Heterogeneous Agents Research Articles

Full‐information estimation of heterogeneous agent models using macro and micro data

We develop a generally applicable full‐information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross‐sections of micro data. To handle unobserved aggregate state variables that affect cross‐sectional distributions, we compute a numerically unbiased estimate of the model‐implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustrations in models with heterogeneous households or firms demonstrate that the proposed full‐information method substantially sharpens inference relative to using only macro data, and for some parameters micro data is essential for identification.

A Perturbational Approach for Approximating Heterogeneous Agent Models

A Perturbational Approach for Approximating Heterogeneous Agent Models

Welfare and Spending Effects of Consumption Stimulus Policies

Using a heterogeneous agent model calibrated to match measured spending dynamics over four years following an income shock (Fagereng, Holm, and Natvik (2021)), we assess the effectiveness of three fiscal stimulus policies employed during recent recessions. Unemployment insurance (UI) extensions are the clear “bang for the buck” winner, especially when effectiveness is measured in utility terms. Stimulus checks are second best and have the advantage (over UI) of being scalable to any desired size. A temporary (two-year) cut in the rate of wage taxation is considerably less effective than the other policies and has negligible effects in the version of our model without a multiplier.

Infinite Debt Rollover in Stochastic Economies

This paper shows that there is more scope for a borrower to engage in a sustainable infinite debt rollover (a “Ponzi scheme”) when interest/growth rates are stochastic. In this context, I prove that the relevant “r vs. g” comparison uses the yield r long to an infinite‐maturity zero‐coupon bond. I show that r long is lower than the risk‐neutral expectation of the short‐term yield when it is variable, and that r long is close to the minimal realization of the short‐term yield when it is highly persistent. The paper applies these results to illustrative heterogeneous agent dynamic stochastic general equilibrium models to obtain similarly weakened sufficient conditions for the existence of public debt bubbles.

How to Build and Solve Continuous-time Heterogeneous Agents Models in Asset Pricing? The Martingale Approach and The Finite Difference Method

How to Build and Solve Continuous-time Heterogeneous Agents Models in Asset Pricing? The Martingale Approach and The Finite Difference Method

Heterogeneous Agents Models in Asset Pricing: The Dynamic Programming Approach and The Finite Difference Method

Heterogeneous Agents Models in Asset Pricing: The Dynamic Programming Approach and The Finite Difference Method

Graphon Games: A Statistical Framework for Network Games and Interventions

In this paper, we present a unifying framework for analyzing equilibria and designing interventions for large network games sampled from a stochastic network formation process represented by a graphon. To this end, we introduce a new class of infinite population games, termed graphon games, in which a continuum of heterogeneous agents interact according to a graphon, and we show that equilibria of graphon games can be used to approximate equilibria of large network games sampled from the graphon. This suggests a new approach for design of interventions and parameter inference based on the limiting infinite population graphon game. We show that, under some regularity assumptions, such approach enables the design of asymptotically optimal interventions via the solution of an optimization problem with much lower dimension than the one based on the entire network structure. We illustrate our framework on a synthetic data set and show that the graphon intervention can be computed efficiently and based solely on aggregated relational data.

Learning Heterogeneous Agent Cooperation via Multiagent League Training

Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practical advantages. However, they also come with challenges compared with homogeneous systems for multiagent reinforcement learning, such as the non-stationary problem and the policy version iteration issue. This work proposes a general-purpose reinforcement learning algorithm named Heterogeneous League Training (HLT) to address heterogeneous multiagent problems. HLT keeps track of a pool of policies that agents have explored during training, gathering a league of heterogeneous policies to facilitate future policy optimization. Moreover, a hyper-network is introduced to increase the diversity of agent behaviors when collaborating with teammates having different levels of cooperation skills. We use heterogeneous benchmark tasks to demonstrate that (1) HLT promotes the success rate in cooperative heterogeneous tasks; (2) HLT is an effective approach to solving the policy version iteration problem; (3) HLT provides a practical way to assess the difficulty of learning each role in a heterogeneous team.

Online Appendix: Average Inflation Targeting in a Behavioral Heterogeneous Agent New Keynesian Model

Online Appendix: Average Inflation Targeting in a Behavioral Heterogeneous Agent New Keynesian Model

An exit contract optimization problem

We study an exit contract design problem, where one provides a universal exit contract to multiple heterogeneous agents, with which each agent chooses an optimal (exit) stopping time. The problem consists in optimizing the universal exit contract w.r.t. some criterion depending on the contract as well as the agents’ exit times. Under a technical monotonicity condition, and by using Bank-El Karoui’s representation of stochastic processes, we are able to transform the initial contract optimization problem into an optimal control problem. The latter is also equivalent to an optimal multiple stopping problem and the existence of the optimal contract is proved. We next show that the problem in the continuous-time setting can be approximated by a sequence of discrete-time ones, which would induce a natural numerical approximation method. We finally discuss the optimization problem over the class of all Markovian and/or continuous exit contracts.

