AbstractÐThis paper examines and proposes an approach to writing software specifications, based on research in systems theory, cognitive psychology, and human-machine interaction. The goal is to provide specifications that support human problem solving and the tasks that humans must perform in software development and evolution. A type of specification, called intent specifications, is constructed upon this underlying foundation. Index TermsÐRequirements, requirements specification, safety-critical software, software evolution, human-centered specifications, means-ends hierarchy, cognitive engineering.

This paper examines the issue of completeness in specification language design. In the mid-80s we identified a set of 26 formal criteria to identify missing, incorrect, and ambiguous requirements for process-control systems. Experimental validation of the criteria on NASA and NASDA spacecraft systems have supported their usefulness in detecting commonly omitted but important information and engineers have been using them in checklist form on real systems. At the same time, we have extended the criteria and now have over 60. This paper shows how most of the criteria can be embedded in a formal specification language in ways that potentially allow automated checking or assist in manual reviews.

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Consumer knowledge is seldom complete or errorless. Therefore, the self-assessed validity of knowledge and consequent knowledge calibration (i.e., the correspondence between self-assessed and actual validity) is an important issue for the study of consumer decision making. In this article we describe methods and models used in calibration research. We then review a wide variety of empirical results indicating that high levels of calibration are achieved rarely, moderate levels that include some degree of systematic bias are the norm, and confidence and accuracy are sometimes completely uncorrelated. Finally, we examine the explanations of miscalibration and offer suggestions for future research. Consumers are overconfident—they think they know more than they actually do. Our simple goal is to evaluate this proposition. Ultimately, we conclude that overconfidence is indeed a robust phenomenon and can be adopted by researchers as a stylized fact about human cognition; however, there are critical qualifications and

Recent studies have demonstrated subadditivity of human probability judgment: the judged probabilities for an event partition sum to more than 1. We report conditions under which people's probability judgments are superadditive instead: the component judgments for a partition sum to less than 1. Weinterpret both directions of deviation from additivity in a common framework, in which probability judgments are often mediated by judgments of evidence. The two kinds of nonadditivity result from di#erences in recruitment of supporting evidence together with reduced processing of nonfocal propositions. 2 Introduction Suppose that an event E has been partitioned into two or more mutually exclusive subevents, and that probability assessments are made for E and for each of these subevents. The assessments are said to be additive if the probability assigned to E is approximately equal to the sum of the probabilities of the subevents. They are subadditive if the probability assigned to E...

We propose a theory that relates perceived evidence to numerical probability judgment. The most successful prior account of this relation is Support Theory, advanced in Tversky and Koehler (1994). Support Theory, however, implies additive probability estimates for binary partitions. In contrast, superadditivity has been documented in Macchi, Osherson and Krantz (1999), and both sub- and superadditivity appear in the experiments reported here. Nonadditivity suggests asymmetry in the processing of focal and nonfocal hypotheses, even within binary partitions. We extend Support Theory by revising its basic equation to allow such asymmetry, and compare the two equations' ability to predict numerical assessments of probability from scaled estimates of evidence for and against a given proposition. Both between- and within-subject experimental designs are employed for this purpose. We find that the revised equation is more accurate than the original Support Theory equation. The implications of...

We model limited attention as incomplete usage of publicly available information. Informed players decide whether or not to disclose to observers who sometimes neglect either disclosed signals or the implications of non-disclosure. These observers may choose ex ante how to allocate their limited attention. In equilibrium observers are unrealistically optimistic, disclosure is incomplete, neglect of disclosed signals increases disclosure, and neglect of a failure to disclose reduces disclosure. Regulation requiring greater disclosure can reduce observers’ belief accuracies and welfare. Disclosure in one arena affects perceptions in fundamentally unrelated arenas, owing to cue competition, salience, and analytical interference. Disclosure in one arena can crowd out disclosure in another.

An ongoing goal in the field of categorization has been to determine how objects ’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed. It is generally accepted that people’s concepts include not only the features and attributes of the entity being represented, but also the ways in which those features are related to one another. For example, we know that hormones can alter a person’s behavior, that chemical structure can affect a substance’s hardness, and that processor

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causalbased generalization (CBG) view included effects of an existing feature’s base rate (Experiment 1), the direction of the causal relations (Experiments 2 and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to-be-generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes. Keywords: Causal-based induction; Generalization; Causal reasoning 1.

We investigate the existence of biases, in particular the so called splitting bias, in environmental decision analysis. The splitting bias suggests that presenting an attribute in more detail may increase the weight it receives. We test whether the splitting bias can be eliminated or reduced through instruction and training. Also the effect of the order of the attribute levels in a value tree is analyzed.