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Evolution and Complexity: Toward a New Synthesis for Economics

edited by David S. Wilson and Alan Kirman

Published by MIT Press Hardcover ISBN:9780262035385 eBook  ISBN: 9780262337700

1 - Introduction - PDF

David S. Wilson, Alan Kirman, and Julia Lupp

Challenges of Integrating Complexity and Evolution

2 - Disequilibrium Adjustment and Economic Outcomes - Abstract - PDF

Alan Kirman and Rajiv Sethi

3 - Two Meanings of Complex Adaptive Systems - Abstract - PDF

David S. Wilson

4 - Evolution and Market Complexity - Abstract

John E. Mayfield

5 - Challenges of Integrating Complexity and Evolution into Economics - Abstract

Robert Axtell, Alan Kirman, Iain D. Couzin, Daniel Fricke, Thorsten Hens, Michael E. Hochberg, John E. Mayfield, Peter Schuster, and Rajiv Sethi

Evolutionary Behavioral Economics

6 - Proximate Mechanisms of Individual Decision-Making Behavior - Abstract - PDF

Paul W. Glimcher

7 - A Typology of Human Morality - Abstract - PDF

Herbert Gintis

8 - Evolutionary Behavioral Economics - Abstract - PDF

Terence C. Burnham, Stephen E. G. Lea, Adrian V. Bell, Herbert Gintis, Paul W. Glimcher, Robert Kurzban, Leonhard Lades, Kevin McCabe, Karthik Panchanathan, Miriam Teschl, and Ulrich Witt

Evolution of Institutions and Organizations

9 - The Diffusion of Institutions - Abstract

Enrico Spolaore and Romain Wacziarg

10 - Robust Institutional Design: What Makes Some Institutions More Adaptable and Resilient to Changes in Their Environment Than Others? - Abstract - PDF

Jenna Bednar

11 - Evolvability of Organizations and Institutions - Abstract

John F. Padgett

12 - Evolution of Institutions and Organizations - Abstract

Thomas Currie, Peter Turchin, Jenna Bednar, Peter J. Richerson, Georg Schwesinger, Sven Steinmo, Romain Wacziarg, and John J. Wallis

Shaping the Evolution of Complex Societies

13 - Adaptation and Maladaptation in the Past: A Case Study and Some Implications - Abstract - PDF

Sander E. van der Leeuw

14 - Innovation Policy as Creating Markets, Not Only Fixing Them: Implications for Complexity Theory - Abstract

Mariana Mazzucato

15 - Complexity Economics and Workaday Economic Policy - Abstract

David Colander

16 - Advancing Agent_Zero - Abstract - PDF

Joshua M. Epstein and Julia Chelen

17 - Not Half Bad: A Modest Criterion for Inclusion - Abstract

Scott E. Page

18 - Shaping the Evolution of Complex Societies - Abstract

John Gowdy, Mariana Mazzucato, Jeroen C. J. M. van den Bergh, Sander E. van der Leeuw, and David S. Wilson

