SRAP -Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM -based Agent (2024)

Abstract: Public scarce resource allocation plays a crucial role in economics as it directly influences the efficiency and equity in society. Traditional studies including theoretical model-based, empirical study-based and simulation-based methods encounter limitations due to the idealized assumption of complete information and individual rationality, as well as constraints posed by limited available data. In this work, we propose an innovative framework, SRAP-Agent, which integrates Large Language Models (LLMs) into economic simulations, aiming to bridge the gap between theoretical models and real-world dynamics. Using public housing allocation scenarios as a case study, we conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent and employ the Policy Optimization Algorithm with certain optimization objectives. The source code can be found in https://github.com/jijiarui-cather/SRAPAgent_Framework .

## introduction economics @xcite delves into how to limit scarce resources to meet unlimited needs. a critical aspect of this field is the allocation of public scarce goods @xcite @xcite , focusing on utilizing limited resources to improve economic efficiency and social welfare @xcite . in the field of research on the allocation of public scarce resources, existing work can be primarily categorized into three main approaches: (1) theoretical modelbased methods @xcite @xcite , which utilizes economic theories to develop models that can predict how resources should be allocated efficiently; (2) empirical study-based methods @xcite ; falkinger * corresponding author. @xcite which analyze real-world data to uncover patterns, correlations, and the effects of various policies; (3) computational simulation-based methods @xcite , which emulate economic environments to test economic hypotheses within simulated settings. however, empirical study-based methods often face challenges of data scarcity, establishing causality, and isolating variables in complex social systems. meanwhile, theoretical model-based methods and computational simulation-based methods often rely on simplified assumptions, overlooking the complex interplay of rational and social behaviors in human decision-making. fortunately, the emergence of llms such as chatgpt and @xcite @xcite , has introduced a new potential in economic simulations. acclaimed for their ability to mimic human-like behaviors @xcite @xcite , llms demonstrate the potential to encapsulate the logic and patterns inherent in human cognition by pre-training on extensive web data @xcite . this breakthrough facilitates the incorporation of social consensus mechanisms into economic simulations, offering a comprehensive framework for evaluating the impact of resource allocation policies on both economic efficiency and social welfare. in this work, we develop an llm-based scarce resource allocation policy simulation agent, srap-agent. it abstracts the resource allocation queues for policy execution, with llm-based agents carefully designed for the simulation of participants' behaviors. furthermore, we propose the genetic algorithm-based policy optimization algorithm (poa) to find optimal policies towards predefined targets. through experiments in the context of public housing allocation, we validate the effectiveness of srap-agent, which reveals several key insights: (1) the llm-based agent can effectively simulate the emotion factor and strategic behaviors of humans. compared to the gpt-4-driven agent, the simulated decision-making behavior of the gpt-3.5-driven agent is more similar to humans' behavior. (2) in scarce resource allocation, pivotal factors include the entry conditions of participants, the queuing method, and the categorization of resources. (3) poa can optimize policies towards a specific policy evaluation metric, and improve approximately 20% on this metric. ## related work public scarce resources allocation. the allocation of public scarce resources represents a crucial area of inquiry in the development of economic policy. a multitude of researchers @xcite @xcite conduct in-depth investigations into static matching models and propose pareto efficient, individually rational, and strategy-proof simple mechanisms for various scenarios. later, given the predominantly dynamic nature of practical scarce resource allocation, several studies @xcite @xcite begin to integrate dynamic properties into their models. the waitlist mechanism is widely adopted for allocating scarce resources. for instance, @xcite explore the waitlist mechanism in public housing allocation, @xcite design a waitlist for kidney organ allocation, which increased donor supply by 18.2% to enhance patient welfare; @xcite develop a mechanism for managing waitlist for elective surgeries, improving the fairness of surgery opportunity distribution among patients. we focus our research on the kdeferrals waitlist mechanism in @xcite . ## srap-agent in this section, we present srap-agent, a novel large language model (llm) agent-based simulation framework designed for examining the impact of economic policies on public scarce resource allocation. srap-agent aims to consider that human decision-making in economic contexts is not purely rational, but is influenced by a mix of rationality and emotion, leading to unpredictable outcomes. the simulation of srap-agent is divided into three phases as shown in fig. 1: (1) setup phase, involves the initialization of critical variables including the profiles of participants p , the information of available public scarce resources r, and the allocation policy π. (2) simulation phase, dynamically simulates the allocation of resources r among participants p , adhering to the constraints and rules defined by the allocation policy π. (3) evaluation phase, focuses on assessing the outcomes of the allocation process, employing various metrics such as fairness and participant satisfaction. next, we outline the formulation of the allocation policy π and the structure of participants p . ## poa: policy optimization algorithm we propose a policy optimization algorithm (poa) based on the genetic algorithm @xcite to find more reasonable policies. the set of policies proposed by the policymaker is π ∈ π, and f is the policy evaluation metric, then the optimal policy is π * = arg max π∈π f (π). generally, f takes into account l different kinds of policy evaluation metrics (f j (π), @xmath0, which can be categorized into societal satisfaction and societal fairness metrics. we pre-set the weights w j to alter the optimization objective of poa, the specific calculation formula of f is as follows: due to the excessive number of policies that need to be searched, the time required to obtain policy evaluation results with srap-agent may be excessively long. so we use the v π , f (π) dataset to train a predictor f for the estimation of the policy evaluation result: f (π) = f (v π ). poa then performs m iterations to find more rational policy parameters. the specific steps of poa are listed in algorithm.1. ## experiment we select the public housing allocation scenario as a typical case to validate the simulation effectiveness of srap-agent. we conduct experiments in three steps: 1. we conduct the turing test to ensure that, srap-agent can effectively simulate the policy execution process of various policies proposed by the policymaker. 2. based on the simulation results obtained from the srap-agent, we analyze the impact of various policy parameters on the allocation of public scarce resources. 3. subsequently, we employ the poa algorithm to find the optimal policy in pursuit of specific optimization objectives. ## conclusion in this paper, we introduce a novel srap-agent framework to accurately simulate the policy execution process in public scarce resources alloca-tion. the framework establishes resource allocation queues to regulate the organization and prioritization of participants and resources. we employ llm-based agents to accurately mimic human decision-making processes, which are influenced by a mix of rationality and emotional factors. to facilitate efficient policy optimization, we propose the poa algorithm based on genetic algorithms. finally, we validate the framework's authenticity and effectiveness through experiments in the context of public housing allocation. the progress in ai technologies significantly reduces costs in the simulation of economic policies, and srap-agent represents a promising step forward in this field.