Authors: Jan Meyerhoff | 18th of December 2016
Patron and academic review: Dr. Tom Brökel
1. Core Elements
Evolutionary economists examine how and why the economy changes. This emphasis on the changing nature of capitalism appears to be the crucial feature which distinguishes them from their non-evolutionary peers. Consequently, popular topics include economic growth, structural change, innovation processes and -systems, technological change, institutional change and economic development. Such topics are not specific to evolutionary economists, since, for instance, neoclassical or “mainstream” models of the economy can also incorporate dynamic elements, such as path dependencies. Proponents of evolutionary economics, however, investigate these phenomena from a different angle. Eike W. Schamp (2012, 121) notes that the aim of evolutionary economists is not case-specific historiography, but the search for general principles of economic change. According to Carsten Herrmann-Pillath (2002, 204), a theory of economic evolution should be able to explain both change (e.g. innovation) and stability (e.g. lock-in processes). Furthermore, the domain of evolutionary economics itself comprises various different approaches, which vary in particular in their interpretation of the term “evolutionary”. In 1987, Ulrich Witt suggested the following elements as the main common pillars of evolutionary economics:
- First, there is a focus on economic dynamics. Dynamics are, however, not conceived as movements between states of equilibrium due to exogenous circumstances. Instead, there are continuous processes, in which new conditions are generated endogenously out of the economic system.
- A further element is the concept of irreversible, historical time. This means that economic development is also influenced by past developments, which have an irreversible direction (path dependency). This does not imply that historical events are viewed as determining factors.
- One last element is the focus on explaining innovation and diffusion.
2. Terminology, analysis and conception of the economy
Concerning the conception of the economy, there is a number of different approaches in evolutionary economics. Hereafter, their most important common terms and concepts will be presented. Dopfer (2007) developed a theoretical frame in which he distinguishes between a micro, meso and macro level of the economy, which will serve as the organizing principle for this section. This micro-meso-macro perspective is only one possible angle of evolutionary economics. Carsten Hermann-Pillath (2002), for instance, highlights the network structure of the economic system.
An essential difference from neoclassical economics relates to the conception of the economy at the micro level. Following Dopfer (2007), evolutionary economics essentially deals with the growth and coordination of economically relevant knowledge. Knowledge is understood as a routine or a combination of routines (Hermann-Pillath, 2002). The notion of “routines” was coined by Nelson and Winter (1982), whereas Dopfer (2007) used the term “rules” for a similar concept. Routines are decision-making rules, which are repeated regularly and which represent the actors' acquired, common behavior. According to Dopfer (2007), such rules or routines comprise phenomena as diverse as technologies and social institutions. Furthermore, a distinction between dynamic and static routines can be drawn. Dynamic routines comprise, for example, rules for the design of new products or the formal organizational structure. Static routines, by contrast, enable the repetition of past activities and represent the everyday activities of an organization or an actor. Knowledge is conceived as integrated in carriers, i.e. entities which embody the respective routines (individuals, organizations, firms etc.), and networks. As such it determines the behavior of these carriers and enables them to perform certain actions, such as production or market transactions. The focus of the evolutionary analysis is the creation, adoption, retention and coordination of rules, which are – in contrast to neoclassical economics – considered to be malleable.
The adoption of a routine within a population is analyzed mostly at the meso level. In evolutionary theories, it is often assumed that several actors form a population. The characteristics of actors then vary within and between populations. Depending on the object of research, market participants, industries or the regions of a country are perceived as populations.
In order to explain economic change, Darwinism offers a specifically evolutionary explanatory concept, which assumes that change is driven by the mechanisms of variation, selection and retention (VSR). The main assumption of this so-called VSR paradigm is that populations are composed of heterogeneous actors. These actors differ from each other in their acquired routines (variety), which in turn can vary over time (e.g. by learning and innovation). These routines are subject to selection pressures, whereas those routines that have the bigger reproductive success will proliferate within a population. Generally, the main selection criterion is the extent by which a routine allows for the efficient usage of the resources that are important for reproduction.
Therefore, as in neoclassical economics, scarcity plays a central role, since it is assumed that competition for scarce resources leads to selection pressure. Routines which are better adjusted are thus reproduced more often. As a consequence, routines that enable more efficient products or production methods than competing routines are more likely to assert themselves in the market (Hermann-Pillath, 2002, 34). The result of this process can be understood as adaptation to the selection environment (Hermann-Pillath, 2002, 206).
