Showing posts with label 悟り. Show all posts
Showing posts with label 悟り. Show all posts

Friday, November 9, 2012

Model Thinking - michigan university coursea

This course will consist of twenty sections. As the course proceeds, I will fill in the descriptions of the topics and put in readings.
Section 1: Introduction: Why Model? In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories:
  1. To be an intelligent citizen of the world
  2. To be a clearer thinker
  3. To understand and use data
  4. To better decide, strategize, and design
There are two readings for this section. These should be read either after the first video or at the completion of all of the videos.
The Model Thinker: Prologue, Introduction and Chapter 1
Why Model? by Joshua Epstein
Section 2: Sorting and Peer Effects We now jump directly into some models. We contrast two types of models that explain a single phenomenon, namely that people tend to live and interact with people who look, think, and act like themselves. After an introductory lecture, we cover famous models by Schelling and Granovetter that cover these phenomena. We follows those with a fun model about standing ovations that I wrote with my friend John Miller.
In this second section, I show a computational version of Schelling's Segregation Model using NetLogo. Netlogo is free software authored by Uri Wilensky or Northwestern University. I will be using NetLogo several times during the course. It can be downloaded here:
NetLogo
The Schelling Model that I use can be found by clicking on the "File" tab, then going to "Models Library". In the Models Library directory, click on the arrow next to the Social Science folder and then scroll down and click on the model called Segregation.
The readings for this section include some brief notes on Schelling's model and then the academic papers of Granovetter and Miller and Page. I'm not expecting you to read those papers from start to end, but I strongly encourage you to peruse them so that you can see how social scientists frame and interpret models.
Notes on Schelling
Granovetter Model
Miller and Page Model
Section 3: Aggregation In this section, we explore the mysteries of aggregation, i.e. adding things up. We start by considering how numbers aggregate, focusing on the Central Limit Theorem. We then turn to adding up rules. We consider the Game of Life and one dimensional cellular automata models. Both models show how simple rules can combine to produce interesting phenomena. Last, we consider aggregating preferences. Here we see how individual preferences can be rational, but the aggregates need not be.
There exist many great places on the web to read more about the Central Limit Theorem, the Binomial Distribution, Six Sigma, The Game of LIfe, and so on. I've included some links to get you started. The readings for cellular automata and for diverse preferences are short excerpts from my books Complex Adaptive Social Systems and The Difference Respectively.
Central Limit Theorem
Binomial Distribution
Six Sigma
Cellular Automata1
Cellular Automata2
Diverse Preferences
Section 4: Decision Models In this section, we study some models of how people make decisions. We start by considering multi criterion decision making. We then turn to spatial models of decision making and then decision trees. We conclude by looking at the value of information..
The reading for multi-criterion decision making will be my guide for the Michigan Civil Rights Initiative. It provides a case study for how to use this technique. For spatial voting and decision models, there exist many great power point presentations and papers on the web. The Decision Tree writings are from Arizona State University's Craig Kirkwood.
Multi Criterion Decision Making Case Study
Spatial Models
Decision Theory
Section 5: Models of People: Thinking Electrons In this section, we study various ways that social scientists model people. We study and contrast three different models. The rational actor approach, behavioral models , and rule based models . These lectures provide context for many of the models that follow. There's no specific reading for these lectures though I mention several books on behavioral economics that you may want to consider. Also, if you find the race to the bottom game interesting just type "Rosemary Nagel Race to the Bottom" into a search engine and you'll get several good links. You can also find good introductions to "Zero Intelligence Traders" by typing that in as well.
Here is a link to a brief primer on behavioral economics that has more references.
Short Primer on Behavioral Economics
Section 6: Linear Models In this section, we cover linear models. We start by looking at categorical models, in which data gets binned into categories. We use this simple framework to introduce measures like mean, variance, and R-squared. We then turn to linear models describing what linear models do, how to read regression output (a valuable skill!) and how to fit nonlinear data with linear models. These lectures are meant to give you a "feel" for how linear models are used and perhaps to motivate you to take a course on these topics. I conclude this section by highlighting a distinction between what I call Big Coefficient thinking and New Reality thinking. The readings for this section consist of two short pieces written by me, but you can find abundant resources on the web on linear models, R-squared, regression, and evidence based thinking.
Categorical Models
Linear Models
Section 7: Tipping Points In this section, we cover tipping points. We focus on two models. A percolation model from physics that we apply to banks and a model of the spread of diseases. The disease model is more complicated so I break that into two parts. The first part focuses on the diffusion. The second part adds recovery. The readings for this section consist of two excerpts from the book I'm writing on models. One covers diffusion. The other covers tips. There is also a technical paper on tipping points that I've included in a link. I wrote it with PJ Lamberson and it will be published in the Quarterly Journal of Political Science. I've included this to provide you a glimpse of what technical social science papers look like. You don't need to read it in full, but I strongly recommend the introduction. It also contains a wonderful reference list.
Tipping Points
DIffusion and SIS
Lamberson and Page: Tipping Points (READ INTRO ONLY)
Section 8: Economic Growth In this section, we cover several models of growth. We start with a simple model of exponential growth and then move on to models from economics, with a focus on Solow's basic growth model. I simplify the model by leaving out the labor component. These models help us distinguish between two types of growth: growth that occurs from capital accumulation and growth that occurs from innovation.
Growth Models
Section 9: Diversity and Innovation In this section, we cover some models of problem solving to show the role that diversity plays in innovation. We see how diverse perspectives (problem representations) and heuristics enable groups of problem solvers to outperform individuals. We also introduce some new concepts like "rugged landscapes" and "local optima". In the last lecture, we'll see the awesome power of recombination and how it contributes to growth. The readings for this chapters consist on an excerpt from my book The Difference courtesy of Princeton University Press.
Diversity and Problem Solving
