Agent-based model generating stylized facts of fixed income markets

Journal of Economic Interaction and Coordination - We develop an agent-based model (ABM) of a financial market with multiple assets belonging either to the fixed income or equity asset classes. The...     

Oligopoly firms with quantity-price strategic decisions

An agent-based model is used to determine market equilibrium with price-setting firms in an oligopoly market. The agent-based model is designed to match the experimental rules that Brandts and Guillen (J Ind Econ 55:453–474, 2007) used with human subjects. Their model uses posted prices and advance production of a perishable good. When the marginal cost is zero, the analytical Bertrand solution is almost perfect competition. When the marginal cost is nonzero, the game does not have a theoretical equilibrium in pure strategies. The agent-based model results show that with one or two firms, prices are at or near the monopoly level, which matches the human experiments. With four firms, prices are always at the perfectly competitive level when particle swarm optimization is used. Results using a genetic algorithm, however, are noisier than those using the particle swarm optimization, and the genetic algorithm falls short of the competitive solution. The triopoly market changes from mostly monopoly to a price in between monopoly and perfect competition when a marginal cost is added. The computerized agents tend to overproduce so that profits are negative in the three- and four-firm cases when production is costly. While the prices in the simulation are close to those observed in experiments with human subjects, the inefficiency due to overproduction is much greater in the agent-based model results. This result suggests that human agents are able to reach solutions, perhaps through social norms, that are missed by the simple agent-based rules used here.     

Allocating U.S. Department of Homeland Security funds to States with explicit equity, population and energy facility security criteria

Allocation of funds to states and local governments by the U.S. Department of Homeland Security (DHS) has been challenged for relying too heavily on the U.S. Patriot Act formula that contains set-asides for each state. Yet, DHS's recent efforts to include more vulnerability/risk information have been criticized for lacking transparency. Using only off-the-shelf data and an off-the-shelf optimization model, which is a compromise between politically grounded formulas and complex analyses supported by massive data manipulations, the authors maximize need (defined and represented here as funds to protect electrical-generating capacity) subject to explicit constraints based on political equity and population size but introduce risk-related criteria as well in the form of ease of securing the facilities and public perception of risk. The model results closely approximate the DHS allocations at the state level when electricity generation and its retail price are maximized subject to moderate set-asides for political equity and population size. The advantages of this approach are that the results are transparent and sensitivity analysis is relatively easy to do. The advantages and disadvantages of the approach presented here are compared with more sophisticated alternatives.     

Effectiveness of Q-learning as a tool for calibrating agent-based supply network models

This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for ‘optimal’ values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the ‘optimal’ parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the ‘optimal’ parameter values in all model settings and scenarios considered.     

An agent-based model for financial vulnerability

This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the ensuing agent-based model for analyzing the vulnerability of the financial system to asset- and funding-based fire sales. The model captures the dynamic interactions of agents in the financial system extending from the suppliers of funding through the intermediation and transformation functions of the bank/dealers to the financial institutions that use the funds to trade in the asset markets. The model replicates the key finding that it is the reaction to initial losses, rather than the losses themselves, that determine the extent of a crisis. By building on a detailed mapping of the transformations and dynamics of the financial system, the agent-based model provides an avenue toward risk management that can illuminate the pathways for the propagation of key crisis dynamics such as fire sales and funding runs.     

Modeling society with statistical mechanics: an application to cultural contact and immigration

We introduce a general modeling framework to predict the outcomes, at the population level, of individual psychology and behavior. The framework prescribes that researchers build a cost function that embodies knowledge of what trait values (opinions, behaviors, etc.) are favored by individual interactions under given social conditions. Predictions at the population level are then drawn using methods from statistical mechanics, a branch of theoretical physics born to link the microscopic and macroscopic behavior of physical systems. We demonstrate our approach building a model of cultural contact between two cultures (e.g., immigration), showing that it is possible to make predictions about how contact changes the two cultures.     

