented in the form of a Taylor series around the ideal point) is reducible to a weighted sum of CP distances. This linkage between utility and compromise (based on a main assumption in which the usual utility functions hold) leads to (i) a method for specification and optimization of usual utility functions by operational technique and (ii) a reformulation of standard CP with the advantage of determining the best CP solution from a utility perspective. © 1997 John Wiley & Sons, Ltd.     

The role of oxytocin in early mother-infant interactions: Variations in maternal affect attunement

The aim of the present research was to investigate the relationship between oxytocin and maternal affect attunement, as well as the role of affect attunement in the relationship between oxytocin and infant social engagement during early mother-infant interactions. Forty-three mother-infant dyads participated in the present study when infants were 4 months. They were observed during (1) a situation where no communication took place and (2) a natural interaction between mother and infant. During this procedure, three saliva samples from mothers and their infants were collected to determine their levels of oxytocin at different time points. Maternal affect attunement (maintaining attention, warm sensitivity) and infant interactive behaviors (gaze, positive, and negative affect) were coded during the natural interaction. Results indicated that overall maternal oxytocin functioning was negatively related to her warm sensitivity, while infant oxytocin reactivity together with maternal affect attunement were associated with infant positive social engagement with their mothers. Specifically, infant oxytocin reactivity was significantly related to their gazes at mother, but only for infants of highly attuned mothers. These results point to the complex role oxytocin plays in parent-infant interactions while emphasizing the need to analyze both overall oxytocin functioning as well as reactivity as different indices of human affiliative behavior.     

交互视角下工作重塑干预的影响效应整合模型

作为一种引导员工主动改变工作的行为, 工作重塑干预成为近年来组织行为学领域研究的重要主题。基于现有研究成果, 系统梳理了工作重塑干预的概念和分类。依据角色-资源接近-回避模型, 归纳和提炼了工作干预的影响效果, 在此基础上, 借鉴AMO理论, 分别从重塑能力(Ability)、重塑动机(Motivation)和重塑机会(Opportunity)层面, 构建了个体和组织交互情境下的工作重塑干预内在影响机制, 进而提出了一个整合性的理论分析框架, 为当前开展工作重塑干预的相关研究提供一定的借鉴和参考。未来研究应更多关注基于中国情境的工作重塑干预影响因素及作用机制。     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.     

社会支持视角下顾客在线互助心理和行为机制

消费结构升级促使顾客通过参与价值共创获得更高层次心理满足感。顾客间通过网络平台参与交流、互动、分享和互助等价值共创行为,能够获得美好的情感体验,有助于形成人际之间的友谊,也使得顾客得以寻求自我表达和自我实现,收获更高层次的满足感。本研究以社会支持理论为基础,聚焦不同属性顾客在不同类型网站中参与在线互助的心理动机、心理收益和行为方式,采用行为实验、实证调查、案例研究、神经营销学等多种研究方法,深入剖析顾客参与在线互助过程中的心理和行为反应机制。预期研究成果将丰富现有顾客参与心理和行为的理论体系,也为企业激励顾客参与在线价值共创提供理论指导。     

Los Degradados: Out,down, dead: Transmitted and inflicted trauma as encountered in the analysis of a 6‐year‐old girl1

The author presents the analysis of a precocious traumatized little girl, which reveals the ways in which historical trauma is transmitted and intrafamilial trauma is both disguised and represented. The play as it evolves is seen to simultaneously communicate what the child struggles with and to resolutely try to hide what has actually happened. Analyst and child together participate in play which utilizes displacement, enactment and interactive enactment, the latter play mode being the very hallmark of profound traumatic experience. Carlotta, the child, helps the analyst to follow her quest for meaning making even as the interaction between them adheres to and departs from the deepening pentimenti of traumatic experience, which needs to be unraveled and reconstructed in order that her own developmental progression can be rejoined. The analysis facilitates Carlotta's capacity to play in a more unfettered fashion and to assist her family's recovery as well.     

Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Logic programs and connectionist networks

One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.