Section 10: Markov Processes In this section, we cover Markov Processes. Markov Processes capture dynamic processes between a fixed set of states. For example, we will consider a process in which countries transition between democratic and dictatorial. To be a Markov Process, it must be possible to get from any one state to any other and the probabilities of moving between states must remain fixed over time. If those assumptions hold, then the process will have a unique equilibrium. In other words, history will not matter. Formally, this result is called the Markov Convergence Theorem. In addition to covering Markov Processes, we will also see how the basic framework can be used in other applications such as determining authorship of a text and the efficacy of a drug protocol.
Markov Processes
Section 11: Lyapunov Functions Models can help us to determine the nature of outcomes produced by a system: will the system produce an equilibrium, a cycle, randomness, or complexity? In this set of lectures, we cover Lyapunov Functions. These are a technique that will enable us to identify many systems that go to equilibrium. In addition, they enable us to put bounds on how quickly the equilibrium will be attained. In this set of lectures, we learn the formal definition of Lyapunov Functions and see how to apply them in a variety of settings. We also see where they don't apply and even study a problem where no one knows whether or not the system goes to equilibrium or not.
Lyapunov Functions
Section 12: Coordination and Culture In this set of lectures, we consider some models of culture. We begin with some background on what culture is and why it's so important to social scientists. In the analytic section, we begin with a very simple game called the pure coordination game In this game, the players win only if they choose the same action. Which action they choose doesn't matter -- so long as they choose the same one. For example, whether you drive on the left or the right side of the road is not important, but what is important is that you drive on the same side as everyone else. We then consider situations in which people play multiple coordination games and study the emergence of culture. In our final model, we include a desire consistency as well as coordination in a model that produces the sorts of cultural signatures seen in real world data. The readings for this section include some of my notes on coordination games and then the Bednar et al academic paper. In that paper, you see how we used Markov Processes to study the model. There is also a link to the Axelrod Net Logo Model.
Coordination Games
Bednar et al. 2010
Axelrod Culture Model in Netlogo
Section 13: Path Dependence
In this set of lectures, we cover path dependence. We do so using some very simple urn models. The most famous of which is the Polya Process. These models are very simple but they enable us to unpack the logic of what makes a process path dependent. We also relate path dependence to increasing returns and to tipping points. The reading for this lecture is a paper that I wrote that is published in the Quarterly Journal of Political Science
Path Dependence
Section 14: Networks
In this section, we cover networks. We discuss how networks form, their structure -- in particular some common measures of networks -- and their function. Often, networks exhibit functions that emerge, but that we mean that no one intended for the functionality but it arises owing to the structure of the network. The reading for this section is a short article by Steven Strogatz.
Strogatz
Section 15:Randomness and Random Walks
In this section, we first discuss randomness and its various sources. We then discuss how performance can depend on skill and luck, where luck is modeled as randomness. We then learn a basic random walk model, which we apply to the Efficient Market Hypothesis, the ideas that market prices contain all relevant information so that what's left is randomness. We conclude by discussing finite memory random walk model that can be used to model competition. The reading for this section is a paper on distinguishing skill from luck by Michael Mauboussin. Mauboussin: Skill vs Luck
Section 16: The Colonel Blotto Game
In this section, we cover the Colonel Blotto Game. This game was originally developed to study war on multiple fronts. It's now applied to everything from sports to law to terrorism. We will discuss the basics of Colonel Blotto, move on to some more advanced analysis and then contrast Blotto with our skill luck model from the previous section. The readings for this section are an excerpt from my book The Difference and a paper that I wrote with Russell Golman of Carnegie Mellon. You need only read the first four pages of the Golman paper.
Blotto from The Difference
Golman Page: General Blotto
Section 17:The Prisoners' Dilemma and Collective Action
In this section, we cover the Prisoners' Dilemma, Collective Action Problems and Common Pool Resource Problems. We begin by discussion the Prisoners' Dilemma and showing how individual incentives can produce undesirable social outcomes. We then cover seven ways to produce cooperation. Five of these will be covered in the paper by Nowak and Sigmund listed below. We conclude by talking about collective action and common pool resource problems and how they require deep careful thinking to solve. There's a wonderful piece to read on this by the Nobel Prize winner Elinor Ostrom
The Prisoners' Dilemma in the Stanford Encyclopedia of Philosophy
Nowak and Sigmund: Five Ways to Cooperate
Ostrom: Going Beyond Panaceas
Section 18: Mechanism Design: Auctions
In this section, we cover mechanism design. We begin with some of the basics: how to overcome problems of hidden action and hidden information. We then turn to the more applied question of how to design auctions. We conclude by discussion how one can use mechanisms to make decisions about public projects. The readings for this section consist of a piece by the Eric Maskin who won a Nobel Prize for his work on mechanism design and some slides on auctions by V.S. Subrahmanian. The Maskin article can be tough sledding near the end. Don't worry about necessarily understanding everything. Focus on the big picture that he describes.
Maskin: Mechanism Design
V.S. Subrahmanian's auction slides
Section 19: Learning: Replicator Dynamics
In this section, we cover replicator dynamics and Fisher's fundamental theorem. Replicator dynamics have been used to explain learning as well as evolution. Fisher's theorem demonstrates how the rate of adaptation increases with the amount of variation. We conclude by describing how to make sense of both Fisher's theorem and our results on six sigma and variation reduction. The readings for this section are very short. The second reading on Fisher's theorem is rather technical. Both are excerpts from Diversity and Complexity
The Replicator Equation
Fisher's Theorem
Section 20: The Many Model Thinker: Diversity and Prediction
In our final section, we cover the value of ability and diversity to create wise crowds when making predictions. We start off by talking about category models and linear models and how they can be used to make predictions. We then cover the Diversity Prediction Theorem, which provides basic intuition for how collective prediction works. We conclude by talking about the value of having lots of models. The reading for this section is a short explanation of the diversity prediction theorem.
Diversity Prediction Theorem
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Model Thinking - Michigan University coursea


