Heterogeneous Agents Research Articles

How Important Are Labor-Market Gender Gaps in the South Caucasus?

In this paper, we use survey data from the South Caucasus countries (Armenia, Azerbaijan, and Georgia) to document the presence of gender gaps in the labor market and examine its consequences. To do the analysis, we use a numerical general-equilibrium occupational choice model with heterogeneous agents in entrepreneurial ability. We then introduce the observed gender gaps in labor-force participants, employers, and self-employed. We find that entrepreneurship gender gaps cause an average GDP loss of 6.2%, while gender gaps in labor-force participation cause an average GDP loss of 9%. Armenia (2007) displays the largest total loss and Georgia (2007, 2014) the smallest ones. We also decompose the gender gaps and their associated costs by households with different education levels and with and without dependents at home. Our results indicate that most of the income losses are driven by households with high education and those with dependents, especially those with both children and elderly at home.

A joint work package sizing and scheduling problem considering resource constraints with a look-ahead heterogeneous reinforcement learning method

Effective work package sizing and project scheduling are crucial for construction project management. However, existing studies often address them as separate optimisation problems, neglecting the interactive nature of these processes. In practical scenarios with limited resources, there is an increasing demand for integrating work package sizing and project scheduling to enhance project management efficiency. This research aims to bridge this gap by developing an integer non-linear programming model that incorporates work package sizing and project scheduling while considering their interaction within a resource-constrained environment. Moreover, we introduce a novel look-ahead heterogeneous reinforcement learning approach that dynamically adjusts work package sizes and project schedules based on observed information at each decision step. A look-ahead sequential decision mechanism is proposed to effectively address the interdependencies and constraints inherent in the joint model. We extend the heterogeneous agent mirror learning technique to our problem to improve sample efficiency and ensure progressive enhancement of the joint policy. To evaluate our approach's effectiveness, we conduct experiments using datasets of 15, 30, 60, 90, and 120 tasks from Rangen and PSPLIB and validate our method in a real-world modular integrated construction project. The experimental results reveal that a joint model integrating work package sizing and project scheduling offers a more comprehensive understanding of their interplay, leading to better resource utilisation and improved project schedules. A comparative analysis against existing state-of-the-art reinforcement learning and priority rule-based methods further substantiates the superior effectiveness of our proposed approach, yielding an approximate 5% reduction in total project cost.

Interest-bearing capital and monetary policy: a heterogeneous agent Marxian model

PurposeIn terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.Design/methodology/approachBased on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.FindingsThe research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.Originality/valueClarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.

Local public good equilibrium

We extend ratio equilibrium to local public good economies by defining Local Public Good Equilibrium (LPGE). We employ “share functions” for individual agents that allow easy comparisons of consumption utility, both across consumption bundles and across jurisdiction memberships. Share functions furthermore aggregate for parsimonious calculation of equilibrium public good provision within each potential jurisdiction, allowing us to establish a link with hedonic games and deploy results from that literature. We demonstrate in examples how this enables us to show LPGE existence for a far wider class of economies than prior work seeking to extend ratio equilibrium to local public good economies. In particular, we demonstrate LPGE existence with heterogeneous agents, a finite number of agents and jurisdictions, endogenous jurisdictional revenues and expenditures, and robustness against coalitional deviations. LPGE admits wide-ranging jurisdiction structures, and we demonstrate how it can generate results on sorting that have attracted interest in the extant literature.

Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization

Vehicle-to-Vehicle (V2V) communication can not only provide unrestricted inter-vehicle information transmission, but also improve spectrum utilization efficiency. However, it also brings uncontrollable co-channel interference, which can not guarantee the quality of service of V2V communication. In this paper, we propose an intelligent resource allocation scheme for V2V communication to improve vehicle connectivity. To enhance cooperation among vehicles and avoid excessive co-channel interference between them, we propose an asynchronous resource allocation method where vehicles choose to send or not to send data based on observed environmental information to ensure stable overall performance. Furthermore, we present a novel resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization (HAPPO) to solve the resource allocation problem in asynchronous vehicular networks. The HAPPO algorithm calculates the global advantage function when each agent makes an action during the training process to ensure that the action taken contributes to the overall performance improvement. Our proposed approach improves the robustness of V2V communication by reducing co-channel interference while maintaining stable overall performance. Simulation results show that the proposed approach can effectively improve the V2V communication connectivity and reduce the packet loss rate compared with the existing methods.

