The Inconsistency of Biological Analogies in Economics
I often tend to use biological analogies either as a simple heuristic or an explanatory model in an analysis, or as a purely illustrative model in support of certain ideas by constructing argumentative structures. For instance, in an attempt to understand how the mechanism of import-export operations at the level of an economy works, I have associated this bidirectional mechanism with the sodium-potassium pump found in the plasma membrane of almost every human cell. But can such analogies actually be used in economics? Can we deal with complex phenomena in economics through such a reductionist approach as biological analogies or, conversely, biological metaphors are a consequence of the complementarity between economics and biology, designed to give rise to a new more complex field, such as evolutionary economics? Nevertheless, one thing is certain: there are reasons for the scepticism towards biological analogies, and the heuristic value of evolutionary biology for economics is still a matter for debate.
Biological analogies in economic geography
The Nelson-Winter framework was of the utmost importance in building up the complex realm of evolutionary economic geography, representing a particular version of evolutionary economics which combines various Darwinian metaphors and concepts (such as variety, selection, novelty, and inheritance) with elements of a behavioural theory of the firm (Martin and Sunley, 2007).
For example, related to the selection concept, it is considered by Nelson and Winter to be a vital agent of transformation. They define firms as “sets of habits and routines competing to fulfil the prevalent economic conditions” and consider them as operating in the presence of two selection processes: an internal selection process of the members and institutions within the company and an external process of selection of routines and habits carried out by the market (Viano, 2005:248). Moreover, there are two different agents that have determined the selection during evolution: an exogenous agent, which is generally associated with the biological and natural environment, that is constantly changing over time and an endogenous agent, respectively social habits, which tends to be static. Economic transformation is therefore seen by Nelson and Winter, as Veblen before them, as a consequence of companies’ adaptation to both the external environment and internal routines, the fundamental difference being represented by the fact that while Veblen emphasizes the conservative and regressive characteristics of evolution on the grounds that old habits prevail over the exogenous environment, Nelson and Winter placed the emphasis on the progressive features of evolution, “assuming the natural prevalence of market over habits” (Viano, 2005: 254).
A critical element in evolutionary doctrine capable of discriminating between different theoretical families is the “emphasis that each theory places on improvement and progress” (Viano, 2005). Despite acknowledging that routines are “unresponsive, or inappropriate, to novel situations” (Nelson and Winter, 1982: 165) and that, if used “massively”, the “routinized control system” can have the “collateral effect of impeding adaptation when adaptation is actually necessary” (Nelson and Winter, 1982: 117), it is argued that inertia, which is determined by habits, exerts a beneficial influence on economic systems. The “tendency for such routines to be maintained over time plays in our theory the same role that genetic inheritance plays in the theory of biological evolution” (Nelson and Winter, 1982: 142), but unlike genes that cannot be modified in a single organism, habits undergo a process of transformation due to innovation: “whereas phenotypes are stuck with their genes, firms are not stuck with their habits” (Nelson, 1995: 69).
The application of self-organization models to economic phenomena
Paul Krugman starts by assuming that self-organization models can be applied to many economic phenomena. For instance, the principle of “order from instability” (in physics, its correspondent is Lohm’s “disaster theory”) which offers an explanation for the growth of hurricanes and embryos, could also explain the formation of cities and business cycles (Martin and Sunley, 2007). According to this principle, when a system is so constituted that a flat or disordered structure is unstable, order spontaneously appears (May, 1999). The classic example in physics is convection which occurs as a result of the inhomogeneous heating of lower layers of the air (in meteorology) or as a direct consequence of the Brownian motion of fluid particles (in liquids). But how is this related to the city formation process? This could be explained through a recent fascinating discovery based on the “intelligence” of a very interesting living organism: the slime mold (Physarum polycephalum).
In 2010, in their paper, “Rules for biologically inspired adaptive network design”, Nakagaki et al. experimentally proved that by applying the behaviour of Physarum polycephalum to complex networks, a better network could be obtained compared to the one given by the Steiner’s minimal tree algorithm (see the picture below).
Source: Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., ... and Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science, 327(5964), 439-442.