Price Level and Inflation Dynamics in Heterogeneous Agent Economies

Price Level and Inflation Dynamics in Heterogeneous Agent Economies

Solving Heterogeneous Agent Models with the Master Equation

Solving Heterogeneous Agent Models with the Master Equation

Evaluating synchromodal transport with agent-based simulation

Synchromodal transport implies using dynamically several modes of transport. Two relevant aspects of synchromodality are collaboration between actors and flexibility, aiming to make as many decisions as possible in real-time, imposing several changes on the logistic system's performance. This study aims to model the interactions between actors and their reactions to disruptions within the transport network. Using an agent-based modelling approach, the system is modelled as a collection of heterogeneous agents with complex interactions. Different collaboration scenarios and reaction strategies are modeled. The result shows improvement in the average cost, as the collaboration between logistics service providers and the flexibility of their response to disruptions increases. This model provides a testbed for stakeholders to assess different scenarios and ideas inexpensively and risk-free.

Analysis of Household Income Dynamics in the Russia Based on the RLMS Database

The goal of the study is to estimate the parameters of the stochastic wage process using data from the Russian Longitudinal Monitoring Survey of Higher School of Economics (RLMS-HSE). The main method of analysis is econometric estimation, which includes two steps. In the first step, the authors estimated a Mincer-type regression. In the second step, they estimated the parameters of the stochastic wage process using the generalized method of moments. As a result, the autoregression coefficient turned out to be lower, and the variance of shocks was higher than in similar foreign studies. The results of the research allow to conclude that labor incomes in Russia are less stable over time and are marked by great uncertainty. The practical value of the work lies in the possibility of using the obtained estimates when calibrating general equilibrium models with heterogeneous agents, which is demonstrated in the framework of estimation of macroeconomic effects from hypothetical tax maneuvers based on the canonical model with heterogeneous agents.

Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question

Peer prediction mechanisms incentivize self-interested agents to truthfully report their signals even in the absence of verification by comparing agents’ reports with their peers. We propose two new mechanisms, Source and Target Differential Peer Prediction, and prove very strong guarantees for a very general setting. Our Differential Peer Prediction mechanisms are strongly truthful : Truth-telling is a strict Bayesian Nash equilibrium. Also, truth-telling pays strictly higher than any other equilibria, excluding permutation equilibria, which pays the same amount as truth-telling. The guarantees hold for asymmetric priors among agents, which the mechanisms need not know ( detail-free ) in the single question setting . Moreover, they only require three agents , each of which submits a single item report : two report their signals (answers), and the other reports her forecast (prediction of one of the other agent’s reports). Our proof technique is straightforward, conceptually motivated, and turns on the logarithmic scoring rule’s special properties. Moreover, we can recast the Bayesian Truth Serum mechanism [ 20 ] into our framework. We can also extend our results to the setting of continuous signals with a slightly weaker guarantee on the optimality of the truthful equilibrium.

Monetary policy and inequality under household heterogeneity and incomplete markets

Motivated by the empirical evidence of the effects of unanticipated nominal interest rate increases on the evolution of household inequality in Mexico, which highlight the importance of insurance mechanisms to deal with idiosyncratic risks, the paper uses a Heterogeneous Agent New Keynesian model to analyze the relationship between monetary policy and household inequality. The model is able to capture the main features that characterise both the business cycle dynamics, as well as the distribution of income and wealth. Results indicate that heterogeneity affects the transmission of monetary policy, and that the design of monetary policy has important distributive effects.

Enter the MATRIX model:a Multi-Agent model for Transition Risks with application to energy shocks.

The global energy crisis that began in fall 2021 and the subsequent spike in energy prices constitute a significant challenge for the world economy that risks undermining the post-COVID-19 recovery. In this paper, we develop and calibrate a new Multi-Agent model for Transition Risks (MATRIX) to analyze the role of energy in the functioning of a complex adaptive system and the economic and distributional effects of energy shocks. The economic system is populated by heterogeneous agents, i.e., households, firms and banks, which take optimal decision rules and interact in decentralized markets characterized by limited information. After calibrating the model on US quarterly macroeconomic data, we assess the economic and distributional impacts of different types of energy shocks, namely: (i) an exogenous increase in the price of fossil fuels (e.g., oil or gas); (ii) a decrease in energy firms’ productivity; (iii) a reduction in the available quantity of fossil fuels. We find that the energy shocks entail similar effects at the aggregate level in terms of higher inflation and lower real GDP. Nevertheless, the distribution of gains and losses across sectors and agents varies significantly depending on the type of shock. Our findings suggest that policymakers should carefully consider the nature of energy shocks and the resulting distributional effects to design effective measures in response to energy crises.

Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.

Stuck at home: Housing demand during the COVID-19 pandemic

The COVID-19 pandemic induced an increase in both the amount of time that households spend at home and the share of expenditures allocated to at-home consumption. These changes coincided with a period of rapidly rising house prices. We interpret these facts as the result of stay-at-home shocks that increase demand for goods consumed at home as well as the homes that those goods are consumed in. We first test the hypothesis empirically using US cross-county panel data and instrumental variables regressions. We find that counties where households spent more time at home experienced faster increases in house prices. We then study various pandemic shocks using a heterogeneous agent model with general equilibrium in housing markets. Stay-at-home shocks explain around half of the increase in model house prices in 2020. Lower mortgage rates explain around one third of the price rise, while unemployment shocks and fiscal stimulus have relatively small effects on house prices. We find that young households and first-time home buyers account for much of the increase in housing demand during the pandemic, but they are largely crowded out of the housing market by the equilibrium rise in house prices.