Another crucial evolutionary concept is path dependency. Proponents of this concept emphasize that the starting point of a development – past events, their specific historical sequence and coincidence – all have an important impact on the final result of economic activities (David, 1984, in Garud and Karnoe, 2001, 4). Thus, current developments are never independent of their history (Hermann-Pillath, 2002, 232).
The macro level is composed of many rules and several populations. As such, it is not a simple aggregation of the micro level but is rather defined by the self-organization of populations and structures at the meso level. This means that processes at the macro level can only be explained by means of the meso level, i.e. the analysis of populations instead of individual actors (Dopfer and Potts, 2007).
In summary, change is mainly explained at the meso level and can be integrated or limited by structures at the micro and macro level (Dopfer et. al., 2004).
While mainstream economics mainly deals with the optimal usage of scarce resources to satisfy individual needs, evolutionary economists suggest that knowledge is the crucial phenomenon. Thereby, uncertainty and fundamental ignorance, i.e. the faultiness of knowledge, are considered as the main economic problems (Herrmann-Pillath 2002, 22). This means that the ontological foundations of evolutionary economics diverge fundamentally from mainstream economics. From the evolutionary perspective, both knowledge and individuals are considered to be real (ontologically existent) phenomena. Herrmann-Pillath (2002, 33) terms this a bimodal ontology. Methodologically, evolutionary economics assumes that the interaction of individuals leads to the formation of new entities, whose characteristics cannot be reduced to the individual level. This postulate is also known as emergentism. Regarding the role of knowledge in such an ontological system, Hermann-Pillath observes that “the fundamental ontological premise of evolutionary economics is that in complex systems of knowledge sharing only the individually and subjectively available knowledge is practically relevant, whereas the performance of the whole system is determined by the overall effective level of knowledge. Thus, the latter has its own ontological status in terms of being an independent cause of economic phenomena” (Hermann-Pillath 2002, 33, own translation).
Consequently, the analysis focuses on economic subjects who are only ‘boundedly rational’ instead of focusing on rational and utility maximizing actors. These economic subjects are neither capable of discerning all possible actions nor of assessing their costs and utility and are therefore unable to calculate an optimal course of action. Instead, it is assumed that the decisions of economic subjects are based on heuristics. When engaging in decision making based on a heuristic, there is no search for an optimal solution, but the alternatives are scanned until a possibility is detected which fulfills the targeted purpose or enables the passing of a certain threshold for achieving a target (“aspiration level”). To describe this behavior, Herbert Simon (1957) coined the term “satisficing”.
Yet, the concept of bounded rationality – just as the neoclassical concept of behavior with its optimization hypothesis – fails to account for the idea of creating new opportunities for action (Witt, 2001). The concept of bounded rationality only explains how decisions are made, based on a set of well-defined alternatives. In the case of neoclassical economics, this decision making process is considered to be perfect, whereas in evolutionary economics it is conceived as being imperfect. Therefore, the goal of evolutionary economists is to transcend the idea of adaptive decision-making processes and to conceive a creative cognitive model, in order to really account for innovative action (Röpke, 1977). Joseph Schumpeter, who is considered to be one of the founding fathers of evolutionary economics, viewed the innovation process as the key driving force of economic development. He conceived innovations as new combinations of available knowledge. Witt (2001) emphasizes that humans have the capability to imagine situations which do not exist yet. In this way, they create new possibilities for actions, test and implement them (Witt, 2001).
The assumption of fundamental ignorance means that knowledge might always turn out to be faulty. Hence, each statement about the world is hypothetical. This premise is associated with the evolutionary epistemology of hypothetical realism. Accordingly, not all knowledge emerges a priori from within the individual. Also, actors do not have access to all knowledge, but perceive different parts of the overall knowledge, which is why methodological individualism cannot provide a sufficient explanation of the system. Knowledge can appear as subjective knowledge or as an emergent phenomenon of the interactions between actors of a network. The overall knowledge is thus bigger than the sum of individual subjective knowledge. Evolutionary epistemology focuses on the emergence and spreading of knowledge rather than treating the question of the truth of knowledge. According to this epistemology, which is primarily supported by Konrad Lorenz, Donald T. Campbell, Gerhard Vollmer and Rupert Riedl, there is at least one reality which is independent of the human being. This reality has a structure with objectively existing causal relations, which are, at least in part, discernible.
