Not Half Bad

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, other. 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.

Robust Institutional Design

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 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.

Proximate Mechanisms of Individual Decision-Making Behavior

In the early 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 responded to these failures by largely abandoning holistic theory in favor of large-scale multi-parameter models that retained many of the key features of the earlier models. Over 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 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.

Disequilibrium Adjustment and Economic Outcomes

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, so that 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.

Shaping the Evolution of Complex Societies

This chapter calls for an approach to economic policy that takes evolutionary and complex systems theory 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. Through illustrative examples, it 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, the use of multilevel selection to inform group design for 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.

Two Meanings of Complex Adaptive Systems

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.

Innovation Policy as Creating Markets, Not Only Fixing Them

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.

Evolution of Institutions and Organizations

Some economists argue that institutions are the most important factor affecting variation in economic growth. We need, however, to better understand how and why institutions emerge and change. This chapter develops a conceptual framework, informed by evolutionary theory and complexity science, 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.

A Typology of Human Morality

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.

Challenges of Integrating Complexity and Evolution into Economics

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 that 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 to more realistic models of actual markets and economies.