What does "causally independent structures" mean?

Question

I'm reading a book (Cummins, R. (1983), "The Nature of Psychological Explanation") where he says "...dispositions that cannot be instantiated in causally independent structures".

Can anyone help explain what he means by this?

Thanks

Answer 1

Short Answer

"...dispositions that cannot be instantiated in casually independent structures"

I'd translate this as "likelihood of events happening that can't be explained by independent events, that is, events with no reasonable causal relationship." Structures is used because causality can be modeled with very complex models, such as those found in this book.

Long Answer

A disposition (SEP) is simply a pattern or probability of behavior. Dispositions and occurrences are fancy philosophical language for saying "tends to occur" rather than "occurred at such and such time". Occurrences and dispositions of things are important because they characterize behaviors; in the philosophy of law, it is important to distinguish an act by a legally culpable agent that occurs once, and one that is seen as among a set of occurrences. When a judge is sentencing a convict, it might bear out in sentencing whether the crime was an occurrence or a disposition, since many people break the law accidentally, but few are repeat offenders likely to fall into a state of recidivism. From SEP:

A glass has certain dispositions, for example the disposition to shatter when struck. But what is this disposition? It seems on the one hand to be a perfectly real property, a genuine respect of similarity common to glasses, china cups, and anything else fragile. Yet on the other hand, the glass’s disposition seems mysterious, ‘ethereal’ (as Goodman (1954) put it) in a way that, say, its size and shape are not. For its disposition, it seems, has to do only with its possibly shattering in certain conditions. In general, it seems that nothing about the actual behavior of an object is ever necessary for it to have the dispositions it has.

In the philosophy of science, occurrences and dispositions are more linked to the notion of certainty. The sun shines on Alps every day, therefore every occurrence is a part of the disposition of the sun, sunlight, and large chunks of dolomite. Of course, understanding the nature of the solar system goes a long way to explaining why there is this particular disposition in events. But what about a different scientific phenomenon like those related to behaviors in a personality disorder? Then, science, in this case psychology, has a certain challenge before it, to explain a behavior that may not be explained in terms of simple Euclidean geometry like our prior example involving the Alps. The human mind is a complex system, and one cannot make deductive arguments, but rather must make probabilistic arguments about its behavior. A person may go their whole life behaving well, and then suddenly snap and commit and egregious crime. What explains the latter case? Again from SEP:

[A]ppeals to dispositions have been made in just about every area of philosophy. There are explicitly dispositional analyses, for example, of mental states, of colours, of value, of properties, and of conditionals.

As for "causally independent structures", what we are likely talking about here are multiple causes with multiple factors that cannot be shown to be independent. Here we are in the language of probability when we are talking the language of independent events. For instance, most would accept that darkness and a human hand play a role in causing a person to throw a light switch on. If I do it, and my neighbor does it at different times and in different places, no matter what sort of graph we could build to explain the necessary and sufficient factors for the light to go on, we could agree that both causal explanations offered for the two events respectively would be independent, ceteris paribus. These would be causally independent structures (of knowledge) in the explanation.

However, now, some pairs of events are correlates, and with correlates, it should presumed that causality as it is understood somehow relates the two events. A good example would be dropping two coins at the same time. Does either coin really cause the other to fall? Of course not. The main factors in the free body diagram would be the independent mass of the coins, the gravitational field, perhaps the resistance of the air they fell through, perhaps magnetic forces if the coins have iron in them. These two coins could be understood as falling independently in the same vicinity, or they could be lumped together in the same model because they started in the same hand, are part of the same region of spacetime, etc. And if dropping these coins is part of a psychological experiment (say, meant to study how students do lab experiments to improve pedagogy), then perhaps the scientific variables are psychological instead of physical. Those two coins being dropped are neither arbitrary nor random psychologically. Let's say a dime and a nickel are called for. Then from the perspective a psychologist, perhaps the physical results of the experiment are impacted or caused by the choices the student makes. Whereas a physicist says the two coins don't cause each other to fall, a psychologist is free to declare that the two coins will ONLY fall when the student has both of them, at least in regards to conducting the experiment. So, it's not clear how to disentangle models that map causes and effects. That's what this passage is getting after.

Causality is a tricky thing philosophically. From WP:

Causality is an abstraction that indicates how the world progresses,6 so basic a concept that it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it.6 Accordingly, causality is implicit in the logic and structure of ordinary language.9

How does one event cause another? How do we know? How do we measure? One common tool is linear regression. One event happens after another, but how do we know they're connected? Obviously post hoc ergo propter hoc can be flagrant, but in scientific experiments, sometimes it's very subtle. If you reward or punish a student, does it cause them to change their attitude or their behavior? Establishing the statistical independence of causal models is a very difficult thing to do when the variables measure parts of the same system, say parts in an engine. Each may have a different proximal cause, but distal causes quickly converge since all of the parts are interlinked and designed to work together.