Labor market policy evaluation with an agent-based model

I develop an agent-based computational economics (ACE) model with which I evaluate the aggregate impact of labor market policies. The findings are that government-financed training measures increase the outflow rate from unemployment to employment. Although the overall effect is positive this effect is achieved by reducing the outflow rate for those who do not receive subsidies. Furthermore, the outflow rate would have been downward-biased had one supposed a matching function that is exogenous to policies.Labor market policy evaluation; agent-based computational model; endogenous matching function; job displacement

Modeling the High-Frequency FX Market: An Agent-Based Approach

The development of computational intelligence‐based strategies for electronic markets has been the focus of intense research. To be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use, and their interactions as well as the patterns emerging as a result of these interactions. In this article, we develop an agent‐based model of the foreign exchange (FX) market, which is the market for the buying and selling of currencies. Our agent‐based model of the FX market comprises heterogeneous trading agents that employ a strategy that identifies and responds to periodic patterns in the price time series. We use the agent‐based model of the FX market to undertake a systematic exploration of its constituent elements and their impact on the stylized facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions that result in the emergence of the stylized facts similarly to the real market data, and formulate a model that closely approximates the stylized facts. We use a unique high‐frequency data set of historical transactions data that enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting

Exploring agent-based methods for the analysis of payment systems: a crisis model for StarLogo TNG

agent-based modeling, payment systems, RTGS, liquidity, crisis simulation Abstract: This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. The main advantage of agent - based modeling is that we can dynamically see what happens to the major variables involved. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants’ liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider. The methodology presented differs from traditional payment systems simulations featuring deterministic streams of payments dealt with in a centralized manner with static behavior on the part of banks. The paper is within a recent stream of empirical research that attempts to model RTGS with agent – based techniques.

Dynamic efficiency of Extended Producer Responsibility (EPR) instruments in a simulation model of industrial dynamics

This paper presents an original approach to the impact of extended producer responsibility instruments for waste prevention upon firms\' innovative strategies and market structure. Our analysis is based on a stylised framework of waste prevention developed in Brouillat (2009a, b). In this framework, products are modelled as multi-characteristic technologies whose evolution depends on firms\' innovation strategies and on the interactions with consumers and post-consumption activities (recycling). This model has been adapted to explore the impact of waste prevention instruments upon industrial dynamics, and more particularly upon firms\' innovative strategies and upon the evolution of products\' characteristics and market structure. We focus on two types of policy instruments: recycling fees and norms. For each instrument, we will consider different policy designs in order to study their effects on industrial dynamics. The main contribution of this paper is to show how this type of simulation model can be used to explore the impact of waste prevention policy instruments on the technological evolution of products, on innovation strategy and on the evolution of firms\' market shares. The introduction of policy instruments in a simulation agent-based model of industrial dynamics enables us to analyse more thoroughly how different policy designs can modify the dynamics of the system and, more particularly, how the incentives and the constraints linked to the policy instruments under consideration shape market selection.waste prevention; industrial dynamics; environmental policy; simulation model; extended producer responsibility

Executing large orders in a microscopic market model

In a recent paper, Alfonsi, Fruth and Schied (AFS) propose a simple order book based model for the impact of large orders on stock prices. They use this model to derive optimal strategies for the execution of large orders. We apply these strategies to an agent-based stochastic order book model that was recently proposed by Bovier, \v{C}ern\'{y} and Hryniv, but already the calibration fails. In particular, from our simulations the recovery speed of the market after a large order is clearly dependent on the order size, whereas the AFS model assumes a constant speed. For this reason, we propose a generalization of the AFS model, the GAFS model, that incorporates this dependency, and prove the optimal investment strategies. As a corollary, we find that we can derive the ``correct'' constant resilience speed for the AFS model from the GAFS model such that the optimal strategies of the AFS and the GAFS model coincide. Finally, we show that the costs of applying the optimal strategies of the GAFS model to the artificial market environment still differ significantly from the model predictions, indicating that even the improved model does not capture all of the relevant details of a real market.Comment: 32 pages, 12 figure

Agent-based Simulation of the Pharmaceutical Parallel Trade Market: A Case Study

The pharmaceutical parallel trade market emerged as a consequence of the European single market for pharmaceuticals, involving multiple players that partake in different types of competitions. These competitions not only affect players’ profit, but also have a significant impact on European people\u27s healthcare access and welfare. Hence, modeling the pharmaceutical parallel trade market provides a way to study the market and to offer valuable decision support to authorities, people, and players involved in the market. Agent-based modeling offers a computational methodology to study macro-level outcomes emerging from individual behaviors while offering to relax conventional assumptions of standard mathematical economic models. Here, we demonstrate a use case of an agent-based model of the European pharmaceutical parallel trade market and investigate its abilities by analyzing various market scenarios

Votes and Lobbying in the European Decision-Making Process: Application to the European Regulation on GMO Release

The paper presents a multi-agent model simulating a two-level public decision game in which politicians, voters and interest groups interact. The objective is to model the political market for influence at the domestic level and at the international level, and to assess how new consultation procedures affect the final decision. It is based on public choice theory as well as on political science findings. We consider in this paper that lobbying groups have different strategies for influencing voters and decision-makers, with long-term and short-term effects. Our computational model enables us to represent the situation as an iterative process, in which past decisions have an impact on the preferences and choices of agents in the following period. In the paper, the model is applied to the European decision-making procedure for authorizing the placing on the market of Genetically Modified Organisms (GMO). It illustrates the political links between public opinions, lobbying groups and elected representatives at the national scale in the 15 country members, and at the European scale. It compares the procedure which was defined by the European 1990/220 Directive in 1990 with the new procedure, the 2001/18 Directive, which replaced it in 2001. The objective is to explore the impact of the new decision rules and the reinforced public participation procedures planned by the 2001/18 Directive on the lobbying efficiency of NGOs and biotechnology firms, and on the overall acceptability of the European decision concerning the release of new GMOs on the European territory.Lobbying, Europe, GMO, Multi-Agent Simulation, Public Choice, Politician, Voter, Group Contest

The Impact of HIV/AIDS in the Context of Socioeconomic Stressors: an Evidence-Driven Approach

In this paper, we present an agent-based simulation model of the social impacts of HIV/AIDS in villages in the Sekhukhune district of the Limpopo province in South Africa. AIDS is a major concern in South Africa, not just in terms of disease spread but also in term of its impact on society and economic development. The impact of the disease cannot however be considered in isolation from other stresses, such as food insecurity, high climate variability, market fluctuations and variations in support from government and non-government sources. The model described in this paper focuses on decisions made at the individual and household level, based upon evidence from detailed case studies, and the different types of networks between these players that influence their decision making. Key to the model is that these networks are dynamic and co-evolving, something that has rarely been considered in social network analysis. The results presented here demonstrate how this type of simulation can aid better understanding of this complex interplay of issues. In turn, we hope that this will prove to be a powerful tool for policy development.Agent-Based Social Simulation, Evidence-Driven Modeling, Socioeconomic Stressors, HIV/AIDS Impact