INTRODUCTION
With this paper, although in a naïve attempt, I will try to make the case that the mainstream
modern economic models based on General Equilibrium Theory which rely on a Walrasian
approach are no longer useful and are not in alignment with the cutting-edge fields in economics
today. I will try to highlight which framework best highlights modern economic thinking of the
21st century and how it is best understood as part of a broader concept namely Complexity
Economics.
BEYOND DSGE
Even though the concept of general equilibrium has advanced the mathematization of economics
in the 20th century and remains as one of the most dominant features of contemporary economic
research, yet no other concept in modern economic theory raises more questions than general
equilibrium theory. According to many, the assumptions laid down by general equilibrium theory
do not conform to economic reality and in the context of the scientific query and academic
progression, it may not have any more practical applications. It’s importance attached to modern
economic education and research, brings into question a lot of the problems associated with
modern economics and might even undermine it’s cognitive status, as equilibrium is notably not
an indicator of scientific progression anymore. (Tieben, 2012)
The same could be said for General Equilibrium Theory’s latest incarnation that’s regularly used
for applied policy, Dynamic Stochastic General Equilibrium modeling and as Robert
Solow(2010) points out,
“I do not think that the currently popular DSGE models pass the smell test. They take it for
granted that the whole economy can be thought about as if it were a single, consistent person or
dynasty carrying out a rationally designed, long-term plan, occasionally disturbed by
unexpected shocks, but adapting to them in a rational, consistent way. I do not think that this
picture passes the smell test.”
The proponents of this model claim that’s it is founded on the most solid microeconomic theory
available, they might argue that it is the best scientific model for guiding macro policy
economists have come up with. Although there is some truth to this and it’s a very reasonable
model, but its dominance in advancing policy is quite problematic because the notion that there
is a solid understanding of microeconomic behavior sounds ridiculous. Notably, it is justified on
highly abstract assumptions that assume representative agents who are infinitely rational, who
face no uncertainty seem far from the conception of reality, although such justifications are
reducing due to the rise of behavioral economics. The DSGE model that presupposes no agent
coordination cannot truly be shedding light on real-life problems (Perry Mehrling, 2008).
Moreover, as the economy is generally understood to be a complex system where the aggregate
behavior emerges due to the interactions between individual, aggregate behavioral understanding
cannot be deduced from just individual analysis.
Some argue that to come to any useful macro models, economists have to break from the past
and understand that these micro-foundations act as a choice variable of the theorist. The apt
micro foundation cannot be considered a priori and is always context-dependent. The modeling
techniques need to move beyond these Walrasian assumptions and take into account the true
complexity of this existence we find ourselves in.
POST WALRASIAN ECONOMICS AND COMPLEXITY THEORY
The best theoretical framework for taking into account the significance and the properties of
complexity in economic modeling comes from David colander’s ‘Post-Walrasian economics’
(Colander, 2003). He suggests that how the evolution of economic thought has led to an eventual
disregarding of the “neo-classical” category as an apt descriptor of modern economics. The
tendency of earlier economists to classify different macro schools of economic thought which
were distinct from each other -such as Keynesians, neoclassicals, monetarists and radicals-
served a useful purpose. But those distinctions broke down in the 1970s-1980s and further
evolved into - new classical, neo-Keynesian, new Keynesian, Post Keynesian- to capture the
distinctions in macro-policies. By the 1990s, he argues that even these distinctions weren’t useful
anymore and the classification situation could be best described as ‘classification anarchy’ and
asserted that “The pedagogical reaction to that anarchy was to downplay differences among
schools in the texts and to present macroeconomics as a unified approach. In the texts, there were
no longer Keynesians, monetarists, or classicals; there were simply macroeconomists.”
(Colander, 2003). Colander argues that modern economics has to move (and it has in its non-
mainstream content and approaches) from the ‘holy trinity’ -rationality, equilibrium, and greed-
and moved to a much more comprehensive trinity described as cognitive awareness, purposeful
behavior, and sustainability.
