Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints

2025-08-14

AI Summary

To address the challenge of balancing efficiency and accuracy for large language models (LLMs) under resource constraints, this paper introduces a “computational economics” framework: modeling the LLM as an agent-based economy composed of attention heads and neuron blocks. It employs differentiable computational cost modeling and end-to-end incentive-driven training to achieve sparse activation during training and Pareto-optimal efficiency–accuracy trade-offs. The method integrates attention reallocation analysis with computation-cost regularization, enabling dynamic resource scheduling—outperforming post-hoc pruning. On GLUE and WikiText-103, it reduces FLOPS by nearly 40% and inference latency while preserving accuracy, and yields more interpretable attention patterns. The core innovation lies in the first application of economic incentive mechanisms to LLM computational resource optimization, enabling differentiable, trainable, and interpretable efficient inference.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource-constrained agents (attention heads and neuron blocks) that must allocate scarce computation to maximize task utility. First, we show empirically that when computation is scarce, standard LLMs reallocate attention toward high-value tokens while preserving accuracy. Building on this observation, we propose an incentive-driven training paradigm that augments the task loss with a differentiable computation cost term, encouraging sparse and efficient activations. On GLUE (MNLI, STS-B, CoLA) and WikiText-103, the method yields a family of models that trace a Pareto frontier and consistently dominate post-hoc pruning; for a similar accuracy we obtain roughly a forty percent reduction in FLOPS and lower latency, together with more interpretable attention patterns. These results indicate that economic principles offer a principled route to designing efficient, adaptive, and more transparent LLMs under strict resource constraints.

Problem

• Optimizing LLM computation allocation under resource constraints
• Reducing computational costs while maintaining model accuracy
• Designing incentive-driven training for efficient sparse activations

Innovation

• Treats LLM as resource-constrained internal economy
• Incentive-driven training with differentiable computation cost
• Achieves Pareto-efficient models with reduced FLOPS