A model of evolution and learning.

Area of Science:

• Artificial Intelligence
• Evolutionary Computation
• Computational Finance

Background:

• Agent-based modeling is crucial for understanding complex adaptive systems.
• The interplay between individual learning and population-level evolution is a key area of research.
• Artificial agents with adaptive learning mechanisms can model real-world economic behaviors.

Purpose of the Study:

• To investigate the interaction between learning and evolution in a population of self-learning agents.
• To analyze how acquired adaptive behaviors are inherited through evolution.
• To model agent-brokers predicting stock price changes and their adaptive strategies.

Main Methods:

• Developed a computational model of evolving populations of self-learning agents.
• Implemented a neural network adaptive critic design for agent behavioral adaptation.
• Simulated scenarios with active evolution, active learning, or both.
• Observed and analyzed the Baldwin effect in agent behavior inheritance.

Main Results:

• Demonstrated the occurrence of the Baldwin effect, where learned adaptive policies become inherited.
• Showcased how initially acquired behavioral adaptations are integrated into the evolutionary process.
• Identified parallels between agent-broker adaptive tactics and animal foraging behaviors.

Conclusions:

• Learning and evolution can synergistically drive adaptation in artificial populations.
• The Baldwin effect provides a mechanism for rapid adaptation by integrating learned traits into the genetic framework.
• Agent-based models offer valuable insights into evolutionary dynamics and adaptive strategies.