Introduction to the special issue on the 24th annual Workshop on Economic science with Heterogeneous Interacting Agents, London, 2019 (WEHIA 2019)

Abstract

Economics is traditionally divided into microeconomics, which studies the behavior of individual agents (such as a firm), and macroeconomics, which studies the behavior of an economy at the aggregate scale (e.g., at a country level), with the connection between the two scales receiving less attention. Economic systems are, however, complex systems (Kirman 2010), where aggregate properties and regularities emerge from interactions between many heterogeneous agents. Understanding the build-up from individual dynamics to aggregate properties is therefore essential to understand the functioning of these systems. Agent-based modeling has proved to be an important computational tool for the exploration of the link between micro and macro, as this approach allows to design and simulate the evolution of artificial but realistic systems that can then be studied at different scales in a controlled setting. This approach has for instance allowed to understand how stylized facts of financial and economic systems can emerge from interactions and coordination among agents (see for instance Lux and Marchesi 1999; LeBaron et al. 1999; Challet et al. 2004; Delli Gatti et al. 2005; Dosi et al. 2010; Dawid and Delli Gatti 2018). Networks are another useful modeling tool in this context, as they can be used to represent complex patterns of relationships between agents and to understand how the aggregate properties of a system are impacted by the structure of its interactions. Networks have, for instance, been successfully employed for the study of trading relationships between countries (see, e.g., Fagiolo et al. 2008, 2013), for quantifying the position of industries within supply chains (see, e.g., Antràs et al. 2012), or to characterize the structural properties of interbank systems and their relation to systemic risk (see, e.g., Boss et al. 2004; Iori et al. 2008; Haldane 2009; Fricke and Lux 2015; Jackson and Pernoud 2021; Bardoscia et al. 2021). Networks and agent-based models have also been recently used for modeling the impact of the COVID-19 pandemic on the economy (see, e.g., Pichler et al. 2020; del Rio-Chanona et al. 2020). Agent-based models and networks represent the common thread in the papers included in the present issue of the Journal of Economic Interaction and Coordination, which is associated with the 24th edition—the last one before the COVID-19 pandemic—of the Workshop on Economic science with Heterogeneous Interacting Agents (WEHIA). This edition was hosted by City University London in 2019. As customarily, the conference was also preceded by a Ph.D. school, which was held at University College London. WEHIA has been over the years an important venue for the promotion and dissemination of research devoted to the modeling of economic systems as complex systems, and it has acted as an engagement platform for researchers coming from different disciplines. The 2019 edition was no exception, and the papers in this special issue well represent the breadth of contributions presented at the workshop: Bartesaghi et al. (2020) introduce a new method based on communicability distances to identify communities in networks. The method works by constructing a graph that connects nodes whose communicability distance is below a given threshold. The threshold is then varied and optimized, so that on one hand the method does not require the number of communities as an input, and on the other hand the robustness of the detected communities can be assessed by changing the threshold. The authors showcase their method by presenting a study of the structure of communities of the world trade network. Macchiati et al. (2021) introduce a model inspired by epidemic spreading processes to discuss how liquidity shortages can propagate in a network of interbank loans. While most analyses of financial contagion focused on individual jurisdictions, here the authors aim at studying the European interbank network. To this end, they explicitly account for country-specific risk factors, and they also extend maximum entropy network reconstruction methods to account for the existence of country-specific blocks in the network. Riccetti et al. (2021) develop an agent-based model of credit relationships between banks and firms, which are then studied in different phases of the business cycle. The model is shown to capture several empirical stylized facts. A policy application of the model to the Basel III countercyclical capital buffer policy shows that the effectiveness of the policy depends on the properties of the business cycle, a fact that should be carefully considered by policy makers. Li et al. (2021) propose a network-based approach for the automatic construction of a researcher’s expertise profile from online databases. The method is showcased on a bibliographic database of life sciences, and it is shown to outperform existing methodologies. The proposed method looks promising in relation to several applications. It could for instance allow funding agencies to identify the most appropriate reviewers for a proposal, or to identify potential gaps of expertise in a team of researchers. Heinrich et al. (2021) develop an agent-based model to study the consequences of the homogeneity of risk models used in the insurance and reinsurance industry. They show that the availability of a small number of risk models increases risk and lowers profits, and they conclude that it would be beneficial for regulators to promote model diversity. Lin et al. (2021) present a comparison between different asset pricing models to dissect returns in the Chinese stock market under two frameworks of cross section and time series factor. The paper shows that, in the framework of cross section factors, the model by Liu et al. (2019), which uses earning-to-price factors, outperforms the one by Fama and French (1993) using the book-to-market factor. Liuzzi and Vié (2022) tackle the issue of the coordination required to respond to the challenges posed by climate change. By means of an agent-based model in which the decision-making process of agents is grounded in the neuroscience literature, they study the emergence of cooperation between agents in the face of an imminent environmental collapse. We thank all contributors for their submissions, which have made this volume another successful one in the Journal of Economic Interaction and Coordination, and we very much hope all the readers of the Journal will enjoy these contributions as much as we did.