Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.

Abstract

Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) reviews the relationship between Complexity and Structural Change; iii) introduces other new models/theories of Structural Change in industries; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of structural change.

Some of the innovations in this Article are that:
i) Structural Change is analyzed and modelled as time-sensitive large-scale aggregated Network-Decisions (or sets of Network-Decisions) in Complex Systems, wherein Structural Change begins, evolves and ends as groupings of decisions made by individuals (mostly), corporations and government agencies over time.
ii) Structural Change is modelled as being subject to “Short-term” Chaos (and short-term sensitivity to “Initial Conditions”, time and variable/processes) which is muted in many cases by changes in “Aggregated Decisions” and Decision Patterns.

Keywords: Structural Change; Globalization; Machine Learning; Portfolio Management; Sustainable Growth; Network-Decisions; Chaos