Vervet Monkey Decision-Making Data for Reward Discount Factor Analysis

Data and code for a methodology using Maximum Entropy Inverse Reinforcement Learning to learn discount factors. The collection includes both clean synthetic data derived from vervet monkey routes and messy empirical field data from vervets, used to test the method. It was authored by Julie A. Teichroeb and last updated on 2026-04-25.

Use Cases

• Testing Maximum Entropy Inverse Reinforcement Learning methods based on the described synthetic and empirical data.
• Modeling animal temporal discounting and future valuation based on vervet monkey route data.
• Comparing algorithm performance on clean synthetic versus messy empirical field data as described.

Strengths

• Includes both synthetic and empirical data types, allowing for method validation across controlled and realistic conditions.
• Methodology is explicitly described as using Maximum Entropy Inverse Reinforcement Learning.
• Last updated on 2026-04-25, indicating recent maintenance.

Limitations

• Row count, file formats, and column-level documentation are unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality and structure require manual inspection after download.

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Data derived from vervet monkey routes in a multi-destination array and empirical field observations.

Time Range

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Freshness

2026-04-25

Geography

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