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Perhaps the most developed field is the analysis of instabilities as well as economic simulation. The main lines of research, however, are clear and represent a departure from the neoclassical theory. They can be summarized thus:. Social values and objectives must be separated from economic theory, that is, we have to separate political economics from pure economic theory.
Economies are systems in continuous evolution. This fact is not appreciated in neoclassical economics which considers only aggregated quantities. The output of economies is primarily the creation of order and complexity, both at the level of products and social structures. Again, this fact is ignored by neoclassical economics, which takes a purely quantitative approach without considering changes in the quality of the output or the power structure of economies.
Economies are never in a state of equilibrium, but are subject to intrinsic instabilities. Economic theory needs to consider economies as physical systems in a physical environment; it therefore needs to take into consideration environmental constraints. Let's now discuss how new directions in economic theory are addressing the above. As mentioned above, economic theory should be clearly separated from political economics. Economies are human artifacts engineered to serve a number of purposes. Most economic principles are not laws of nature but reflect social organization.
As in any engineering enterprise, the engineering objectives should be kept separate from the engineering itself and the underlying engineering principles and laws. Determining the objectives is the realm of political economics; engineering the objectives is the realm of economic theory. One might object that there is a contradiction between the notion of economies as engineered artifacts and the notion of economies as evolving systems subject to evolutionary rules. This contradiction is akin to the contradiction between studying human behavior as a mechanistic process and simultaneously studying how to improve ourselves.
We will not try to solve this contradiction at a fundamental level. Economies are systems whose evolution is subject to uncertainty. Of course the decisions we make about engineering our economies are part of the evolutionary process. Pragmatically, if not philosophically, it makes sense to render our objectives explicit. The separation between objectives and theory is not always made clear, especially in light of political considerations. Actually, there should be multiple economic theories corresponding to different models of economic organization.
Currently, however, the mainstream model of free markets is the dominant model; any other model is considered either an imperfection of the free-market competitive model or a failure, for example Soviet socialism. This is neither a good scientific attitude nor a good engineering approach. The design objectives of our economies should come first, then theory should provide the tools to implement the objectives. New economic thinking is partially addressing this need.
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In the aftermath of the — financial crisis and the subsequent questioning of mainstream economics, some economists are tackling socially-oriented issues, in particular, the role and functioning of the banking system, the effect of the so-called austerity measures, and the social and economic implications of income and wealth inequality. There is a strain of economic literature, albeit small, known as meta-economics, that is formally concerned with the separation of the objectives and the theory in economics.
The term metaeconomics was first proposed by Karl Menger, an Austrian mathematician and member of the Vienna Circle 5. Influenced by David Hilbert's program to give a rigorous foundation to mathematics, Menger proposed metaeconomics as a theory of the logical structure of economics. The term metaeconomics was later used by Schumacher [ 30 ] to give a social and ethical foundation to economics, and is now used in this sense by behavioral economists. Metaeconomics, of course, runs contrary to mainstream economics which adheres to the dogma of optimality and excludes any higher-level discussion of objectives.
What are the characteristics of evolutionary complex systems such as our modern economies? An introduction can be found in Beinhocker [ 31 ]. Georgescu-Roegen [ 32 ] distinguishes two types of evolution, slow biological evolution and the fast cultural evolution typical of modern economies. Thus, the term bioeconomics.
The entropy accounting of the second law of thermodynamics implies that any local increase of order is not without a cost: it requires energy and, in the case of the modern economies, produces waste and pollution. Georgescu-Roegen argued that because classical economics does not take into account the basic laws of entropy, it is fundamentally flawed. When Georgescu-Roegen first argued his thesis back in the s, economists did not bother to respond. Pollution and depletion of natural resources were not on any academic agenda.
But if economics is to become a scientific endeavor, it must consider the entropy accounting of production. While now much discussed, themes such as energy sources, sustainability, and pollution are still absent from the considerations of mainstream economics. It should be clear that these issues cannot be solved with a mathematical algorithm. As a society, we are far from being able, or willing, to make a reasonable assessment of the entropy balance of our activities, economic and other. But a science of economics should at least be able to estimate perhaps urgently the time scales of these processes.
Economic growth and wealth creation are therefore based on creating order and complexity. Understanding growth, and eventually business cycles and instabilities, calls for an understanding of how complexity evolves—a more difficult task than understanding the numerical growth of output. Older growth theories were based on simple production functions and population growth. Assuming that an economy produces a kind of composite good, with appropriate production functions, one can demonstrate that, setting aside capital, at any time step the economy increases its production capabilities and exhibits exponential growth.
An increase of complexity is the key ingredient of economic growth. The study of economic complexity is not new. At the beginning of the twentieth century, the Austrian School of Economics introduced the idea, typical of complex systems, that order in market systems is a spontaneous, emerging property.
As mentioned above, this idea was already present in Adam Smith's invisible hand that coordinates markets. The philosopher-economist Friedrick Hayek devoted much theoretical thinking to complexity and its role in economics. There, under the direction of the economist Bryan Arthur, researchers developed one of the first artificial economies. Complexity economics is now a subject of research at many universities and economic research centers.