Introduction: Why Model? (Section 1)


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No Free Lunch Theorem:

No Free Lunch Theorem: "states that any two optimization algorithms are equivalent when their performance is averaged across all possible problems."

i.e. any heuristic is the same as any other when averaged across infinite number of cases.

Definitions:

Six Sigma: Six Sigma seeks to improve the quality of process outputs by minimizing variability in manufacturing and business processes, and identifying and removing the causes of defects (errors). (reduce variability to increase production quality)
Fisher's fundamental theorem: The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time. (More variance = better at adapting)

When use them??

Use Six Sigma to reduce variation when rugged landscape is fixed.
--> Concentrate when not much in environment changes.

Use Fisher's fundamental theorem when rugged landscape moves.
More variation allows for more chance of adpation.
--> Variance when environment has changed a lot.




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http://en.wikipedia.org/wiki/Adaptive_landscapes
http://en.wikipedia.org/wiki/Hill_climbing
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Wednesday, November 7, 2012

Untangling Skill and Luck

Untangling Skill and Luck

 http://www4.gsb.columbia.edu/ideasatwork/feature/7317814/Untangling+Skill+and+Luck#?ref=tw

Why is it so important to distinguish skill from luck?
We all know that the outcomes in many activities in life combine both skill and luck. Understanding the relative contributions of each can help us assess past outcomes and, much more importantly, anticipate future outcomes.
It is axiomatic that outcomes will revert to the mean in a system that combines skill and luck. An extremely favorable or unfavorable single outcome is going to be followed by an outcome that has an expected value closer to the average of all results. If a system reverts quickly to the mean, you know that it has lots of luck. If a system is slow to revert to the mean, you know that a good amount of skill is contributing to the outcomes.
Sports provide us with some good examples. In baseball, over a long season the best teams win about 60 percent of their games. But over shorter stretches it’s not unusual to see a team win or lose a whole bunch in a row. Reversion to the mean tells you the expected value of the whole season is still closer to 50-50 or slightly above or below. The Red Sox started off 2011 by losing six games in a row, so their fans were fretting. The Baltimore Orioles started off winning four in a row and their fans are excited. It’s a long season. Reversion to the mean will ultimately assert itself because there’s a lot of randomness in baseball.
In contrast, running races are almost all skill. That means the fastest runners will dominate: line up the same people five or 10 times in a row and the fastest person will win every time. There is no reversion to the mean because there is no luck. Runners finish in the same order every time.
How do you sort out skill from luck?
In sports, you can use statistical analysis to calculate where a given activity falls on the skill-luck continuum. Basically, you figure out what the results would look like if only luck mattered — the win-loss records for teams in a league would follow a binomial distribution — and then you figure out what the results would look like in a world of pure skill. You can then blend the two distributions in a proportion that matches the empirical results. The weightings of the blend indicate the relative contributions of skill and luck.
This leads directly into defining what makes for a good statistic. You see statistics used in many realms, but not all stats are equally useful. Basically, a good statistic should have two features. First, it should be persistent — that is, the current period result should have a decent correlation with the prior result. Second, it should be predictive — that is, if you do well or poorly as measured by the stat, the outcome will be good or bad. You’d be amazed how many stats fail this simple two-feature test.
While measuring skill and luck in business or investing isn’t as simple as sports, you can use these methods to take concrete steps in understanding the relative contributions of both skill and luck in these realms.