In contrast to the comparative-static approach of neoclassical economics, evolutionary economics deals with the dynamics of economic systems in historical time. Evolutionary economists undertake both deductive and inductive research (Boschma and Frenken, 2006, 291). Yet, they do not always aim for generalization. Instead, it is recognized that knowledge can be limited to a specific context of space and time.
Furthermore, evolutionary economics is neither solely based on methodological individualism (reductionism), nor methodological collectivism. Instead, there are selection mechanisms both at the level of routines and individuals; and at the level of super-ordinate entities (Bowles, 2004, 479). Furthermore, formalized models and quantitative-empirical methods are used as well as qualitative methods. Evolutionary economics is in itself very interdisciplinary since it not only applies concepts and terms (e.g. from Biology) but also methods of other disciplines (e.g. Social Network Analysis - SNA).
Alongside the well-established regression techniques, evolutionary economics use Social Network Analysis to investigate the evolution of networks; agent-based and computational modeling; and evolutionary game theory. This bunch of methods is complemented by surveys as well as qualitative methods, such as interviews. Thereby, evolutionary economics approaches the aspiration of a pluralistic methodology – in line with Feyerabend’s “anything goes”. Hence, the methods that lend themselves to the analysis of developments and dynamics are the most widely used.
6. Ideology and political goals
The positive interpretation of innovation and change can be considered as an ideological aspect of evolutionary economics. Correspondingly, the innovative and adaptive economy can be interpreted as a normative point of reference, since positive economic development is attributed to the ability to innovate and to adapt to the (changing) technological and economic environment. Consequently, great importance is attributed to policies concerning research, innovation and technology. It can be interpreted as ideological that innovation is perceived as a guiding principle and that the factors determining innovation, derived from evolutionary economics theories, are used as the basis for policy advice. On the other hand, the neoclassical ideal of Pareto-efficient markets tends to be rejected. Evolutionary theories do not imply that the aggregate welfare is maximized by perfect markets with perfect competition or that a political economic strategy should aim at the creation of those kinds of competitive markets.
Consequently, economic policy should not only address the market but rather “refer to the entirety and complexity of the networks and their dimensions in which the processes that it aims to influence are embedded” (translation of Hermann-Pillath, 2002, p.441, own translation). For instance, promoters of the innovation system approach argue that, analogous to market failure, a failure of the national innovation system (“system failure”) might occur, but this failure could be averted by the state and thus justify policy interventions. However, there is no agreement on how an adaptive and innovative economy should be organized, i.e. how a failure in the innovation system should be averted. Since no political actor has perfect knowledge, political interventions might also fail. In addition to this limited cognitive ability of actors, the problems to be solved are complex. Thus, rational or deductive decision-making processes tend to be rejected. Instead, inductive processes which are based on experiments are endorsed. In this context, the importance of past experiences and available knowledge for decision making is emphasized (Metcalfe, 1994).
For instance, evolutionary theories found their way into regional policies of the European Union, which amongst other things pursue the objective of increased research and development (R&D) activity in European regions. An example is the so called strategy of “smart specialization” which aims at making Europe an innovative economic area. Although it was coined smart specialization, the strategy is actually about diversification. The novelty of this strategy is that decisions on diversification should be closely guided by the available knowledge of the region, i.e. new economic sectors should be “related” to old ones since this enables knowledge transfer and thus increases the likelihood of successful diversification and innovation. In addition, the decision on new diversifications should emerge out of a social discourse (“entrepreneurial discovery”) which includes the relevant stakeholders (Boschma and Gianielle, 2014).
7. Current debates and analysis
A central debate concerns the applicability of Darwinism to economic evolution as a social phenomenon. The universal Darwinist hypothesis of variation, selection and inheritance is not generally accepted within the community of evolutionary economists. In particular, opponents claim in particular that the evolution of economic systems lacks a mechanism that can be conceived heredity. Instead, economic evolution follows its own rules, since as a part of cultural evolution it is subject to significantly faster developments. This is referred to as the continuity hypothesis. Furthermore, the formation of innovation in the socio-economic realm is not only pure coincidence, as is the case in genetics.
