Complexity Economics

This compilation of some key resources in complexity economics is a reader for both students and faculty engaged in a postgraduate course in complexity economics or planning such a course. After developing and teaching such a course at Jindal School of Government and Public Policy (JSGP) we recognised the need to bring together in one place suitable pedagogic materials from a variety of sources to support the teaching and growth of this young but important economics discipline. Complexity economics sees the economy as a complex adaptive system with all the associated characteristics like dynamic non-linearities, feed back loops, far from equilibria states and heterogeneous agents making rational and non-rational decisions driven by both self-interest and other motivations. Some of its tools are computational in nature, such as a range Agent Based Models, Network Analysis etc, but some are more qualitative in nature with a focus on sense making , heuristics, complex thinking, etc.

Complex adaptive system’s approach in economic science and public policy has evolved in different paths over the last few decades. There have been different perspectives developed towards understanding the economy as a complex adaptive system. Computational approaches, sense-making framework, evolutionary biology based concepts, to name some seminal frameworks, have evolved in parallel. The CEASP (Centre for complexity economics, applied spirituality and public policy) at JSGP, OP Jindal Global University, has compiled this volume of seminal works in the multitude of approaches, which as a whole provides an on-to-date but still emerging field of complex social sciences, where complexity economics plays a central role. 
 

First section deals with foundational questions and frameworks, here we compile the seminal works since late 1980s which have formally organised complexity economics questions. Central concepts like economy as far from equilibrium system, economy as ecology of forecast models, agents being heterogeneous, non-linear relations between dynamic variables, computational modelling and its differences from DSGE paradigm, are focus of these works. Students can see the historical development of complexity economics. We also include more recent foundational contributions from evolutionary economics stand point, which is an interface between evolutionary biology, or psychology and complex social system understanding. 

Macroeconomics has traditionally been source of many intriguing problems in the history of economic thought, for example problems related to aggregation or fallacy of composition, which provided vital inputs to emergence literature. Similarly multiple equilibria in theoretical neoclassical models have remained a thorny issue, not only for economists but also for policy architects. Multiple equilibria presents a puzzle since theoretical modelling itself cannot provide any insight on which equilibrium solution would be chosen eventually, collectively by agents. Hence welfare economics traditionally have posed many normative principles in such scenarios, making Pareto optimality a necessary but never a sufficient criterion for economy as a whole. In the third section then we make a brief de-tour of important recent works on multiple equilibria problems in Macroeconomics. This section equips students with deeper understanding of problems in standard macro theory, which would provide further insights into why we need to embrace complexity economics view of non-equilibrium dynamics.

Currently complexity economics is motivated by computational frameworks, which we have explored in the earlier sections. However, we observe here that agent based modelling though very fruitful as a theoretical foundation, might not always be practicable when urgent policy interventions are required. Also that students, particularly from different backgrounds than mathematically oriented, might find computational models not in their forte. Fortunately, we have seen that complexity frameworks with practical policy insights can be built in alternative ways too. One major work in this direction has been sense-making framework, which provides a thorough qualitative template to distinguish between simple, complex, complicated and chaotic systems. This categorization is vital since in many research and policy programs, it remains unclear which type of system we are dealing with, complex does not by default mean complicated! Hence this section provides an introductory coverage of sense-making framework with its applications in different domains including management science.

The Cynefin framework helps leaders determine the prevailing operative context so that they can make appropriate choices. Each domain requires different actions. Simple and complicated contexts assume an ordered universe, where cause-and-effect relationships are perceptible, and right answers can be determined based on the facts. Complex and chaotic contexts are unordered—there is no immediately apparent relationship between cause and effect, and the way forward is determined based on emerging patterns.   (Read full paper)
 

Cognitive capture is a well-known in policy literature, where policy makers are biased by the long term training they undergo (may be in graduate schools) in a particular school of modelling, which is in most case general equilibrium or DSGE. Hence for policy students, for example as in our University, it is critical that policy implications of complexity economics is being explored. This section explores current papers in that direction. Albeit, policy implications of complexity economics is still an unfolding domain, some seminal texts are already written which we refer to.

We conclude our compendium by arranging some very recent frontiers works, which potentially might enrich the complex adaptive systems view. Inputs from evolutionary biology is again emphasized. Another parallel development has been quantum-like modelling framework, which is an interface between mathematical and conceptual tools in quantum science and human cognition. Copious empirical literature show that decision making under contexts of uncertainty (conceptually different from hedge-able risk) does not follow standard Boolean logical set up, hence can be better described by quantum-like logic with its own mathematical apparatus. Quantum-like modelling can contribute significantly to complexity thinking, since both heterogeneity of agents and behaviour of such agents in uncertain contexts are fundamental to complexity modelling, particularly economics modelling. Other cutting edge ideas like ontological emergence in economy is also covered briefly in this section. Such ideas would motivate students to move on the wonderful paths of self-study and further research.