How can systems increase their complexity spontaneously, thereby evolving? Lessons from biology might help. Chaitin [ 12 ] proposed a mathematical theory based on the theory of algorithmic complexity that he developed to explain Darwinian evolution. Chaitin's work created a whole new field of study—metabiology—though his results are not universally accepted as proof that Darwinian evolution works in creating complexity. While no consensus exists, and no existing theory is applicable to economics, it is nevertheless necessary to understand how complexity is created if we want to understand how economies grow or eventually fail to grow.
Assuming the role of complexity in creating economic growth and wealth, how do we compare the complexity of objects as different as pasta, washing machines and computers? And how do we measure complexity? While complexity can be measured by a number of mathematical measures, such as those of the algorithmic theory of complexity, there is no meaningful way to aggregate these measures to produce a measure of the aggregate output.
Mainstream economics uses price—the market value of output—to measure the aggregate output. But there is a traditional debate on value, centered on the question of whether price is a measure of value. A Marxist economist would argue that value is the amount of labor necessary to produce that output. We will stay within market economies and use price to measure aggregate output. The next section discusses the issues surrounding aggregation by price. Aggregating so many eventually rapidly changing products 7 quantitatively by physical standards is an impossible task.
We can categorize products and services, such as cars, computers, and medical services but what quantities do we associate to them? Economics has a conceptually simple answer: products are aggregated in terms of price, the market price in free-market economies as mentioned above, or centrally planned prices in planned economies. But there are two major problems with this. First, in practice, aggregation is unreliable: Not all products and services are priced; many products and services are simply exchanged or self-produced; black and illegal economies do exist and are not negligible; data collection can be faulty.
Therefore, any number which represents the aggregate price of goods exchanged has to be considered uncertain and subject to error. Second, prices are subject to change. If we compare prices over long periods of time, changes in prices can be macroscopic. For example, the price of an average car in the USA increased by an order of magnitude from a few thousand dollars in the s to a tens of thousands of dollars in the s. Certainly cars have changed over the years, adding features such as air conditioning, but the amount of money in circulation has also changed.
The important question to address is whether physical growth corresponds to the growth of nominal GNP. The classical answer is no, as the level of prices changes. But there is a contradiction here: to measure the eventual increase in the price level we should be able to measure the physical growth and compare it with the growth of nominal GNP.
But there is no way to measure realistically physical growth; any parameter is arbitrary. The usual solution to this problem is to consider the price change increase or decrease of a panel of goods considered to be representative of the economy. This process has two important limitations. First, the panel of representative goods does not represent a constant fraction of the total economy nor does it represent whole sectors, such as luxury products or military expenditures. Second, the panel of representative goods is not constant as products change, sometimes in very significant ways.
Adopting an operational point of view, the meaning of the real GNP is defined by how it is constructed: it is the nominal GNP weighted with the price of some average panel of goods. Many similar constructions would be possible in function of different choices of the panel of representative goods. There is therefore a fundamental arbitrariness in how real GNP is measured.
The growth of the real GNP represents only one of many different possible concepts of growth. Growth does exist in some intuitive sense, but quantifying it in some precise way is largely arbitrary. Here we are back to the fundamental issue that economies are complex systems. Describing mathematically the evolution of the complexity of an economy is a difficult, perhaps impossible, task.
When we aggregate by price, the problem becomes more tractable because there are constraints to financial transactions essentially due to the amount of money in circulation and rules related to the distribution of money to different agents. But it does not make sense to aggregate by price the output of an entire country. We suggest that it is necessary to model different sectors and understand the flows of money. Some sectors might extend over national boundaries. Capital markets, for example, are truly international we do not model them as purely national ; the activity of transnational corporations can span a multitude of countries.
What is required is an understanding of what happens under different rules. Here we come upon what is probably the fundamental problem of economics: the power structure. Who has the power to make decisions? Studying human structures is not like studying the behavior of a ferromagnet. Decisions and knowledge are intertwined in what the investor George Soros has called the reflexivity of economies.
In neoclassical economics, finance is transparent; in real-world economies, it is far from being the case. Real economies produce complexity and evolve in ways that are difficult to understand. Generally speaking, the financial and banking systems allow a smooth evolution of the economic system, providing the money necessary to sustain transactions, thereby enabling the sale and purchase of goods and services. While theoretically providing the money needed to sustain growth, the financial and banking systems might either provide too little money and thereby constrain the economy, or provide too much money and thereby produce inflation, especially asset inflation.
Asset inflation is typically followed by asset deflation as described by Minsky [ 33 ] in his financial instability hypothesis. Minsky argued that capitalist economies exhibit asset inflations due to the creation of excess money, followed by debt deflations that, because of the fragile financial systems, can end in financial and economic crises. Since Minsky first formulated his financial instability hypothesis, many changes and additional analysis have occurred. First, it has become clear that the process of money creation is endogenous, either by the central banks or commercial banks.