8. Delineation: subschools, other economic theories, and other disciplines
A clear delineation of subschools is difficult, but specific elements of different theories can be identified. Some approaches refer more or less to terms and concepts of evolutionary biology while others focus on the concepts of path dependency, self-organization of complex systems or institutional–cultural change.
Universal Darwinism conceives economic evolution as directed change which emerges from the formation, selection and conservation of new routines (knowledge). This change requires diversity but change itself also creates diversity. According to Essletzbichler (2012, 129), Universal Darwinism is a theoretical frame for understanding evolution in complex population systems. Populations of heterogeneous entities evolve by interacting among themselves and with the environment, which they also shape. Universal Darwinism is inspired by genetics and comprises the “heredity of replicator-instructions by individual entities, a variation of replicators and interactors and a process of selection of the interactors in a population” (translation of Hodgson/Knudsen, 2010, 65, in Essletzbichler, 2012).
Variation is generated by endogenous transformation and the emergence of new characteristics, both by coincidence and the deliberate search of intentional actors for improvement. The biological term phenotype corresponds to the term “interactor” in evolutionary economics. Essletzbichler (2012, 133) defines an interactor as an “entity which interacts directly with its environment as a cohesive whole”. For each interactor, there is a range of replicators. The term “replicator” corresponds to the biological term “genotype”. Replicators are certain characteristics of interactors (mostly their routines), which are considered to be their genes. Individuals, organizations, but also countries or regions can be interactors. They are considered to be the “carriers” of replicators (i.e. of routines). Information on the successful adaptation of interactors (phenotypes) is passed on by the replicators (genotypes) over time. The latter impede the immediate adaptation to changes in the environment. Thereby, it is guaranteed that there are different kinds of variation which is a necessary condition for selection to take place. The selection results in higher survival rates for interactors who better fit the specific local and historical context. This means that phenotypes pass on their genotypes with a higher rate (Essletzbichler, 2012, 130).
For Neo-Schumpeterians, the concept of selection is the core element of evolutionary economics. This includes the approach by Nelson and Winter in An Evolutionary Theory of Economic Change (Cordes, 2014, 2; Nelson and Winter, 1982). They imported biological concepts to economics in a rather metaphorical way, while for instance Metcalfe (1994) directly applies a model of natural selection to economic competition. In part, the approach of innovation systems (Lundvall, 2010) belongs to the neo-Schumpetarian tradition.
Naturalistic concepts within evolutionary economics assume that the biological heredity of humans has a lasting impact on their current behavior and that it limits economic evolution (Cordes, 2014, 5). According to Cordes (2014, 5), Thorstein Veblen's (1898) theory of institutional change belongs to this subschool (while he is also more generally argued to be an American institutionalist. This kind of institutional change is also discussed by Friedrich von Hayek and Douglas North. Also the domain of ‘bioeconomics’, established by Nicholas Georgescu-Roegen, could be assigned here. The latter highlights the long-term limits of economic evolution due to biological evolution (Cordes, 2014, 8).
Theories of path dependency do not necessarily use biological terms and concepts. The proponents of this approach emphasize the importance of the genesis of a phenomenon in order to account for its’ subsequent development and its’ present characteristics. Garud and Karnoe (2001) complemented the concept of path dependency with the concept of “path-creation”. With the notion of path creation, they refer to the mechanisms by which new economic and technological paths emerge and gain momentum.
To some extent, complexity theories are also associated with evolutionary economics. This especially includes the approaches of complexity economics that emerged from the interdisciplinary cooperation of, amongst others, physicists, biologists and economists at the Santa Fe Institute in Santa Fe, New Mexico (USA). This cooperation was initiated by Kenneth Arrow. Current representatives are Brian Arthur or Samuel Bowles. Complexity economists use concepts such as dynamic, open systems, self-organization and the cumulative causation of socio-economic processes (positive feedbacks). Furthermore, path dependencies and selection processes often play an important role. Complexity is described separately as one perspective on this webpage (link).
