It is not only the world economy that is in crisis. The teaching of economics is in crisis too, and this crisis has consequences far beyond the university walls. What is taught shapes the minds of the next generation of policymakers, and therefore shapes the societies we live in. We, 42 associations of economics students from 19 different countries, believe it is time to reconsider the way economics is taught. We are dissatisfied with the dramatic narrowing of the curriculum that has taken place over the last couple of decades. This lack of intellectual diversity does not only restrain education and research. It limits our ability to contend with the multidimensional challenges of the 21st century - from financial stability, to food security and climate change. The real world should be brought back into the classroom, as well as debate and a pluralism of theories and methods. This will help renew the discipline and ultimately create a space in which solutions to society’s problems can be generated.(...)
There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.
We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.
A cosa serve l'approccio della complessità...? Bertuglia e Vaio danno da sempre svariate risposte pratiche ed efficaci a numerosi ambiti anche delle Scienze Sociali, dell'Economia, dell'Urbanistica. Un articolo su come, in pratica, la Complessità ci aiuti a decidere e a vivere meglio.
Visualize, for a moment, the industrial economy as a massive system of conveyor belts—one that directs materials and energy from resource-rich countries to manufacturing powerhouses, such as China, and then spirits the resulting products onward to the United States, Europe, and other destinations, where they are used, discarded, and replaced. While this image is an exaggeration, it does capture the essence of the linear, one-way production model that has dominated global manufacturing since the onset of the Industrial Revolution.
Changing consumer behaviour is key to reducing the environmental effects of industrialised societies. Social practice theories provide an integrated approach to understanding consumer behaviour. The mechanisms underlying the emergence and diffusion of social practices are however until now poorly understood. This paper presents a conceptual framework and an abstract agent-based simulation model for generating social practices which use and extend approaches from social practice theories. The main results are twofold. First, the simulation model is able to generate social practices, what confirms that the conceptual framework captures relevant elements and processes. Second, a new mechanism for behavioural lock-in is identified that provides additional insights into the widely acknowledged challenge of changing social practices and respective consumption.
by Georg Holtz
Journal of Artificial Societies and Social Simulation 17 (1) 17
This course is anchored on the seven main sections associated with the key Economics areas where the complex systems studies approach to economy has been known to have important influence. These sections are: Section I: A Philosophical and Methodological approach to Economy using Complexity Sciences; Section II: The structure of interaction; Section III: Macroeconomics and Growth; Section IV: Financial Markets; Section V: International and Monetary Economy Dynamics; Section VI: Regional Economic Systems; Section VII: Evolutionary Economic Dynamics. Other than discussing the literature, the students will be invited to model, implement and discuss some of the underlying mentioned models using social simulation programming libraries.
Figuring out why financial crises emerge in seemingly stable economies is tough. Widespread collapses are notoriously difficult to predict - to do so requires a comprehensive view of a complex, interconnected system. But help may be at hand: experts in finance are now looking to certain fields of ecology to help provide this viewpoint.
We investigate the failure mechanisms of load sharing complex systems. The system is composed of multiple nodes or components whose failures are determined based on the interaction of their respective strengths and loads (or capacity and demand respectively) as well as the ability of a component to share its load with its neighbors when needed. We focus on two distinct mechanisms to model the interaction between components' strengths and loads. The failure mechanisms of these two models demonstrate temporal scaling phenomena, phase transitions and multiple distinct failure modes excited by extremal dynamics. For critical ranges of parameters the models demonstrate power law and exponential failure patterns. We identify the similarities and differences between the two mechanisms and the implications of our results to the failure mechanisms of complex systems in the real world.
What makes a system complex? It is a perplexing problem--both its description and its quantification. One might think that the description of a system as complex would suggest it has many subsystems each acting in accordance with its own rules, and interacting with each of the other subsystems in ways that we find difficult to describe. But there are systems involving very few "parts" which exhibit the kind of behaviour we call complex.
by Wolfram Elsner, Torsten Heinrich, Henning Schwardt
The Microeconomics of Complex Economies uses game theory, modeling approaches, formal techniques, and computer simulations to teach useful, accessible approaches to real modern economies. It covers topics of information and innovation, including national and regional systems of innovation; clustered and networked firms; and open-source/open-innovation production and use. Its final chapter on policy perspectives and decisions confirms the value of the toolset.
Written so chapters can be used independently, the book includes simulation packages and pedagogical supplements. Its formal, accessible treatment of complexity goes beyond the scopes of neoclassical and mainstream economics. The highly interdependent economy of the 21st century demands a reconsideration of orthodox economic theories.
Economics is a stately subject, prim and respectable, one that’s altered little since its modern foundations were laid in Victorian times. Now it is changing rapidly, thanks to the work of a small group of researchers over the last two decades in New Mexico. (...)
This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.
Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.
Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.
Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, et al. (2013) Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries. PLoS ONE 8(12): e81707. http://dx.doi.org/10.1371/journal.pone.0081707
The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.
Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agent-based simulations were conducted to investigate how collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding.
Maximizing returns on financial investments depends on accurately understanding and effectively accounting for weather and climate risks, according to a new study by the American Meteorological Society (AMS) Policy Program.
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