opportunity cost

In neoclassical >equilibrium economics, opportunity costs and equilibrium values of goods converge. In EE, opportunity costs are the most important category of deliberative choice within the confines of an actor's referential knowledge.

Every actor decides according to his or her ranking of alternatives, with the opportunity cost being the subjective value of the forgone alternative when one has been selected. However, in an economic system with distributed >knowledge and >novelty it constantly happens that actors learn ex post that other actors have discovered a better ranking. Hence, the "true" opportunity costs become only known ex post and are the object of >learning. Convergence of opportunity costs means that the valuation becomes common knowledge in a population of actors. However, this convergence is almost always disturbed because of the continuous emergence of novelty.

One fundamental theorem related to opportunity costs is Arrow's paradox on the impossibility of optimizing over an uncertain search for alternatives: If we do not know all relevant alternatives, we need to invest time and effort to learn about these. However, we have no way to optimize this search by rational choice, unless we knew all the foregone alternatives after the stopping point of our search. But if we knew, we would not need to search. Opportunity costs, hence, are an evolutionary non-equlibrium category.

Arrow's paradox is of central importance for the theory of the division of labour and entrepreneurship: Very often, other people discover alternatives beyond our stopping points in searching for alternatives.

Basic References

The classic on opportunity costs is:
James Buchanan, Cost and Choice. An Inquiry in Economic Theory. Chicago: University of Chicago Press, 1969.

Semantic Field
decision   learning
opportunity costs