Answer Sets and Qualitative Optimization

One of the major reasons for the success of answer set programmingin recent years was the shift from a theorem proving to a constraintprogramming view: problems are represented such that stablemodels, respectively answer sets, rather than theorems correspondto solutions. This shift in perspective proved extremely fruitfulin many areas. We believe that going one step further from a"hard" to a "soft" constraint programming paradigm, or, in otherwords, to a paradigm of qualitative optimization, will proveequally fruitful. In this paper we try to support this claimby showing that several generic problems in logic based problemsolving can be understood as qualitative optimization problems,and that these problems have simple and elegant formulationsgiven adequate optimization constructs in the knowledge representationlanguage.     

WEIRDness abounds! Sample characteristics,restricted variance,and why they matter for international selection and assessment research

There is increasing awareness that reliance on samples from Western, Educated, Industrialized, Rich, Democratic (WEIRD) countries may have skewed our view of human phenomena. Nowhere is this more true than in international selection and assessment (ISA) research. In the present paper, we describe a way of understanding the impact of WEIRDness on ISA phenomena: restricted variance (RV). The WEIRDness problem is usually one of cross level interactions in which a country-level variable (e.g., individualism/collectivism) might moderate the relationship between two individual-level variables. RV can help to see not only where such cross level interactions might exist, but also provide a specific, mathematical reason for their existence. We use several examples from IJSA and related sources to illustrate the role of RV in ISA-based WEIRDness.     

A tribute to Howard Rachlin and his two-parameter discounting model: Reliable and flexible model fitting

Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes.     

The tunneling method for global optimization in multidimensional scaling

This paper focuses on the problem of local minima of the STRESS function. It turns out that unidimensional scaling is particularly prone to local minima, whereas full dimensional scaling with Euclidean distances has a local minimum that is global. For intermediate dimensionality with Euclidean distances it depends on the dissimilarities how severe the local minimum problem is. For city-block distances in any dimensionality many different local minima are found. A simulation experiment is presented that indicates under what conditions local minima can be expected. We introduce the tunneling method for global minimization, and adjust it for multidimensional scaling with general Minkowski distances. The tunneling method alternates a local search step, in which a local minimum is sought, with a tunneling step in which a different configuration is sought with the same STRESS as the previous local minimum. In this manner successively better local minima are obtained, and experimentation so far shows that the last one is often a global minimum.This paper is based on the 1994 Psychometric Society's outstanding thesis award of the first author. The authros would like to thank Robert Tijssen of the CWTS Leiden for kindly making available the co-citation data of the Psychometric literature. This paper is an extended version of the paper presented at the Annual Meeting of the Psychometric Society at Champaign-Urbana, Illin., June 1994.     

Assessing control by elements of complex stimuli in delayed matching to sample.

A series of six experiments examined delayed identity matching-to-sample performances of subjects with mental retardation. The stimuli were either one or two simultaneously displayed forms. When the reinforcement contingencies required that only one form exert discriminative control, all subjects achieved high accuracy scores. However, accuracy scores were substantially lower when the contingencies required discriminative control by two forms, suggesting restricted stimulus control. The decline in matching accuracy appeared to reflect selective losses of conditional control by sample stimuli and shifts in control to features of the comparison stimulus displays. The experiments suggest improved techniques for assessing control by complex stimuli and for evaluating the effects of procedures that seek to broaden restricted stimulus control. The results challenge interpretations based on stimulus-generalization decrement or shared attention.     

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.     

关于肿瘤专科诊疗最优化与过度医疗

肿瘤专科诊疗最优化准则有狭义、广义之分,二者既有区别,更有联系.肿瘤专科诊疗的过度医疗的内容是动态发展的,在特定情况下有其积极意义.实现肿瘤专科诊疗最优化,避免过度医疗,要重视矛盾问题"底线"的研究,提供相对宽松的技术、学术、文化氛围;医者持续的、有计划的专业学习与培训是增强其主体性的保证和前提;要充分考虑"可承受性"原则;用新的医德价值观指导现实中的医学道德实践;积极发挥医院伦理委员在肿瘤专科诊疗决策中的作用;建立与利用医务人员诚信档案.     

Application of the Within- and Between-Series Estimators to Non-normal Multiple-Baseline Data: Maximum Likelihood and Bayesian Approaches

In single-case research, multiple-baseline (MB) design provides the opportunity to estimate the treatment effect based on not only within-series comparisons of treatment phase to baseline phase observations, but also time-specific between-series comparisons of observations from those that have started treatment to those that are still in the baseline. For analyzing MB studies, two types of linear mixed modeling methods have been proposed: the within- and between-series models. In principle, those models were developed based on normality assumptions, however, normality may not always be found in practical settings. Therefore, this study aimed to investigate the robustness of the within- and between-series models when data were non-normal. A Monte Carlo study was conducted with four statistical approaches. The approaches were defined by the crossing of two analytic decisions: (a) whether to use a within- or between-series estimate of effect and (b) whether to use restricted maximum likelihood or Markov chain Monte Carlo estimations. The results showed the treatment effect estimates of the four approaches had minimal bias, that within-series estimates were more precise than between-series estimates, and that confidence interval coverage was frequently acceptable, but varied across conditions and methods of estimation. Applications and implications were discussed based on the findings.     

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.