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Agent-based Computational Economics and emotions for decision-making processes
No full textPreliminary remarks: Agent-based computational economics has been developed extensively in recent years, using sophisticated algorithms of evolutionary computation and artificial life. Surely, the trend has been to shape systems, frameworks, and environments to be as much like a human being as possible. However, to adequately characterize the economic systems viewed as complex adaptive systems, we must integrate emotional aspects with agent-based technology in order to facilitate behavioral shortcuts for the development of fast and adaptive decisional skills, which are innate human behaviors.
Theoretical foundation and state of art: There is a growing consensus among researchers in agent-based computational economics that teamwork models can enable flexible coordination among heterogeneous entities. These models are based on a belief-desire-intention (BDI) architecture. We integrate analysis with a review of recent emotions theories that could be useful for integration into agent-based computational economics environments.
Analysis and tools: Our role is to integrate agent-based architectures with emotions. So we consider, the emotions, “behavioural shortcuts”. Our future purpose is to develop artificial agents that incorporate emotions to run simulations and create frameworks that can be used cooperatively with business intelligence technologies to understand the different ways that enterprises decline or improve as a consequence of the actions of their managers.
Results: The theoretical results are expected to be of considerable importance in terms of providing a defensible, functional approach for the analysis of future applications, and, above all, they will provide the essential basis for creation of human-based systems.
Discussion and conclusions: The literature relevant to emotions and agent-based areas will be reviewed, the elements of the model will be described, suggestions for future work will be presented, and the many implications for theory, research, and practice will be discussed
Synchronization of a biofeedback system with an eye tracker through an audiovisual stimulus marker
No full textIn the last few years, many psychophysiology scientists have begun to use eye-tracking methodologies in conjunction with standard biofeedback systems. This approach has proven to be useful for analyzing visual stimuli and the physiological reactions they produce. To be fully effective, however, it is essential to have a marker on the biofeedback signals so that the exact time of a presented stimulus can be determined. While a specific scientist may not work on ‘‘evocated potential’’ or, in general, with short-time stimuli, it is essential to establish the time between the presented stimulus and when the physiological response occurs. In addition, synchronization between the eye tracker and biofeedback allows the addition of a series of signals from the eyes, such as dilation of the pupils and the distance between the pupils, to standard neurophysiology signals, allowing the assessment of the degree of relaxation or stress felt by the subjects. In this work, we show a technical solution for synchronizing eye tracker Tobii 1750 with a biofeedback ‘‘Procomp infiniti’’ using a TT-AV Sync. Also, we added some algorithms to this hardware tool in order to be able to conduct the desired data analysis. We used a Tobii 1750 monitor and a Biograph ‘‘Procomp infiniti’’. We conducted 500 synchronizations with the device in order to establish its precision, which we determined to be ±0.1 s. We also found that we could reduce the errors by using synchronization based on a visual marker (from black to white) simultaneously with synchronization based on an audio marker (from silence to beep). The hardware mentioned above for synchronizing biofeedback and the eye tracker is the TT-AV Sync, which was configured through a physical channel on biofeedback
Looking at One's Self Through Facebook Increases Mental Stress: A Computational Psychometric Analysis by Using Eye-Tracking and Psychophysiology
No full text: The aim of this study was to investigate if Mental Stress was superior, inferior, or equal navigating on Facebook own profile or others profiles. An experimental manipulation would invalidate the results since it would force the participants to navigate in only one condition each time. To overcome this problem, we used an eye-tracker to get clear time markers that identified the areas where the participants focused during all of the Facebook navigation. While the gazes were being recorded for 30 participants, we simultaneously recorded their psychophysiological signals, which were extracted and paired with each specific focus area. Consequently, we obtained the psychophysiological correlates of Facebook navigation for both the conditions related to "own" and "others." The areas related to own were about the own profile (such as exploring and focusing on one's own information, posting one's own news, and similar activities). The areas related to others were about Facebook friends (e.g., exploring others' profiles and reading comments). The results showed that, based on cardiovascular measures (strong measurements of psychological stress), looking at one's own profile increased mental stress level. Bayesian analyses showed that these differences between the two conditions were not due to the cognitive load or the different attentional and emotional content in the two conditions. The study posed new questions about the expression of one's self to others, and indicated potential detrimental effects of chronic stress deriving from being more oriented to the self than the others
Investigations of executive functions using Virtual Multiple Errands Test and Psychophysiological measures
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