WorkSim: A Calibrated Agent-Based Model of the Labor Market Accounting for Workers’ Stocks and Gross Flows

This paper presents an agent-based model of the labor market. It simulates the market in the recent period at the aggregate level and at the level of the principal categories of labor, on the basis of the decisions of heterogeneous agents, firms and individuals, who interact. These decisions rely on individual computations of profits and utilities, although rationality is bounded in such a complex environment. The theoretical structure that underlies the decisions is the search concept. We apply this framework to the case of France in 2011. The model is at a scale of 1/4700. It is fairly detailed on the institutions of the labor market that constrain the agents’ decisions. Finally it is calibrated by a powerful algorithm to reproduce a large number of variables of interest. The calibrated model presents a consistent accounting system of the gross flows of the individuals between the main states, employment, distinguishing open ended contracts and fixed duration contracts, unemployment and inactivity. The simulation of the gross flows accounts enables us to analyze the patterns of mobility in a way that the observed statistics on gross flows, which are partial, cannot do. The model then characterizes the nature of the labor market under study, reproducing the high proportion of the fixed duration contracts in the hiring flows, and it points to a dualism of the French labor market.     

A computational model of labor market participation with health shocks and bounded rationality

This paper presents a computational agent-based model of labor market participation, in which a population of agents, affected by adverse health shocks that impact the costs associated with working efforts, decides whether to leave the labor market and retire. This decision is simply taken by looking at the working behaviors of the other agents, comparing the respective levels of well-being and imitating the more advantageous decision of others. The analysis reveals that such mechanism of social learning and imitation suffices to replicate the existing empirical evidence regarding the decline in labor market participation of older people. As a consequence, the paper demonstrates that it is not necessary to assume perfect and unrealistic rationality at the individual level to reproduce a rational behavior in the aggregate.     

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange (FX) market, and we use an agent‐based market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero‐intelligence directional‐change event strategy, and a genetic programming‐based strategy. A series of experiments were conducted, and the results were compared with those of a high‐frequency FX transaction data set. Our results show that the zero‐intelligence directional‐change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market.     

Simulation Analysis Using Multi-Agent Systems for Social Norms

With the existence of the social customs or norms, Naylor demonstrates a possibility of stable long-run equilibria of support for a strike in a labor market, and this implies that at least some individuals will behave cooperatively and hence the prisoners’ dilemma could be escaped. In this paper, using an agent-based simulation model in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms, we compare the results of our simulation analysis with that of the mathematical model by Naylor. In particular, while Naylor’s model is based on rationality as it relates to individual utility maximization, agents behave adaptively in our agent-based simulation model; agents make decisions by trial and error, and they learn from experiences to make better decisions.      

Shocks and Endogenous Institutions: An Agent-based Model of Labor Market Performance in Turbulent Times

We develop an agent-based model of labor market regulation to study the consequences of employment protection legislations for labor market performance. Unlike most of the existing studies of labor market regulation we endogenize the institutional setting. Workers cast their vote on labor market regulation depending on the past payoffs that they accrued when one of two competing parties with different labor market policy platforms was in power. We identify important interactions with exogenous shocks. In more turbulent economic times, employment protection systems can affect labor market performance for some periods even after the shock has subsided.     

Polyhedral Flows in Hybrid Automata

A hybrid automaton is a mathematical model for hybrid systems, which combines, in a single formalism, automaton transitions for capturing discrete updates with differential constraints for capturing continuous flows. Formal verification of hybrid automata relies on symbolic fixpoint computation procedures that manipulate sets of states. These procedures can be implemented using boolean combinations of linear constraints over system variables, equivalently, using polyhedra, for the subclass of linear hybrid automata. In a linear hybrid automaton, the flow at each control mode is given by a rate polytope that constrains the allowed values of the first derivatives. The key property of such a flow is that, given a state-set described by a polyhedron, the set of states that can be reached as time elapses, is also a polyhedron. We call such a flow a polyhedral flow. In this paper, we study if we can generalize the syntax of linear hybrid automata for describing flows without sacrificing the polyhedral property. In particular, we consider flows described by origin-dependent rate polytopes, in which the allowed rates depend, not only on the current control mode, but also on the specific state at which the mode was entered. We identify necessary and sufficient conditions for a class of flows described by origin-dependent rate polytopes to be polyhedral. We also propose and study additional classes of flows: strongly polyhedral flows, in which the set of states that can be reached up to a given time starting from a polyhedron is guaranteed to be a polyhedron, and polyhedrally sliced flows, in which the set of states that can be reached at a given time starting from a polyhedron is guaranteed to be a polyhedron. Finally, we discuss an application of the above classes of flows to approximate exponential behaviours.     

