3. Cause and Effect
Our general concern in this early part of the course is with the form of assertions about the relation between society and technology, and with the ways in which such assertions might be justified. We have already remarked that the assertions often have the form of reductions, and are very often implicitly justified by their status as folk theories, i.e., as "common sense." In addition, we should notice that these assertions usually have the form of a cause/effect relation, i.e., they assert that one thing causes another. Before going further with our theme, it is very worthwhile to spend a little time examining the situation in the physical sciences.
The first thing to notice is that there are no cause/effect laws in the physical sciences. For example, Newton's third law,
Nevertheless, physicists, chemists and engineers do use the language of cause and effect, for example, in an experiment where a magnetic force is applied to a metal ball in a vacuum, the experimenter thinks of the force as the cause and the acceleration as the effect. All cause/effect language arises through asymmetries that are introduced by an observer in a similar way. They are not part of nature, they are part of human culture; they are social. In fact, natural language does not provide good support for talking about the kind of acausal laws that are the actual content of physics.
A second thing to notice is that physical laws are not true! They are approximations that are useful under certain assumptions. For example, Newton's third laws fails for velocities near the speed of light, where relativistic corrections are required; it also fails for quantum level phenomena. Similarly, the general gas law fails for very low temperatures, since any gas will eventually start to liquify. In general, it is not very clear what are the exact boundaries within which any given law can be applied; the boundary between Newtonian and quantum physics is fuzzy, which is a symptom of the fact that quantum gravitational phenomena are at present very poorly understood.
A third thing to notice is that physical laws cannot be proved; instead, they can fail to be refuted. The standard methodology of science calls for making hypotheses, and then trying to refute them with experiments. No single experiment can show that a physical law is true, because the law is supposed to apply to an infinite number of different situations. However, a single experiment can refute a hypothesis. A hypothesis that withstands more and more tests gradually comes to be more and more accepted. And as already noted, even experiments that contradict a law may not serve to refute it totally, but only to restrict the range of phenomena within which it is considered to be usefully valid.
If this is the situation for well-established and widely used physical theories, just think how bad things must be for the social sciences!
It is often said that good science is reductionist, and the success of the hard sciences is often given as an example that the social sciences should try to follow. A prime example is the reduction of chemistry to physics. However, if we look carefully at what really happens in chemistry, we will see that chemists are not doing a specialized kind of physics. On the contrary, they are using concepts at the level of chemistry, such as valance. Since it is impossible in practice to solve Schroedinger's equation for any but the very simplest atoms, calculations in quantum physics cannot be used to do chemistry.
This situation is often described by saying that chemistry is an emergent level above physics, meaning that partial reductions are possible and can be very valuable when they occur, but concepts and theories that are distinctly chemical (and not quantum mechanics) are primary for the practical applications. This does not deny that reduction might be possible in principle, and most scientists believe that this is the case.
If we look at higher levels, such as biology, psychology, and sociology, we again see emergent phenomena, but it becomes progressively more difficult to support the belief that reduction to lower levels must be possible in principle, and indeed most social scientists today do not believe this.
So where does this leave us? It seems that simple cause/effect explanations are not characteristic of the hard sciences, and moreover that reductionism does not take the simple form that is often claimed. So we conclude that arguments in favor of technological determinism based on a claim that it is in some sense more scientific than alternatives are fatally flawed.
Going a little further, I think we should conclude that it is very wise to be suspicious of simplistic principles and simplistic arguments in complex areas like ethics and the relationship between technology and society. Technological determinism is a prime example of such a simplistic principle.
But then, Why, given the deficiencies of technological determinism, do people find it so persuasive? Why is it so common in advertisements, newspaper and magazine articles, websites, and other places?
One answer is that causal explanations are built into our language. For example, the sentence
Linguistics has developed extensive theories of stories, which can add many interesting details to this discussion. The narrative presupposition applies to any story, but especially to oral narratives of personal experience; it says that the order of clauses is the same as the order of the events that they describe unless there are explicit contrary indications (this term was introduced by William Labov). For example, if we hear
Additional evidence concerning the narrative and causal presuppositions comes from studies of the Balinese language, in which the narrative presupposition is replaced by the default presupposition that, given clauses A, B in that order, the corresponding events happen concurrently, possibly with mutual interaction (see papers by Alton Becker). In computer science terms, we might say that in English, the default semantic connection between subsequent clauses is ";" rather than "||" , whereas the opposite holds in Balinese.
Much more information on the theory of narrative can be found on the web through the links on the narratology page at the Media and Communication Studies site at the University of Aberdeen (e.g., the paper The Narrative Emergence of Identity by Mark Gover, from Michigan State University). More details of my own approach can be found in the essay Notes on Narrative.
