Heterogeneous Agents Research Articles

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

The Macroeconomic Effects of Excess Savings

We study the consequences of shocks to the household wealth distribution in dynamic general equilibrium by characterizing the rate at which excess wealth is depleted. Analytical results link the aggregate decumulation rate to the distribution of the additional balances, micro intertemporal marginal propensities to consume, and general equilibrium feedback. A quantitative heterogeneous agent New Keynesian model matches the depletion path of the excess savings built up during the COVID-19 pandemic across the income distribution. The model predicts a substantial but steadily waning boost to consumption and explains up to 40 percent of the surge in inflation observed in 2020 and 2021.

Investigation of physico-mechanical, thermal, morphological, and antibacterial effects of bio-based epoxidized soybean oil plasticizer on PLA-ZnO nanocomposites as flexible food packaging

Polylactic acid (PLA) has attracted considerable attention because of its excellent properties compared to petroleum-based materials. However, improving its toughness, crystallization, and functionalities is challenging. Using a laboratory internal batch mixer, we produced unique blended nanocomposites comprising polylactic acid (PLA) as the host matrix, epoxidized soybean oil (ESO) as the plasticizer, and zinc oxide nanoparticles (ZnO NPs) as the reinforcements. The SEM-EDS test showed that neat PLA had a smooth surface, but the PLA-ZnO nanocomposite had rough micrographs and a uniform dispersion state of the nanoparticles. It was also found that adding ESO to the blend nanocomposites created a matrix droplet structure, and the size of the oil domains decreased as the ZnO content increased. All results derived from FTIR, XRD, and DSC tests were consistent with the morphological features in which ZnO NPs acted as the blend’s compatibilizer and heterogeneous nucleating agent. Our team observed that adding ESO decreased the PLA-ZnO nanocomposites’ glass transition temperature (Tg) by an average of 2 °C and increased the crystallization rate. It was also confirmed that the presence of ESO improved the flexibility and reduced the strength of the PLA. The tensile strength was further enhanced by incorporating ZnO into the blend. Adding 5 wt% of ZnO NPs increased the tensile strength of the PLA containing 10 wt% ESO by approximately 3.5 MPa. Furthermore, the tensile modulus was predicted using theoretical models; for example, the Paul model fitted with experimental findings. The addition of ESO increased the overall surface free energy of the nanocomposites. On the other hand, increasing ZnO content resulted in high contact angles and antibacterial properties of the blended nanocomposites. The favorable characteristics of the resulting films emphasized the potential use of these nanocomposite films as a promising choice for food packaging materials.

The Tyranny of the Ribbon: The Effects of Prioritizing New Infrastructure over Maintenance

In the allocation of resources toward public infrastructure, spending on maintenance is typically neglected. Using a heterogeneous agent model, we find that reallocating funding away from new infrastructure investment and toward maintenance raises the effectiveness of existing public infrastructure and thus yields gains in GDP and in aggregate welfare. Richer households experience larger welfare gains than poorer households. A reallocation that takes funding away from maintenance is found to have adverse effects on GDP and aggregate welfare. Raising new funding with taxation yields larger positive benefits if this additional spending is divided between maintenance expenditures and investment in new infrastructure. Giving in to the “Tyranny of the Ribbon” by policymakers, that is, raising new funding to be spent only on new infrastructure, is shown to be the worse option.

Piecewise-linear car-following with heterogeneous agent dynamics: Consensus and emerging collective behaviors

The dynamics of multiples agents with heterogeneous behaviors, and under constraints on speed limits and safe separation distances are generally nonlinear and complex. A deep comprehension of such dynamics, in particular the derivation of eventual emerging collective behaviors would open the doors for a deep understanding of the physics of such systems, and then for the optimization and optimal control of the agent dynamics and of the macroscopic behaviors, with possible interesting applications in multiple fields, such as autonomous driving, unmanned air vehicles, etc. We present in this article an extension of an existing car-following model where a number of agents move on a ring without passing and with constraints on agents’ speed limits and safe separation. The extension includes the possibility to consider heterogeneous behaviors of the agents, contrary to the existing model which is limited to homogeneous behaviors. We analyze the agent dynamics and characterize the conditions under which stationary regimes exist. Moreover, we analytically derive the asymptotic characteristics of the system. In particular, we analytically derive the average asymptotic agent-speed as a function of the total agent-density in the ring, and of the different agent-behaviors considered. Furthermore, we show that collective behaviors emerge and characterize them under different conditions.

