A study of large-scale web-clicking data employed this theory to explain why certain distributions of web page hits emerge on web sites. Huberman et al. [362] proposed a mathematical model that assumes that at any page, users decide to continue clicking as long as its information scent exceeds some threshold. This information scent can be computed using information foraging theory (IFT).
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IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested. Note that the term information does not refer to the information-theoretic concept but to subjective interest; here, information means anything that users find interesting.
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The roots of IFT lie in optimal foraging theory, originally proposed in biology, which describes the hunting and food search behaviors of animals.
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A solution strategy—or
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Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search.
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MDP is a formalism that originates from studies of sequential decision-making in artificial intelligence and operations research. Instead of the choice between n actions, MDP deals with environments where rewards are delayed (or distal). This requires an ability to plan actions as part of sequences instead of one-shot choices.
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