@phdthesis{Saulin2023,
  author    = {Saulin, Anne Christin},
  title     = {Sustainability of empathy as driver for prosocial behavior and social closeness: insights from computational modelling and functional magnetic resonance imaging},
  doi       = {10.25972/OPUS-30555},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305550},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Empathy, the act of sharing another person's affective state, is a ubiquitous driver for helping others and feeling close to them. These experiences are integral parts of human behavior and society. The studies presented in this dissertation aimed to investigate the sustainability and stability of social closeness and prosocial decision-making driven by empathy and other social motives. In this vein, four studies were conducted in which behavioral and neural indicators of empathy sustainability were identified using model-based functional magnetic resonance imaging (fMRI). Applying reinforcement learning, drift-diffusion modelling (DDM), and fMRI, the first two studies were designed to investigate the formation and sustainability of empathy-related social closeness (study 1) and examined how sustainably empathy led to prosocial behavior (study 2). Using DDM and fMRI, the last two studies investigated how empathy combined with reciprocity, the social norm to return a favor, on the one hand and empathy combined with the motive of outcome maximization on the other hand altered the behavioral and neural social decision process. The results showed that empathy-related social closeness and prosocial decision tendencies persisted even if empathy was rarely reinforced. The sustainability of these empathy effects was related to recalibration of the empathy-related social closeness learning signal (study 1) and the maintenance of a prosocial decision bias (study 2). The findings of study 3 showed that empathy boosted the processing of reciprocity-based social decisions, but not vice versa. Study 4 revealed that empathy-related decisions were modulated by the motive of outcome maximization, depending on individual differences in state empathy. Together, the studies strongly support the concept of empathy as a sustainable driver of social closeness and prosocial behavior.},
  subject      = {Einf{\"u}hlung <Motiv>},
  language  = {en}
}
@phdthesis{KuhnAndriotti2009,
  author    = {Kuhn Andriotti, Gustavo},
  title     = {Prospect Theory Multi-Agent Based Simulations for Non-Rational Route Choice Decision Making Modelling},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-40483},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2009},
  abstract  = {Simulations (MASim) and non-rational behaviour. This non-rational behaviour is here based on the Prospect Theory [KT79] (PT), which is compared to the rational behaviour in the Expected Utility Theory [vNM07] (EUT). This model was used to design a modified Q-Learning [Wat89, WD92] algorithm. The PT based Q-Learning was then integrated into a proposed agent architecture. Because much attention is given to a limited interpretation of Simon's definition of bounded-rationality, this interpretation is broadened here. Both theories, rationality and the non-rationality, are compared and the discordance in their results discussed. The main contribution of this work is to show that an alternative is available to the EUT that is more suitable for human decision-makers modelling. The evidences show that rationality is not appropriated for modelling persons. Therefore, instead of fine-tuning the existent model the use of another one is proposed and evaluated. To tackle this, the route choice problem was adopted to perform the experiments. To evaluate the proposed model three traffic scenarios are simulated and their results analysed.},
  subject      = {Mehragentensystem},
  language  = {en}
}