a theory

a theory

A theory not only explains known facts; it also allows scientists to make predictions of what they should observe if a theory is true. Scientific theories are testable. New evidence should be compatible with a theory. If it isn’t, the theory is refined or rejected. The longer the central elements of a theory hold–the more observations it predicts, the more tests it passes, the more facts it explains–the stronger the theory.
But for scientists, a theory has nearly the opposite meaning. A theory is a well-substantiated explanation of an aspect of the natural world that can incorporate laws, hypotheses and facts. The theory of gravitation, for instance, explains why apples fall from trees and astronauts float in space. Similarly, the theory of evolution explains why so many plants and animals–some very similar and some very different–exist on Earth now and in the past, as revealed by the fossil record.

What is the purpose of business? While most agree that business minimally involves the creation of value, a blurred double image of value haunts our discussion of purpose. The image of what counts as value for a single firm is laid atop an image of what counts as value for business in general. These two images cannot match. Indeed, the resulting conceptual blurriness is a classic example of a composition fallacy. We should never mistake the properties of a part for the properties of the whole. A theory of the firm is ill equipped to handle the many expectations we hold for business practice. As such, we seek to establish the beginnings of a theory of business, one that is both empirical and normative. Offering four central propositions about the purpose, accountability, control and success of business, we close with a consideration of several important theoretical issues and practical opportunities that await us in the years ahead.
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This paper proposes a decision-theoretic framework for experiment design. We model experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters’ preference for randomization, and clarifies the circumstances in which randomization is optimal: when the available sample size is large enough or robustness is an important concern. We illustrate the practical value of such a framework by studying the issue of rerandomization. Rerandomization creates a trade-off between subjective performance and robustness. However, robustness loss grows very slowly with the number of times one randomizes. This argues for rerandomizing in most environments.

University of DelawareUniversity of Cincinnati
University of DelawareUniversity of Cincinnati