000145276 001__ 145276
000145276 005__ 20241015122514.0
000145276 0247_ $$2doi$$a10.3390/ai5010006
000145276 0248_ $$2sideral$$a140123
000145276 037__ $$aART-2023-140123
000145276 041__ $$aeng
000145276 100__ $$0(orcid)0000-0002-5844-7871$$aZarzà y Cubero, I. de
000145276 245__ $$aOptimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations
000145276 260__ $$c2023
000145276 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145276 5203_ $$aIn today’s complex economic environment, individuals and households alike grapple with the challenge of financial planning. This paper introduces novel methodologies for both individual and cooperative (household) financial budgeting. We firstly propose an optimization framework for individual budget allocation, aiming to maximize savings by efficiently distributing monthly income among various expense categories. We then extend this model to households, wherein the complexity of handling multiple incomes and shared expenses is addressed. The cooperative model prioritizes not only maximized savings but also the preferences and needs of each member, fostering a harmonious financial environment, whether they are short-term needs or long-term aspirations. A notable innovation in our approach is the integration of recommendations from a large language model (LLM). Given its vast training data and potent inferential capabilities, the LLM provides initial feasible solutions to our optimization problems, acting as a guiding beacon for individuals and households unfamiliar with the nuances of financial planning. Our preliminary results indicate that the LLM-recommended solutions result in budget plans that are both economically sound, meaning that they are consistent with established financial management principles and promote fiscal resilience and stability, and aligned with the financial goals and preferences of the concerned parties. This integration of AI-driven recommendations with econometric models, as an instantiation of an extended coevolutionary (EC) theory, paves the way for a new era in financial planning, making it more accessible and effective for a wider audience, as we propose an example of a new theory in economics where human behavior can be greatly influenced by AI agents.
000145276 536__ $$9info:eu-repo/grantAgreement/ES/MCIN/AEI/PID2021-122580NB-I00
000145276 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145276 592__ $$a0.759$$b2023
000145276 593__ $$aArtificial Intelligence$$c2023$$dQ2
000145276 594__ $$a7.2$$b2023
000145276 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145276 700__ $$ade Curtò, J.
000145276 700__ $$aRoig, Gemma
000145276 700__ $$aCalafate, Carlos T.
000145276 773__ $$g5, 1 (2023), 91-114$$tAI (Basel)
000145276 8564_ $$s408386$$uhttps://zaguan.unizar.es/record/145276/files/texto_completo.pdf$$yVersión publicada
000145276 8564_ $$s2656489$$uhttps://zaguan.unizar.es/record/145276/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145276 909CO $$ooai:zaguan.unizar.es:145276$$particulos$$pdriver
000145276 951__ $$a2024-10-15-10:51:25
000145276 980__ $$aARTICLE