Agent-Based Market Simulation Calibration via Type Distribution
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Solution Overview
Problem
There is a need for a mechanism to create a realistic agent-based simulation of an over-the-counter market and to calibrate the simulator using data from the real market, as existing methods are costly and inefficient.
Innovation Solution
The method involves assigning type values to agents based on their state, receiving agent-specific data related to market observations, actions, and rewards, generating a market simulation using these type values and data, and calibrating the simulation using actual market data through a reinforcement learning calibration algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents are trained using a shared policy network with decentralized execution, then training efficiency is improved, but the complexity of calibrating the system to match external targets increases
Solution Approach 1:
A calibration algorithm is introduced as an intermediary component that takes the outputs from the trained shared policy network and adjusts the agent type distribution parameters to match external calibration targets. This mediator separates the training process from the calibration process, allowing efficient training while managing calibration complexity through a dedicated optimization layer.
Solution Approach 2:
The system changes parameters of the agent type distribution (such as the proportion of different agent types) to achieve calibration. By adjusting these distribution parameters rather than retraining the entire policy network, the system maintains training efficiency while achieving the desired calibration to external targets.
2Manufacturing precision
If multiple agent types are introduced to achieve calibration targets, then calibration precision is improved, but the computational cost of training increases
Solution Approach 1:
The system performs preliminary training of the shared policy network with a simplified agent population before introducing the full calibration process. This preliminary action establishes a baseline model that can then be efficiently calibrated by adjusting type distribution parameters rather than training from scratch with complex agent heterogeneity.
Solution Approach 2:
Instead of training separate policies for each agent type or using complex heterogeneous agent architectures, the system achieves precision by changing the parameters of the agent type distribution. This allows multiple agent types to be represented through a single shared policy network with adjusted population proportions, significantly reducing computational costs.
3Adaptability or versatility
If the simulation is calibrated repeatedly to match different market conditions, then adaptability is improved, but the time required for calibration increases
Solution Approach 1:
The calibration system is designed to be dynamic and flexible, allowing the agent type distribution parameters to be adjusted according to different market conditions. The calibration algorithm can quickly adapt to new conditions by modifying distribution parameters rather than performing lengthy retraining processes, enabling rapid adaptation to changing market environments.
Solution Approach 2:
A preliminary training phase establishes a robust baseline policy network that is pre-configured with various agent types. This preliminary preparation allows subsequent calibration to different market conditions to proceed much faster, as the system only needs to adjust the distribution of pre-existing agent types rather than learn from scratch.
Data Source
AI summary
A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.