Numerical Simulations on Stationary States for Rotating Two-Component Bose-Einstein Condensates

We numerically study stationary states such as ground, symmetric and central vortex states as well as their energy diagrams for rotating two-component Bose-Einstein condensates (BECs), which are stationary solutions of time-independent coupled Gross-Pitaevskii equations with an angular momentum rotational term. We compute these stationary states by using normalized gradient flows with a backward Euler finite difference discretization, which is proved to be efficient and accurate. By using this discretization, we find various ground state configurations with several vortices for the two-component BECs, which are novel but not found in rotating two-component BECs; we also find that the critical angular velocity at which the ground states lose symmetry.     

Price suppression stimulation in a local market

In the paper, we study possible organization of a free multi-agent multi-commodity local market that stimulates price suppression. Market participants (agents) produce different commodities and services sold both in the local and external markets. Agents are interdependent: produce of one agents is a product of industrial consumption of other agents. Agents independently make decisions about shares of a product sent to external and internal markets and about prices that they fix in the local market. Prices of sale and purchase in the external market are fixed and do not depend on actions of the agents of the local market. Commodities of industrial consumption required for the agents cam be bought both in the local or external markets. We study a mechanism of price suppression based on the priority of access to resources granted to more compliant agents. It is shown that at sufficiently soft conditions of “valid competition,” all agents of the local market can obtain an increase in profitability concerning the strategy of purchase and sale in the external market only. Small and low-profitable enterprises obtain the highest relative gains in profitability.     

Comparative dynamics in an overlapping-generations model: the effects of quasi-rational discrete choice on finding and maintaining Nash equilibrium

Many models of Nash Equilibrium are complex enough that it becomes difficult to ascertain if and under what conditions the economic players can find and maintain this equilibrium. Using an analytical overlapping- generations model of goods, labor, and banking markets and quasi-rational discrete choice decision making, we find through agent-based simulations that Nash Equilibrium in goods market prices is stable when firms are sufficiently sensitive to changes in profits. In addition to verifying the analytical Nash outcome, the simulations verify that their economic agents, decision rules, and other protocols correspond to and maintain consistency with the analytical theory and identify important bounds of the analytical model.      

Asset Pricing and Productivity Growth: The Role of Consumption Scenarios

The paper analyzes the performance of asset prices implied by an aggregate macroeconomic growth model under two different consumption hypotheses: overlapping generations of agents with two period lives versus the infinitely lived agent. The production side of the economy is described by a random growth model with a competitive labor market and an exogenously given random dividend payout ratio. For an isoelastic technology with multiplicative production shocks this implies a random dynamical system for the firm’s rate of profit with a unique asymptotically stable random fixed point for a large class of productivity growth and dividend payout ratio processes. Based on an extensive numerical study of stationary solutions we show that the two consumption scenarios imply a limited number of diverse effects regarding equity and bond returns and equity premia.     

人工神经网络在劳动力供求预测中的应用

劳动动力市场是一个非线性系统,常规方法对其建模进行预测的准确率不高。通过引入机器学习领域的研究成果,结合劳动力市场自身的特点,建立BP人工神经网络模型,对劳动力市场的模拟和供求量的预测都得到较好的结果,提出了将人工神经网络应用于劳动力市场建模的一般方法,可以用来定量研究劳动力市场的各种问题,具有可操作性和实用性,比现有方法的拟合效果好,预测准确率高。     

A Survey on Visual Traffic Simulation: Models,Evaluations, and Applications in Autonomous Driving

Virtualized traffic via various simulation models and real-world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state-of-the-art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data-driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.     

A design chain for embedded systems

Today's embedded systems design culture produces custom products from scratch. However, as electronic products become more complex and global competition demands shorter time to market, the industry is moving toward a design process that integrates commodity system-on-chip (SoC) platforms. The article looks at how we go about developing such integration-oriented SoC design flows and evolving a new platform-based design culture     

Cyclic Economic Processes in Systems with Monopsonic Labor Markets

A dynamical macromodel of an economic system with a competitive commodity market and a monopsonic labor market is considered. For this model, conditions of the onset of economic cycles that are similar to classical business cycles are investigated. Mechanisms of formation of such cycles as a result of interaction between the commodity market and the labor market are analyzed.     