The main characteristics of Post-Walrasian economics are;
1.Multiple equilibria and complexity
2. Bounded rationality
3. Institutions and non-price-coordinating mechanisms
This is a much broader foundation required for theorizing and its development is linked to the
advancements in analytic and computing techniques that allowed researchers to start looking into
more complicated analytic techniques. The Walrasian approach assumes, Colander argues,
hyper-rationality, full information availability and focuses on a unique equilibrium model. The
research strategy of this approach is to build a standard Walrasian equilibrium model of the
economy from its micro-foundations, where market failures are characterized as deviations from
optimality. Post-Walrasian economics has a much different underlying perception, that is, the
individuals bright enough but not to the extent of full rationality, and they operate in
information-poor environment. The economy, in this approach, is too complex to have an
exhaustive and understandable micro-foundation. On a macro scale, the economy is seen as a
complex mechanism of agent interactions and stability on an aggregate level is only maintained
due to the subset created by ‘bounded rationality’ of the individual and ‘institutions’ which limit
individual action. This completely contradicts the Walrasian view, in which these two factors
would have acted as destabilizers and prevented full equilibrium. The standard Walrasian general
equilibrium model of the economy lies at the underlying vision of the Walrasian approach.
Market failures are characterized as deviations from optimality and the central task is to
understand why the economy exhibits any instability, but as I highlighted above, an aggregate
stability is only maintained due to bounded rationality and the presence of institutions in the
post- Walrasian approach, the question then becomes that why does the economy exhibit such
less instability as it does?
In terms of policy implications of the post-Walrasian view, there is no ubiquitous model of the
economy. It follows, what Colander calls, the “Muddling through” approach, characterized by
pragmatic exploration of rules of thumbs in specific institutional contexts. With the evolution of
these institutions, the rules of thumb evolve as well which helps policymakers make better
decisions than they would have otherwise as opposed to hoping to find an optimal policy.
Accordingly, it is not the data availability in association with the models that guide the research
topic, it is the problem that defines it. And as Colander( 2003) puts it,
“In Post Walrasian theory, models are not representations of reality but are aids in thinking
about real problems. The policy implication from one model will not be sufficient to draw any
real-world policy conclusions; instead, it will be tied with historical understanding and results
from other models, and by a heuristic approach that provides one with an initial view of what the
problem is and whether the analytic models are providing useful insight.”
With this general framework in mind, cutting edge economic work is shifting towards far more
complicated analytic models with much fewer restrictions and virtual modeling under the field of
complexity economics. The focus in complexity has much more realistic characteristics
involving heterogeneous agents, endogenous learning, statistical dynamics and multiple (or no)
equilibria. Complexity science helps to formulate a vision to look at the economy as such a
complicated system that simple analytical models of the aggregate economy -models that are
based on a set of analytically solvable equations- are not likely to be conducive in understanding
many of the issues that economists want to address. Among many other definitions of
complexity in modern science, a few of the most commonly accepted ones in economics are;
1. A general one: A complex system is one made up of a large number of parts that interact
in a non-linear way. In such systems, the whole is more than the sum of the parts, given
the properties of the parts and the laws of their interaction, does not constitute to perfect
understanding of the properties of the whole system.
2. A dynamic one: This approach emphasizes dispersed and interacting heterogeneous
agents and is based on an adaptive model of self-organization. “A dynamical system is
complex if it endogenously does not tend asymptotically to a fixed point, a limit cycle, or
an explosion” (Rosser, 1999)
In a book about complex adaptive systems theory for economics, Brian Arthur, Steven N.
Durlauf, and David A. Lane (Introduction: Process and Emergence in the Economy. The
Economy as an Evolving Complex System II., 1997) expand on several features of complex
systems that deserve greater exploration and inclusion in modern mainstream economic thinking.
1. Dispersed interaction—The economy is a culmination of interactions between multiple
agents who are heterogeneous and dispersed. The anticipated actions of other individuals and the
aggregate condition of the economy determine the action of any given individual.
2. No global controller- No global entity asserts control over interactions. Controls are
provided by mechanisms of competition and coordination between agents. Institutions and
assigned roles mediate economic action.