Therefore, in this experiment, the slime mold was invited to explore a territory covered in oats, the centre oat representing the city of Tokyo with the surrounding oats symbolizing the suburban railway stations. The plasmodium grew out from the initial food source and spread out in a branching pattern, forming a network, a connection at each knot of oats encountered, continuing to progressively colonize each of the food sources. After 26 hours, the spreading mycelium – resolved into a reticular structure of tubes – succeeded in establishing quite a firm network by interconnecting the food sources. The remarkable discovery that was made through this experiment was that, in fact, the slime mold had managed to replicate the Tokyo transport network – a complex system that has shown a tremendous development down through the decades to the present, requiring the work of numerous community dwellings, civil engineers and urban planners. Basically, the slime mold accomplished in more than a day what took us more than 100 years. The conclusion from the experiment was that Physarum polycephalum can form efficient networks comparable to those of real-world infrastructure networks (the Tokyo rail system) in terms of efficiency, fault tolerance, and cost (Tero et al., 2010). As a consequence, the slime mold can be assimilated to a biological computer (yet, a big unanswered science question is related to this protist’s intelligence – if the slime mold does not have a central nervous system or a brain, how is this organism capable of performing behaviours associated with the brain’s functions, such as learning, remembering, solving problems, making decisions – where exactly is its intelligence?).
Paul Krugman’s contribution, as an evolutionary economist, to the field of economic geography is not limited to the promotion of the agglomeration concept, but also to the application of self-organization principles to economic phenomena. In this regard, in order to test his hypotheses, being inspired by the behaviour of slime mold cells, Krugman developed a simple one-dimensional model in which expanding firms self-organize into cities. This analogy was reinforced by the discovery of biologists that individual slime mold cells follow simple rules, without central direction, forming various structures and acting as a community (Barker, 2012). This finding has influenced research in many fields, including urban economics (Barker, 2012). Krugman (1998) applied slime mold behaviour models to the growth of cities and ascertained that simulated firms that follow simple economic rules form cities. He has demonstrated, by applying a reaction-diffusion mathematical model to the formation of cities, that there are certain similarities between a city and a living organism, even if we might consider it irrational due to the fact that this kind of restriction to purely local interaction is found naturally in both chemical and biological models – so the question is how are we supposed to try to impose this restriction on a model of economic behaviour? Notwithstanding this question, an immediate resemblance is related to the formation of urban concentrations that implies, despite the presence of some large developers, “the independent movement of large numbers of individual households and firms”, the interaction among them being responsible for generating the corresponding dynamics that determine the process of city formation (Krugman, 1998: 15). Krugman identifies another similarity between spatial economic models and models of biological/chemical/physical self-organization, associating the tension between “centripetal” and “centrifugal” forces (involved in the first model) with the tension between positive and negative feedback (involved in the second model). Also, according to Goodwin, the distribution of firms and households across the landscape represents an “excitable medium” of the kind that he pretends to be “the generic representation of self-organizing spatial systems”. Goodwin has also demonstrated that slime mold becomes a sort of “advertisement” of the fact that common principles of self-organization can be applied to complex dynamic systems in many different fields of activity, focusing on the similarity between the process of coalescence in slime molds and self-organizing chemical systems – such as the famous Belousov-Zhabotinsky reaction.
On the other hand, the second principle of “order from random growth” can explain the rules that determine the dimensions of earthquakes, meteorites and metropolitan areas (Krugman 1996, also Martin and Sunley, 2007). As stated by this principle, various structures or phenomena in science, from earthquakes to asteroids, seem to respect a distribution of the size of the law of power (May, 1999). In addition, I personally consider Mandelbrot’s work on fractals extremely relevant due to its emphasis on the importance of power law distributions in the field of geoscience, respectively on the distribution of sizes of various geological objects and structures, such as lakes, fault gouges, oil reservoirs, sedimentary layers and so on.
In conclusion, Krugman’s thesis is based on its conclusive argument that common principles of self-organization operate on any system, including physical, chemical, biological and even socio-economic systems, configuring a new vision of how the economy structures itself in space and time. We can also observe how Krugman’s ideas about self-organization had anticipated the experimental results from Nakagaki’s research, namely that drawing inspiration from biology has led to “useful approaches to problem-solving such as neural networks, genetic algorithms, and efficient search routines developed from Ant Colony Optimization algorithms” (Tero et al., 2010). It is important to mention that I have tried to outline Krugman’s perspective as an evolutionary economist because of his keen interest in comparing evolutionary economics and biology. From my point of view, Krugman was able to highlight many of the characteristic problems of the classical economic discipline and economic thinking in the 1980s. For this reason, he constantly strove to borrow from biology, to borrow from geography, taking certain risks, trying to constantly reform the economic discipline.