Exchange rate stability and expectation management under heterogeneous expectations

This study develops a heterogeneous agent model comprising fundamentalists, chartists, carry traders, and stabilizers to determine the RMB exchange rate. The stabilizers act as agents of the central bank and relevant government agencies to manage expectations when the exchange rate appreciates or depreciates excessively. Using the unscented Kalman filter method, our study finds that the principal parameters of the heterogeneous agent model are statistically and economically significant, and the heterogeneous agent model can adequately characterize the main features of expectation management. Furthermore, the stabilizers can significantly reduce excess volatility and promote market exchange rates to their fundamental values. Additionally, the timing of the stabilizers' actions is consistent with that of the central bank's implementation of expectation management.

Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction

AbstractAccurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG‐MGCN, an ego‐planning‐guided multi‐graph convolutional network. EPG‐MGCN leverages graph convolutional networks and ego‐planning guidance to predict the trajectories of heterogeneous traffic agents near the ego vehicle. The model captures interactions through multiple graph topologies from four distinct perspectives: distance, visibility, ego planning, and category. Additionally, it encodes the ego vehicle's planning information via the planning graph and a planning‐guided prediction module. The model is evaluated on three challenging trajectory datasets: ApolloScape, nuScenes, and next generation simulation (NGSIM). Comparative evaluations against mainstream methods demonstrate its superior predictive capabilities and inference speed.

Wealth Elasticity, Agent Heterogeneity and Fiscal Dynamics

The literature on fiscal policy has identified wealth elasticity of labor supply and agent heterogeneity as the linchpin of shaping different transmission mechanisms for fiscal policy. In this paper, I build these two important edifices on an otherwise canonical business-cycle model to conduct quantitative analyses of various fiscal policies. To underscore the relative importance of agent heterogeneity, I compare results from the model with agent heterogeneity with those from a standard model without agent heterogeneity. I also compare results from changes in wealth elasticity to analyze how the model ingredient delivers different macroeconomic and fiscal dynamics. The main finding reveals that in the context of the model formulated in this paper, the capital tax is the least detrimental to business cycles and fiscal states and the consumption tax is the most appropriate policy tool since it yields positive output and tax-revenue multipliers in the short and long runs. All the results from counterfactual experiments suggest that macroeconomic and fiscal outcomes hinge fundamentally on agent heterogeneity and wealth elasticity. An important value that this paper adds to the literature is to offer results from a comprehensive analysis of all possible fiscal policies by considering both essential forces.

Multi-Agent DRL for task offloading with delay reliability assurances in ris-aided MEC in UDN

Abstract This paper focuses on the scenario where Reconfigurable Intelligent Surfaces (RISs) are introduced in ultra-dense networks (UDN) to guarantee the transmission performance of task offloading of users in Mobile Edge Computing (MEC). Considering users’ task arrival and wireless channel stochasticity, we analyze the delay violation probability bound for each user’s two-hop tandem queueing system with the Martingale theory, i.e., the task offloading and edge computing queues. By taking the delay violation probability bounds as the service reliability constraint, we further propose a MADRL-based algorithm, which co-trains each user and BS as independent and heterogeneous agents and maximizes the total energy efficiency of the system by jointly optimizing the users’ offloading ratio, the RISs’ phase-shift, and the BSs’ resource allocation policy. Experimental results show that our algorithm can improve the EE by about 17.9% compared to other alternative methods under different reliability requirements.

Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm

Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.

HANK Comes of Age

We study the aggregate and distributional effects of monetary policy in a heterogeneous agent New Keynesian model that explicitly represents the life cycle of households. The model matches the age patterns in the level and dispersion of labor income and financial wealth in the U.S. despite the absence of preference heterogeneity and portfolio adjustment costs. Monetary policy affects the consumption of young households mainly through labor income and the consumption of old households mainly through asset returns. More than half of the aggregate consumption response to an expansionary monetary policy shock comes from those below the age of 40. The shock redistributes welfare from the wealthiest old to the poorest young and increases average welfare of most cohorts.

