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Felix Stalder, PhD Student,
Last update: May, 1997
Table of Contents1. Introduction 2. The financial markets, a sketch 2.1. The origins 2.2. Some basic figures 3. Elements of the markets 3.1. Derivatives 3.2. Forecasting approaches 3.3. The clearinghouse
4. The nature of networks
5. Outlook: development without control?
"The two great inventions of the human mind are
writing and money-the common language of intelligence
and the common language of self-interest."
Karl Marx wrote in Grundrisse, Foundations of a Critique of Political Economy (1857): "Money as medium of circulation becomes coin, mere vanishing moment, mere symbol of value it exchanges. ... As the most superficial (in the sense of driven out onto the surface) and the most abstract form of the entire production process [money circulation] is in itself quite without content." (quoted in: Spivak, 1987, p.32) In this sense, money circulation1 is a 'pure medium', much as light was a pure medium in the eyes of Marshall McLuhan, a medium without content. The underlying thesis of this short paper is that financial networks offer a uniquely transparent view on the dynamics of networks in their pure form. The nature of networks itself becomes visible in the workings of the financial markets. The financial markets are, unlike any other aspect of today's economy or culture, completely embedded in computer networks, many of its instruments are unthinkable in any other media environment. Their phenomenal growth over the last two decades is a direct result of the power created by the linking of high-end computational hard- and software with nearly unlimited, or at least abundant, telecommunicational bandwidth2. The long-term perspective of my interest is to describe the structure and nature of computer-based communication networks to determine their impact on society and culture. The area of electronic money can serve as an empirical testing ground to discern patterns that will be more difficult so see in other areas where they are likely to be 'distorted' as they form combinations with patterns of other environments, their different natures and modes of structuring. Evidently, this paper is only a first step into that direction and it's goal is mainly to test a number of speculations and to present preliminary results.
The first section will sketch the origins and the present
state of the financial markets3, the second section
will describe some of its mechanisms and the tools
employed and the final section will interpret the findings
in the light of their 'networkness', it will try to
abstract from the empirical evidence, an analysis of
the inherent culture4 of networks.5
In the 19th century the nation-states began to (re)form around national economies under the influence of liberal and later conservative economic theorists the introduction of the gold standard marked an important shift of power from the ruling aristocracy to the market and the merchants. Until then, a method of balancing the budget popular among rulers was to change the gold to metal ratio in the coins, in other words, to start inflation. This habit complicated the money exchange and international trade enormously. Inspired by ideas of the political economist David Ricardo (1772-1823) the gold standard6 was created in England in the first half of the 19th century. By 1880 all major countries had adopted this standard (Eichengreen, 1985). The gold standard remained valid until W.W.I and was reintroduced 1944 in the Bretton Woods agreements regulating the post-war international economy (Millan, 1995, p.43, Eichengreen 1996). Ironically, the end of the gold standard, 1971, marked again a shift of power from the political ruler, this time the nation-state, to the markets and the merchants. Several independent developments increased the power of the markets culminating in end of the gold standard, trends most visible in the emergence of the Eurodollar7.
During this time of rapid transnational expansion, the ratio of money to gold remained relatively stable at ten to one (Hammond, 1996, p.148). However, in the 1960s the system of fixed currency exchange started to become too inflexible to represent an increasingly dynamic international economy. The political problems, such as the crisis of France (May 68), played a certain role in accelerating the destabilization of the international financial system as it had existed since W.W.II (Millan 1995, pp.65-94, Eichengreen, 1996). In 1971 when the system broke down and erstwhile president Richard Nixon abolished the gold standard, the 'Eurodollars' became the seeds of new electronic money and the global financial markets (Kurtzman, 1993, p.85).
While those investments represent more the intra-company infrastructure that is required to successfully participate in these markets, the markets themselves, or at least an important part of them, take place in the semi-public10 arena of the closed private information networks, such as the financial networks of Reuters. Reuters, which started in 1850 with a pigeon carrier to send stock exchange data from Brussels to Aachen in order to bridge the gap between the Belgian and the German telegraph lines (Read, 1992), is today's leading provider of news to the financial markets, a service that is delivered over a proprietary network. It brings news and prices directly to customer screens, providing datafeeds to financial markets, and the software tools to analyze the data. This data covers currencies, stocks, bonds, futures, options and other instruments. Its main customers are the world's leading financial institutions, traders, brokers, dealers, analysts, investors and corporate treasurers. Even though the there are several competitors in these markets, Reuters regards itself as "a market leader in most of these sectors." (From the Reuters webpage) However, Reuters does not only provide the news for the market, Reuters is also the environment of the markets themselves. It provides the tools for dealers to contact counterparts through a Reuters communications network in order to do the actual tradings. Through proprietary instruments, such as Dealing 2000-211, Reuters enables traders to deal from their keyboards in such markets as foreign exchange, futures, options, and securities. Foreign exchange dealers can either converse with chosen trading partners or use an automated matching system. The underlying size and potential of these communicational services are well kept company secrets. If the revenue is any indicator of the company's activities then the financial services are about 10 times bigger than the traditional news agency, which only yields less than 10% of the company's revenues, but still is the largest of its kind worldwide. (Read, 1992, The Annual Report 1993, p. 25) The effective size of the financial markets is difficult to determine, since the sources are often not comparable. However, a few key figures may serve as an indicator of the enormous amounts of money that circulates in these markets. The foreign exchange market, virtually unexisting in the early 70s, was the first sector of the financial market to globalize and still is in many aspects the only real global market12. In the mid 70s their daily turnover rate was $15 billion, since then it has expanded to $60 billion/day in 1980, to $300billion/day in 1986, and to (more than) $1 trillion/day in 1995. Only about 1.5-3% of that sum is connected to the world trade, the movement of physical commodities from one currency system into another and the world trade is no triviality (Sassen, 1996, p.40). The rest is purely the virtual movement of money, done by banks and foreign currency speculators, without any direct relation to physical goods13. Virtual, however, does not mean without real gains. For most of the banks this from of trading has developed into one of the most profitable areas of business. Citibank, one of the leading banks worldwide, made in the early1990s an average of $500 million/year profit in the foreign currency exchange market. The US comptroller of currency calculates that foreign currency exchange dealing accounts for half of the profits made by the big commercial banks during that period (Valdez, 1993, p.126). An other figures estimates that at the same time the overall daily exchange within the world's financial center, NYC, was about $ 1.9 trillion/day. Within 3 days this equals the total output of American companies and it's work force of one year (Kurtzmann, 1993 p.17). Ray Hammond, in his 1996 book Digital Economy, presents an even more impressive figure estimating that 90% of the world's wealth is transmitted from account to account over the closed financial networks (Hammond, 1996, p.154). Such comparisons are somewhat problematic because it is dubious whether these figures are really comparable in any meaningful way. The quality of the values that circulate around the globe is fundamentally different from that stored in physical goods, such as real estate or machinery. Money within the networks represents a informational flow and as such it is dependent on the qualities of flows, which are determined by factors as speed and the number of participants. In contrast, the values stored in physical goods represent possession, and as such they are subject to completely other dynamics, dependent on scarcity and physical location. In spite of conceptual ambiguities, the capital markets represent today one of the most important power centres in the developing global culture. They emerged into this role trough innovation in previously unexisting territory, the electronic networks. The rationale of seeking these possibilities has always been exclusively to search for a higher yield, capitalizing on and accelerating long range social and economic trends. The markets and their instruments developed outside any direct governmental control, driven by profit seeking, and enabled by technology. The following section takes a short look at three elements of the capital markets, as they developed in the last few years. The first is a financial instrument, derivatives, the second are models for forecasting and the third is a controlling institution, the clearinghouse.
To simplify matters, I will use the example of options to sketch the basic features of derivatives in general. An option conveys a right, but not the obligation to buy (or sell) stock, bond or any other financial instrument at a specific time for a specific price. This right can be traded. It is separate from but related to the price of the underlying product. An option is an instrument to speculate on the future movement of the markets. If a dealer expects the market to rise, he or she can pay the holder of an asset for the right to buy it within or after a period, usually 1-12 month, for a fixed price. If the market rises over the prearranged price then the dealer exercises the option and buys the underlying asset, only to sell it immediately to the higher market price. The profit arises from the difference between the actual market price and the prearranged price in the option. If the market moves in the other direction, then the dealer does not have to exercise the option, it simply expires and the dealer looses what he or she paid for the option. Options an be understood as a second, and if they are traded as a third, fourth or fifth layer of abstraction in order to gain control over more assets than otherwise possible. Instead of buying the 'real' asset for 100% of its actual price, an option can be bought for, let's say, 5% granting the right to buy the asset in the future. If the price of the asset rises then the option is exercised if not, the option expires. With this mechanism very small movements of the underlying asset can have very significant impacts. Evidently, there is no sure gain in speculation. If the price of the underlying asset moves downwards, let's say 1%, then the speculator looses 100% because it would make no sense to exercises the option and buy the underlying asset at a price higher than it would be currently available on the market14. The powerful element of derivatives is not only that they allow speculation on relative small market movements but also that they are indefinitely expandable and that they allow to tie together originally unrelated things. Really complex derivatives may give, to quote Kevin Kelly, "the option of buying milk at a certain price in New Zealand while simultaneously selling oil in Taiwan. Third- and fourth-order derivatives -- those betting on an option based on a bet that hinges on another gamble -- increase the complexity and incomprehensibility of these financial instruments" (Kelly, 1994b). As an effect, the markets that were previously unrelated become more and more integrated (Millan, 1994, p.xiii) and the number of factors to be taken into account rises exponentially. In such an environment, where many factors influence the movements and tiny movements can be translated into massive consequences forecasting becomes more important, more rewarding, and, more complex. Traditional models have lost their usefulness for predicting the movement of prices in such an environment and alternative models are being developed to monitor the new territories.
3.2. Forecasting approachesTwo principally different forecasting approaches exist in the financial markets: fundamental analysis and technical analysis. Fundamental analysis is a technique of modeling the market based on the movement of fundamental economic variables. Its underlying concept is that the financial markets represent directly in a linear and deterministic way the traditional economy. The financial markets are, according to this model, the dependent variable while the 'real' economy is the independent one. In terms of communication theory: the real economy is the input and the financial markets are the out put. In short, this approach is rooted in the classical Newtonian logic that every effect (the financial market) has an independent cause (the physical economy). In contrast, technical analysis tries to recognize the patterns within the movements of prices in the financial markets. As a method it started in the 1950s when the first scientists and researchers claimed to predict the markets more efficiently by looking at the past behaviour of the segment in question than by analyzing the fundamental variables. To do so they began to draw huge charts, hence their name "Chartists", to make patterns visible to the naked eye and use this insight as the basis of forecasting (Mills, 1992). Predicting the future especially in the financial markets is somewhat paradoxical, because it contradicts one of the corner-stones of the neo-liberal economic theory, "the efficient market hypothesis" (Mills, 1992). An efficient, open market is believed to incorporate all knowledge immediately leading to randomness in the movement of the prices. The unpredictability is the result of independent participants who try to use information before others can use it . This constant feedback of information to the markets guarantees the efficiency that everyone has the same information. Predictability, as it turns out, is the sign of an inefficient market itself. How deeply the nature of the markets changed since they have become network-based is reflected in the landslide in popularity between the two forecasting models. Since the late 70s or early 80s the number of practitioners of fundamental analysis in the financial markets has fallen sharply and the number of technical analysts risen considerably (Metha, 1995 p.184). A new environment needs new conceptual models.
The old model of fundamental analysis came to its limits
on several levels:
The current models for the markets move away from the
analysis of the interconnection between the markets
and the underlying economy. The more the markets are
seen as interconnected the more the impact of 'distortions'
- the self produced side-effects - are taken into focus.
New models take into account that the independent variables
can no longer be defined15, and also that the systems
as such becomes increasingly non-linear. This results
for the following reasons: Today, it is the prevailing wisdom of financial economist, that the price fluctuations not due to external influences [news] are dominated by noise and they can be modeled by stochastic processes. The models for such an environment are based on completely or partially deterministic but nonlinear dynamics. Such models are rooted in artificial intelligence and especially in neural networks as self-learning systems. In the financial world, the interest in 'intelligence' is very prosaic. What makes it worth the heavy investment is the promise that artificial intelligence can make statistical inferences without any a priori assumptions about the data and that 'intelligent' systems are therefore capable of detecting dependencies despite complex, nonlinear behaviour. These systems try to make use of what chaos theory calls the two kinds of complexity: inherent and apparent complexity. Inherent complexity is the 'true' complexity of chaotic systems. It leads to final unpredictability. The other kind of complexity is the compliment of chaos. Apparent complexity only looks like chaos, but with the right mathematical concepts, the veil of obscurity can be lifted and an exploitable order appears. These two forms of complexity make chaotic systems unpredictable in the long run, but in the short term there are patterns leading to predictable behaviour. "The key question to ask in beating the stock market is, what patterns should you pay attention to? Which ones disguise order? Learning to recognize order, not causes, is the key." (Kelly, 1994b) The rise of the pattern-oriented models represents a paradigm shift in the cognition of observable activities. This shift is directly related to the nature of the environment and the new paradigms of interconnected networks. The models applied are principally different from the ones that build on classical Newtonian logic. They assume no relationship between the financial markets and the underlying economy. This does not imply that there is non, but for the successful player this long-term relationship is simply not relevant, his cycles move at a completely different speed. In the currency markets fortunes are made within minutes, hours or days and not within months or years. Instead of analyzing the vertical relationship between underlying cause and its derived effect, the alternative models focus on how effects reproduce themselves horizontally and what this reveals about their inherent dynamics. The model is built on the assumption it is the effect that moves the effect in a constant feedback loop instead of a linear cause-effect relation. The search for pattern replaces the search for reasons. The patterns in complex systems, however, are tricky. They are, at best, only reliable for very short moments, in the terms of the economic theory, these are the market inefficiencies. The small pockets of predictability. However, they help to navigate in an volatile, fast-paced environment. Considering the huge amounts of money involved, even a small reliability can be worth a lot. Random guessing has a chance to make the right choice of 50% since there are only to possibilities, buy or sell. If a complex system can augment these chances by 10-20 percentage points then this system could yield a fortune, much higher than the average return in the financial markets. Huge amounts of money slosh in virtually unpredictable patterns through the global communication system. To keep that flow uninterrupted at such a high speed, technical connectivity and sophisticated analytical methods are needed but also a structural context that assures a certain, and in the case of the financial markets, absolute reliability. In the physical world this context of reliability is built either through direct knowledge of the other, through the presentation of trustworthy additional information such as passport or credit card or by researching otherwise available data about the person, such as credit history, criminal records, work reports etc.
The first method is obviously not applicable in a global
economy. But also the other methods have common weaknesses: To guarantee the necessary reliability in the network environment without any slow-down of the flow of information the financial markets established several institutions, one of them is the clearinghouse.
A clearinghouse is a legally independent entity but
it is usually connected with an exchange facility.
It works upon two premises: Today clearinghouses are central institutions for keeping the market flowing despite network related complexities such as anonymity and speed. Both factors makes it impossible to determine the contextual background of the data provided on, say, the Reuters screens. This is especially important for third, forth and fifth level tradings where the determination of the original source would be so complex that it would be impossible to deal derivative instruments at all. The largest private sector payments network in the world is Clearing House Interbank Payments System (CHIPS) in N.Y.C.. About 182,000 interbank transfers valued at nearly $1.2 trillion are made daily through the network17. This represents most of the exchange of the capital markets within N.Y.C. (From the webpage of the Federal Reserve Bank of NYC) Even though the institution of the clearinghouse is more than 120 years old, its central position is directly related to the nature of computer networks. With the growth of the networks the number of clearinghouses (and other similar institutions) is rising. Clearinghouses can be viewed as one of the first private, global legal institutions that regulate the communication in a supposedly deregulated market (Sassen, 1996)18.
Central network-related characteristics of the clearinghouse
are:
4. The nature of networks4.1. Networks as environmentFinancial networks provide their own complete environment. They are content and context at the same time. The surrounding larger social and economic environment is structurally separated and its relevance is relevance is assesed regarding whether it has the ability to invade the closed universe of the financial market, for example in form of new that are regarded as important. But which information is important and which is not is decided within the markets and has nothing to do with the 'value' of the information as such. The context of the market defines the content of the information. If everyone expects a company, or a country, to report huge losses, then the news of moderate losses can boost the price or currency, in contrast, if everyone expects the opposite then the same piece of information can have a devastating influence on the market value. As a complete environment the (financial) networks are fully self-referential, or to quote Marshall McLuhan: "New media are not bridges between man and nature, they are nature" (quoted in, McLuhan, Zingrone, 1995 p.272). Everything that counts is what happens within the networks. What are the other participants doing? Since the direct connection to other environments, or subsystems, is broken, the ultimate determination takes place within the markets themselves. Evidently, the markets react very fast on new information and the connection to political and economic events is almost immediate. Nevertheless, it is indirect. The markets as a closed system react on news because its active elements, the dealers, expect each other to react and try to react before the others. What the others plan to do is the most important information. John M. Keynes described this structure is his famous beauty contest analogy:
"Professional investment be likened to those newspaper
competitions in which the competitors have to pick
out the six prettiest faces from a hundred photographs,
the prize being awarded to the competitor whose choice
most nearly corresponds to the average preferences
of the competitors as a whole; so that each competitor
has to pick, not those faces he finds himself the prettiest,
but those which he thinks likeliest to catch the fancy
of the other competitors, all of whom are looking at
the problem from the same point of view. it is not
the case of choosing those which, to the best of one's
judgment, are really the prettiest, not even those
which average opinion genuinely thinks the prettiest.
We have reached the third degree, where we devote our
intelligence to anticipating what average opinion expects
average opinion to be. And there are some, I believe,
who practice the fourth, fifth and higher degrees."
(Keynes, 1936 p.156)
For Jean Baudrillard this reversal in the relationship of sign and object is the principal characteristic of post-modernity. In his sombre prose he analyzes that the simulation is no longer 'representational imaginary' "rather, genetic miniaturisation is the dimension of simulation. The real is produced from miniaturised units, from matrices, memory banks and command models ... It no longer has to be rational, since it is no longer measured against some ideal or negative instance. It is nothing more than operational." (Baudrillard, 1983, p.32) At first hand strange and unenvisioned in the gloomy metaphors of Baudrillard, the effect of that reversal is cooperation. Since networks are tools and environment at the same time, everyone who uses the tools has a certain need to maintain the environment19. This does not imply any idyll, the cooperation is only on the level of the environment, and not within the environment. There are exceptions to this rule, evidently. Networks function efficiently when information can actually be taken at face value. To guarantee this they have to be structurally separated from other environments. In this regard the institution of the clearinghouse can be read as a one trillion dollar per day cooperative buffer against the invasion of external context. The clearinghouse provides the world economy's most substantial resources, ultimately the funds of the most relevant firms in the markets, to guarantee the constant flow within the networks, uninterrupted by external defaults which would be translated directly into the network and not only indirectly through the interpretation of the players. This direct impact would destroy the face value of the information. If the financial networks are the global brain, or parts of it, then the clearinghouse is the helmet that prevents the direct, not translated impact of the hammer of bankruptcy from crushing the skull. Without this helmet the speed in which the information would be allowed to flow would be much slower.
In the network environment, then, the condition of staying
a member of the network is to provide information that
can be taken at face value. The position of a player
is determined by the information he, she, or it delivers
to the other players, the faster and the more accurate
it is, the more relevant the source becomes. Since
everyone is connected with everyone reliable information
gets delivered to the environment as such. The connectiveness
forces even in the most competitive environments a
certain form of collaboration. What seems paradoxical
is the nature of the network, it needs collaboration
to stabilize itself, building the stage for competition
at the same time.
It is only logic that such a convergence is also expressed
organizationaly. Reuters does not only provide the
news and information about the financial markets to
the markets but it also provides the tools for the
markets to make the information. Consumer of news and
producer of news converge and the network displays
instantly to everyone what everyone else does. Or,
in other words, its only content are the users.
"The paradox of scale and control is most visible in the financial sector where heavy investment in technologies designed to enhance the predictability and responsiveness has been blamed for exacerbating instability: when a multitude of different and competing actors seek to improve their control capacities, the result at the level of the system is a breakdown of control. What is rational at the micro level becomes highly irrational at the macro level." (Mulgan, 1991 pp. 28/29) With the number of connections and the speed of communication rising the predictability and controllability of the system as such is decreasing.
5. Outlook: development without control?The interconnected environment is driven by an internal logic that is not reducible to the planning of the small number of centers. Even though there are clearly visible major nodes within the network they are not in a central command position. The are in the same way interconnected to the network at large as any smaller node. On the other hand, development is not random. It is the result of great number of planned efforts to survive in that environment. Each environment rewards its members if they use specific strategy to seek that survival, these strategies, or rules of conduct, evolve over the time out of interaction of the members who constantly feed back the success of their actions regarding the reaction of the other members of the network to readjust their own strategy. The two paradoxes of networks, control/chaos and cooperation/competition, produce two completely equally adequate ways to describe them. Form the inside, on the level of the usage they are complex and unpredictable, chaos and competition are dominating the picture. From the outside, on the level of effects, they are very predictable and unimodal, cooperation and control seem to be their prime features.
6. Bibliography
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Endnotes1 The case is different for money itself. This has always to be tied to something, either to something physical as gold or something imaginary as a "system of belief and confidence" (Hammond, 1996, p.148).
2 No other sector in society has been so radically transformed
by new technology as the financial markets. No other
sector has gained so much power in the last two decades.
No other sector has this profit rate. It is popular
in the financial literature to describe this transformation
with an analogy to the impact of the atomic bomb. (Lowell,
Farrell 1996 p.2; Millan, 1995 p.vii)
3 The financial markets are in many ways fragmented.
In this paper I will use examples from different areas
to exemplify characteristics of the markets as a whole
even though there are substantial variations between
the different segments.
4 Culture is understood in a very broad definition as
the way humans interact and the artifacts, institutions
and values into which these interactions stabilize.
5 I would like to thank Jesse Hirsch for editing my
paper and helping me mastering the finer details of
the English language
6 Gold standard means that the government guarantees
that each bill can be exchanged against a fixed and
predetermined amount of gold. After W.W.II only the
US $ guaranteed to such an exchange. But since all
other major currencies were tied to the dollar, the
gold standard of the dollar was the de facto gold standard
of the western world.
7 The term Eurodollar is slightly misleading since all
foreign-held US $ outside the US, whether they are
in Europe or not, are called Eurodollars.
8 It is generally difficult to estimate such figures
precisely because neither is the term 'financial markets'
sharply defined nor the concept of 'working in' these
markets. There is a large gray zone of subcontracted
and supporting services where people work for but not
in the institutions that make up the markets.
9 This estimate is based on figures from the 1995 Annual
report of Reuters. It is the number of institutions
accessing the Reuters' instruments for dealing. In
regard of the central position of Reuters in financial
markets one can assume that virtually every institution
that deals in these markets is also a Reuters customer.
10 Semi-public in the sense that the access only restricted
by the access fees.
11 This instrument was launched in 1992. It enables
all traders linked to the product to see the best buy
or sell price for a currency pair simultaneously. This
automatic, anonymous service matches bid and offer
orders using a central computer, verifying that the
counterparts have sufficient and mutually acceptable
credit.
12 A market is regard as truly global not only when
it reaches all national markets but when there are
no regulatory or other differences between those national
markets.
Therefor, constitutive elements of a truly global market
are:
13 There are two more parties involved in foreign currency
exchange, tourists and governments (e.g. payment for
US troops overseas). But the amounts they move are
so small that they have no influence on the exchange
rate. (Valdez, 1993, p.126)
14 Example: A dealer buys for 5m an option on an asset
worth 100m. If the assets becomes worth 110m at the
time the option can be exercised, then the dealer buys
it at the prearranged price of 100m and immediately
resells it for 110m. The revenue is 10m. He paid 5m
for the option to do so, earning the dealer a profit
of 5m which equals 100% of the original investment.
On the other side, if the price of the asset moves
to 99m (-1%) then it makes no sense to buy them for
100m. The option, originally worth 5m, becomes worthless.
15 The conceptual background of this inability to define
the independent variable is the insight that, potentially,
everything can be connected with everything. That the
environment of the financial markets is not vertically
derived from the 'real' economy, but that is forms
a vertical, interconnected network.
16 It is not surprising that this institution was founded
in Chicago in the late 19th century. At this time,
Chicago was the trading place for most of the commodities
of the Mid West, mainly agricultural products. Since
they are usually delivered only once a year, at the
time of the harvest, new dealing instruments were introduced,
such as futures, where a farmer and a dealer could
agree on a price and delivery in advance. These new
ways of dealing required new regulations.
17 An example how it works: suppose a London bank wants
to transfer $1 million from its account at one New
York correspondent bank "A," to an account
at second New York correspondent bank "B."
Banks "A" and "B" are both CHIPS
participants.
18 Clearinghouses are a striking example that deregulation
does not mean that the number of regulations is decreasing
but that the power to regulate is transferred from
the nation state to the market place.
19 The same could be said regarding the natural environment
in general. But there are principal differences. The
main differences are the fragility and speed of networks
that make their breakdown a comprehensible reality.
They are man made and therefore easily destroyed by
man. Cooperation is needed even for short term gains.
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