Formal definitions of information and knowledge and their role in growth through structural change

• Computer science definitions of information and knowledge are applied to economics.
• Shannon entropy and mutual information emerge from our evolutionary equations.
• Information and knowledge become quantifiable ingredients of growth.
• A direct relationship is shown between information and growth potential.
• Changing economic structures according to this information optimizes growth.

The article provides a way to quantify the role of information and knowledge in growth through structural adjustments. The more is known about environmental patterns, the more growth can be obtained by redistributing resources accordingly among the evolving sectors (e.g. bet-hedging). Formal equations show that the amount of information about the environmental pattern is directly linked to the growth potential. This can be quantified by treating both information and knowledge formally through metrics like Shannon's mutual information and algorithmic Kolmogorov complexity from information theory and computer science. These mathematical metrics emerge naturally from our evolutionary equations. As such, information becomes a quantifiable ingredient of growth. The policy mechanism to convert information and knowledge into growth is structural adjustment. The presented approach is applied to the empirical case of U.S. export to showcase how information converts into growth potential.

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