基于多代理的人工供应链仿真模型分析与设计

采用多代理计算机建模技术构建了一类能够模拟复杂供应链的人工供应链仿真模型,并开发了相应的实验平台,通过实验分析验证了模型的有效性。旨在通过构建多代理供应链仿真模型简化供应链仿真实验的难度,通过仿真实验模拟和数据分析得出一些有效的结论,进而指导企业实践。     

Agentenbasierte Simulation zur Analyse und Lösung betriebswirtschaftlicher Entscheidungsprobleme

Complex systems frequently reveal decision problems which cannot be addressed by classical analytical or optimization methods. Markets consisting of players that interact with heterogeneous goals or enterprises with multi-faceted intertwined processes are typical examples of such systems within the global economy. During recent years, the agent-based simulation (ABS) has spread as a technology that helps to simulate such systems and better understand their inner causes and effects. In this work, we present the potential of ABS with respect to business applications and review a broad set of case studies from academia as well as from corporate practice. To conclude, we analyze the application process for the utilization of an ABS, spanning from the conception and modeling to implementation and the processing of the simulation.     

Information dissemination in an experimentally based agent-based stock market

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.     

A causal model for the development of long-term facilities: a case in Taiwan

Elder care is an important issue in many developed countries such as U.S., Japan, and Taiwan. With the advent of population aging throughout the world, the development of long-term care facilities has become a very vital topic. In actuality, long-term care systems involve government oversight agencies as well as interaction between factors such as laws, social environment, culture, long-term care facilities, residents, and the families of residents, forming a complex and dynamic system. This paper uses system dynamics methodology to model the developmental structure of Taiwanese long-term care facilities to explore its system behaviors. The developmental structure of long-term care facilities in Taiwan is primarily composed of the four levels: overall satisfaction, overall service quality, the skill of administrative and medical care personnel and facility hardware resources. Time delay, complex, and dynamic relationships are present in the overall structure. The overall service quality of facilities is one of the most important factors in facilities’ development and that the skills of administrative and medical care personnel is the main critical factor in improving overall service quality. Finally, some suggestions are discussed relevant strategies for the government and the industry.     

东北特殊钢集团作业成本管理案例研究

本文以东北特殊钢集团为例,着重对特钢集团成本管理理论和应用体系进行研究。通过分析特钢集团业务流程特点建立了作业成本管理模型,在现代集成制造环境下构建了成本管理与其他企业管理职能间的集成关系,分析了各职能部门在成本管理中的作用,并提出了在软件信息交互关系及分布式环境下的实现体系,分析了案例企业的应用效果。通过案例分析表明该研究有利于细化企业成本管理和控制粒度,对我国特钢集团成本管理具有直接的参考价值。     

Statistical Implementations of Agent-Based Demographic Models

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.     

Diffusion and spatial equilibrium of a social norm: voting participation in the United States, 1920–2008

Social conformity can spread social norms and behaviors through a society. This research examines such a process geographically and over time for voting, which is strongly influenced by the norm that citizens should vote. A mathematical model for the spread of voting participation under the influence of social conformity is developed based on the diffusion equation, and predictions are tested with spatial analysis of state-level voter turnout in American presidential elections from 1920 to 2008. Results show that voter turnout has converged to a stable equilibrium in its geographical distribution across the states—but it is an equilibrium that results in persistent differences at the state level. Results are compared to individual-level and agent-based models. The model may be applied to other types of social diffusion that depend on specific geographical location.     

Using difference equations to find optimal tax structures on the SugarScape

The use of equations to describe agent-based model dynamics allows access to mathematical theory that is not otherwise available. In particular, equation models can be effective at solving optimization problems—that is, problems concerning how an agent-based model can be most effectively steered into a particular state. In order to illustrate this strategy, we describe a modified version of the well-known SugarScape model and implement taxation. The optimization problem is to determine tax structures that minimize deaths but maximize tax income. Tax rates are dependent upon the amount of sugar available in a particular region; the rates change over time. A system of discrete difference equations is built to capture agent-based model dynamics. The equations are shown to capture the dynamics very well both with and without taxation. A multi-objective optimization technique known as Pareto optimization is then used to solve the problem. Rather than focusing on a cost function in which the two objectives are assigned weights, Pareto optimization is a heuristic method that determines a suite of solutions, each of which is optimal depending on the priorities of the researcher. In this case, Pareto optimization allows analysis of the tradeoff between taxes collected and deaths caused by taxation. The strategies contained here serve as a framework for a broad class of models.     

