Our civilization is deeply involved with technology, and recently, especially with information technology; therefore so are all of us. Many people want to know, Where is it all going? Newspapers, magazines, TV, radio and of course the internet, are all full of predictions, both dire and glorious. The result is enormous confusion, with thoughtfulness sacrificed for flashiness, i.e., for media market share. So better questions than "Where is it all going?" are "How do we think about all this?" and "How do we tell truth from trash?" In a way, this course is about being cynical, about questioning what you read, hear and see in the media. It is also about learning to think for yourself.

The basic question addressed in this lecture was: What counts as an explanation? (for the relationship between society and technology).

Technological determinism is the theory that technology is an autonomous force that changes society. This provides explanations for many changes that can be observed in society, and it has a very simple cause/effect form.

Social determinism is the theory that society is an autonomous force that changes technology. This provides explanations for many changes that can be observed in technology, and it also has a very simple cause/effect form. It is the converse of technological determinism.

Both of these theories come in hard and soft forms, where the "soft" form only claims that this is one influence among many, and not an absolute determinant. The hard forms claim that the force is irresistible. Both of these determinisms are forms of reductionism. A reductionist theory reduces some class of phenomena to some (allegedly) simpler phenomena of another class.

The direct opposite to reductionism is holism, where a holistic theory says that some process or phenomenon cannot be broken into parts, and therefore certainly cannot be explained by reduction; the phenomenon only works as a whole. In general, this theory is probably true of very complex phenomena, but since it does not explain anything, it is not useful as a theory.

Social scientists today almost universally reject determinist and reductionist explanations of complex social phenomena, despite their appeal.

Marshall McLuhan introduced the special case of media determinism, which tries to explain various social phenomena through the nature of the media employed. His most famous quote is "The medium is the message". Claims that writing, or later on printing, changed society have been around for a long time. McLuhan extended this to newspapers, radio, and television. The media love to cover this sort of theory.

Voluntarism is the oposite of determinism. We wre free to make our own choices.

In physics, there are no cause/effect laws. Newton's third law, F = ma, is a relationship between measurable quantities: it says that force, mass and acceleration are related in a certain regular way; it does not say that acceleration causes force, or that force causes acceleration. It allows either possibility, and it even allows mass to be used to alter force, as when an aircraft jetisons fuel before a dangerous landing. All of the equations of physics, chemistry, and engineering have this same acausal character

Nevertheless, physicists 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. In fact, we lack a good language for talking about the kind of acausal laws that are used in physics.

It is often said that good science is reductionist, and the success of the hard sciences is 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. It is impossible in practice to solve Schroedinger's equation for any but the very simplest atoms, so quantum calculations 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 physical as such are regularly used, and in fact are primary for practical applications. This does not deny that reduction might be possible in principle, and most scientists believe that it is in this case.

If we look at higher levels, such as biology, psychology, and sociology, we again see emergent phenomena, but it is 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 all this leave us? It seems that simple cause/effect explanations are not characteristic of the hard sciences, and even the general principle of reductionism does not take a simple form in the hard sciences. 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 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

John hit the ball.

An example where we can clearly see the interplay of an underlying acausal 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 in class.

It is difficult to understand such a system by contemplating Volterra-Lotka differential 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.

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.

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., see the right sides of the Volterra-Lotka equations for the derivatives of the two variables). 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 been used to describe such systems for centuries.

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.

        (This topic continues in the notes for the second meeting.)