Certezas e conjecturas IV

Limits to prediction II

Kevin Ke
lly sobre "The limits to growth":

The Limits to Growth model has many things going for it. Among them: It is not overly complex; it is pumped by feedback loops; it runs scenarios. But among the weaknesses I see in the model are the following:

Narrow overall scenarios. Rather than explore possible futures of any real diversity, Limits to Growth plays out a multitude of minor variations upon one fairly narrow set of assumptions. Mostly the "possible futures" it explores are those that seem plausible to the authors. Twenty years ago they ignored scenarios not based on what they felt were reasonable assumptions of expiring finite resources. But resources (such as rare metals, oil, and fertilizer) didn't diminish. Any genuinely predictive model must be equipped with the capability to generate "unthinkable" scenarios. It is important that a system have sufficient elbowroom in the space of possibilities to wander in places we don't expect. There is an art to this, because a model with too many degrees of freedom becomes unmanageable, while one too constrained becomes unreliable.

Wrong assumptions. Even the best model can be sidetracked by false premises. The original key assumption of the model was that the world contains only a 250-year supply of nonrenewable resources, and that the demands on that supply are exponential. Twenty years later we know both those assumptions are wrong. Reserves of oil and minerals have grown; their prices have not increased; and demand for materials like copper are not exponential. In the 1992 reissue of the model, these assumptions were adjusted. Now the foundational assumption is that pollution must rise with growth. I can imagine that premise needing to be adjusted in the next 20 years, if the last 20 are a guide. "Adjustments" of this basic nature have to be made because the Limits to Growth model has...

No room for learning. A group of early critics of the model once joked that they ran the Limits to Growth simulation from the year 1800 and by 1900 found a "20-foot level of horse manure on the streets." At the rate horse transportation was increasing then, this would have been a logical extrapolation. The half-jesting critics felt that the model made no provisions for learning technologies, increasing efficiencies, or the ability of people to alter their behavior or invent solutions.

There is a type of adaptation wired into the model. As crises arise (such as increase in pollution), capital assets are shifted to cover it (so the coefficient of pollution generated is lowered). But this learning is neither decentralized nor open-ended. In truth, there's no easy way to model either. Much of the research reported elsewhere in this book is about the pioneering attempts to achieve distributed learning and open-ended growth in manufactured settings, or to enhance the same in natural settings. Without decentralized open-ended learning, the real world will overtake the model in a matter of days.

In real life, the populations of India, Africa, China, and South America don't change their actions based upon the hypothetical projections of the Limits to Growth model. They adapt because of their own immediate learning cycle. For instance, the Limits to Growth model was caught off-guard (like most other forecasts) by global birth rates that dropped faster than anyone predicted. Was this due to the influence of doomsday projections like Limits to Growth? The more plausible mechanism is that educated women have less children and are more prosperous, and that prosperous people are imitated. They don't know about, or care about, global limits to growth. Government incentives assist local dynamics already present. People anywhere act (and learn) out of immediate self-interest. This holds true for other functions such as crop productivity, arable land, transportation, and so on. The assumptions for these fluctuating values are fixed in Limits to Growth model, but in reality the assumptions themselves have coevolutionary mechanisms that flux over time. The point is that the learning must be modeled as an internal loop residing within the model. In addition to the values, the very structure of the assumptions in the simulation-or in any simulation that hopes to anticipate a vivisystem-must be adaptable.

World averages. The Limits to Growth model treats the world as uniformly polluted, uniformly populated, and uniformly endowed with resources. This homogenization simplifies and uncomplicates the world enough to model it sanely. But in the end it undermines the purpose of the model because the locality and regionalism of the planet are some of its most striking and important features. Furthermore, the hierarchy of dynamics that arise out of differing local dynamics provides some of the key phenomena of Earth. The Limits to Growth modelers recognize the power of subloops-which is, in fact, the chief virtue of Forrester's system dynamics underpinning the software. But the model entirely ignores the paramount subloop of a world: geography. A planetary model without geography is...not the world. Not only must learning be distributed throughout a simulation; all functions must be. It is the failure to mirror the distributed nature-the swarm nature-of life on Earth that is this model's greatest failure.

The inability to model open-ended growth of any kind. When I asked Dana Meadows what happened when they ran the model from 1600, or even 1800, she replied that they never tried it. I found that astonishing since backcasting is a standard reality test for forecasting models. In this case, the modelers suspected that the simulation would not cohere. That should be a warning. Since 1600 the world has experienced long-term growth. If a world model is reliable, it should be able to simulate four centuries of growth-at least as history. Ultimately, if we are to believe Limits to Growth has anything to say about future growth, the simulation must, in principle, be capable of generating long-term growth through several periods of transitions. As it is, all that Limits to Growth can prove is that it can simulate one century of collapse.

"Our model is astonishingly 'robust,' " Meadows told me. "You have to do all kinds of things to keep it from collapsing....Always the same behavior and basic dynamic emerges: overshoot and collapse." This is a pretty dangerous model to rely on for predictions of society's future. All the initial parameters of the system quickly converge upon termination, when history tells us human society is a system that displays marvelous continuing expansion.