Given that business and investing are more complex systems, can you talk more about how skill and luck play out there?
First, it’s important to understand the paradox of skill, which says that as people in an endeavor become more skillful, luck actually becomes more important in determining outcomes. This seems backwards, but another sports example can illustrate the point. Seventy years ago, Ted Williams was the last baseball player to have a full-year batting average over .400. He hit .406 in 1941 and no one’s really come close ever since. Why?
One possible explanation is that players aren’t as good as they used to be, but that’s implausible because training techniques and technology have improved dramatically.
No one has come close to the record because everyone has gotten better: pitchers, batters, fielders. Consequently, while the mean of the batting averages has been relatively stable over the decades, the standard deviation of batting averages is much smaller: neither the best nor worst hitters are as far away from the average hitters as they used to be. When you have more skill, results become more uniform and so luck has more room to influence outcomes. Since the right tail of batting averages is closer to the mean today than it was in 1941, no player has been good enough to sustain a batting average over .400. And Williams, were he playing today, wouldn’t be able to do it either.
The paradox of skill is one reason it is so hard to beat the market. Everybody is smart, has incredible technology, and the government has worked to ensure that the dissemination of information is uniform. So information gets priced into stocks quickly and it’s very difficult to find mispricing. By the way, the standard deviation of mutual fund returns has been declining for the last 50 years or so, just as it has for batting averages.
So where would you place business and investing on the continuum?
Some academics have argued that all market outperformance is attributable to luck. But it’s been demonstrated, by statistical tests and common sense, that there is a component of skill involved. There’s a big difference between saying investing is all luck and saying it has a lot of luck. But as an investor it is important to acknowledge that, on the continuum, investing is closer to the luck side. That doesn’t mean that investors aren’t skillful — it is rather a reflection of the paradox of skill.
This leads to an important mindset: whenever you observe any outcome, you should always ask, “What would I expect by chance?” If the actual outcomes are different than what chance would dictate, there’s likely some element of skill.
So what constitutes skill in a field where probability dominates? The key is to have a good process. In all probabilistic fields, the best performers dwell on process. This is true for great value investors, great poker players, or great sports team managers. It’s all process stuff. It’s hard to do psychologically, emotionally, organizationally, but that is how you get paid.
You highlight the market’s march toward efficiency, which suggests a much more competitive landscape. What does it mean for the future of investment management?
Until we change human nature, or evolution weeds out these kinds of behaviors — which isn’t going to happen anytime soon — there will be opportunities in markets.
Think about handicapping in horseracing. There are two separate issues to consider. One is how fast the horse is likely to run in a particular race. The second is the odds priced on the tote board. If the horse’s chances of success are fully reflected in the odds, you are not going to make any money. You are looking for discrepancies between the horse’s prospects and what’s priced on the board.
The failure to distinguish between the fundamentals of how a company will perform and expectations — that is, what is priced into the security — is probably the biggest error in the investment business. Our natural tendency is to buy when things are going well and sell when things are going badly, irrespective of what’s priced in.
So the behavioral piece will continue to be enormous. Our behavioral finance courses routinely address the common heuristics that humans use and the biases that emanate from these heuristics. These biases include overconfidence, anchoring, confirmation bias, and recency bias. If you’re a normal human being, you exhibit all of these biases. The goal is to develop a process that weaves in tools and techniques to allow you to mitigate or manage those biases.
Seth Klarman, founder of the Baupost Group, has a great line: “Value investing is at its core the marriage of a contrarian streak and a calculator.” The contrarian streak says that you must be willing and able to do something different than what the consensus is telling you to do. That’s extremely difficult. And sometimes the consensus is right: if the movie house is on fire, you should run out the door, not in. So adding the calculator part is key; being a contrarian makes sense if it leads to a mispricing between fundamentals and expectations. That is a market opportunity.
The types of opportunities that present themselves tend to evolve. Think about them like the game Whac-A-Mole, where the moles are opportunities in markets. A mole pops up, you whack it, another one pops up. If an academic publishes a paper on a certain strategy that generates alpha, investors will exploit the strategy and by their very actions will compete away the excess returns. But the good news is that another opportunity pops up somewhere else.