Self-organization plays a leading role in evolutionary economic geography, being defined – along with the concept of emergence – as “the key mechanism in the evolution of economic landscapes” (Martin and Sunley, 2015:722). The idea of self-organization is correlated with the notion of autopoiesis, a concept that refers to the dynamics of a system characterized by a non-equilibrium state, being capable of reproducing itself, generating its own components with the purpose of preserving its organized structure that in turn gives rise to those components. A canonical example of an autopoietic system is the eukaryotic cell which, for example, at the level of the Golgi apparatus, synthesizes its polysaccharides which are then packed into membrane-bound vesicles that will migrate to the cytoplasmic membrane to permanently recreate its structure. In this regard, the geographical structures that compose the economic landscape – the urban system, center-periphery models, clusters, industrial districts – can be regarded, in a particular way, as emerging phenomena of self-organization.
However, there is a prodigious difference between the socio-economic realm and the biological field: while self-organization is often considered a spontaneous process within biological systems, in the socio-economic realm we take into account the fact that the development of all these spatial structures (such as industrial clusters or regional economies) implies the existence of two deciding factors – “myriad individual actions and interactions of economic agents that generate outcomes that serve to reproduce those same space systems” (common element to any biological system); and also “the intentional behaviours and learning of economic agents pursuing their own objectives” (the glaring disagreement between the two doctrines) (Martin and Sunley, 2015: 722). For instance, in Human Action, Ludwig von Mises describes human behaviour as volitional, based on will and purpose (telos), so deliberate, in stark contrast to unconscious behaviour. Thus, “some agents may possess and exert more influence and power than others over the precise form and function of spatial economic self-organization” (MartinandSunley, 2015: 722). Yet, even in the purely biological case we can find such examples of greater power of certain agents, in genetics for example, when one type of gene is predominant over another.
Geographical determinism problems
In economic geography, biological analogies are especially used on a conceptual level. A relevant example would be the concept of diffusion that comes from biology – representing the movement of molecules/atoms/ions from a region of higher concentration to a region of lower concentration – being applied in economic geography in order to observe how a phenomenon extends from an origin pole at which there is a higher concentration (of this particular economic event) to a certain limit, the emphasis being placed equally on both the modality in which the phenomenon is formed within the origin point and also on the manner in which it has variably affected certain territories, certain spaces, as well as on its capacity to respond positively to a negative element or, on the contrary, to respond negatively to a positive one.
For instance, we can think about the significance of the diffusion process of the economic crisis in the United States generated by real estate loans granted without prudence or collateral and how they affected the European Union or Eastern Europe. In Asia, for example, the crisis effects were quickly transmitted through the trade channel, such that most companies reduced their production in response to a low demand. Furthermore, the decline in production and exports was particularly pronounced for certain manufactured goods, such as machinery, steel and electronics, while industrial production fell by exceptionally large quantities in countries where these types of goods account for a significant share of total production (Edey, 2009). Therefore, biological analogies can be found predominantly at the level of spatial concepts, otherwise there will be a great possibility that these analogies will cause geographical determinism problems.
A practical example of such geographical determinism can be discovered in the work of Jeffrey Sachs and John Gallup, “Agriculture, climate and technology: why are the tropics falling behind?”, in which they misinterpret the biological elements that are applied to certain geographical areas. The authors end up substantiating their theories on the conception that African people live in extreme poverty, in underdevelopment as an inevitable consequence of the existence of some simple geographical and climatological factors that make this region’s agriculture inherently less productive than in other parts of the world, respectively than in other regions of the tropics (McKay and Thorbecke, 2015). Nevertheless, Johnson and Evenson (2000) argue that Africa is not inherently less productive than other regions, but that the effects of the research conducted in other parts of the world are hardly propagated there (McKay and Thorbecke, 2015). Part of the reason for the direct non-proliferation of these effects is the lack of interest of foreign nations in small markets with low technological infrastructure. If sub-Saharan Africa had enjoyed even an average level of such foreign infusions of agricultural know-how, growth would have been much faster, although an outstanding growth could not have been realistic in what concerns the costs involved in the development of technological infrastructure, long distances and climate constraints. Africa is not only far from the source, but it distances itself even more with the passage of time (Evenson, 2000). Therefore, returning to the work of Sachs and Gallup, we can remark that all these elements that are not understood from an Anglo-Saxon perspective seem strange and are misinterpreted. The biological analogy in this case consists of treating tropical countries as patients in need of intervention, as in the case of any real suffering patient.