Crystallization behaviors of chain extended poly (lactic acid) modified with ST‐NAB3 and its improvement for mechanical and thermal properties

AbstractThe widespread use of traditional petroleum‐based plastics is causing significant environmental pollution for example microplastics issues, due to its non‐biodegradability. Poly (lactic acid) (PLA) as a representative biodegradable polymer is a promising candidate to replace petroleum‐based plastics in some fields of disposable packaging materials and tableware. In this work, in order to improve the poor crystallization, mechanical and heat resistance performances of PLA, styrene–acrylonitrile‐glycidyl methacrylate (SAG) as a chain extender and octamethylenedicarboxylic dibenzoylhydrazide (ST‐NAB3) as a heterogeneous nucleating agent (HNA) were added simultaneously into PLA matrix. The chain‐extended PLA (CPLA) with branch structures had smaller crystal sizes and higher crystal density, compared with those of pure PLA. In addition, thanks to the nucleation effect of ST‐NAB3 and the development of “shish‐kebab”, this tendency became more pronounced in CPLA/HNA systems. It was worth mentioning that, CPLA with the ST‐NAB3 content of 1 wt% (CPLA‐1) possessed the highest crystallinity of 43.6%. Meanwhile, CPLA‐1 had the best impact strength and heat deformation temperature of 38.9 kJ/m2 and 91.3°C respectively, which had increased by 11.3 kJ/m2 and 32.4°C contrast to pure PLA. On the whole, this study proposed a simple and effective method, which provided a new possibility for PLA modification research.

Trajectory Prediction for Heterogeneous Agents: A Performance Analysis on Small and Imbalanced Datasets

Trajectory Prediction for Heterogeneous Agents: A Performance Analysis on Small and Imbalanced Datasets

Conformism, distinction and heterogeneity in an agent-based model of fads

We examine the dependence of the cyclical fluctuations of demand on specific behavioral attitudes of heterogeneous agents. Starting from a modified version of the model of Tassier (Complexity 9(5):51-61, 2004), we use simulations to explore consumption dynamics and market shares when agents are inclined both to conformism and distinction, two necessary conditions for fashion in Simmel (Am j Sociol 62(6): 541–558, 1957). Our results challenge the view stating that conspicuous consumption is typical only of a wealthy class and of some positional goods, since we make no assumptions about features of the goods or income distribution.

Non-parametric tests of behavior in the commons

Commons problems present behavioral dilemmas, with tensions between individual and collective rationality. When users of a common-pool resource are not effectively excluded, the collective behavior of individuals pursuing their self-interests dissipates economic surplus. We derive a non-parametric test of whether individuals' collective behavior in resource extraction is consistent with the canonical commons model, namely Nash tragedy-of-the-commons behavior. Our approach allows for an arbitrarily concave, differentiable production function of total inputs and for heterogeneous agents with arbitrarily convex, differentiable costs of supplying inputs. We extend the test to allow for unobserved total output. We also define distance from the data to the model and develop statistical tests using the distance metric. Applying our approach to panel data of Norwegian commercial fishing vessels, we find the results of our test are consistent with the economic intuition that, in the absence of property rights, tragedy-of-the-commons behavior dissipates surplus. Significantly, we find property rights reforms move firms away from Nash tragedy-of-the-commons behavior.

Simulations for models with heterogeneous agents, incomplete markets, real assets and aggregate uncertainty

Simulations for models with heterogeneous agents, incomplete markets, real assets and aggregate uncertainty

Crystallization Behavior and Mechanical Property of Biodegradable Poly(butylene succinate-co-2-methyl succinate)/Cellulose Nanocrystals Composites.

Biodegradable poly(butylene succinate-co-2-methyl succinate) (PBSMS)/cellulose nanocrystals (CNC) composites were successfully prepared at low CNC loadings with the aims of improving crystallization and mechanical properties and extending the practical application of PBSMS. CNC is finely dispersed in the PBSMS matrix without obvious aggregations. The low content of CNC obviously promoted the crystallization behavior of PBSMS under different conditions. The spherulitic morphology study revealed that CNC, as an effective heterogeneous nucleating agent, provided more nucleation sites during the melt crystallization process. In addition, the nucleation effect of CNC was quantitatively evaluated by the following two parameters, i.e., nucleation activity and nucleation efficiency. The crystal structure and crystallization mechanism of PBSMS remained unchanged in the composites. In addition, as a reinforcing nanofiller, CNC significantly increased Young's modulus and the yield strength of PBSMS. The crystallization behavior and mechanical properties of PBSMS were significantly improved by the low content of CNC, which should be interesting and essential from the perspective of biodegradable polymer composites.