A central organizing principle in contemporary economic theory is the notion of equilibrium: all individuals make plans that are optimal, given beliefs that are mutually consistent. The equilibrium method is effective in generating sharp predictions, but it sidesteps important questions about how equilibrium can be attained, optimality assessed, and available alternatives enumerated. This chapter describes an alternative approach in which the process of adjustment is a central theme. Individuals adapt to changes in their environment by making incremental changes in their behavior. These changes alter the environment faced by others, which leads to further dynamic adjustments. Trajectories may eventually converge to an equilibrium, but this is not inevitable. Even when convergence does occur, it may be to one of several conceivable equilibria, in which case the dynamics operate as an equilibrium selection device. These ideas are explored primarily through the example of homophily in social interactions, with other potential applications also briefly considered.
In complex systems theory, two meanings of a complex adaptive system (CAS) need to be distinguished. The first, CAS1, refers to a complex system that is adaptive as a system; the second, CAS2, refers to a complex system of agents which follow adaptive strategies. Examples of CAS1 include the brain, the immune system, and social insect colonies. Examples of CAS2 include multispecies ecosystems and the biosphere. This chapter uses multilevel selection theory to clarify the relationships between CAS1 and CAS2. The general rule is that for a complex system to qualify as CAS1, selection must occur at the level of the complex system (e.g., individual-level selection for brains and the immune system, colony-level selection for social insect colonies). Selection below the level of the system tends to undermine system-level functional organization. This general rule applies to human social systems as well as biological systems and has profound consequences for economics and public policy.
Many complexities in our world come about through the use of preexisting purposeful information. This information may be structured in various ways (e.g., instructions, recipes, algorithms, rules, rules of thumb, business plans, and expert knowledge) and, if followed, directs the formation of something which otherwise would not have existed. This chapter argues that information organized in this way must ultimately arise as the output of an evolutionary computation. Because of this, an evolutionary process underlies most everything that characterizes human existence. This principle includes economics and markets. This chapter addresses whether or not understanding the fundamental role of evolutionary computation for enabling human and biological complexity provides useful insight into market behaviors and introduces the basic concepts necessary to have this discussion.
Complex systems theory and evolutionary theory hold important insight for economics, yet to date they have played a limited role in shaping modern economic theory. This chapter reviews different notions of equilibrium and explores four distinct areas relevant to the incorporation of evolutionary and complexity ideas into economics, finance, and policy. It investigates the determinants of major economic transitions, such as the Industrial Revolution or the collapse of the Soviet Union. It asks whether evolutionary processes should lead to an increase in complexity, on average, of economic and social systems over time. It reviews modern theories of group learning in biology, which have both evolutionary and complexity dimensions, to see if they might be relevant to human social institutions, such as firms. It analyzes whether the structure of human interactions or individual human intelligence is primarily responsible for the performance of our institutions. Finally, it finds the methods of evolutionary analysis and of complex systems to be extremely useful in capturing the open-ended, evolving nature of an economy composed of interactive agents and suggests that these methods be used to create more realistic models of actual markets and economies.
In the early part of the twentieth century, neoclassical economic theorists began to explore mathematical models of maximization. The theories of human behavior that they produced explored how optimal human agents, who were subject to no internal computational resource constraints of any kind, should make choices. During the second half of the twentieth century, empirical work laid bare the limitations of this approach. Human decision makers were often observed to fail to achieve maximization in domains ranging from health to happiness to wealth. Psychologists working during this period responded to these failures by largely abandoning holistic theory in favor of large-scale multiparameter models that retained many of the key features of the earlier models. Over the course of the last two decades, scholars combining neurobiology, psychology, economics, and evolutionary approaches have begun to examine alternative theoretical approaches. Their data suggest explanations for some of the failures of neoclassical approaches and have revealed new theoretical avenues for exploration. While neurobiologists have largely validated the economic and psychological assumption that decision makers compute and represent a single decision variable for every option considered during choice, their data also make clear that the human brain faces severe computational resource constraints which force it to rely on very specific modular approaches to the processes of valuation and choice.
This chapter suggests a typology of human morality based on gene–culture coevolution, the rational actor model, and behavioral game theory. The basic principles are that human morality is the product of an evolutionary dynamic in which evolving culture makes new behaviors fitness enhancing, thus altering our genetic constitution. It is thus predicated upon an evolved set of human genetic predispositions and consists of the capacity to conceptualize and value a moral realm governing behavior beyond consequentialist reasoning.
This chapter explores how the economic model of individual behavior can be improved by incorporating a number of insights from evolutionary theory and complex systems theory. Insights from psychology, the neurosciences, and the behavioral strand of economics may be better understood from an evolutionary and complexity perspective. It takes an integrated interdisciplinary approach to economic phenomena. Core concepts in economic theory (preference and choice) are clarified and Tinbergen’s “four questions” about the origins of behavior are used to provide a framework. Informed by Tinbergen, areas from behavioral science are presented which may be useful for understanding economic behavior: some are directly evolutionary, while others come from scientific contexts informed by evolutionary theory. Each area has yielded well-researched ideas that provide considerable insight into human nature. It concludes with a review of where research stands today and where it could be directed in the future.
