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Bounded Rational Decision Networks With Belief Propagation
Gerrit Schmid1, Sebastian Gottwald2, Daniel A Braun3
1Ulm University Institute of Neuroinformatics, 89081 Ulm, Germany gerrit.schmid@uni-ulm.de.
Neural Computation
|October 9, 2024
Summary
Complex systems like the brain use local communication between specialized units. This study shows cyclical information flow and feedback loops can improve decision-making performance in networks with bounded rationality.
Area of Science:
- Computational neuroscience
- Network science
- Decision theory
Background:
- Complex information processing systems, analogous to the human brain, feature specialized, collaborating units.
- Locality, where information is exchanged locally without a central controller, is a key property of these networks.
Purpose of the Study:
- To investigate networks of bounded rational decision makers using a decision-theoretic approach.
- To explore the impact of cyclical information processing paths and local communication on system performance.
Main Methods:
- Adaptation of message-passing algorithms to facilitate local information flow.
- Analysis of networks with specialized units and cyclical communication paths.
- Examination of decision-making agents with bounded rationality.
Main Results:
- Cyclical information processing and local communication enable circular dependency structures.
- Repeated communication can enhance performance in systems with limited unit processing capabilities.
- Suboptimal utility arises from systems with insufficient or excessive connections and feedback loops.
Conclusions:
- Networks of bounded rational agents benefit from specialized units and local, cyclical communication.
- The degree of connectivity and feedback is critical for optimizing utility in complex decision-making systems.
- Message-passing algorithms can be adapted for studying networks with feedback, advancing understanding of emergent intelligence.
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