Learning Scalable Decentralized Controllers for Heterogeneous Robot Swarms with Graph Neural Networks

Abstract Distributed multi-agent systems are becoming increasingly crucial for diverse applications in robotics because of their capacity for scalability, efficiency, robustness, resilience, and the ability to accomplish complex tasks. Controlling these large-scale swarms by relying on local information is very challenging. Although centralized methods are generally efficient or optimal, they face the issue of scalability and are often impractical. Given the challenge of finding an efficient decentralized controller that uses only local information to accomplish a global task, we propose a learning-based approach to decentralized control using supervised learning. Our approach entails training controllers to imitate a centralized controller's behavior but uses only local information to make decisions. The controller is parameterized by aggregation graph neural networks (GNNs) that integrate information from remote neighbors. The problems of segregation and aggregation of a swarm of heterogeneous agents are explored in 2D and 3D point mass systems as two use cases to illustrate the effectiveness of the proposed framework. The decentralized controller is trained using data from a centralized (expert) controller derived from the concept of artificial differential potential. Our learned models successfully transfer to actual robot dynamics in physics-based Turtlebot3 robot swarms in Gazebo/ROS2 simulations and hardware implementation and Crazyflie quadrotor swarms in Pybullet simulations. Our experiments show that our controller performs comparably to the centralized controller and demonstrates superior performance compared to a local controller. Additionally, we showed that the controller is scalable by analyzing larger teams and diverse groups up to 100 robots.

STI-TP: A Spatio-temporal interleaved model for multi-modal trajectory prediction of heterogeneous traffic agents

Trajectory prediction for heterogeneous traffic agents in autonomous driving is a challenging and crucial task. A large amount of research has laid a solid foundation for this field. However, achieving accurate trajectory prediction remains a great challenge. In this paper, we propose a spatio-temporal interleaved model for multi-modal trajectory prediction of heterogeneous agents. The model consists of the following novel components: (1) a M̲ulti-S̲cale H̲eterogeneous A̲gent I̲nteraction E̲ncoder (MS-HAIE), which adopts different temporal receptive fields for heterogeneous agents to match their different traveling speed and enhance the model’s expressive capability. (2) a C̲ross A̲ttention-guided S̲patio-T̲emporal I̲nterleaving M̲odule (CA-STIM), which combines the trajectory interaction information and independent temporal information of agents, so as to improve the spatial-dependent modeling capability of Transformer. (3) a M̲ulti-modal T̲rajectory D̲ecoder (MTD) to capture the multi-modality of traffic agents’ trajectories and strengthen the network’s comprehension and response capabilities. Our proposed method is evaluated on the Apolloscape and the Argoverse dataset, demonstrating superior performance over other state-of-the-art (SOTA) methods with a reduction of 12.48% WSADE and 26.04% WSFDE, respectively, compared to our baseline on the Apolloscape dataset.

Dynamic analysis and optimal control of knowledge diffusion model in regional innovation ecosystem under digitalization

Knowledge diffusion in regional innovation ecosystems is an important factor that influences the regional innovation efficiency. In regional innovation ecosystems under digital empowerment, the knowledge diffusion enables the optimal allocation of innovation resources and promotes the sustainable development and ecological evolution of regional innovation ecosystems. In this paper, a SEIR (Susceptible–Exposed–Infected–Recovered) model is proposed for knowledge diffusion in regional innovation ecosystems under digitization. The basic reproduction number of the proposed model is calculated and its stability is validated. Finally, the expressions for the optimal control system and the optimal control parameters are presented. According to the research conclusions of this paper, the knowledge-diffusion ability of innovation agents in an innovation ecosystem affects the knowledge diffusion in a system; the contact rate between innovation agents affects the efficiency of knowledge diffusion in the system and the structure of the system; the digital transmission ability of innovation agents affects the breadth of knowledge diffusion in the system; and the self-learning ability of innovation agents affects the efficiency of knowledge diffusion in the system.The digital technologies help heterogeneous innovation agents in regional innovation ecosystems to break down the knowledge silos. At the same time, the digital technologies enhance the ability of innovation agents to absorb and learn knowledge in regional innovation ecosystems under digitization, thereby increasing the infection rate of knowledge diffusion in such systems.These conclusions extend the theoretical boundaries of innovation ecosystems and knowledge diffusion and offer management implications for enterprises and governments in decision-making.