Emergence from Emergence: Financial Market Simulation via Learning with Heterogeneous Preferences Article Swipe
Hashimoto, Ryuko
,
Takata Ryosuke
,
Suzuki Masahiro
,
Tanaka Yuki
,
Izumi Kiyoshi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.05207
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.05207
Agent-based models help explain stock price dynamics as emergent phenomena driven by interacting investors. In this modeling tradition, investor behavior has typically been captured by two distinct mechanisms -- learning and heterogeneous preferences -- which have been explored as separate paradigms in prior studies. However, the impact of their joint modeling on the resulting collective dynamics remains largely unexplored. We develop a multi-agent reinforcement learning framework in which agents endowed with heterogeneous risk aversion, time discounting, and information access collectively learn trading strategies within a unified shared-policy framework. The experiment reveals that (i) learning with heterogeneous preferences drives agents to develop strategies aligned with their individual traits, fostering behavioral differentiation and niche specialization within the market, and (ii) the interactions by the differentiated agents are essential for the emergence of realistic market dynamics such as fat-tailed price fluctuations and volatility clustering. This study presents a constructive paradigm for financial market modeling in which the joint design of heterogeneous preferences and learning mechanisms enables two-stage emergence: individual behavior and the collective market dynamics.
Related Topics
Concepts
Reinforcement learning
Constructive
Volatility (finance)
Stock market
Financial market
Economics
Microeconomics
Computer science
Social learning
Fictitious play
Dynamics (music)
Collective behavior
Behavioral economics
Joint (building)
Agent-based model
Trading strategy
Experimental economics
Learning theory
Game theory
Market segmentation
Metadata
- Type
- preprint
- Landing Page
- https://doi.org/10.48550/arxiv.2511.05207
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7104737661
All OpenAlex metadata
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https://openalex.org/W7104737661Canonical identifier for this work in OpenAlex
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- Title
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Emergence from Emergence: Financial Market Simulation via Learning with Heterogeneous PreferencesWork title
- Type
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preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-07Full publication date if available
- Authors
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Hashimoto, Ryuko, Takata Ryosuke, Suzuki Masahiro, Tanaka Yuki, Izumi KiyoshiList of authors in order
- Landing page
-
https://doi.org/10.48550/arxiv.2511.05207Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48550/arxiv.2511.05207Direct OA link when available
- Concepts
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Reinforcement learning, Constructive, Volatility (finance), Stock market, Financial market, Economics, Microeconomics, Computer science, Social learning, Fictitious play, Dynamics (music), Collective behavior, Behavioral economics, Joint (building), Agent-based model, Trading strategy, Experimental economics, Learning theory, Game theory, Market segmentationTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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