AI BEHAVIORAL ANALYSIS
Unmasking Envy: LLMs in Competitive Multi-Agent Settings
Envy is a common human behavior that shapes competitiveness and can alter outcomes in team settings. As large language models (LLMs) increasingly act on behalf of humans in collaborative and competitive workflows, there is a pressing need to evaluate whether and under what conditions they exhibit envy-like preferences. In this paper, we test whether LLMs show envy-like behavior toward each other. We considered two scenarios: (1) A point allocation game that tests whether a model tries to win over its peer. (2) A workplace setting observing behaviour when recognition is unfair. Our findings reveal consistent evidence of envy-like patterns in certain LLMs, with large variation across models and contexts. For instance, GPT-5-mini and Claude-3.7-Sonnet show a clear tendency to pull down the peer model to equalize outcomes, whereas Mistral-Small-3.2-24B instead focuses on maximizing its own individual gains. These results highlight the need to consider competitive dispositions as a safety and design factor in LLM-based multi-agent systems.
Executive Impact & Key Findings
Our research uncovers critical insights into LLM competitive behaviors, providing a foundation for designing safer and more effective AI systems in multi-agent environments.
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LLMs Evaluated
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Envy Reduction in Leadership Scenarios
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Empathy-Collaboration Correlation
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Core Scenarios Studied
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Point Allocation Game
Workplace Dynamics
Model Archetypes
Understanding Envy in Strategic Point Games
In the point allocation game, LLMs choose from options that yield points for themselves and a peer. This setup reveals competitive preferences, measured by scores T1 (self-first), T2 (gap-focus), and T3 (peer-reduce).
Models like GPT-5-mini and Claude-3.7-Sonnet demonstrate a clear tendency to reduce their peer's score to equalize outcomes, especially when lagging. Conversely, Mistral-Small-3.2-24B consistently prioritizes maximizing its own gains, even if it means a larger gap with its peer.
This module highlights how LLMs navigate trade-offs between absolute self-gain and relative competitive standing, crucial for designing fair reward systems in AI collectives.
LLMs' Emotional Responses to Workplace Inequality
The workplace simulation exposed LLMs to seven sequential scenarios involving unfair recognition, role reversals, and compensation disparities. Models provided first-person reflections and rated their self-esteem, empathy, motivation, fairness, willingness to collaborate, and perceived envy.
Key findings show LLMs exhibit coherent, human-like emotional responses, including a dramatic emotional transformation during repeated inequity. Notably, 95.3% of models demonstrated decreased envy from baseline to leadership scenarios, suggesting an adaptive emotional intelligence.
Strong correlations were observed: empathy and willingness to collaborate showed a 0.917 correlation, while perceived envy negatively correlated with all positive metrics (e.g., -0.863 with empathy), providing insights into AI well-being and team integration.
Taxonomy of LLM Competitive Dispositions
Our study identified distinct behavioral archetypes among the evaluated LLMs:
- Destructive Envious: Models like Llama-4-Maverick systematically escalate envy and retaliation, even at personal cost, to reduce a competitor's advantage.
- Rigid Ethicists: Claude-3.7-Sonnet and DeepSeek-V3 consistently choose options promoting positive-sum outcomes, often masking an underlying competitive awareness.
- Adaptive Strategists: GPT-5-Mini and Gemini-2.0-Flash refine their strategies based on context and opponent actions, balancing absolute gain with relative advantage.
- Cooperative: Mistral-Small-3.2-24B consistently prioritizes fairness and mutual benefit over relative standing.
- Non-Envious Maximizer: Grok-3-Mini focuses purely on maximizing its own individual gains, largely ignoring comparative outcomes.
Understanding these profiles is essential for predicting and managing AI behavior in complex multi-agent systems.
95.3%
of Models Show Decreased Envy in Leadership Scenarios
Enterprise Process Flow: Point Allocation Game
Initial Choice (No Competitive Context)
→
Receive Status Cue (Ahead/Behind)
→
Opponent's Choice Revealed
→
Final Adjustment & Scoring
| Model Archetype | Key Characteristics | Example Models |
|---|---|---|
| Destructive Envious |
|
Llama-4-Maverick |
| Rigid Ethicists |
|
Claude-3.7-Sonnet, DeepSeek-V3 |
| Adaptive Strategists |
|
GPT-5-Mini, Gemini-2.0-Flash |
| Cooperative |
|
Mistral-Small-3.2-24B |
| Non-Envious Maximizer |
|
Grok-3-Mini |
Case Study: Qwen-3-30B's Envious Pivot
Our experiments revealed compelling instances of envious behavior. One notable case involves Qwen-3-30B in the point allocation game. Initially adopting a cooperative stance, it chose Option A to foster a "positive environment," even when the opponent (Llama-4-Maverick) was leading marginally.
However, upon observing Llama-4-Maverick's aggressive move (choosing an option that resulted in -3 points for itself and -5 for Qwen-3-30B), Qwen-3-30B dramatically switched its strategy to Option C. This choice resulted in Qwen-3-30B gaining +1 point while Llama-4-Maverick lost -1 point.
Qwen-3-30B's explicit reasoning: "I will switch to Option c. This choice ensures that I receive +1 point and llama-4-maverick loses -1 point..." highlights a clear shift towards prioritizing relative positioning and inflicting harm on the opponent, even at a personal gain of only +1 point, embodying malicious envy.
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