So the conclusion here is that the nature of narrative, especially the causal presupposition, predisposes us towards assuming that simple cause/effect relations really do exist in the world of our experience, and therefore predispose us towards accepting assertions of the form that technological determinism uses.
3.2 Complex Systems
An example where we can clearly see the interplay of an underlying acausal (i.e., non-causal) model with human causal explanation is a simple ecological system, with one predator species and one prey species, say wolves and rabbits. The basic Volterra-Lotka differential equation is well known, and has as a solution (given suitable coefficients and initial values) two periodic functions with a time lag; that is, the numbers of wolves and rabbits fluctuate up and down over some fixed time period, as illustrated by the applet below. (Of course, most real ecological systems are much more complex than this, but simple cases where the assumptions of this model are satisfied have actually been observed in nature.)
For more on Volterra-Lotka models of predator-prey systems, see the Lascaux Graphics webpages, and for much more background information on ecological systems and their models, see the Population Ecology Reference List (by Alexei Sharov, of the Dept. Entomology at Virginia Tech), such as the Volterra-Lotka simulation server linked there (but last time I tried, it was not working). The Voltera-Lotka equations for a simple predator-prey system have been given as follows:
It is difficult to understand such a system just by contemplating these equations! In fact, our intuition is much better served by causal assertions, such as "a large number of wolves will decrease the number of rabbits" and "a small number of rabbits will decrease the wolf population". It is not just beginners who find such assertions helpful; even experts often use this kind of causal language informally among themselves. The human mind did not develop under evolutionary pressure to deal with differential equations, whereas there certainly was evelutionary pressure to deal with simple cause/effect relations.
A good example of a non-obvious consequence of the equations is the possibility of chaos under certain conditions. Another consequence (although this requires a more complex model) is that the rabbits will die out if there are no wolves, because they will increase to a point where they consume all available food. (Note that this statement has a cause/effect form.)
So we should not conclude that causal assertions cannot be used at all, but rather we should be aware of their limitations. Causal assertions are normal and useful for talking about entities that have intentions. However, they are often misleading in talk about non-intentional entities. Moreover, they are open to deliberate misuse, e.g., in advertisements for new technical devices. (Applying concepts to non-humans that are only appropriate for humans is called anthropomorphism.)
Another example is the relationship between two stock market variables: the Dow-Jones Industrial Average and investor confidence. It is easy to find articles which say things like "Decreases in the Dow have eroded investor confidence" or conversely that "Decreased investor confidence is eroding the Dow." But it is hard, maybe impossible, to find articles which say that these two variables are mutually interdependent, reflecting a complex system where simple cause/effect assertions can be misleading.
So the conclusion here is that if we look more carefully into real social systems, we will see that, insofar as there is lawfulness, the laws tend to be like the laws of physical science, that is, acausal relationships among variables, rather than assertions of cause/effect relationships. On the other hand, cause/effect relationships are truly useful in understanding the actions of people, as well as corporations, governments, etc., because such entities do have (or can be said to have) intentions - that is, goals - and they do carry out actions in order to achieve their goals. This is in sharp contrast to systems, like the stock market and the global telecommunications system, which do not themselves have goals, and do not intentionally perform actions, but for practical purposes, can be successfully described as satisfying various laws.
In fact, what is strikingly clear for complex systems such as eco-systems (and is almost definitional for complex systems) is that there are complex mutual interdependencies (e.g., the derivatives on the left sides of the Volterra-Lotka equations are defined by the right sides, which use both of the variables, each of which is a function of time). This is difficult to translate into our ordinary language of cause/effect relationships, because it really denies the essence of such relationships. However, phrases like "mutual causation" and "interdependent origination" have long been used to describe complex systems, the latter going back over 2,500 years to the Buddha (the orignal Sanskrit word for this is "pratityasamutpada"). One point is that positive and negative forces coexist in a dynamic balance; for example, the wolves are a negative force for the rabbits, but could face extinction without them.
Another point that the study of complex systems drives home is that anything that we call a system is an abstraction, emphasizing some particular things and ignoring others. Since in the real world, everything is interdependent, it is necessary to draw a line somewhere, and call what is inside the line "the sytem" and what is outside of it "the environment." Sometimes it is pretty clear where to draw such a line, but other times, especially for complex systems with a significant social component, it is much less clear, and different choices can lead to very different analyses and results. For example, the optimizations done by large corporations tend to ignore many variables of social importance, such as the quality of air, diversity of the biosphere, and the depletion of non-renewable resources. The actor-network theory that we will study later on takes an entirely different approach from drawing an artificial line demarcating a system.
The final point is that our knowledge of the necessity for abstraction in describing complex systems is another factor that makes us vulnerable to the kind of manipulation using technological determinism that we have found to be so common in advertisements for (e.g.) communications and internet devices; this knowledge means that we are not surprised to see simple cause/effect assertions instead of complex networks of socio-technical factors.