 

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The paradox of skill.

The paradox of skill. As people become better at an activity, the difference between the best and the average and the best and the worst becomes much narrower. As people become more skillful, luck becomes more important. 


The reason that luck is so important isn't that skill isn't relevant when you bring the best of the best together. It's that skill is very high and consistent. That said, over longer periods, skill has a much better chance of shining through.
In the short term you may experience good or bad luck [and that can overwhelm skill], but in the long term luck tends to even out and skill determines results.

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You may do the right things and get bad outcomes or do the wrong things and get good outcomes. So performance results can be very deceptive.
The better way to do it is to focus on the process of decision making that people use and to look for numerical measures that can be proxies for that.

If you look at streaks, not just in investing but in any endeavor, almost by definition they combine skill and luck. You have to have above-average skill and above-average luck to have a streak. If you look at it in the realm of sports, all the streaks are held by the most skillful players, although not all skillful players have streaks. 

http://online.wsj.com/article/SB10000872396390444734804578062890110146284.html
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Saturday, October 13, 2012

A Study of History

A Study of History
http://en.wikipedia.org/wiki/A_Study_of_History

A Study of History is the 12-volume magnum opus of British historian Arnold J. Toynbee, finished in 1961, in which the author traces the development and decay of all of the major world civilizations in the historical record. Toynbee applies his model to each of these civilizations, detailing the stages through which they all pass: genesis, growth, time of troubles, universal state, and disintegration.
The major civilizations, as Toynbee sees them, are: Egyptian, Andean, Sinic, Minoan, Sumerian, Mayan, Indic, Hittite, Hellenic, Western, Orthodox Christian (Russia), Far Eastern, Orthodox Christian (main body), Persian, Arabic, Hindu, Mexican, Yucatec, and Babylonic. There are four 'abortive civilizations' (Abortive Far Western Christian, Abortive Far Eastern Christian, Abortive Scandinavian, Abortive Syriac) and five 'arrested civilizations' (Polynesian, Eskimo, Nomadic, Ottoman, Spartan).

Contents



http://en.wikipedia.org/wiki/World_Systems_Theory

World-systems theory

World-systems theory (also known as world-systems analysis)[1] is a multidisciplinary, macro-scale approach to world history and social change.[1][2]
World-systems analysis stresses that the world-system (and not nation states) should be the primary (but not exclusive) unit of social analysis.[1][3] World-system refers to the international division of labor, which divides the world into core countries, semi-periphery countries and the periphery countries.[2][3] Core countries focus on higher skill, capital-intensive production, and the rest of the world focuses on low-skill, labor-intensive production and extraction of raw materials.[4] This constantly reinforces the dominance of the core countries.[4] Nonetheless, the system is dynamic, in part as a result of revolutions in transport technology, and individual states can gain or lose the core (semi-periphery, periphery) status over time.[4] For a time, some countries become the world hegemon; throughout last few centuries during which time the world system has extended geographically and intensified economically, this status has passed from the Netherlands, to the United Kingdom and most recently, to the United States.[4]
The most well-known version of the world-systems approach has been developed by Immanuel Wallerstein in 1970s and 1980s.[2][5] Wallerstein traces the rise of the world system from the 15th century, when European feudal economy suffered a crisis and was transformed into a capitalist one.[5] Europe (the West) utilized its advantages and gained control over most of the world economy, presiding over the development and spread of industrialization and capitalist economy, indirectly resulting in unequal development.[3][4][5]
Wallerstein's project is frequently misunderstood as world-systems "theory," a term that he consistently rejects.[6] For Wallerstein, world-systems analysis is above all a mode of analysis that aims to transcend the structures of knowledge inherited from the 19th century. This includes, especially, the divisions within the social sciences, and between the social sciences and history. For Wallerstein, then, world-systems analysis is a “knowledge movement”[7] that seeks to discern the “totality of what has been paraded under the labels of the… human sciences and indeed well beyond."[8] “We must invent new language,” Wallerstein insists, to transcend the illusions of the “three supposedly distinctive arenas” of society/economy/politics.[9] This trinitarian structure of knowledge is grounded in another, even grander, modernist architecture – the alienation of biophysical worlds (including those within bodies) from social ones. “One question, therefore, is whether we will be able to justify something called social science in the twenty-first century as a separate sphere of knowledge.”[10][11]
Significant work by many other scholars has been done since then.[3]