What has become apparent, especially since the — financial crisis, is that central banks can create money greatly in excess of economic growth and that this money might not flow uniformly throughout the economy but follow special, segregated paths or flows , eventually remaining in the financial system, thereby producing asset inflation but little to no inflation in the real economy.
Another important change has been globalization, with the free flow of goods and capital in and out of countries, in function of where it earns the highest returns or results in the lowest tax bill. Some Western economies have been successful in specializing in added-value sectors such as financial services.
These countries have experienced huge inflows of capital from all over the world, creating an additional push toward asset inflation. But within a few decades of the beginning of globalization, some of those economies that produced low-cost manufactured goods have captured the entire production cycle from design, engineering, manufacturing, and servicing.
Unable to compete, Western economies started an unprecedented process of printing money on a large scale with, as a result, the recurrence of financial crashes followed by periods of unsustainable financial growth. Studying such crises is a major objective of economics. ETH-Zurich's Didier Sornette, who started his career as a physicist specialized in forecasting rare phenomena such as earthquakes, made a mathematical analysis of financial crises using non-linear dynamics and following Minsky's financial instability hypothesis.
Together with his colleague Peter Cauwels, Sornette and Cauwels [ 34 ] hypothesize that financial crises are critical points in a process of superexponential growth of the economy. As discussed above, the mathematical analysis of complex system is difficult and might indeed be an impossible task. To overcome this problem, an alternative route is the development of agent-based artificial economies. Artificial economies are computer programs that simulate economies. Agent-based artificial economies simulate real economies creating sets of artificial agents whose behavior resembles the behavior of real agents.
The advantage of artificial economies is that they can be studied almost empirically without the need to perform mathematical analysis, which can be extremely difficult or impossible. The disadvantage is that they are engineered systems whose behavior depends on the engineering parameters. The risk is that one finds exactly what one wants to find. The development of artificial markets with zero-intelligence agents was intended to overcome this problem, studying those market properties that depend only on the trading mechanism and not on agent characteristics.
There is by now a considerable literature on the development of artificial economies and the design of agents. See Chakraborti et al. Among classical economists, efforts are underway to bring the discipline closer to an empirical science. Some of the adjustments underway are new versions of DSGE theories which now include a banking system and deviations from perfect rationality as well as the question of liquidity.
As observed above, DSGE models are a sort of complex system made up of many intelligent agents. It is therefore possible, in principle, to view complex systems as an evolution of DSGEs. However, most basic concepts of DSGEs, and in particular equilibrium, rational expectations, and the lack of interaction between agents, have to be deeply modified. In this paper we have explored the status of economics as an empirical science. We first analyzed the epistemology of economics, remarking on the necessity to carefully analyze what we consider observations e.
- Major recent departures from the standard model.
- Strong Enough.
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In physics, observables are processes obtained through the theory itself, using complex instruments. Physical theory responds to empirical tests in toto ; individual statements have little empirical content. In economics, given the lack of a comprehensive theory, observations are elementary observations, such as prices, and theoretical terms are related to observables in a direct way, without cross validation. This weakens the empirical content of today's prevailing economic theory. We next critiqued neoclassical economics, concluding that it is not an empirical science but rather the study of an artificial idealized construction with little connection to real-world economies.
This conclusion is based on the fact that neoclassical economics is embodied in DSGE models which are only weakly related to empirical reality. We successively explored new ideas that hold the promise of developing economics more along the lines of an empirical science. Econophysics, an interdisciplinary effort to place economics on a sure scientific grounding, has produced a number of results related to the analysis of financial time series, in particular the study of inverse power laws. But while econophysics has produced a number of models, it has yet to propose a new global economic theory.
Other research efforts are centered on looking at economies as complex evolutionary systems that produce order and increasing complexity. Environmental constraints due to the accounting of energy and entropy are beginning to gain attention in some circles.
As with econophysics, the study of the economy as a complex system has yet produced no comprehensive theory. The most developed area of new research efforts is the analysis of instabilities, building on Hyman Minsky's financial instability hypothesis. Instabilities are due to interactions between a real productive economy subject to physical constraints, and a financial system whose growth has no physical constraints. Lastly, efforts are also being made among classical economists to bring their discipline increasingly into the realm of an empirical science, adding for example the banking system and boundedly rational behavior to the DSGE.
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Among the major proponents of neoclassical economic thinking are Robert Lucas and Eugene Fama, both from the University of Chicago and both recipients of the Nobel Prize in Economics. Attempts are being made to address some of the shortfalls of neoclassical economics, such as the consideration of the banking system, money creation and liquidity.
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To determine simultaneity, we perform operations based on sending and receiving signals that travel at finite speed. Given the invariance of the speed of light, these operations make simultaneity dependent on the frame of reference. See Section Econophysics and Econometrics below. Here we use metaeconomics in analogy with Chaitin's metabiology. Google Scholar. Two Dogmas of Empiricism Bridgman WP. The Logic of Modern Physics Hempel CG. New York: Free Press Feyerabend P.
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