# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 6 Results  
As described in the experimental setup, four simulation scenarios were implemented to evaluate the performance of the proposed shared storage system. Scenarios 1 and 2 varied warehouse capacities (one vs. five units), while Scenarios 3 and 4 introduced a capacity pooling mechanism, allowing agents to forward unaccepted offers. This structure enabled a systematic assessment of how warehouse size and pooling affect agent performance and resource utilization. In Scenario 1, each agent operated a single storage slot without the pooling mechanism. The LLM-driven AI agent outperformed random counterparts, winning three out of five simulations and earning an average of 249 tokens, compared to 232 for the random agents. As shown in Figure 7, this success can be attributed to more consistent and complete warehouse utilization. This result indicates that the AI agent strategically manages limited resources to maximize efficiency. In Scenario 2, warehouse capacity was increased to five slots. Here, the AI agent won only two of five games--equal to Agent 4. The AI reserved later slots for potentially higher-value offers, but this strategy resulted in underutilizing slots 3-5, as shown in Figure 8. The mismatch between offer frequency and the AI's expectations illustrates the limitations of the stateless prompt strategy in more complex configurations. Scenario 3 introduced the capacity pool while retaining the single-slot configuration. The AI agent won four out of five games and led in token earnings. With the ability to forward less favorable offers, the AI reduced reliance on chance, achieving superior utilization (Figure 9). This performance reinforces and provides strong evidence that capacity pooling significantly enhances decision quality and consistency. Figure 8: Average warehouse utilization simulation Scenario 2. Figure 7: Average warehouse utilization simulation Scenario 1. Scenario 4, the most complex and realistic configuration, combined five storage slots with the pooling mechanism. The AI again won four out of five games and earned the most tokens (Figure 10). Although storage utilization was similar across agents (Figure 11), the AI achieved greater token efficiency, selectively forwarding, accepting, and rejecting offers. As shown in Figure 12, the AI submitted the highest number of offers to the pool and achieved the most successful submissions. Although it accepted fewer offers from the pool than other agents (Figure 13), it optimized the results through a higher token-per-offer ratio. Figure 11: Average warehouse utilization. Figure 9: Average warehouse utilization simulation Scenario 3. These findings confirm that AI effectively adapts to complex environments by making context-sensitive, profit-optimized decisions. The decision-making process is illustrated in Figure 14. The AI consistently preferred submitting to the pool over rejecting offers. Even in its worst round, it achieved the highest average tokens per accepted offer (3.9) compared to other agents (3.7 and 3.4), showcasing robustness under uncertain conditions. Despite having a larger storage capacity, the AI opted to accept fewer offers from the pool, likely recognizing that keeping the storage less occupied would maximize its relative token yield. Therefore, AI managed to achieve an average of over four tokens per accepted offer, which is the highest score across all scenarios. This exceptional performance is likely due to the complexity of the scenario, which allowed for the largest number of possible decision combinations, potentially leading to optimal outcomes. The [PERSON] employed a combination of efficient decisions, including accepting a higher number of offers, which is reflected in its optimal storage utilization, where the [PERSON] fully utilized all five storage slots better than the other players. Figure 14 shows the players' decision-making process in relation to rejected offers. Once again, the AI recognized that the expected profit from submitting offers to the pool was consistently higher than from rejecting them.