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Information foraging theory is being developed in order to understand and improve human—information interaction. The framework assumes that humans adapt to the world by seeking and using information. As a result humans create a glut of information, causing a poverty of attention and a greater need to allocate that attention effectively and efficiently. The framework draws upon concepts in optimal foraging theory and computational cognitive psychology. Theories are developed and tested via rational analysis and computational cognitive models. Rational analysis involves an engineering style model of (a) what environmental problem is solved and (b) why a given system is a good solution to the problem. Computational cognitive models provide the details of how the human cognitive architecture achieves information foraging tasks in given information environments. The framework and methodology are illustrated using an example of the task of finding a good, inexpensive hotel on the Web.

Research on human information interaction (HII) focuses on people’ s relationship with information, rather than with technology (as in human-computer interaction) or with the information agency (as in librarianship). HII is inherently a multidisciplinary field, encompassing areas ranging from human-computer interaction to computer-supported cooperative work, human factors, social informatics, management, and library and information science (LIS). LIS is the only field that has given HII a significant position among its areas as the field of human information behavior (HIB). This chapter provides an overview of HII and some of its research disciplines. It discusses relevance judgment in information seeking and presents examples of current and potential areas of research related to HII, including knowledge organization, information retrieval, and information and communication technologies.

Cognitive work analysis (CWA) can play an important role in realizing convergence between research in human information interaction (HII) and information systems design. CWA has the potential to establish relations between various elements that have been fragmented in human information behavior research and in other HII areas. It can also connect theory with practice, various areas of research expertise, seemingly diverse areas of research, and various theoretical traditions and conceptual constructs. Moreover, CWA can help researchers decide about which variables to study. As an ecological approach, CWA can also be useful to the design of context-specific systems through its focus on environmental constraints.

As a multidisciplinary field, human information interaction (HII) includes basic concepts with a variety of definitions and interpretations. Researchers give these concepts the meanings common in their discipline, but researchers in the same discipline may come up with differing interpretations. This chapter provides an overview of some basic concepts about HII and their definitions, first by elucidating the terms human, information, and ecological approach. It then explains what a community of actors is, along with system, the functional elements of a system, a system’s environment and constraints, context-specific and general-context information systems, and the systems approach used to account for the multidisciplinary nature of HII.

Cognitive work analysis (CWA) is a context- and work-centered conceptual framework that guides an analysis of cognitive work leading to design requirements. Under CWA, any activity that requires decision making is considered “cognitive work.” Given its emphasis on explicit analysis of the constraints impressed upon actors by the work ecology, CWA provides an ecological approach to the study of human information interaction (HII). This chapter describes CWA’s dimensions of analysis and discusses its potential application to information systems design. There are five dimensions of analysis: analysis of the work environment, the work domain analysis, activity analysis, organizational analysis, and analysis of actors’ resources and values.

Cognitive science is a science of intelligent systems. This chapter proposes that cognitive science can provide useful perspectives for research on technology-mediated human-information interaction (HII) when HII is cast as emergent behaviour of a coupled intelligent system. It starts with a review of a few foundational concepts related to cognitive computations and how they can be applied to understand the nature of HII. It discusses several important properties of a coupled cognitive system and their implication to designs of information systems. Finally, it covers how levels of abstraction have been useful for cognitive science, and how these levels can inform design of intelligent information systems that are more compatible with human cognitive computations.

Information need is a foundational concept related to both information seeking and human information interaction. Robert S. Taylor, a scholar of human information behavior, made significant contributions to research on information need. This chapter discusses the definitional and operational challenges associated with the study of information need. It also examines the decision ladder, a model of the decision making process introduced by Jens Rasmussen (1986) and his colleagues as part of a conceptual framework which they developed for cognitive work analysis. The decision ladder describes decision making as a process involving three phases: situation analysis, evaluation, and planning.

Cognitive work analysis (CWA) can address the complexity of human information interaction by parceling out the investigated phenomenon while preserving the relationships among the resulting parts. The CWA dimensions suggest that attributes may be organizational, cultural or individual, or determined by the work and subject domains. To harness complexity, CWA provides analytical tools for each dimension, such as the analysis of actors’ resources and values, strategies analysis, and decision analysis. Decision analysis, task analysis, and work environment analysis are all carried out using an abstraction hierarchy tool that provides a means-ends analysis.