Affective analytics of demonstration sites. (May 2019)

• Record Type: Journal Article
• Authors: Kaklauskas, A.; Jokubauskas, D.; Cerkauskas, J.; Dzemyda, G.; Ubarte, I.; Skirmantas, D.; Podviezko, A.; Simkute, I.
• Is Part Of: Engineering applications of artificial intelligence. Volume 81(2019)
• Page Start: 346
• Page End: 372
• Publication Date: 2019-05
• Subjects: Intelligent decision support system -- Crowd -- Biometrics -- Neuro decision matrix -- Public spaces -- Demonstration sites; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.03.001
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Multiple-criteria decision-making (MCDM) typically assumes that crowds make completely rational decisions. In MCDM, a crowd as a whole, or its individual members, generally make decisions free from any influence of valence, arousal, emotional state or environment. In contrast, various theories dealing with crowd psychology (Gustave Le Bon, Freudian, Deindividuation, Convergence, Emergent norm, Social identity) analyze, in one form or another, the emotions of the crowd. According to above theories, crowd is influenced by a range of behavioral factors, such as physical, social, psychological, culture, norms, and emotions. It can be argued that the emotional state, valence and arousal of crowds affect their decision making to a considerable degree and multiple criteria crowd behavior modeling must, therefore, consider this impact as well. In this light, the integration of crowd simulation and biometric methods, behavioral operations research and emotions in decision making has taken a prominent place as it leads to a better understanding of crowd emotions and crowd decision making. In this context, the authors developed the A ffective An alytics of De monstration S ites (ANDES) that added to this body of research in four ways. The crowd analysis and simulations conducted with ANDES used a neuro decision matrix. The matrix contains a detailed description of demonstration sites (public spaces) in question and the emotions, valence, arousal and physiological parameters of people present there. With ANDES's Remote Sensor Network, emotional (emotions, valence, arousal) and physiological (average crowd facial temperature, crowd composition by gender and age group, etc.) parameters of people present at demonstration sites can be mapped. ANDES can assist experts in more effective implementations of public spaces planning and a participation process by attendees by collecting and examining various layers of data on the emotional and physiological parameters of visitors based on a visitors-centric public spaces planning approach. ANDES can determine the public space and real estate values. Highlights: ANDES analyze biometric and physiological signals of the crowd. ANDES can determine the public space and real estate values (DS). A neuro decision matrix was used in the integrated analysis of the crowd and DS. ANDES create affective and physiological maps of sites in question. Based on such maps, ANDES give tips for stakeholder groups.