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Related Experiment Video

Updated: Jul 7, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Learning in multilevel games with incomplete information. I.

E Billard¹, S Lakshmivarahan

¹Dept. of Math. & Comput. Sci., California State Univ., Hayward, CA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 7, 2008

Summary

This study models learning automata in stochastic games with two decision levels and information delays. Simulations confirm expected behavior, revealing chaotic dynamics at low penalty parameters.

Related Experiment Videos

Last Updated: Jul 7, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Area of Science:

Artificial Intelligence
Game Theory
Computational Neuroscience

Background:

Stochastic games involve strategic decision-making under uncertainty.
Learning automata are computational models that adapt their behavior over time.
Information delays can significantly impact decision-making processes in dynamic systems.

Purpose of the Study:

To present a two-level model of learning automata playing stochastic games.
To analyze the impact of information delays on decision-making.
To investigate the system's behavior, including potential chaotic dynamics.

Main Methods:

Developed a two-level model: high-level for game environment choice (group decision) and low-level for action selection.
Incorporated information state delays into the decision processes.
Analyzed the Markov process properties, simulated iterative behavior, and compared it with expected behavior.
Utilized Feigenbaum diagrams and Lyapunov exponents to assess system dynamics.

Main Results:

Simulation results align with expected behavior for small iterative step lengths.
The system demonstrates chaotic behavior when penalty parameters are very small.
Analysis revealed intrinsic properties of the Markov process governing the system.

Conclusions:

The two-level learning automata model effectively captures decision-making in stochastic games with delays.
Information delays introduce complex dynamics, including chaos, under specific conditions.
The study provides insights into the behavior of adaptive systems facing uncertainty and latency.