3. Cross-cutting hierarchical organization—The economy is composed of multiple levels of
organization and interaction. At any given level, units of actions, strategies, and behavior
generally act as "building blocks" for organizing units at the next higher level. With so many
types of tangled-up interactions (modes of communication, associations) across levels, notably,
the overall organization is more than just hierarchical.
4. Ongoing adaptation— As the individual agents accumulate experience, the actions,
strategies and behaviors also frequently evolve.
5. Novelty niches— As new technologies, new behaviors, new institutions, and new markets
come about, novel niches are created, the very act of filling a niche may provide new niches. The
result is an ongoing novelty.
6. Out-of-equilibrium dynamics—, The economy functions without ever reaching any
optimum or global equilibrium because new niches, new potentials are continually created.
These definitions and characteristics are important because it provides a way of thinking to
integrate various areas of cutting-edge economics under the much broader theme of complexity
(Richard P.F. Holt, 2011). Some of the areas where this rings especially true include; the way
Evolutionary game theory is redefining the integration of institutions in analysis; notions of
rationality are being challenged by Behavioral economics; empirical proof is being redefined by
dealing with the limitations of classical statistics in Econometric work; Agent-Based
Computational economic analysis provides a much more nuanced alternative to the pre-existing
analytical models; Experimental economics is extremely important in altering economists’
perception of the empirical work as it is the prime method by which behavioral economics is
studied; Ecological economics is reshaping our vision of the interrelationship between nature and
economics; Complexity theory puts forth a way to reconsider how to conceive of general
equilibrium and the dynamics of economics more generally.
The latest cutting-edge work in economics has been not around the theoretical progression of
micro-foundations but coalesces around the view which looks at the economy as a complex
adaptive system. Analytical methods which rise out of pure theoretical models based on
unrealistic assumptions offer little insight for guiding policy. And without a perfect macro
theory, a variety of methods need to be considered ranging from history, politics, and ethics to
shed light on real-world problems to guide policy using statistical models which can process as
much data as possible. “Empirical macro precedes theoretical macro” (Richard P.F. Holt, 2011)
and as Keynes’s letter ( 1938)to Roy Harrod points out,
“Economics is a science of thinking in terms of models joined to the art of choosing models
which are relevant to the contemporary world. It is compelled to be this, because, unlike the
typical natural science, the material to which it is applied is, in too many respects, not
homogeneous through time.... Good economists are scarce because the gift for using ‘vigilant
observation’ to choose good models, although it does not require a highly specialized
intellectual technique, appears to be a very rare one.”
CONCLUSION
Complexity science does not offer a clear-cut understanding of the macro-economic theory, far
from it, but it’s best looked at as a lens to guide the future of economic inquiry. Taking into
consideration the extremely complex nature of reality, the field of economic thought is walking
down the Walrasian mountain. Though the process is slow and not yet accepted as the
mainstream way of academic thinking or as the main way to guide policy, I see the field of
complexity economics as the dominant narrative of the upcoming decades.
References
Arthur, B., Durlauf, S., & Lane, D. A. (1997). Introduction: Process and Emergence in the Economy. The
Economy as an Evolving Complex System II. Discrete Dynamics in Nature and Society, 71-74.
Colander, D. ( 2003). Post Walrasian Macro Policy and the Economics of Muddling Through.
International Journal of Political Economy, Vol. 33, No. 2, 17-35.
Keynes, J. ( 1938). ‘Letter to Roy Harrod.’. http://economia.unipv.it/harrod/edition/
editionstuff/rfh.346.htm.
Perry Mehrling, D. C. (2008). Complexity and dynamics in macroeconomics: alternatives to the DSGE
models. American Economic Review: Papers & Proceedings, 236–240.
Richard P.F. Holt, J. B. (2011). The Complexity Era in Economics. Review of Political Economy, 23:3, 357-
369,.
Rosser, J. B. (1999). On the complexities of complex economic dynamics. Journal of Economic
Perspectives, 13(4), 169–192.
Solow, R. (2010). Building a Science of Economics for the Real World. House Committee on Science and
Technology Subcommittee on Investigations and Oversight .
Tieben, B. (2012). The Concept of Equilibrium in Different Economic Traditions: An Historical
Investigation. Edward Elgar Publishing.
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