ORGANISATION

Kirana Chain

Large scale synthetic dataset containing 1 Million negotiation turn records across 125000 distributor retailer bargaining trajectories modeling informal credit supply chain disruptions multilingual dialogue and decentralized retail decision making in India.

About Dataset

KiranaChain is a large scale behavioral intelligence benchmark designed to model decentralized retail negotiations within the Indian Kirana ecosystem. The dataset contains 1000000 turn level records grouped into 125000 complete negotiation trajectories spanning distributor retailer interactions across 12 Indian states. The dataset captures informal credit allocation mechanisms Udhar dynamics trust score evolution liquidity stress indicators supply chain disruptions environmental externalities multilingual bargaining behavior and structured negotiation telemetry. Each trajectory consists of 8 sequential negotiation turns enabling trajectory based research in Reinforcement Learning Multi Agent Systems Behavioral Economics Credit Risk Modeling Graph Learning and Conversational AI. KiranaChain contains 40 structured features organized across agent profiles financial indicators environmental conditions and negotiation state variables. The generation framework combines Gaussian Copula based dependency modeling Non Stationary Markov negotiation processes and Gaussian Process behavioral perturbations to create statistically consistent large scale synthetic negotiation data. The dataset is released by Atlas AI Labs and developed by undergraduate researchers (Dhadi Sai Praneeth Reddy, Mididuddi Dhatri, Biradar Amulya) from Vasavi College of Engineering Autonomous Hyderabad India. See the full description on the dataset page: https://huggingface.co/datasets/Atlas-AI-Labs/KiranaChain.

Purpose of Dataset

Kiranachain Is Intended To Support Research And Development In Artificial Intelligence Machine Learning And Computational Social Science. The Dataset Enables Experimentation With Reinforcement Learning Negotiation Agents Multi Agent Systems Informal Credit Risk Assessment Supply Chain Intelligence Graph Neural Networks Trajectory Forecasting And Multilingual Conversational Modeling. The Dataset Provides A Reproducible Benchmark For Studying Decentralized Market Behavior Under Financial Constraints Environmental Disruptions And Supply Chain Uncertainty. It Is Particularly Useful For Developing And Evaluating Algorithms For Settlement Prediction Price Forecasting Trust Modeling Default Risk Estimation Dialogue Generation And Behavioral Decision Intelligence. Because The Dataset Is Synthetic It Can Be Freely Used For Academic Research Benchmarking Education And Model Development Without Exposing Personally Identifiable Information Or Real Financial Records.

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Geographical coverage

India

Sector

Social

Author

Dhadi Sai Praneeth Reddy

Source Organisation

Atlas AI Labs

Uploaded by

DHADI SAI PRANEETH REDDY

Data Quality Score (Beta)

Dataset type

Structured

Frequency

Static

Time Granularity

Static

Year range

07/05/2025 - 02/06/2025

Date & Time

03/06/26 17:21:02

Visibility

Open

Primary Key / Indicator

Transaction_id

Hosted / Redirected

Redirected

Data Type

Synthetic

If Redirection which source

Hugging Face

Data Collection Method

Procedural Simulation Using Gaussian Copulas Non Stationary Markov Chains Gaussian Process Behavioral Noise Multilingual Dialogue Synthesis And Trajectory Based Agent Modeling Of Decentralized Retail Negotiations.