Understanding and Changing Behavior

P eople routinely engage in relationships whereby they influence and are influenced by other humans. In the near future, we envision people confronting machines that can support such relationships as well. B.J. Fogg refers to these machines as persuasive systems.1 A notable difference between persuasion in human-machine interaction—as opposed to human-human interaction— is the limited (if any) ability of machines to have a real-time understanding of people’s individual traits, activities, and social dynamics. Most persuasive systems are designed offline and subsequently lack the flexibility required to personalize or adapt messages to the usage context. This can only be remedied by real-time analysis of human behavior during interactions. New technology and algorithms for human behavior analysis are giving computers an awareness that lets them attribute meaning to users’ attitudes, preferences, personality, and social relationships and helps them understand users’ activities, routines, and lifestyles. This could redefine the relationship between computers and the interacting human, changing the computer’s role from that of passive observer to a socially active participant that can influence people’s attitudes and behaviors in their everyday, natural environments. The goal of this special issue is to contribute to the advancement of ubiquitous information societies, where computers and humans are part of the same ecosystem. A crucial property of entities living in the same ecosystem is that they mutually influence and affect each other’s behavior and internal states (such as attitudes) in a variety of ways—including implicitly and indirectly. This special issue, organized as a follow-up to the Second International Workshop on Human Behavior Understanding,2 investigates several domains where behavior change starts with a real-time understanding of human behavior. Albert Ali Salah Boğaziçi University