Employment Shift in Response to a Technology Shock: An Analysis of Two Rigidities and Two Agents

This paper examines the relationship between a technology shock and employment, considering price, wage rigidities, and heterogeneous agents. To explore this relationship, we utilized a Dynamic Stochastic General Equilibrium (DSGE) model, incorporating households with varying savings rates. For empirical validation, we conducted a Structural Vector Autoregression (SVAR) analysis using data from two economies with distinct savings patterns—the United States and China. This approach allowed us to assess the impact of technology shocks on employment dynamics across different savings environments. Under these conditions, we observe that the effect of technology on aggregate employment is initially positive. Still, it gradually decreases in the mid-term, eventually switching to a negative impact before slowly recovering to equilibrium. The reason for this phenomenon depends on (i) the magnitude of fluctuations in price and wage, precisely, which variable’s fluctuations have a greater magnitude, and (ii) which effect, between income effect and substitute effect, is preferred by restricted and unrestricted households. Due to (i), real wages change, and because of (ii), households make different labor supply decisions, leading to fluctuations in employment in response to technology shocks.

From Empty Parking Lots to Border Fences: A Visual and Sensory Ethnography of the Finnish–Russian Borderscapes in ‘Times of Stress’

ABSTRACT This article studies everyday bordering processes at the Finnish – Russian border during ‘times of stress’, specifically, following the COVID-19 pandemic and the onset of the war in Ukraine. The research is grounded in ethnographic fieldwork conducted in the summer and autumn of 2022 in four border cities (Imatra and Lappeenranta in Finland, and Svetogorsk and Vyborg in Russia). The study centres on visual and sensory observations made during the fieldwork, as well as discussions taking place in local newspapers and their social media channels. The Finnish – Russian borderland is approached as a ‘borderscape’ – a site where borders are constructed, deconstructed and reconstructed in everyday life by heterogeneous agents. The research illustrates that the political context shaped the Finnish – Russian borderscapes concerning the material border landscape, people’s experiences and imaginations. The study argues that visual and sensory ethnography can add deeper and more nuanced layers to the understanding of borderscapes as well as provide alternative ways of conveying research findings.

Crystallization behavior and thermal properties of octa-phenyl-substituted silsesquioxane-modified polylactide (PLA)

This study aims to understand the effects of adding octa-phenyl-substituted silsesquioxane (phSQ) on the crystallization process and thermal stability of polylactide (PLA). Nowadays, PLA is the most industrially used compostable polymer, but its uses are limited by its low crystallization and thermal degradation during processing. The possibility of introducing functionalized silsesquioxanes (SQs) to improve thermal stability and increase its crystallinity and ductility in a controlled way is desirable. The nanometric size of the Si-O-Si cage, coupled with the influence of the functional groups attached to its structure, enables it to function as a heterogeneous nucleating agent. In this work, a specially synthesized octa-phenyl-substituted SQ (phSQ) was added to the PLA in 0.5–5 wt%. Crystallization in non-isothermal and isothermal conditions was conducted and monitored using differential scanning calorimetry (DSC); the course of the spherulite formation under identical conditions to DSC was also assessed using optical microscopy in polarized light. The results showed that phSQ increases the degree of crystallinity of PLA by introducing additional sites of heterogeneous nucleation but does not increase the spherulite growth coefficient. Additionally, the analysis of thermal properties indicates that the presence of phSQ could not have a positive impact on thermal stability. The agglomeration of the nanometric particles and changes in the main structural features of the polymeric matrix could be present in the samples, affecting the obtained results.Graphical abstract

Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph.

Heterogeneous multiagent systems are characterized by diverse task distributions, which are prevalent in practical scenarios, such as distributed decision making and robotic collaboration. A significant challenge in these systems is the constraint of limited observations, where each agent has access only to partial information. Many studies facilitate information exchange by employing shared parameters among agents. However, this approach is generally more effective for homogeneous systems where agents have similar observation or action spaces. In heterogeneous systems, indiscriminate parameter sharing can significantly increase the exploration cost required for effective adaptation. To address this challenge, we propose a novel communication complementary graph model (CCGM) for enhancing collaboration in heterogeneous multiagent systems. Our approach builds upon the training framework of heterogeneous agent reinforcement learning (HARL) with trust region learning and nonparameter sharing. This model utilizes advantage function decomposition and sequential updates to promote policy convergence. Within this framework, we introduce a novel communication method inspired by signaling games, where agents acting as receivers, process messages from other agents alongside their own observations. CCGM aligns the messages with observations in a graph-based communication module, which establishes communication relationships and supplements observational information. Subsequently, agents generate self-interested information, which they then share with others as senders. We evaluate our algorithm across various environments, including multiagent particle environments (MPE) and multiagent MuJoCo (MAMuJoCo) robot experiments. The results demonstrate the effectiveness of CCGM in enhancing HARL-based algorithms.

