Combined reasoning by automated cooperation

Different reasoning systems have different strengths and weaknesses, and often it is useful to combine these systems to gain as much as possible from their strengths and retain as little as possible from their weaknesses. Of particular interest is the integration of first-order and higher-order techniques. First-order reasoning systems, on the one hand, have reached considerable strength in some niches, but in many areas of mathematics they still cannot reliably solve relatively simple problems, for example, when reasoning about sets, relations, or functions. Higher-order reasoning systems, on the other hand, can solve problems of this kind automatically. But the complexity inherent in their calculi prevents them from solving a whole range of problems. However, while many problems cannot be solved by any one system alone, they can be solved by a combination of these systems.We present a general agent-based methodology for integrating different reasoning systems. It provides a generic integration framework which facilitates the cooperation between diverse reasoners, but can also be refined to enable more efficient, specialist integrations. We empirically evaluate its usefulness, effectiveness and efficiency by case studies involving the integration of first-order and higher-order automated theorem provers, computer algebra systems, and model generators.     

Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questions

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.     

Quantified coalition logic

We add a limited but useful form of quantification to Coalition Logic, a popular formalism for reasoning about cooperation in game-like multi-agent systems. The basic constructs of Quantified Coalition Logic (QCL) allow us to express such properties as “every coalition satisfying property P can achieve φ” and “there exists a coalition C satisfying property P such that C can achieve φ”. We give an axiomatisation of QCL, and show that while it is no more expressive than Coalition Logic, it is nevertheless exponentially more succinct. The complexity of QCL model checking for symbolic and explicit state representations is shown to be no worse than that of Coalition Logic, and satisfiability for QCL is shown to be no worse than satisfiability for Coalition Logic. We illustrate the formalism by showing how to succinctly specify such social choice mechanisms as majority voting, which in Coalition Logic require specifications that are exponentially long in the number of agents.     

Are some negotiators better than others? Individual differences in bargaining outcomes

The authors address the long-standing mystery of stable individual differences in negotiation performance, on which intuition and conventional wisdom have clashed with inconsistent empirical findings. The present study used the Social Relations Model to examine individual differences directly via consistency in performance across multiple negotiations and to disentangle the roles of both parties within these inherently dyadic interactions. Individual differences explained a substantial 46% of objective performance and 19% of subjective performance in a mixed-motive bargaining exercise. Previous work may have understated the influence of individual differences because conventional research designs require specific traits to be identified and measured. Exploratory analyses of a battery of traits revealed few reliable associations with consistent individual differences in objective performance-except for positive beliefs about negotiation, positive affect, and concern for one's outcome, each of which predicted better performance. Findings suggest that the field has large untapped potential to explain substantial individual differences. Limitations, areas for future research, and practical implications are discussed.     

The prediction of Honesty–Humility-related criteria by the HEXACO and Five-Factor Models of personality

We examined two questions involving the relative validity of the HEXACO and Five-Factor Models of personality structure. First, would the HEXACO model outpredict the Five-Factor Model (FFM) with regard to several diverse criteria that are conceptually relevant to the Honesty–Humility dimension of personality? If so, would the addition of a proxy Honesty–Humility scale—as computed from relevant facets of the FFM Agreeableness domain—allow the FFM to achieve predictive validities matching those of the HEXACO model? Results from self- and observer ratings in three samples (each N > 200) indicated that the HEXACO model showed considerable predictive validity advantages over the FFM. When a measure of Honesty–Humility derived from the FFM was added to the original five domains of that model, the predictive validity reached that of the HEXACO model for some criteria, but remained substantially below for others.     

纵向Rasch模型在大学新生适应性追踪研究中的应用

对1952名大学新生进行适应性调查,其中285人接受了2次以上的追踪调查,所得的多级评分重复测量数据采用纵向Rasch模型进行统计分析。研究应用SAS的GLIMMIX过程对多层Rasch模型参数估计作了新的尝试。结果表明：（1）新生在第一学年内,学习和情绪适应总体呈上升趋势,人际适应呈下降趋势;（2）不同个体入学时的适应状况差异显著,但是随时间变化的趋势、快慢相同;（3）学习适应分量表的项目稳定性较好,而人际、情绪适应的部分项目难度存在时间效应。研究结果对新生辅导具有启示意义     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

Using FMRI to test models of complex cognition

This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event-locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal with the scan-to-scan correlations in the errors of measurement of the BOLD signal. This approach is illustrated using a "sacrificial" ACT-R model that involves mapping 6 modules onto 6 brain regions in an experiment from Ravizza, Anderson, and Carter (in press) concerned with equation solving. The model's visual encoding predicted the BOLD response in the fusiform gyrus, its controlled retrieval predicted the BOLD response in the lateral inferior prefrontal cortex, and its subgoal setting predicted the BOLD response in the anterior cingulate cortex. On the other hand, its motor programming failed to predict anticipatory activation in the motor cortex, its representational changes failed to predicted the pattern of activity in the posterior parietal cortex, and its procedural component failed to predict an initial spike in caudate. The results illustrate the power of such data to direct the development of a theory of complex problem solving, both at the level of a specific task model as well as at the level of the cognitive architecture.     

It takes two to tango: An interdependence analysis of the spiraling of perceived trustworthiness and cooperation in interpersonal and intergroup relationships

We present three spiral reinforcement models that describe how mutual perceptions of trustworthiness and/or mutual cooperation may develop in dyadic interpersonal and intergroup relationships: a perceived trustworthiness spiral model, a cooperation spiral model, and a perceived trustworthiness-cooperation spiral model that posits a relatively more complex spiraling between trustworthiness perceptions and cooperation. Actor-Partner Interdependence Model (Kashy & Kenny, 2000) analyses of data from two joint venture business simulations provided support for the spiral models in interpersonal and intergroup relationships. While all three models received some support, the perceived trustworthiness-cooperation spiral received the most consistent support, suggesting that trust perceptions play a crucial mediating role in the development of cooperation in both interpersonal and intergroup interactions. Overall, it does “take two to tango”: the development of mutual trust and cooperation involves an intricate dance that spirals over time and is fundamentally affected by partners’ initial moves.