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 launching point for determining underlying theoretically-based quantitative relationships. Possibly this difference reflects the greater ease of quantifying and isolating variables in the physical sciences. Such sweeping generalizations are simplistic, however -- economics is an extremely quantitative social science.

All concepts of cause-and-effect assume that identical sets of initial conditions yield identical effects. Yet, quantum mechanics demonstrates that this fundamental scientific premise is invalid at the scale of individual atoms. For example, radioactive decay is intrinsically unpredictable for any one atom. If certainty is impossible at the atomic level, the same must be true for larger-scale phenomena involving many atoms. Werner Heisenberg, a champion of atomic-scale indeterminacy, carried this logic to a conclusion that sounds almost like a death knell for causality [Dillard, 1974]: “method and object can no longer be separated. The scientific world-view has ceased to be a scientific view in the true sense of the word.”

Some non-scientists have seized on Heisenberg’s arguments as evidence of the inherent limitations of science. Heisenberg’s indeterminacy and the statistical nature of quantum mechanics are boundary conditions to causal description of particle physics, but not to causal explanation in general. Particle physicists emphasize that virtual certainty can still be obtained for larger-scale phenomena, because of the known statistical patterns among large numbers of random events. The pragmatic causality of scientists finds atomic indeterminacy to be among the least of its problems. Far more relevant is the overwhelming complexity of nature. Heisenberg may have shaken the foundations of science, but few scientists other than physicists felt tremors in the edifice.

It seems that a twentieth-century divergence is occurring, between theoretical concepts of causality and the working concepts used by scientists. One can summarize the differences among these different concepts of causality, using the following symbols: A is the cause, B is the effect, ⇒ means ‘causes’, ≠> means ‘does not necessarily cause’, ∴ means ‘therefore’, Ai is an individual observation of A, and $\overline{A}$ is average behavior of A.

The different concepts of causality are then: Sufi and Trobriand patterns:. . . . A, B,. . . . Aristotle: A⇒B, in order to. . . Hume: If A, then B; or A, ∴B logical positivist: theory C predicts ‘A, ∴B’, & observation confirms it Quantum mechanics: Ai ≠> Bi, yet $\overline{A}$ ⇒ $\overline{B}$ scientific consensus: If A, then probably B, possibly because. . ..

Scientists’ working concept of causality remains unchanged, effectively useful, and moderately sloppy: if one event frequently follows another, and no third variable is controlling both, then infer causality and, if feasible, seek the underlying physical mechanism. Ambiguities in this working