Evolution of unplanned coordination in a market selection game

This paper examines the evolution of unplanned coordination among independent agents in a market selection game, which is a noncooperative repeated game with many agents and several markets. Every agent is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price is determined by the total supply of products. For example, if many agents choose a particular market, the market price at that market is low. The point of the market selection is to choose a market that is not chosen by many other agents. In this paper, game strategies are genetically updated by localized selection and mutation. A new strategy of an agent is probabilistically selected from its neighbors' strategies by the selection operation or randomly updated by the mutation operation. It is shown that the maximization of each agent's payoff leads to the unplanned coordination of the market selection where the undesired concentration of agents is avoided. The unplanned coordination is compared with the planned global coordination obtained by the maximization of the total payoff over all agents     

Homoclinic and Heteroclinic Bifurcations in an Overlapping Generations Model with Credit Market Imperfection

We analyze a nonlinear OLG model with credit market imperfection and endogenous labor supply. When the investors’ protection is perfect, the model reduces to the standard one sector growth model proposed by Reichlin (JET 40:89–102, 1986), while the model reduces to the one studied by Matsuyama (Econometrica 72:853–884, 2004) when the agents’ labor supply is exogenous. Our goal is to highlight that the local analysis of the perfect foresight equilibrium may lead to misleading conclusions because the local analysis neglects the occurrence of different global bifurcation scenarios. In particular, the existence of a heteroclinic connection or the occurrence of a homoclinic bifurcation may be associated with global indeterminacy even when all steady states are locally determinate.     

Coordination of multiple agents with double-integrator dynamics under generalized interaction topologies

The problem of the convergence of the consensus strategies for multiple agents with double-integrator dynamics is studied in this paper. The investigation covers two kinds of different settings. In the setting with the interaction topologies for the position and velocity information flows being modeled by different graphs, some sufficient conditions on the fixed interaction topologies are derived for the agents to reach consensus. In the setting with the interaction topologies for the position and velocity information flows being modeled by the same graph, we systematically investigate the consensus algorithm for the agents under both fixed and dynamically changing directed interaction topologies. Specifically, for the fixed case, a necessary and sufficient condition on the interaction topology is established for the agents to reach (average) consensus under certain assumptions. For the dynamically changing case, some sufficient conditions are obtained for the agents to reach consensus, where the condition imposed on the dynamical topologies is shown to be more relaxed than that required in the existing literature. Finally, we demonstrate the usefulness of the theoretical findings through some numerical examples.     

Optimization problems of transportation in communication networks with variable capacities

In this paper, formulations of the synthesis and analysis problems in multiterminal communication networks with variable edge capacities are considered. In contrast with traditional formulations, the notions of flow density, flow speed, and flow intensity are introduced and the time characteristics of a transfer of given volumes through a network are analyzed. Related problems are found in various fields of human activities, e.g., with studies of traffic flows in evacuation models and, e.g., in models of the requalification of people on the labor market. The algorithm for solving the transportation problem in a multiterminal network with variable capacities is presented; under certain conditions, this algorithm allows one to avoid dealing with complex nonlinear large-scale problems of mathematical programming.     

PRICE DYNAMICS, INFORMATIONAL EFFICIENCY, AND WEALTH DISTRIBUTION IN CONTINUOUS DOUBLE-AUCTION MARKETS

This paper studies the properties of the continuous double-auction trading mechanism using an artificial market populated by heterogeneous computational agents. In particular, we investigate how changes in the population of traders and in market microstructure characteristics affect price dynamics, information dissemination, and distribution of wealth across agents. In our computer-simulated market only a small fraction of the population observe the risky asset's fundamental value with noise, while the rest of the agents try to forecast the asset's price from past transaction data. In contrast to other artificial markets, we assume that the risky asset pays no dividend, thus agents cannot learn from past transaction prices and subsequent dividend payments. We find that private information can effectively disseminate in the market unless market regulation prevents informed investors from short selling or borrowing the asset, and these investors do not constitute a critical mass. In such case, not only are markets less efficient informationally, but may even experience crashes and bubbles. Finally, increased informational efficiency has a negative impact on informed agents' trading profits and a positive impact on artificial intelligent agents' profits.