The growth of an economy can be divided into two parts: growth of population, and growth of output per person, which is commonly known as "productivity." The McKinsey Global Institute looks at these patterns over th
We show that the behaviour of Bitcoin has interesting similarities to stock and precious metal markets, such as gold and silver. We report that whilst Litecoin, the second largest cryptocurrency, closely follows Bitcoin's behaviour, it does not show all the reported properties of Bitcoin. Agreements between apparently disparate complexity measures have been found, and it is shown that statistical, information-theoretic, algorithmic and fractal measures have different but interesting capabilities of clustering families of markets by type. The report is particularly interesting because of the range and novel use of some measures of complexity to characterize price behaviour, because of the IRS designation of Bitcoin as an investment property and not a currency, and the announcement of the Canadian government's own electronic currency MintChip.
By W. Brian Arthur; External Professor, Santa Fe Institute; Visiting Researcher, Palo Alto Research Center.
Economics is a stately subject, one that has altered little since its modern foundations were laid in Victorian times. Now it is changing radically. Standard economics is suddenly being challenged by a number of new approaches: behavioral economics, neuroeconomics, new institutional economics. One of the new approaches came to life at the Santa Fe Institute: complexity economics.
Complexity economics got its start in 1987 when a now-famous conference of scientists and economists convened by physicist Philip Anderson and economist Kenneth Arrow met to discuss the economy as an evolving complex system. That conference gave birth a year later to the Institute’s first research program – the Economy as an Evolving Complex System – and I was asked to lead this. That program in turn has gone on to lay down a new and different way to look at the economy.
Which famous economist are you most similar to? To find out, answer the questions below and watch your dot move around the graph. Click on blue circles to see economist webpages. Click on questions to see survey data.
All questions and data were taken from the excellent IGM Economic Experts Panel, a survey of a diverse set of economists.
To understand market perturbations like crashes and bubbles, SFI Distinguished Professor Geoffrey West and three co-authors advocate a revised view that treats an economy like biologists would think about an ecosystem rife with evolutionary dynamics. "Here, we emphasize the importance of an ecosystems perspective: it is precisely due to the web of interdependencies among all companies that the unrestrained growth of one, or a few, companies leads to systematic imbalance." The growth of such imbalances, they say, is a result of evolutionary processes often leading to feedback loops. Drawing on their recent paper in Proceedings of the Royal Society A, for example, the authors suggest that two mechanisms "act as catalysts for the emergence of a crisis. The first is banks copying the business models of the most (short-term) successful bank, which leads to loss of both diversity and resilience. The second is investors such as fund managers increasing their appetite for risk by trying to outperform competitors."
Economic models of animal behaviour assume that decision-makers are rational, meaning that they assess options according to intrinsic fitness value and not by comparison with available alternatives. This expectation is frequently violated, but the significance of irrational behaviour remains controversial. One possibility is that irrationality arises from cognitive constraints that necessitate short cuts like comparative evaluation. If so, the study of whether and when irrationality occurs can illuminate cognitive mechanisms. We applied this logic in a novel setting: the collective decisions of insect societies. We tested for irrationality in colonies of Temnothorax ants choosing between two nest sites that varied in multiple attributes, such that neither site was clearly superior. In similar situations, individual animals show irrational changes in preference when a third relatively unattractive option is introduced. In contrast, we found no such effect in colonies. We suggest that immunity to irrationality in this case may result from the ants’ decentralized decision mechanism. A colony's choice does not depend on site comparison by individuals, but instead self-organizes from the interactions of multiple ants, most of which are aware of only a single site. This strategy may filter out comparative effects, preventing systematic errors that would otherwise arise from the cognitive limitations of individuals.
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.
The field of Big Data requires more clarity and I am a big fan of simple explanations. This is why I have attempted to provide simple explanations for some of the most important technologies and terms you will come across if you’re looking at getting into big data.
THIRTY kilometres south of central Chennai, just out of earshot of the honking, hand-painted lorries roaring up Old Mahabalipuram Road, you seem to have reached rural India. The earth road buckles and heaves. Farmers dressed in Madras-checked dhotis rest outside huts roofed with palm leaves. Goats wander about. Then you turn a corner, go through a gate, and arrive in California. Lakewood Enclave is a new development of 28 large two-storey houses, wedged tightly together. The houses are advertised as “Balinese-style”, although in truth they are hard to tell apart from any number of suburban homes around the world. Outside, the houses are painted a pale pinkish-brown; inside, the walls are white, the floors are stone and the design is open-plan. They each have three bedrooms (middle-class Tamil families are small these days) and a covered driveway to protect a car from the melting sun. Just one detail makes them distinctively Indian: a cupboard near the door for Hindu gods. (...)
As more and more users access social network services from smart devices with GPS receivers, the available amount of geo-tagged information makes repeating classical experiments possible on global scales and with unprecedented precision. Inspired by the original experiments of Milgram, we simulated message routing within a representative sub-graph of the network of Twitter users with about 6 million geo-located nodes and 122 million edges. We picked pairs of users from two distant metropolitan areas and tried to find a route between them using local geographic information only; our method was to forward messages to a friend living closest to the target. We found that the examined network is navigable on large scales, but navigability breaks down at the city scale and the network becomes unnavigable on intra-city distances. This means that messages usually arrived to the close proximity of the target in only 3–6 steps, but only in about 20% of the cases was it possible to find a route all the way to the recipient, in spite of the network being connected.
Complexity in Economics: Cutting Edge Research (New Economic Windows) [Marisa Faggini, Anna Parziale] on Amazon.com. *FREE* shipping on qualifying offers. In this book, leading experts discuss innovative components of complexity theory and chaos theory in economics. The underlying perspective is that investigations of economic phenomena should view these phenomena not as deterministic
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.