A Dynare toolbox for social learning expectations

Social learning (SL) is a behavioral model in which expectations and the resulting aggregate dynamics stem from the interactions of a large number of heterogeneous agents. Nonetheless, this framework has so far lacked a parsimonious development with a general-solution method. This paper bridges this gap and introduces a Dynare toolbox to solve any linear state-space model with SL expectations, opening up a wide range of potential applications. As an illustration, optimal monetary policy rules are studied in a microfounded New Keynesian (NK) model under SL and rational expectations (RE).

Distributed misbehavior monitors for socially organized autonomous systems

In systems in which many heterogeneous agents operate autonomously, with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by “social” rules imposed on the behavior of individual agents. Social laws are structured in a way that they can be verified just by using local information made available to an agent by a small number of its neighbors. Automobile mobility with traffic rules and logistics robots in warehouses are canonical examples of such “regulated autonomy”, but many other fairly-competing autonomous systems are to be expected shortly. In these systems, detecting whether an agent is not abiding by the rules is crucial for raising an alert and taking appropriate countermeasures. However, the limited visibility due to the local nature of the information makes the problem of misbehavior detection hard for any single agent, and only an exchange of information between agents can provide sufficient clues to arrive at a decision. This paper attacks the misbehavior detection problem for a class of socially organized autonomous systems, where the behavior of agents depends on the presence or absence of other neighbors. We propose a solution involving a “local monitor”, which runs on each agent and includes a hybrid observer and a set-valued consensus node. Based on whatever visibility is available, it reconstructs a set-valued occupancy estimate of nearby regions and combines it with communicating neighbors to reach a shared view and a mismatch discovery. We provide a formal framework for describing social rules that unify many different applications and a tool to generate code automatically for local monitors. The technique is demonstrated in various systems, including self-driving cars, autonomous forklifts, and distributed power plants.

Information consistency and inhibition impact on diffusion in networks

This paper introduces the Principal-Agent mechanism to study the diffusion of information in complex networks. Hot list information is defined as principal information, information that is homogeneous with hot list information is defined as homogeneous agent information, and information that is heterogeneous with hot list information is defined as heterogeneous agent information. The theory of diffusion dynamics is used to study the phenomenon that homogeneous information promotes each other and heterogeneous information inhibits each other in the process of competitive diffusion of network information. Based on the traditional SIR model, this paper introduces the Principal-Agent mechanism, establishes the PA_SIR diffusion model and studies the mutual influence of information in the process of competitive diffusion of multiple information. The research shows that in the process of competitive diffusion of multiple information, principal information has a positive incentive effect on homogeneous agent information through people’s interest orientation, herd mentality and topic public opinion and has a negative incentive effect on heterogeneous agent information. Therefore, if you want your published information to attract more attention, the best release time is when homogeneous information is on the hot search list, which will help to attract people’s attention more quickly.

Mapping a food donation supply chain and the implications for social policies in Brazil

PurposeThe coronavirus disease 2019 (Covid-19) pandemic raised global alarms about hunger and food insecurity worldwide and the corresponding need for public policies, particularly in emerging countries. One possible solution for addressing this important topic is a food donation supply chain, which comprises food banks and involves their relationships with suppliers (donors), customers (people in need) and other potential stakeholders. The aim of this research is to understand how different agents in a food donation supply chain use different but interrelated capital dimensions (human, structural and relational) to leverage their resources and build knowledge in their relationships with other stakeholders.Design/methodology/approachWe conducted an abductive single case study with the food bank as a focal organisation in a direct supply chain. Data were collected by way of 15 semi-structured interviews with representatives from each tier in the chain (food bank, donor, social impact company and social movement), non-participant observation and secondary data (e.g. codes of practice, websites and media). All data sources were analysed individually and then cross-checked.FindingsOur findings show that tangible dimensions, such as those that are structural, are present upstream while intangible dimensions – those that are relational and human, for example – are prevalent downstream. The political dimension was identified as an important mechanism that allows stakeholder engagement and access to resources, funding and government incentives.Originality/valueBy applying a multi-tier approach, the present research explores how different stages in this supply chain use intellectual capital (IC) to better manage relationships. The heterogeneity of agents within the food donation supply chain (public, private and third sector), each of which has distinct levels of IC dimensions, can share their knowledge to improve efficiency and social protection mechanism policies. Since the focal organisation is a non-profit organisation, this study also contributes to IC theory.

Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry

Abstract We measure the distribution of welfare losses from non-competitive behaviour in the U.S. credit card industry during the 1970s and 1980s. The early credit card industry was characterized by regional monopolies. Ensuing legal decisions led to competitive reforms that resulted in greater, but still limited, oligopolistic competition. We measure the distributional consequences of these reforms by developing and estimating a heterogeneous agent, defaultable debt framework with oligopolistic lenders. The transition from monopoly to oligopolistic competition yields welfare gains equivalent to a one-time transfer worth $3,600 (in 2016 dollars) for the bottom decile of earners (roughly 50% of their annual income) versus $1,200 for the top decile of earners. As the credit market expands, low-income households benefit more since they rely disproportionately on credit to smooth consumption. Greater competition also explains rising bankruptcies, chargeoffs, and credit to income ratios. Lastly, we bound the welfare gains from competition by computing a perfectly competitive benchmark. Aggregate welfare gains are 40% larger from perfect competition but distributed similarly to oligopolistic competition.

Welfare effects of health insurance reform: The role of elastic medical demand

Some medical demand is inelastic to price fluctuations, but not all. This paper examines the role of the price elasticity of medical demand on the welfare effects of public health insurance reform. I develop a computational general equilibrium life-cycle model for heterogeneous agents with varying income, wealth, and health that allows for endogenous medical spending. I calibrate the model for the Japanese economy. If medical spending is totally price-inelastic, increasing copayments will improve the welfare of future generations, but harm all current generations, particularly older people with low income or poor health. In contrast, the welfare gain for newborns is significantly greater in the empirically observed situation where medical spending includes some price-elastic components. Moreover, the reform reduces welfare losses for current individuals and may benefit younger generations.

Study on foaming behavior of block copolymer/inorganic particles co‐construction of toughened epoxy resin foams

AbstractLow toughness and brittleness limit its application range of thermosetting epoxy resin (EP) based foams. In current research, inorganic particles are commonly utilized as heterogeneous nucleating agents to enhance foaming quality and EP toughness. However, the increasing inorganic particle content usually leads to particle agglomeration, which negatively impacts foaming quality and reduces EP performance. Block copolymers, which appear as liquid to effectively mitigate agglomeration, are employed as toughening materials for EP. In this study, a small number of modified calcium silicate particles (MCS) were used as anchor points to induce the distribution of block copolymers (P‐F68) to improve EP toughness and effectively enhance cell nucleation, thus, EP/MCS/P‐F68 foams performance was improved. The research results showed that EP foams obtained excellent cell structure and very small foam density (0.256 g/cm3) at the P‐F68 content of 10 wt%. The continuous increase of P‐F68 content would lead to the thickening of cell wall and the increase of cell size. Simultaneously, the core‐shell structure formed by P‐F68 in EP effectively enhanced the toughness of EP foams and mitigated its brittleness, offering crucial theoretical guidance for toughening and regulating the cell structure of EP foams.

Standard Errors for Calibrated Parameters

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive conservative standard errors and confidence intervals for the structural parameters that are valid even under the worst-case correlation structure. In the over-identified case, we show that the moment weighting scheme that minimizes the worst-case estimator variance amounts to a moment selection problem with a simple solution. Finally, we develop tests of over-identifying or parameter restrictions. We apply our methods empirically to a model of menu cost pricing for multi-product firms and to a heterogeneous agent New Keynesian model.

A reappraisal of fundamentalists in a cobweb model with heterogeneous agents

A reappraisal of fundamentalists in a cobweb model with heterogeneous agents

Consumption tax cuts vs stimulus payments

Recent work shows that in macroeconomic models with non-Ricardian consumer behavior, uniform transfers are equivalent to interest rate cuts. That is, policymakers can use stimulus payments to substitute for conventional monetary policy when, say, the zero lower bound on short-term rates binds. We argue that in the same class of models, temporarily reducing consumption taxes delivers more stimulus than transfers — at the same cost to the taxpayer. Consumption tax cuts activate both income and substitution channels and prompt a strong response from all consumers across the wealth distribution. Simulating these policies in a quantitative heterogeneous agent New Keynesian model, we find output expands twice as much.

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.