Risk-aware Markov Decision Processes Using Cumulative Prospect Theory
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916737090322432 |
|---|---|
| author | Brihaye, Thomas Chatterjee, Krishnendu Mohr, Stefanie Weininger, Maximilian |
| author_facet | Brihaye, Thomas Chatterjee, Krishnendu Mohr, Stefanie Weininger, Maximilian |
| contents | Cumulative prospect theory (CPT) is the first theory for decision-making under uncertainty that combines full theoretical soundness and empirically realistic features [P.P. Wakker - Prospect theory: For risk and ambiguity, Page 2]. While CPT was originally considered in one-shot settings for risk-aware decision-making, we consider CPT in sequential decision-making. The most fundamental and well-studied models for sequential decision-making are Markov chains (MCs), and their generalization Markov decision processes (MDPs). The complexity theoretic study of MCs and MDPs with CPT is a fundamental problem that has not been addressed in the literature.
Our contributions are as follows: First, we present an alternative viewpoint for the CPT-value of MCs and MDPs. This allows us to establish a connection with multi-objective reachability analysis and conclude the strategy complexity result that memoryless randomized strategies are necessary and sufficient for optimality. Second, based on this connection, we provide an algorithm for computing the CPT-value in MDPs with infinite-horizon objectives. We show that the problem is in EXPTIME and fixed-parameter tractable. Moreover, we provide a polynomial-time algorithm for the special case of MCs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk-aware Markov Decision Processes Using Cumulative Prospect Theory Brihaye, Thomas Chatterjee, Krishnendu Mohr, Stefanie Weininger, Maximilian Logic in Computer Science Cumulative prospect theory (CPT) is the first theory for decision-making under uncertainty that combines full theoretical soundness and empirically realistic features [P.P. Wakker - Prospect theory: For risk and ambiguity, Page 2]. While CPT was originally considered in one-shot settings for risk-aware decision-making, we consider CPT in sequential decision-making. The most fundamental and well-studied models for sequential decision-making are Markov chains (MCs), and their generalization Markov decision processes (MDPs). The complexity theoretic study of MCs and MDPs with CPT is a fundamental problem that has not been addressed in the literature. Our contributions are as follows: First, we present an alternative viewpoint for the CPT-value of MCs and MDPs. This allows us to establish a connection with multi-objective reachability analysis and conclude the strategy complexity result that memoryless randomized strategies are necessary and sufficient for optimality. Second, based on this connection, we provide an algorithm for computing the CPT-value in MDPs with infinite-horizon objectives. We show that the problem is in EXPTIME and fixed-parameter tractable. Moreover, we provide a polynomial-time algorithm for the special case of MCs. |
| title | Risk-aware Markov Decision Processes Using Cumulative Prospect Theory |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2505.09514 |