An Agent-Based Model for Integrated Contagion and Regulation of Negative Mood

Through social interaction, the mood of a person can affect the mood of others. The speed and intensity of such mood contagion can differ, depending on the persons and the type and intensity of their interactions. Especially in close relationships the negative mood of a depressed person can have a serious impact on the moods of the ones close to him or her.For short time durations, contagion may be the main factor determining the mood of a person; however, for longer time durations individuals also apply regulation mechanisms to compensate for too strong deviations of their mood. Computational contagion models usually do not take into account such regulation.This paper introduces an agent-based model that simulates the spread of negative mood amongst a group of agents in a social network, but at the same time integrates elements from Gross’ emotion regulation theory, as the individuals’ efforts to avoid a negative mood.Simulation experiments under different group settings pointed out that the model is able to produce realistic results, that explain negative mood contagion and emotion regulation behaviours posed in the literatur

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Lämpimän käyttöveden lämmittäminen suomalaisissa kesäolosuhteissa

Tämä diplomityö on tehty TA-Yhtiöille. Työn ensisijaisena tavoitteena on löytää ympäristöystävällisempi ja taloudellisempi käyttöveden lämmitysmuoto erilaisiin asumismuotoihin Suomen kesäisissä olosuhteissa tutkimalla lämpöpumppuja sekä aurinkoenergiaa. Tätä kautta pyritään paitsi säästämään energiakustannuksissa niin myös keventämään ympäristöön kohdistuvaa päästökuormitusta. Työssä selvitetään nykyisten lämmitysmuotojen ongelmakohtia, kustannuksia ja päästöjä Suomessa. Tämä tehdään tarkastelemalla käytössä olevia tekniikoita sekä tutkimalla näiden kannalta keskeisimpiä Suomen ilmasto-olosuhteita. Lisäksi tutkitaan mahdollisuuksia ja kannattavuutta integroida maalämpölämmitysjärjestelmän rinnalle aurinkokerääjiä tai ilmalämpöpumppuja sekä uusissa kohteissa, että myös korjausrakentamiskohteissa. Näiden pohjalta etsitään taloudellisin, energiatehokkain ja ympäristöystävällisin vaihtoehto lämpimän käyttöveden lämmittämiseen ja osittain myös asuinrakennuksen lämmittämiseen. Työn tuloksina esitettyjä kuvaajia sekä tietokoneohjelmaa voidaan jatkossa käyttää pohjana lämmitysmuodon valinnassa sekä uudis- että saneerauskohteissa, Lisäksi ohjelman avulla voidaan analysoida, miten päästöt riippuvat lämmitysjärjestelmästä. Jotta tuloksista saataisiin konkreettisempia, on niiden avulla tehty valittujen esimerkkikohteiden tapauksiin sopivat kannattavuuslaskelmat

Analysis of beliefs of survivors of the 7/7 London bombings: application of a formal model for contagion of mental states

During emergency scenarios, the large number of possible influences inter se between cognitive and affective states of the individuals involved makes it difficult to analyse their (collective) behaviour. To study the behaviour of collectives of individuals during emergencies, this paper proposes a methodology based on formalisation of empirical transcripts and agent-based simulation, and applies this to a case study in the domain of the 7/7 London bombings in 2005. For this domain, first a number of survivor statements have been formalised. Next, an existing agent-based model has been applied to simulate the scenarios described in the statements. Via a formal comparison, the model was found capable of closely reproducing the real world scenarios

Agent-Based Modelling of the Emergence of Collective States Based on Contagion of Individual States in Groups

Abstract. This paper introduces a neurologically inspired computational model for the dynamics and diffusion of agent states within groups. The model combines an individual model based on Damasio’s Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of individual states such as emotions, beliefs and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group beliefs and intentions, while, in addition there is a positive feeling with these common states amongst the group members. A particular issue addressed is how certain types of states may affect other types of states, for example, emotions have an effect on beliefs and intentions, and beliefs may effect emotions.

Modelling the Interplay of Emotions, Beliefs and Intentions within Collective Decision Making Based on Insights from Social Neuroscience

Abstract. Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in Social Neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes