Complex Copyright

Mapping the Information Ecosystem

Deborah Tussey

• Publisher: Routledge
• Year: 2016
• ISBN:
  9781317162810
• Edition: 1
• Topic: Law
• Subtopic: Copyright Law
• Index: Law

This book draws on a wide selection of interdisciplinary literature discussing complex adaptive systems - including scholarship from economics, political science, evolutionary biology, cognitive science, and religion - to apply general complexity tenets to the institutions, conceptual framework, and theoretical justifications of the copyright system, both in the United States and internationally. The author argues that copyrighted works are the products of complex creative systems and, consequently, designers of copyright regimes for the global 'information ecosystem' should look to complexity theory for guidance. Urging legal scholars to undertake empirical studies of real-world copyright systems, Tussey reveals how the selection of workable configurations for the copyright regime is larger than that encompassed by the traditional, entirely theoretical, debate between private property rights and the commons. Finally, this unique study articulates how copyright law must tolerate certain chaotic elements that may be essential to the sustainability of complex systems.

Chapter 1
Are Publishers Really Like Prairies? Copyright Systems as Complex Adaptive Systems

It should be evident from the preceding chapter that the universe comprised of physical copyright-related systems is extraordinarily complicated. Copyright law, as a conceptual system, often strays past mere complication into near incomprehensibility. It bears the hallmarks of several hundred years in which the discipline was a backwater familiar only to copyright specialists and the copyright industries themselves. Complicatedness is not, however, the same thing as complexity. “Complexity” refers to behavior as well as structure. A complicated system like an automobile performs in a linear, predictable manner. Its overall behavior is the sum of the behavior of its parts. Complex adaptive systems, on the other hand, exhibit properties and behaviors different from those of their underlying parts. The aggregated interactions of system components produce entirely new patterns and behaviors. Complex adaptive systems change in response to internal and external stimuli and evolve over time—they “learn” in that they adjust their behavior to the world around them. This chapter explores the threshold question of whether copyright systems, both physical and conceptual, might constitute complex adaptive systems.

To start with a very general proposition, the formation of systems appears to be an inherent feature in the universe. Interacting, interdependent elements create a unified whole—a system.1 Modern science teaches that, from the moment of the Big Bang, the universe began to self-organize, forming layers of structure from the primordial soup: subatomic particles, atoms, molecules, right on up to planets, stars, and galaxies, all captured in the encompassing fabric of space-time.2 Once life appeared, organisms evolved from single cells into a profusion of elaborate, multi-cellular creatures. In Darwin’s words, “... from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.” 3 Systems may remain simple, with few parts engaged in mostly linear interactions, but most systems seem to evolve toward greater complexity. Discrete components, such as electrons or elephants, become part of larger systems, like atoms or herds. Those systems, in turn, become part of still greater systems such as elements and ecosystems.

Ecosystems and their constituent parts are paradigmatic complex adaptive systems. Many of the inhabitants of ecosystems are themselves complex adaptive systems. Humanity, at its current stage of evolution, both results from the evolution toward complex organization and contributes to it. Our own bodies are complex adaptive systems composed of complex adaptive systems such as the brain, the nervous system, and the immune system, which are themselves miniature ecosystems colonized by microbes. Like other animals, we inhabit natural ecosystems. We also collectively create complex social, political, economic, and legal systems in which we participate throughout our lives. As conscious beings, we attempt to control both natural and human-created complex adaptive systems.

Economists, political scientists, and sociologists have devoted considerable study to complexity in human systems. It may seem unlikely that diverse human and non-human systems could have much in common—that an ecosystem such as a prairie is really like an information system such as a publishing house. Complexity scholars in varied fields have, however, established that complex adaptive systems do share certain, general characteristics, whether the systems are natural or manmade. This chapter describes these characteristics, using examples taken from natural ecologies and from human systems, and then discusses similar characteristics displayed within the copyright system.

The Complexity Crash Course

Despite some variations in terminology across disciplines, there is general consensus on the essential properties of complex adaptive systems. For purposes of measuring the copyright system against these properties, they can be categorized as: intricate architecture, nonlinearity, emergence, and adaptiveness. As a result of these characteristics, the long-term behavior of complex adaptive systems is unpredictable and human attempts to intervene are likely to produce unanticipated consequences. The most sustainable complex systems maintain a state described as self-criticality or positioning at the “edge of chaos.” The following discussion synthesizes, very much in lay terms, some basic principles of complexity drawn from a variety of sources.4

Architecture: Nests, Networks, and Loops

First, complex adaptive systems have a distinctive architecture. They typically contain many, heterogeneous elements, sometimes referred to as agents or actors. In a prairie, for example, herd animals, grasses, predators, and prey are all actors playing important roles in an ecosystem where biodiversity is critical to sustainability. In a market economy, regulatory organizations, financial institutions, corporate and individual producers, sellers, and buyers are all actors in the system. Complex adaptive systems often “nest” within each other so that the components of the primary system under observation are themselves complex adaptive systems that contain yet other complex adaptive systems. The prairie ecosystem includes the bison herds that include individual bison, each of which includes a brain, a nervous system, and so on. A market economy includes corporations and other institutions comprised of individual human beings, each of them composed of the complex adaptive systems of the human body. Thus, any agent, whether individual or institutional, is simultaneously involved in a multitude of complex adaptive systems, which overlap and interact with each other.

The constituent elements of each system tend to be densely interconnected in networks. Network connectivity enables the flow of information, money, products, energy, nutrients, or whatever other medium flows through the system. Consequently, actors within the system are directly or indirectly involved in interdependent relationships with other actors. Most direct interactions occur between neighboring actors, but the network interconnections assure that the effects of a change in conditions affecting one system element will be propagated throughout the system, often triggering cascades of further change. Where system elements are interdependent, change in one element may constrain or facilitate change in others.

Predators and prey on the prairie, for example, exist in interconnected food webs. If humans eradicate prairie dogs, coyotes will have to find alternate food sources or starve, and a responsive decline in predator populations may occur. The decrease of beneficial burrowing activities may also adversely impact the growth of grasses, thereby affecting the food supply and, hence, the survival of elk, bison, and other grazers. Market economies display similar interdependencies. The stock market is an enormous network of businesses and their investors, both individual and institutional, now supported by computer networks that facilitate instantaneous trading worldwide. The 2008 crash of the market as a result of risky investments in questionable financial instruments, and the resulting devastation to global economies, made painfully clear the interdependencies among investors, financial systems, and the overall health of market economies.

All of these interconnections contribute to a feature that straddles the divide between structure and behavior: the existence of feedback loops. Since complex adaptive systems are self-organizing, their interconnected architecture lays the groundwork for feedback and may then build new architecture along the feedback paths.5 Denser interconnections create more feedback. Feedback may be positive or negative. In positive feedback, a change sets off reinforcing pressures that amplify change. Network effects, which cause the value of a good or service like an Internet connection to increase as more people use it, are a form of positive feedback. Positive feedback simply reinforces whatever change is occurring, whether that change is upward growth or a downward spiral. In negative feedback, a change triggers forces that counteract it and dampen the effects of change. A negative loop returns the system to approximately its original position in an attempt to keep the system in a stable condition known as homeostasis. Thermostats that respond to temperature changes by turning heating systems on or off to maintain a constant temperature are negative feedback devices. Traditional equilibrium economics assumes that price acts like a thermostat in the economy: if demand increases, price goes up, causing increases in supply, which causes price to fall until supply and demand are in balance.6 Positive feedback loops contribute to instability; negative loops contribute to stability.

In predator–prey relationships, as the number of prey, like prairie dogs, increase, so do the number of predators, such as coyotes—a positive feedback loop. As coyotes grow too numerous, the prairie dog population declines which, in a negative feedback loop, causes a decrease in coyote populations. In a bullish stock market, the faith that value will increase creates positive feedback that encourages more investment, but the increasing cost of shares simultaneously produces negative feedback that deters some investors.7 Recent events make clear that that positive feedback loops can push the markets down. The failure of important financial institutions in 2008 generated negative media coverage that made investors wary of further investments. As investment declined, stock prices dropped, starting a self-reinforcing downward spiral that continued until the market got so low that investors reentered the market to grab bargains.8 Additionally, changes in one system not only feed back within that system, but they may cause change in interacting systems, which then feeds back to the primary system. Thus, the stock market drop quickly impacted economies around the world, drying up new development, causing business failures and job losses that, in turn, adversely impacted the stock market.

It is evident from the preceding examples that the effects of an agent’s behavior also feed back to that agent, affecting its future behavior. Studies have shown that prey animals change their behavior in order to thwart predators.9 Consumers caught short by the bursting of the housing bubble started saving more and buying less. Where system agents are human beings, they also try to anticipate the behavior of others, like the investors who reenter the market when they anticipate that the bottom has been reached.

Nonlinearity: Deterministic Rules, Butterfly Effects, and Path Dependence

Complex adaptive systems are dynamic and, not surprisingly given a networked structure typified by feedback loops, their processes are nonlinear in the long term. In linear systems, effects are proportional to causes. In nonlinear systems, effects are not necessarily proportional to their causes. In the case of the prairie dog and the coyote, the predator–prey relationship is not likely to be directly proportional because both species interact with other species and respond to many other factors in the ecosystem. In a market economy, stimulus spending should not foster merely an equal amount of production, but rather should kick-start businesses, forestall job losses, and, with luck, produce economic gains larger than the spending itself because of the diverse interactions between elements of supply and demand in the economy.

Complex adaptive systems do follow defined, deterministic rules that, in the short term, produce relatively predictable behaviors. Ant colonies operate under rules in which pheromone trails communicate vital information about such important topics as food locations or imminent threats. Bison herds follow established patterns in mating and migration. In human-constructed systems, we consciously set many of the rules of interaction. Actors in market economies follow accepted rules set by domestic laws, international treaties, and commercial and social norms. However, because of the networked interdependencies and feedback loops among system actors, a change at one point cascades unpredictably throughout the system producing chaotic behavior.

In the complexity lexicon, “chaotic” does not mean “random.” Chaotic systems follow deterministic rules of interaction, but when those rules are followed in the aggregate, they result in unpredictable behavior or “deterministic randomness.” John Casti provides the following example of deterministic randomness: a saltwater taffy–pulling machine repeatedly performs the same mechanical function; however, two raisins initially placed very close together in the taffy will over time end up in dramatically different positions and it is impossible to predict their future, relative positions. The rule of action, taffy pulling, is deterministic, but the results are unpredictable.10 Meteorologists know a great deal about the rules guiding planetary weather systems, yet they can rarely predict the weather more than a day or two in advance. Those systems produce the storms whose lightning sets prairies ablaze at entirely unpredictable times, producing beneficial, indeed essential, ecosystem effects by clearing out old growth and making way for new. Financial markets follow accepted rules, yet the unexpected demise of Lehmann Brothers engendered panic among investors and sent the stock market into a tailspin.

Systems exhibiting chaotic behavior are described as “sensitive to initial conditions.” A tiny change in the initial conditions to which a system responds can produce wide divergences in system development. Certain patterns may recur but never in exactly the same way because initial conditions will have changed. This phenomenon is popularly known as the “butterfly effect.” A butterfly flapping its wings in the Amazon rain forest may, on one occasion, set off a chain of events in the atmosphere that, a month later, produces a tornado in Oklahoma. On a different occasion, the butterfly’s actions may have no effect at all. The butterfly example, which exists in many variations, demonstrates two aspects of nonlinearity: (1) very small actions may have very large consequences, and (2) even slight changes in initial conditions may produce very different outcomes. For actors in a particular system, sensitivity to initial conditions means that their very actions change the conditions of the system so that the same action, taken at a later point in time, may not produce the same result. A small investment in Apple stock when its price is low (a rare occurrence in recent years) may be part of a wave of such investments that produces gains as the stock goes up. The exact same investment made at a later time may produce no gain at all or even a loss dependent on many other factors affecting Apple stocks.

Sensitivity to initial conditions means that complex adaptive systems are path dependent. They have a history that influences their future trajectories; future options may be limited by past choices. New architecture may form along a chosen path so that changes in the system “lock in” and prove difficult to reverse. For example, complexity economists studying technology markets have suggested that an initial advantage for a technology or standard may, because of sensitivity to initial conditions and historical “lock-in,” have adverse effects even in free markets. They offer the example of the QWERTY keyboard, which became the industry standard because the large majority of keyboard users learned to type on the QWERTY system and resisted learning another system.11 In the environment, a species like the bison may adapt so completely to a particular niche that it cannot re-adapt quickly enough to survive environmental change. In financial markets, financial deregulation permitted the growth of financial architectures that made some companies “too big to fail” without taking the global economic system with them. Opinions differ over the true “irreversibility” of path-dependent behavior—after all, the bison were saved, financial institutions can be re-regulated—but, at a minimum, path dependence creates resistance to change; at maximum, it may foreclose some options.

Emergence: Self-organized, Systemic Patterns

Against this resistance to change must be set the quality of emergence, the indispensible characteristic of all complex adaptive systems. The aggregated behavior of system actors following simple rules of interaction produces unexpected, system-wide patterns and behavior that may have no clear relationship to a p...