优化财务规划:将个人和合作预算编制模型与 LLM 建议相结合

AI Pub Date : 2023-12-25 DOI:10.3390/ai5010006
I. de Zarzà, J. de Curtò, Gemma Roig, C. T. Calafate
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引用次数: 0

摘要

在当今复杂的经济环境中,个人和家庭都在努力应对财务规划的挑战。本文介绍了个人和合作(家庭)财务预算的新方法。我们首先提出了一个个人预算分配的优化框架,旨在通过在不同支出类别之间有效分配每月收入来最大限度地节省开支。然后,我们将这一模型扩展到家庭,从而解决了处理多种收入和共同支出的复杂性问题。合作模式不仅优先考虑最大化储蓄,还考虑每个成员的偏好和需求,无论是短期需求还是长期愿望,都能营造和谐的财务环境。我们的方法中一个值得注意的创新是整合了大型语言模型(LLM)的建议。鉴于其庞大的训练数据和强大的推理能力,LLM 为我们的优化问题提供了初步可行的解决方案,为不熟悉财务规划细微差别的个人和家庭起到了指路明灯的作用。我们的初步结果表明,LLM 推荐的解决方案所产生的预算计划既经济合理,即符合既定的财务管理原则,促进了财政的弹性和稳定性,又符合相关各方的财务目标和偏好。这种将人工智能驱动的建议与计量经济学模型相结合的做法,作为扩展的共同进化(EC)理论的一个实例,为财务规划的新时代铺平了道路,让更多的人更容易理解和使用财务规划,因为我们提出了一个经济学新理论的例子,在这个例子中,人类行为可以受到人工智能代理的极大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations
In today’s complex economic environment, individuals and households alike grapple with the challenge of financial planning. This paper introduces novel methodologies for both individual and cooperative (household) financial budgeting. We firstly propose an optimization framework for individual budget allocation, aiming to maximize savings by efficiently distributing monthly income among various expense categories. We then extend this model to households, wherein the complexity of handling multiple incomes and shared expenses is addressed. The cooperative model prioritizes not only maximized savings but also the preferences and needs of each member, fostering a harmonious financial environment, whether they are short-term needs or long-term aspirations. A notable innovation in our approach is the integration of recommendations from a large language model (LLM). Given its vast training data and potent inferential capabilities, the LLM provides initial feasible solutions to our optimization problems, acting as a guiding beacon for individuals and households unfamiliar with the nuances of financial planning. Our preliminary results indicate that the LLM-recommended solutions result in budget plans that are both economically sound, meaning that they are consistent with established financial management principles and promote fiscal resilience and stability, and aligned with the financial goals and preferences of the concerned parties. This integration of AI-driven recommendations with econometric models, as an instantiation of an extended coevolutionary (EC) theory, paves the way for a new era in financial planning, making it more accessible and effective for a wider audience, as we propose an example of a new theory in economics where human behavior can be greatly influenced by AI agents.
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