Growth,unemployment and heterogeneity

The paper analyzes unemployment in a medium-run growth model, where aggregate demand and supply interact, using a top-down approach. The aim of the essay is the study of a nonlinear system where both aggregate demand and supply are endogenous and generate bounded unemployment, followed by a methodological effort direct to identify possible lines of convergence with the agent based models (ABM) approach. This is a by-product of the presence of heterogeneity in the model. Heterogeneity acts through two different channels and operates among class of agents: it comes into the aggregate consumption function where households are assumed employed or unemployed; it changes the learning process of pessimists and optimists. The analysis is carried on through simulations. The resulting system is fairly stable to changes in main structural parameters. On one hand, autonomous demand drives the dynamics of the system, while heterogeneity in the consumption function, due to the presence of unemployment, strengthens the links with supply aspects. On the other hand, both the rate of growth of labor productivity and labor supply are endogenous. Two major results are obtained. First, unemployment allows the so called Harrodian reconciliation between aggregate demand and supply. Second, unemployment remains bounded meaning that the interaction between aggregate demand and supply thwarts instability. These results are in keeping with those obtained by means of a bottom-up approach, typical of ABM. Possible explanations and implications of this convergence are put forward and open the venue to further deepening of complementarities among the two modeling strategies.

     

Validation of a social cohesion theoretical framework: a multiple group SEM strategy

Social cohesion dates back to the end of the nineteenth century. Back then, society experienced epochal transformations, as are also happening nowadays. Whenever there are epochal changes, a social order (cohesion) matter arises. The paper provides a conceptual scheme of social cohesion identifying its constituent dimensions subdivided by three spheres (macro, meso, micro) and two perspectives (objective and subjective). The overarching aim is to test the validity of the operationalization of the social cohesion model provided. Firstly, we conducted an exploratory factor analysis introducing an approach implemented in Mplus named exploratory structural equation modeling that shows several useful characteristics. Afterward, through a structural equation modeling approach, we performed several confirmatory factor analyses adopting a multiple group SEM strategy in order to cross-validate the social cohesion model.     

From the kinetic theory of active particles to the modeling of social behaviors and politics

This paper deals with the modeling of complex social systems by methods of the mathematical kinetic theory for active particles. Specifically, a recent model by the last two authors is analyzed from the social sciences point of view. The model shows, despite its simplicity, some interesting features. In particular, this paper investigates the ability of the model to describe how a social politics and the disposable overall wealth may have a relevant influence towards the trend of the wealth distribution. The paper also outlines various research perspectives.     

基于多智能体的分销链协同一体化运作建模与流程分析

预测-计划-调度是分销链运作研究的重要内容．协同一体化的运作模型构建及流程分析有助于优化分销链的管理。在比较了基于对象建模和基于Agent建模的基础上．给出了分销链运作与Agent的对应关系。根据多智能体技术的建模方法和流程．运用该技术对分销链协同一体化的运作模型进行了优化。并给出了基于多智能体技术的预测-计划-调度的智能运作模型．并时该模型的运作流程进行了分析。     

Happiness sentiments and the prediction of cross-border country exchange-traded fund returns

This research examines whether social media (Twitter) happiness sentiment and country-level happiness sentiment indices predict cross-border ETF returns. To account for complicated associations between happiness sentiment and ETF returns, we use a quantile regression approach and find that Twitter and trading market (U.S.) happiness sentiments are strong predictors of future ETF returns, for which both have far greater predictive power than those of their home countries. Home country happiness indices exhibit asymmetric impacts across quantiles, suggesting the importance of trading country (U.S.) and Twitter happiness sentiments. Higher U.S. and home countries’ freedom to make life choices, absence of corruption perception, and confidence in national government precede higher ETF returns, while U.S. GDP, social support, health life expectancy, positive affect, and negative affect precede lower (abnormal) returns. We find that higher return quantile country ETFs provide a safe haven for U.S. investors during a U.S. bear market.