Abstract. The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting denser subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolution. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity based approach and its generalizations can be viewed as particular cases of the hedonic games.

Decision theory has been characterized, for much of its history, by a debate about whether human decision processes are inherently flawed. The remarkable part of this debate is that for virtually its entire duration, it has been conducted without reference to detailed data on how people actually make decisions in everyday settings. In recent years, this issue has come to the forefront in the work of Cohen (1981); Barwise and Perry (1983); Klein, Orasanu, Calderwood, and Zsambok (1993); and others, who have pointed out the fundamental differences between decision making as it has been studied using traditional decision theory and as it occurs in socially situated naturalistic settings. The resulting naturalistic decision theory has emphasized highly detailed, almost ethnographic, studies of decision processes in specific domains. This had resulted in dense data but primarily prose representations and analyses.In parallel to the rise of naturalistic decision theory, cognitive science and human-computer interaction (HCI) researchers were developing increasingly powerfid analysis methods that collectively were called cognitive task analysis techniques. The purpose of these techniques was to analyze and model the cognitive processes that gave rise to human task performance in specific domains, as the basis for design and evaluation of computer-based systems and their user interfaces. The Tactical Decision Making Under Stress (TADMUS) project provided a unique opportunity for these two avenues of inquiry to come together. This chapter describes research that combined the highly formal methods and tools of the HCI community with the theoretical orientation of naturalistic decision theory. The aim of the research reported here was to create a detailed and domain-The authors acknowledge the contributions made by Janine k c e l l to the research reported here as well as the cooperation and effort of the many individuals who acted as participants in the data collection effort. The efforts of Don MacConkey and John Pollen in supporting the data collection and analysis effort on the Navy side are also gratefully acknowledged.

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The Decision Support Systems (DSS) field has grown rapidly drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure, and poor linkage between research and practice. This paper presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e. functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision maker's need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are: process models which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across and/or alternatives; information control techniques which help in storage, retrieval, organization and integration of data and knowledge; analysis and reasoning techniques which support application of problem-specific expert reasoning procedures; representation aids which assist in expression and manipulation of a specific representation of a decision problem; and judgement amplification/refinement techniques, which help in quantification and de-biasing of heuristic judgements. Additional distinctioms are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.

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