Social and individual learning in the Minority Game

ArXiv ID: 2307.11846 “View on arXiv”

Authors: Unknown

Abstract

We study the roles of social and individual learning on outcomes of the Minority Game model of a financial market. Social learning occurs via agents adopting the strategies of their neighbours within a social network, while individual learning results in agents changing their strategies without input from other agents. In particular, we show how social learning can undermine efficiency of the market due to negative frequency dependent selection and loss of strategy diversity. The latter of which can lock the population into a maximally inefficient state. We show how individual learning can rescue a population engaged in social learning from such inefficiencies.

Keywords: Minority Game, social learning, individual learning, frequency dependent selection, strategy diversity, Financial Markets (Agent-based modeling)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced stochastic processes and evolutionary game theory with precise formulas for payoffs and imitation probabilities, placing it in high math complexity. However, it relies entirely on simulation results of a theoretical model with no real-world data, backtests, or implementation details, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal:<br>Roles of Social & Individual Learning<br>in Minority Game Outcomes"] --> B["Data/Inputs:<br>Social Network Structure<br>Strategy Sets<br>Market Parameters"]
    B --> C["Methodology:<br>Agent-based Modeling<br>Simulations of:<br>Social Learning vs. Individual Learning"]
    C --> D{"Computational Process:<br>Iterative Game Rounds"}
    D --> E["Outcome A:<br>Social Learning leads to<br>Loss of Diversity & Efficiency"]
    D --> F["Outcome B:<br>Individual Learning rescues<br>Population from Inefficiency"]
    E --> G["Key Findings:<br>1. Social learning undermines market efficiency<br>2. Negative frequency dependent selection<br>3. Individual learning prevents inefficiency traps"]
    F --> G