9. Delineation from the Mainstream
The position of evolutionary economics within the economic sciences is contentious. Some proponents simply view it as a sub-discipline, which is concerned with innovation, entrepreneurship and technological change. Within this rather narrow definition, neoclassical or mainstream assumptions are not necessarily rejected. In this way, evolutionary economics is considered to be a sub-discipline among others, such as environmental economics. Others view it as a fundamentally different approach to research on the economy (vgl. Herrmann-Pillath 2002: 21-22).
- European Association for Evolutionary Political Economy (EAEPE)
- Internationale Joseph A. Schumpeter Gesellschaft (ISS)
- Association for Evolutionary Economics (AFEE)
- Journal of Evolutionary Economics
- Research Policy
- Industrial and Corporate Change
- Journal of Institutional Economics
- Journal of Economic Issues
- Journal of Economic Geography
- Papers on Economics and Evolution, Max-Planck-Institut für Ökonomik: http://www.econ.mpg.de/deutsch/research/EVO/discuss.php
- Evonomics (Blog): http://evonomics.com/
Boschma, R., Frenken, K. (2006). Why is economic geography not an evolutionary science? Towards an evolutionary economic geography. Journal of Economic Geography, 6, 273–302.
Boschma, R., & Gianelle, C. (2014). Regional branching and smart specialization policy. JRC technical reports, (06/2104).
Bowles, S. (2009). Microeconomics: behavior, institutions, and evolution. Princeton University Press.
Cordes, C. The Application of Evolutionary Concepts in Evolutionary Economics. Papers on Economics & Evolution. Max Planck-Institut für Ökonomik. Nr. 1402.
David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337.
Dopfer, K. (2007): Grundzüge der Evolutionsökonomie - Analytik, Ontologie und theoretische Schlüsselkonzepte. Discussion Paper Universität St. Gallen.
Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14(3), 263-279.
Dopfer, K., Potts, J. (2007): The General Theory of Economic Evolution. Routledge.
Essletzbichler, J. (2012). Generalized Darwinism, group selection and evolutionary economic geography. Zeitschrift für Wirtschaftsgeographie, 56 (3): S. 129 – 146.
Feyerabend, P. K., & Vetter, H. (1976). Wider den Methodenzwang: Skizze einer anarchistischen Erkenntnistheorie. Frankfurt/Main: Suhrkamp.
Lundvall, B. Å. (Ed.). (2010). National systems of innovation: Toward a theory of innovation and interactive learning (Vol. 2). Anthem Press.
Garud, R., & Karnoe, P., (2001). Path Creation as a Process of Mindful Deviation: Dependence and Creation. London: Lawrence Erlbaum Associates, 1-38.
Herrmann-Pillath, Carsten 2002: Grundriß der Evolutionsökonomik, Wilhelm Fink Verlag: München
Hodgson, G. M., & Knudsen, T. (2010). Generative replication and the evolution of complexity. Journal of Economic Behavior & Organization, 75(1), 12-24.
Metcalfe, J. S. (1994). Evolutionary economics and technology policy. The economic journal, 104(425), 931-944.
Nelson, R. R., & Winter, S. G., (1977). In Search of Useful Theory of Innovation. Research Policy, 6 (1), 36-76.
Schamp, E. W. (2012). Evolutionäre Wirtschaftsgeographie. Eine kurze Einführung in den Diskussionsstand. Zeitschrift für Wirtschaftsgeographie. 56 (1-2), 121-128
Simon, H. A. (1957). Administrative theory: A study of decision-making processes in administrative organization. New York: Macmillan.
Witt, U. (2001). Learning to consume–A theory of wants and the growth of demand. Journal of Evolutionary Economics, 11(1), 23-36
Witt, U. (1987). Individualistische Grundlagen der evolutorischen Ökonomik (Vol. 47). Mohr Siebeck.
Assigned course modules
|Emergence Theory||Think Academy||-||self paced||beginner|
|Complex adaptive systems||Complexity Labs||-||self paced||advanced|
|Network Theory Introduction||Complexity Labs||-||self paced||advanced|
|Capitalism: Competition, Conflict, Crisis||Anwar Shaikh||The New School||flexible||advanced|
Organisations and links
The General Theory of Economic Evolution
Year of publication: 2007
Elgar Companion to Institutional and Evolutionary Economics
Year of publication: 1994
Edward Elgar Publishing