Contents

Origins

Influences and major thinkers

World-systems theory traces emerged in the 1970s.[1] Its roots can be found in sociology, but it has developed into a highly interdisciplinary field.[3]
World-systems theory was aiming to replace modernization theory.[2] Wallerstein criticized modernization theory due to:
  1. its focus on the state as the only unit of analysis,[2]
  2. its assumption there is only a single path of evolutionary development for all countries,[2]
  3. its disregard of transnational structures that constrain local and national development.[2]
Three major predecessors of world-systems theory are: the Annales school, Marxist, and dependence theory.[2][3] The Annales School tradition (represented most notaby by Fernand Braudel) influenced Wallerstein in focusing on long-term processes and geo-ecological regions as unit of analysis.[2] Marxist theories added
World-systems theory was also significantly influenced by dependency theory - a neo-Marxist explanation of development processes.[2]
Other influences on the world-systems theory come from scholars such as Karl Polanyi, Nikolai Kondratiev and Joseph Schumpeter (particular, from their research on business cycles and the concepts of three basic modes of economic organization: reciprocal, redistributive, and market modes, which Wallerstein reframed into a discussion of mini-systems, world-empires, and world-economies).[2]
Overall, Wallerstein sees the development of the capitalist world economy as detrimental to a large proportion of the world's population.[5] Similar to Marx, Wallerstein predicts that capitalism will be replaced by a socialist economy.[2]
World-systems thinkers include Samir Amin, Giovanni Arrighi, Andre Gunder Frank, and Immanuel Wallerstein with major contributions by Christopher Chase-Dunn, Beverly Silver, Volker Bornschier, Janet Abu Lughod, Thomas D. Hall, Kunibert Raffer, Theotonio dos Santos, Dale Tomich, Jason W. Moore, and others.[3] In sociology, a primary alternative perspective is world polity theory as formulated by John W. Meyer.

Dependency theory

Main article: Dependency theory
World systems theory builds on but also parts from the proposition of dependency theory. Fernando Henrique Cardoso described the main tenets of the dependency theories as follows:
  • there is a financial and technological penetration of the periphery and semi-periphery countries by the developed capitalist core countries
  • this produces an unbalanced economic structure within the peripheral societies and among them and the centers
  • this leads to limitations upon self-sustained growth in the periphery
  • this favors the appearance of specific patterns of class relations
  • these require modifications in the role of the state to guarantee the functioning of the economy and the political articulation of a society, which contains, within itself, foci of inarticulateness and structural imbalance[12]
Dependency and world system theory propose that the poverty and backwardness of poor countries are caused by their peripheral position in the international division of labor. Since the capitalist world system evolved, the distinction between the central and the peripheral nations has grown and diverged.
In recognizing a tripartite pattern in division of labor, world-systems analysis criticized dependency theory with its bimodal system of only cores and peripheries.

Wallerstein

The most well-known version of the world-systems approach has been developed by Immanuel Wallerstein, who is seen as one of the founders of the intellectual school of world-systems theory.[2][5]
Wallerstein notes that world-systems analysis calls for an unidisciplinary historical social science, and contends that the modern disciplines, products of the 19th century, are deeply flawed because they are not separate logics, as is manifest for example in the de facto overlap of analysis among scholars of the disciplines.[1]
Wallerstein offers several definitions of a world-system. He defined it, in 1974, briefly, as:
a system is defined as a unit with a single division of labor and multiple cultural systems.
—[13]
He also offered a longer definition:
…a social system, one that has boundaries, structures, member groups, rules of legitimation, and coherence. Its life is made up of the conflicting forces which hold it together by tension and tear it apart as each group seeks eternally to remold it to its advantage. It has the characteristics of an organism, in that it has a life-span over which its characteristics change in some respects and remain stable in others. One can define its structures as being at different times strong or weak in terms of the internal logic of its functioning.
—[14]
In 1987, Wallerstein's, defines world-system as:
...not the system of the world, but a system that is a world and which can be, most often has been, located in an area less than the entire globe. World-systems analysis argues that the units of social reality within which we operate, whose rules constrain us, are for the most part such world-systems (other than the now extinct, small minisystems that once existed on the earth). World-systems analysis argues that there have been thus far only two varieties of world-systems: world-economies and world empires. A world-empire (examples, the Roman Empire, Han China) are large bureaucratic structures with a single political center and an axial division of labor, but multiple cultures. A world-economy is a large axial division of labor with multiple political centers and multiple cultures. In English, the hyphen is essential to indicate these concepts. "World system" without a hyphen suggests that there has been only one world-system in the history of the world.
—[1]
Wallerstein characterizes the world system as a set of mechanisms which redistributes resources from the periphery to the core. In his terminology, the core is the developed, industrialized part of the world, and the periphery is the "underdeveloped", typically raw materials-exporting, poor part of the world; the market being the means by which the core exploits the periphery.
Apart from these, Wallerstein defines four temporal features of the world system. Cyclical rhythms represent the short-term fluctuation of economy, while secular trends mean deeper long run tendencies, such as general economic growth or decline.[1][3] The term contradiction means a general controversy in the system, usually concerning some short term vs. long term trade-offs. For example the problem of underconsumption, wherein the drive-down of wages increases the profit for the capitalists on the short-run, but considering the long run, the decreasing of wages may have a crucially harmful effect by reducing the demand for the product. The last temporal feature is the crisis: a crisis occurs, if a constellation of circumstances brings about the end of the system.
In Wallerstein's view, there have been three kinds of societies across human history: mini-systems or what anthropologists call bands, tribes, and small chiefdoms, and two types of world-systems - one that is politically unified and the other, not (single state world-empires and multi-polity world-economies).[1][3] World-systems are larger, and ethnically diverse. Modern society, called the "modern world-system" is of the latter type, but unique in being the first and only fully capitalist world-economy to have emerged, around 1450 - 1550 and to have geographically expanded across the entire planet, by about 1900. Capitalism is a system based on competition between free producers using free labor with free commodities, 'free' meaning its available for sale and purchase on a market.