This chapter explores the fundamental drivers of economic development and political institutions. It provides a novel empirical analysis of the determinants of institutional differences and the diffusion of institutional innovations across societies. A critical discussion of the recent literature is presented, documenting how economic and political outcomes are affected by traits that have deep historical and geographic roots and that are passed on from generation to generation. The hypothesis is presented that intergenerationally transmitted traits affect current outcomes by acting as barriers to the diffusion of technological and institutional innovations: a longer historical separation time between populations creates greater barriers. Hence, the degree of ancestral distance between a given society and the society at the frontier of institutional and technological development should be associated with higher barriers and lower adoption. This hypothesis is tested empirically with cross-country data. Empirical findings provide substantial support for the proposition that long-term historical distance from the frontier affects both current institutions and development.
Institutions are designed to alter human behavior. To remain effective over time, institutions need to adapt to changes in the environment or the society the institution is meant to regulate. Douglas North (1994) referred to this property as adaptive efficiency and suggested the need for a model of how institutions change to remain effective. This essay contributes to a theory of adaptive efficiency by relating it to the burgeoning literature in robust system design. It reviews five models of institutional change, paying particular attention to each model’s ability to explain institutional adaptation. It isolates three common structural features of a robust, adaptive institutional system: diversity, modularity, and redundancy. It illustrates the theory with a brief application to federal systems, and closes by describing some open research questions relating to institutional adaptive efficiency.
One (not the only) way to operationalize this Forum’s agenda of blending evolutionary theory with complexity theory is Padgett and Powell’s The Emergence of Organizations and Markets. There “evolutionary theory” means “autocatalysis,” and “complexity theory” means “dynamic multiple networks in regulatory feedback.” Together (but not separately), these two theoretical building blocks can explain the sudden emergence or invention of novel forms of organizations not previously observed in history. This chapter draws on an empirical case study from the book, the emergence of international finance in medieval Tuscany, to illustrate the theory.
Some economists argue that institutions are the most important factor affecting variation in economic growth. There is a need, however, to better understand how and why institutions emerge and change. Informed by evolutionary theory and complexity science, this chapter develops a conceptual framework that follows models of cultural evolution in viewing institutions as part of a nongenetic system of inheritance. This framework is used to examine how broad historical factors (not just economic factors) influence present-day institutional arrangements and economic outcomes, as well as how noninstitutional aspects of culture (e.g., values, beliefs) interact with institutions to shape behavior in particular contexts. Overall, this framework emphasizes the processes by which institutions evolve, and how they can coevolve with other institutions and culture. This approach is illustrated using four examples to demonstrate how evolution theory and complexity science can be used to study institutional emergence and change. Explicit models of the processes of institutional evolution need to be developed and then tested and assessed with data. This framework holds promise to bring together and synthesize the findings and insights from a range of different disciplines.
Adaptation and maladaptation are best viewed as different phases in the relationship between a society and its (social and natural) environment. This chapter looks at that relationship over two scales (millennial and centennial) and attributes the transitions (“tipping points”) between adaptation and maladaptation to the unintended consequences of human actions. These, in turn, are due to the difference in dimensionality between the environment and humans’ perception of it. Transitions between adaptation and maladaptation occur when a society’s “value space” (i.e., the total set of values that the society knows, which keep that society functionally together) does not expand at a sufficient pace to keep up with the growth of the society’s population. This seems to be linked to a second-order dynamic that develops as societies are overwhelmed by the consequences of their own (earlier) actions; their focus turns inward and becomes short term (i.e., tactical instead of strategic). This chapter argues that this is the case in the current, Western-dominated global system, and suggests that an inversion of global information flows (i.e., spreading information rather than concentrating it in the West) has the potential to reenergize the global economic system. This needs to be achieved while respecting the environment, hence the term green growth. It implies rephrasing the current economic and political debates from “burden sharing” to “opportunity creation,” both for the developing and for the developed world.
Successful innovation policies are those that actively create and shape markets, not only fix them. In the past this has been achieved through “mission-oriented” policies aimed not at fixing market failures or minimizing government failures, but rather on maximizing the transformative impact of policy. Countries around the world are currently striving to achieve innovation-led growth that is both inclusive and sustainable. For this to happen, public policy needs to support innovation and direct future activities. Innovation policy must focus on building more “symbiotic” (less parasitic) innovation “ecosystems.” This chapter discusses new types of policy questions needed to address the collective, uncertain, and persistent nature of innovation and posits four key areas: directing public policy, evaluating public policy, organizational change to accommodate risk taking and exploration, and the socialization of risks and rewards.
Much of what filters down to standard economists about complexity economics are summaries of abstract analysis that are generally seen as having little direct impact on the workaday policy analysis that most economists do. This chapter argues that complexity theory has significant implications for workaday economic policy. Even if economists do not accept that the complexity scientific theory of the economy is ready for prime time, the complexity vision, which pictures an economy as a complex evolving system undergoing continual evolutionary change, has direct relevance for their workaday applied policy. The reason is that good applied policy is not applied science but rather more like engineering. This chapter explains why applied policy should not be viewed as applied science and explores some implications and examples of how using a complexity frame for economic policy changes workaday applied economic policy analysis.

