@article{3345332,
    title = "On the Adaptive Value of Mood and Mood Contagion",
    author = "Tzafestas, E.",
    journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    year = "2022",
    volume = "13499 LNAI",
    pages = "205-216",
    publisher = "Springer Science and Business Media Deutschland GmbH",
    doi = "10.1007/978-3-031-16770-6_17",
    keywords = "Artificial intelligence;  Computers, Adaptive value;  Decisions makings;  Evolution;  Learning;  Mood;  Mood contagion;  Performance;  Personal strategies, Decision making",
    abstract = "We are presenting a study on mood that purports to contribute to an understanding of its evolution as a personal strategy and as a prosocial automatic contagion-based mechanism that both improve decision making. We present an environment where an agent interacts with objects of varying difficulty and where its mood is both a dispositional factor that influences its decision making and an information variable that aligns with the history of its interactions. We show that for very competent and very incompetent agents the mood has a positive effect on their obtained performance and that on average for the whole population the mood mechanism would be selected by evolution. We also examine a mechanism of automatic mood contagion that favors all but the most incompetent agents and on average the whole population and we show that contagion would again be selected by evolution. We delineate the implications of our model for further research on moods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG."
}