Research questions

World-systems theory asks several key questions:
  • How is the world-system affected by changes in its components (nations, ethnic groups, social classes, etc.)?[3]
  • How does the world-system affect its components?[3]
  • To what degree, if any, does the core need the periphery to be underdeveloped?[3]
  • What causes world-systems to change?[3]
  • What system may replace capitalism?[3]
Some questions are more specific to certain subfields; for example, Marxists would concern themselves whether world-systems theory is a useful or unhelpful development of Marxist theories.[3]


http://en.wikipedia.org/wiki/International_inequality

International inequality

International inequality is inequality between countries (cf. Milanovic 2002). Economic differences between rich and poor countries are considerable. According to the United Nations Human Development Report 2004, the GDP per capita in countries with high, medium and low human development (a classification based on the UN Human Development Index) was 24,806, 4,269 and 1,184 PPP$, respectively (PPP$ = purchasing power parity measured in United States dollars).[1]

Contents

International wealth distribution

See also: List of countries by distribution of wealth
A study by the World Institute for Development Economics Research at United Nations University reports that the richest 1% of adults alone owned 40% of global assets in the year 2000, and that the richest 10% of adults accounted for 85% of the world total. The bottom half of the world adult population owned barely 1% of global wealth. Extensive statistics, many indicating the growing world disparity, are included in the available report, press releases, Excel tables and Powerpoint slides.
The major component of the world's income inequality (the global Gini coefficient) is comprised by two groups of countries (called the "twin peaks" by Quah [1997]).
  • The first group has 13% of the world's population and receives 45% of the world's PPP income. This group includes the United States, Japan, Germany, the United Kingdom, France and Australia, and comprises 500 million people with an annual income level over 11,500 PPP$.
  • The second group has 42% of the world's population and receives only 9% of the world PPP income. This group includes India, Indonesia and rural China, and comprises 2,100 million people with an income level under 1,000 PPP$. (See Milanovic 2001, p. 38).
Economic inequality very closely matches lognormal distribution as one traverses the strata of national and world societies from top-to-bottom.
During the 20th century there was considerable divergence between the economic wealth of developed and developing countries. Richer countries like the United States and many European countries converged together towards a GDP per capita much greater than developing countries such as India and Ethiopia.
The evolution of the income gap between poor and rich countries is related to convergence. Convergence can be defined as "the tendency for poorer countries to grow faster than richer ones and, hence, for their levels of income to converge" [2]. Convergence is a matter of current research and debate, but most studies have shown lack of evidence for absolute convergence based on comparisons among countries (with regard to this debate see for instance Cole and Newmayer (2003) or [3]).
According to current research, global income inequality peaked approximately in 1970s when world income was distributed bimodally into "rich" and "poor" countries with little overlap. Since then inequality have been rapidly decreasing, and this trend seems to be accelerating. Income distribution is now unimodal, with most people living in middle-income countries.[2][3]


