Prospect Theory Multi-Agent Based Simulations for Non-Rational Route Choice Decision Making Modelling

Prospect Theorie basierte Multi-Agenten Simulationen für nicht-rationalle Route Entscheidung Modellierung

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-40483
  • 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 andSimulations (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.zeige mehrzeige weniger

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Metadaten
Autor(en): Gustavo Kuhn Andriotti
URN:urn:nbn:de:bvb:20-opus-40483
Dokumentart:Dissertation
Titelverleihende Fakultät:Universität Würzburg, Fakultät für Mathematik und Informatik
Institute der Universität:Fakultät für Mathematik und Informatik / Institut für Informatik
Datum der Abschlussprüfung:20.11.2009
Sprache der Veröffentlichung:Englisch
Erscheinungsjahr:2009
Allgemeine fachliche Zuordnung (DDC-Klassifikation):5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Normierte Schlagworte (GND):Mehragentensystem; Bestärkendes Lernen <Künstliche Intelligenz>
Freie Schlagwort(e):Q-Learning; Route Entscheidung
MASim; Prospect Theory; Q-Learning; Route Choice; Traffic
Datum der Freischaltung:25.11.2009
Betreuer:Prof. Dr. Dietmar Seipel