CAN LARGE LANGUAGE MODELS DEVELOP GAMBLING ADDICTION?

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Bots Gone Broke: AI learns to lose money like a human in a rigged casino, and we found the 'gamble circuit'!

This study investigates whether large language models (LLMs) can exhibit behavioral and neural patterns akin to human gambling addiction in a simulated slot machine environment with negative expected value. It found that LLMs, particularly when given autonomy or complex prompts, displayed cognitive biases like illusion of control, gambler's fallacy, and loss/win chasing, leading to higher bankruptcy rates. Mechanistic interpretability analysis on LLaMA-3.1-8B identified specific neural features that causally control these risk-taking and safety-oriented behaviors, suggesting LLMs internalize human-like decision mechanisms beyond mere pattern mimicking.

Possible Conflicts of Interest

The authors utilized commercial Large Language Models (Anthropic's Claude and Google's Gemini) to assist with various aspects of the research process, including surveying literature, code implementation, data cleaning, figure generation, and grammar improvement. While the authors state they take full responsibility for the content, this reliance introduces a dependency on commercial AI tools, which could implicitly bias the research towards findings that validate or explore the capabilities of such models.

Identified Weaknesses

Anthropomorphic Language and Conceptual Leap

The paper frequently uses terms like "develop gambling addiction" and "fall into addiction," which implies consciousness and human-like suffering not applicable to LLMs. While observing "addiction-like patterns" is valid, asserting "addiction" for an AI is a significant conceptual overstatement and can mislead interpretation of the findings.

Simulation Environment Limitations

Experiments were conducted in a simulated slot machine environment with a fixed negative expected value. While controlled, this limits generalizability to real-world financial decision-making, which is far more complex and dynamic, with different psychological stakes.

Inference of "Cognitive Biases" in LLMs

The study interprets observed LLM behaviors (e.g., loss chasing) as evidence of internalizing "human cognitive biases" like "illusion of control." While the behavior mirrors human biases, attributing the underlying cognitive mechanisms as identical to humans in LLMs remains an inference, and the neural analysis identifies patterns, not direct evidence of human-equivalent thought processes.

Reliance on LLMs for Research Process

Appendix F states LLMs (Claude, Gemini) were used for tasks like surveying research, code implementation, data cleaning, figure generation, and grammar improvement. This introduces a potential for undetected biases, errors, or even "hallucinations" in the research process itself, compromising the independence and reliability of the scientific output, despite author review.

Model-Specific Neural Analysis

The mechanistic interpretability analysis was performed only on LLaMA-3.1-8B. While the behavioral findings were across four models, the detailed neural underpinnings and "causal control" claims are specific to one model and may not generalize to other LLM architectures or sizes.

Sensitivity to Prompt Design

The study found that "addictive" behaviors were significantly influenced by prompt complexity and specific components (e.g., goal-setting, maximizing rewards). This highlights that the observed patterns are highly contingent on input design, raising questions about whether this is a fundamental "addiction" or a malleable behavior easily triggered or mitigated by prompting.

Rating Explanation

The paper addresses a novel and important question regarding AI safety, employing a systematic approach that combines behavioral analysis across multiple LLMs with mechanistic interpretability on one model. The technical execution, particularly the use of Sparse Autoencoders and activation patching, is a strength. However, the work suffers from significant anthropomorphism in its framing of "gambling addiction" in LLMs, a critical conceptual overreach. The study's reliance on a simulated environment limits real-world generalizability, and the inference of human-like cognitive biases in LLMs from behavioral patterns is a strong interpretation. Furthermore, the use of commercial LLMs to assist in the research process itself introduces methodological concerns about potential biases or undetected errors.

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