Specifically, it is argued that complexity policy opens up economics to a wide range of policies that go beyond the standard allocation policies that economists tend to focus on in the standard policy approach, and supplements them with a set of policies designed to influence the ecostructure within which individuals operate. This adds what might be called formation policy to allocation policy. Formation policy does not see the market and government as opposites, but rather views them as coevolving institutions. Formation policy is designed to influence that coevolution. An example of how complexity policy differs from standard policy can be seen in distribution policy. The standard approach to distribution policy tends to focus on redistributive taxes such as progressive income and wealth taxes. The complexity policy approach to distribution focuses more on modifying the length and nature of evolving property rights as embedded in patent and copyright law.

Agent_Zero is a mathematical and computational individual that can generate important, but insufficiently understood, social dynamics from the bottom up. First published by Epstein (2013), this new theoretical entity possesses emotional, deliberative, and social modules, each grounded in contemporary neuroscience. Agent_Zero’s observable behavior results from the interaction of these internal modules. When multiple Agent_Zeros interact with one another, a wide range of important, even disturbing, collective dynamics emerge. These dynamics are not straightforwardly generated using the canonical rational actor which has dominated mathematical social science since the 1940s. Following a concise exposition of the Agent_Zero model, this chapter offers a range of fertile research directions, including the use of realistic geographies and population levels, the exploration of new internal modules and new interactions among them, the development of formal axioms for modular agents, empirical testing, the replication of historical episodes, and practical applications. These may all serve to advance the Agent_Zero research program.
To understand a complex system (e.g., an economy, an ecosystem, the global climate system), scientists often rely on models. Models simplify reality by focusing on certain parts of a system, and the relationships between them, while ignoring, by necessity, others. Advocates of complexity theory often boldly claim (partly by virtue of greater realism) that they can improve upon the standard neoclassical economic framework. A much weaker claim supports the promotion of this new class of models or any class of models: even if the complexity framework makes less accurate predictions than the neoclassical approach, the complexity framework can be of use because its models differ.
This chapter calls for an approach to economic policy that takes evolutionary and complex systems theories into account. Such an approach alters the way that economic policy is framed and how policy co-depends on understanding markets as outcomes of nonmarket interactions, incomplete information, path dependency, and coordination failures. Using several illustrative examples, the chapter explores the application of evolutionary and complexity thinking to policy criteria, goals, instruments, and policy assessment. These examples—the transition to a low carbon economy, using multilevel selection to inform group design in various human organizations, policy making as shaping and creating markets, government failures in Greek farm policy, and protecting the Sudd Wetland in South Sudan—are used to identify key issues for an evolutionary and complexity approach to public policy.