Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon’s notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the authors held a competition between the satisficing “Take The Best ” algorithm and various “rational ” inference procedures (e.g., multiple regression). The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. Organisms make inductive inferences. Darwin (1872/1965) observed that people use facial cues, such as eyes that waver and lids that hang low, to infer a person’s guilt. Male toads, roaming through swamps at night, use the pitch of a rival’s croak to infer its size when deciding whether to fight (Krebs & Davies, 1987). Stock brokers must make fast decisions about which of several stocks to trade or invest when only limited information is available. The list goes on. Inductive

One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counterintuitive less-is-more effect in which less knowledge is better than more for making accurate inferences.

A ‘‘building code’ ’ for preference measurement is needed in a world in which many expressions of preference are constructed when people are asked a valuation question. Construction of preferences means that preference measurement is best viewed as architecture Ž building a set of values. rather than as archaeology Ž uncovering existing values.. We describe potential faults in the process of preference construction, offer guidelines for measuring constructed preferences Ž a ‘‘building code’ ’. to mitigate these faults, and discuss how the code must be sensitive to the purpose of the valuation Ždesign vs. prediction.. Key words: constructive preferences, value measurement, decision aiding JEL Classification: D80

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57 % after performing tradeoff tasks. Tradeoff support also significantly increased users’ confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User

this article, we propose a new task-goal hypothesis regarding the prominence effect: The prominent attribute receives more weight in tasks whose goal is to differentiate among options than in tasks whose goal is to equate options. We use this hypothesis to generalize the prominence effect beyond choice and matching to several additional tasks, including the choice-based matching and difference comparison methods that are widely employed in decision analysis. The results of three studies provide strong support for the task-goal account of the prominence effect and cast doubt on competing explanations. We discuss the implications of these findings for descriptive decision theory and for preference measurement in decision analysis, public policy, and marketing

ion. Requirements may be distinguished based on the abstraction level of their description. A requirement may be further defined by add new details defined in more specialized subrequirements. Through specialization of abstract requirements, or generalization of detailed requirement, a requirement abstraction hierarchy can be defined. . Development p roperties . Requirements may be distinguished based on their development properties. For example, a requirement may have just been proposed. Late r, it may be accepted or rejected. . Representational properties. Requirements may be distinguished based on their representation. A requirement may begin as an informal sketch, then become a natural language sentence (e.g., "The system shall ..."). Finall y, more formal representations, such as UML, Z, or predicate cal- Requirements Interaction Management - Definition and scope 6 1999 William N. Robinson Requirements Interaction Management GSU CIS 99-7 culus, may be used to express a requir...

Much research on judgment and decision making has focussed on the adequacy of classical rationality as a description of human reasoning. But more recently it has been argued that classical rationality should also be rejected even as normative standards for human reasoning. For example, Gigerenzer and Goldstein (1996) and Gigerenzer and Todd (1999a) argue that reasoning involves ‘‘fast and frugal’ ’ algorithms which are not justified by rational norms, but which succeed in the environment. They provide three lines of argument for this view, based on: (A) the importance of the environment; (B) the existence of cognitive limitations; and (C) the fact that an algorithm with no apparent rational basis, Take-the-Best, succeeds in an judgment task (judging which of two cities is the larger, based on lists of features of each city). We reconsider (A)–(C), arguing that standard patterns of explanation in psychology and the social and biological sciences, use rational norms to explain why simple cognitive algorithms can succeed. We also present new computer simulations that compare Take-the-Best with other cognitive models (which use connectionist, exemplarbased, and decision-tree algorithms). Although Take-the-Best still performs well, it does not perform noticeably better than the other models. We conclude that these results provide no strong reason to prefer Take-the-Best over alternative cognitive models.

this paper, and Eran Chajut for stimulating conversations and help with collecting some of the data. Address reprint requests to Dan Ariely, Department of Psychology, The University of North Carolina, Chapel Hill, NC 27599-3270. E-MAIL: ariely@cs.unc.edu

Developing requirements from a group of analysts and system users is a difficult task. In addition to the usual problems of individual requirements acquisition, group requirements acquisition entails conflict detection, resolution generation, and resolution choice. In essence, requirements must be negotiated. In this paper, we summarize our model for requirements negotiation and its automated support. The model calls for the independent representation of user requirements followed by their negotiation. The model centers around three themes: user participation, resolution generation, and negotiation records. To support these themes, we have built a tool, called Oz, which provides: (1) automated methods for conflict detection and resolution generation, (2) an interactive resolution choice procedure, and (3) records of the negotiation process. This paper overviews our negotiation method and tool support. 1 Introduction Requirements negotiation is a common and difficult problem...

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