Further facts

Wealth:
  • 6% of the world population own 52% of the global assets. The richest 2% of the world population own more than 51% of the global assets, the richest 10% own 85% of the global assets.
  • 50% of the world population own less than 1% of the global assets.[4]
  • The whole global assets volume is about 125 trillion US$.[5]
  • 1,125 Dollar-Billionaires own 4.4 trillion US$. They own 4 times more than the 50% poor people of the world.[6]
  • over 80% of the world population lives on less than 10 US$/day.;[7] over 50% of the world population lives on less than 2 US$/day;[8] over 20% of the world population lives on less than 1.25 US$/day [9]
Income:
  • In 2005, 43% of the world population (3.14 billion people) have an income of less than U.S. $2.5/day. 21.5% of the world population (1.4 billion people) have an income of less than US$1.25/day.[10]
  • In 1981, 60.4% of the world population (2.73 billion people) had an income of less than US$ 2.5/day and 42.2% of the world population (1.91 billion people) had an income of less than US$ 1.25 /day. But first of all these improvements were reached in China. In all other developing countries only the percents decreased (by the swelling world population) but the absolute numbers increased.
  • In 2008, 17% of the people in the developing countries are on the verge of starvation.[11]
  • The proportion of poor people (with less than US$ 3,470 per year) is 78%. The proportion of rich people (with more than US$ 8,000/year) is 11%.[12]
Welfare spending: If East Asia and southern Latin American countries are taken out of the equation, the differences in government spending between the industrialized and developing states are striking, with the latter registering extremely low levels of spending.
  • Social expenditure as a proportion for GDP for Indonesia or the Dominican Republic registers around the 2-3 per cent mark, compared to Sweden or France which at the moment hover just under the 30 per cent mark.
  • In contrast to the industrialized states, from 1980 to 1990 many southern states experienced a decline in social spending as a percentage of overall government spending.
Therefore, in contrast to the North, the developing states are far more vulnerable to the pressures arising from economic globalization. Overall, social spending is far lower in the South, with some regions registering just a few per cent of GDP.[13] However, some people argue that decrease in welfare spending is not an issue of global inequality but rather a common phenomenon in an era of globalization.[14]


http://en.wikipedia.org/wiki/Oecumene

Ecumene

Ecumene (also spelled œcumene or oikoumene) is a term originally used in the Greco-Roman world to refer to the inhabited universe (or at least the known part of it). The term derives from the Greek οἰκουμένη (oikouménē, the feminine present middle participle of the verb οἰκέω, oikéō, "to inhabit"), short for οἰκουμένη γῆ "inhabited world".[1] In modern connotations it refers either to the projection of a united Christian Church or to world civilizations.

Contents

  • 1 Ancient world
  • 2 Byzantine usage
  • 3 Modern usage
  • 4 Ecumenes in fiction
  • 5 References
  • 6 External links


    http://en.wikipedia.org/wiki/Arnold_J._Toynbee
    Arnold J. Toynbee


    Arnold Joseph Toynbee CH (14 April 1889 – 22 October 1975) was a British historian whose twelve-volume analysis of the rise and fall of civilizations, A Study of History, 1934–1961, was a synthesis of world history, a metahistory based on universal rhythms of rise, flowering and decline, which examined history from a global perspective. A religious outlook permeates the Study and made it especially popular in the United States,[citation needed] for Toynbee rejected Greek humanism, the Enlightenment belief in humanity's essential goodness, and the "false god" of modern nationalism. Toynbee in the 1918–1950 period was a leading British consultant to the government on international affairs, especially regarding the Middle East.

    Contents

  • 1 Biography
  • 2 Foreign policy
  • 3 Study of History
  • 4 Civilizations
  • 5 Influence
  • 6 Reception and criticism
  • 7 Family connections
  • 8 Allusions in popular culture
  • 9 Works
  • 10 See also
  • 11 References
  • 12 Further reading
  • 13 Notes
  • 14 External links

  • Study of History

    In 1934-1954, Toynbee's ten-volume A Study of History came out in three separate installments. He followed Oswald Spengler in taking a comparative topical approach to independent civilizations. Toynbee's said they displayed striking parallels in their origin, growth, and decay. Toynbee rejected Spengler's biological model of civilizations as organisms with a typical life span of 1,000 years.
    Of the 21 civilizations Toynbee identified, sixteen were dead by 1940 and four of the remaining five were under severe pressure from the one named Western Christendom - or simply The West. He explained breakdowns of civilizations as a failure of creative power in the creative minority, which henceforth becomes a merely 'dominant' minority; that is followed by an answering withdrawal of allegiance and mimesis on the part of the majority; finally there is a consequent loss of social unity in the society as a whole.[7]
    Toynbee explained decline as due to their moral failure. Many readers, especially in America, rejoiced in his implication (in vols. 1-6) that only a return to some form of Christianity could halt the breakdown of western civilization which began with the Reformation. Volumes 7-10, published in 1954 abandoned the religious message and his popular audience slipped away, while scholars gleefully picked apart his mistakes.[8]



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