03 Apr Causal Chains in Action

Expectations are often influenced by our understanding of cause and effect. In physical interactions between our bodies and the external environment, and in social interactions between other complex people, we are capable of predicting an outcome long before it actually comes to pass. We are also capable of predicting an outcome immediately before it comes to pass, as we do instinctively when we trip over an unseen obstacle or a slippery patch. Our instinctive reaction usually involves positioning the arms to help prevent a painful impact, contracting and relaxing muscles in the legs and hips to shift our weight, scrunching our eyes and any number of other reflex responses to the situation.

There is a simple correlation between human behaviors associated with causal chains, and computer behaviors. People reason about what to do next using logical propositions like:

If x (where x is a condition such as “it’s raining”)

Then I’ll respond with y (where y is a decision such as “put on a raincoat”).

Computers use “IF – THEN” statements to make branching decisions that process input and deliver output. Put a bunch of these together end-to-end, and you have a chain.

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Section 5 #33

Table of Context

Our ability to develop expectations and use them to interpret every little thing is critical to our survival and success. The following chart identifies different forms of cause and effect relationships and shows whether we normally think of the chain in affirmative, negative, or ambivalent ways.

Causal chains can be described in simple terms in which a single cause can be identified for a single effect. Unfortunately, the behavior of complex systems is seldom this easy to describe. Typically, many different laws operate to bring about any change (effect). For example, the phrase “the auto damage was the result of an accident” is clear and correct, but it fails to capture the cause of the accident – slippery roads, inattention, stupidity, whatever. As humans, we fill in the gaps with common sense assumptions. The more context we know, the more of our assumptions will be valid. Teaching computers to build out assumptions to fill in the gaps is another matter entirely.

Causality in Complex Systems

The laws of physics describe behavior of things in the physical universe. The universe is certainly a complex system, but its organization seems to be 100% consistent: it obeys its own laws. The law of cause and effect is an example: “For every action there is an equal and opposite reaction.” In the domain of physics, this law of cause and effect is broadly accepted. Does the same rule apply to less regularly organized systems than the physical universe?

Consider the system of a society. In that system, people often perform actions. In interpersonal relations in society there are many rules described as social norms, mores, civil laws, and the like, which prescribe behavior but which do not necessarily prescribe reaction. One famous teacher described a social rule which departs from the physical law of cause and effect: “And as ye would that men should do to you, do ye also to them likewise” (New Testament, Luke 6:31). In fact, the implication that social (or anti-social) actions can prompt (or cause) like reactions rather than opposite is empirically credible, though certainly not universally applicable.

Understanding behavior of complex systems is a key component of cybernetic research. The brain, though complex, is governed by the same physical laws as the universe, thus it is a regular system that obeys its own laws. We are now beginning to understand those laws. In irregular systems, such as society, the way to understand the laws is to observe and develop an empirical framework. Wherever possible, scientific methods should be applied to test the framework. In the end, there are so many exceptions or irregularities that anecdotes and other empirical observations inevitably form an important part of the overall theory.

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