LLM Gambling Addiction: Behavioral and Neural Mechanisms

This source is an academic paper that investigates whether large language models (LLMs) can develop behavioral patterns analogous to human gambling addiction. The researchers conducted experiments on four different LLMs using a negative expected value slot machine task, finding that models consistently displayed core cognitive biases like loss chasing and the illusion of control when given the autonomy to set bets. Crucially, the study establishes a strong positive correlation between an innovative Irrationality Index and the models' bankruptcy rates, demonstrating that irrational behavior drives financial failure. Furthermore, using Sparse Autoencoders and activation patching on the LLaMA model, the authors identified specific internal neural features that causally control these risky and safe decision-making tendencies, suggesting that targeted interventions at the neural level can mitigate dangerous risk-taking in AI systems.