Joseph Schumpeter’s “creative destruction”
Dynamics occupy a central position in the evolution of spatial economics, especially owing to the fact that, every day, new companies, technologies, products, new industries and new jobs are introduced into the economy, replacing the older ones that will eventually disappear. This steady flow was once depicted by Joseph Schumpeter, in his famous work, Capitalism, Socialism and Democracy, as a “creative destruction” process of industrial mutation that “incessantly revolutionizes the economic structure within, incessantly destroying the old, constantly creating a new one” (Schumpeter, 1942: 82). In other words, the economy is evolving (Boschma and Martin, 2007).
Witt argues that evolutionary economics is primarily based on the processes and mechanisms through which the economy self-transforms from within, highlighting the novelty effect as “the ultimate source of self-transformation” (Boschma and Martin, 2007: 537). Therefore, economic evolution and adaptation are determined by two key factors: the creative capacity of economic agents (individuals and firms) and the creative functions of markets; while spatial effects are frequently correlated with the industry evolution (Witt, 2003; 2006) as a natural consequence of the impact of initiatives, capabilities, endowments and institutional facilities characteristic of certain locations on new products and innovative production methods (Antonelli, 2001). According to Witt (1994), the emergence of “industrial clusters” is specifically linked to the growth of such complementary and interdependent local innovative activities that triggers a process of “self-augmentation of company growth and firm founding activities in close spatial proximity” (Witt, 1994:11). In the early stages of the industry life cycle, a considerable part of the appropriate national or international innovative industrial activity could be aggregated in such locations, Silicon Valley representing the clearest case. In such regions, incomes and employment are extremely high (Witt, 1994). Nevertheless, cluster research has a tendency to contain a normative bias (Markusen, 1996), outlining an exclusive view from the bright side of economic geography, contributing to the portrayal of clusters as a political panacea – a final state model that each region could achieve. In the case of Silicon Valley, for example, despite high incomes and high employment, we cannot ignore the extreme asymmetries of development between the centre and the peripheral regions.
Thus, although “creative destruction” was originally referring to the revolutionary process by which a new product or method replaces the older product or the less current method, Schumpeter describes a laborious process for which the term also seems appropriate, that in which capitalism, through its creative success, leads to its own destruction and paves the way for a socialist economic system to overcome it (Elliott, 1980). In this broader sense, “creative destruction” is common to both Marx and Schumpeter. Although Marx saw how capitalism could be reinvented, he also believed that the inherent tendency to self-destruction will lead to its end. The “essential point grasp” about capitalism, however, according to Schumpeter, is that it is an “evolutionary process”, as “was long ago emphasized by Karl Marx” (Elliott, 1980: 46).
Regarding this reference to capitalism containing the seeds of its own destruction, a creative one, which is succeeded by socialism, the thesis is reductionist and ultimately false. This is due to the fact that the only type of capitalism that can self-destruct is the state capitalism or (as Chomsky calls it) “crony capitalism”. Why? Because only in a system in which taxation appears, some classes commence to thrive on account of other classes. Thus, the conflict between them is imminent and has the potential to degenerate into a socialist (dis)order.
Biological analogies in economy – examples and criticisms
Darwin’s “struggle for existence” or “natural selection”
Mises argues that no matter how effective is someone (in absolute terms) in everything he knows how to do, he still has comparative advantages, namely where his inefficiency is relatively less pronounced. On the other hand, no matter how effective is someone (in absolute terms) in everything he knows how to do, it is still worthwhile, from Mises’ perspective, to cooperate with the less effective ones, because of the added gain. Therefore, the possible relations with economically inferior individuals would not be reduced only to considerations related to charity, the inexperienced or inferior ones representing no longer only a potential enemy or a potential beggar, but also a potential partner.
Taking the implications further, the understanding of the concept of competition in Economics acquires completely different values, the similarity with the struggle for survival or the natural selection fading completely. And this is owing to the fact that, in light of the principle of comparative advantage, the most terrible thing that can happen to an individual engaged in the economic competitive struggle is not his disappearance, but precisely his placement where he has comparative advantage or advantages. Or, by virtue of the principle of comparative advantage, everyone has such a thing.
This is per se a biological analogy, because the predatory nature is usually characteristic of wild animals, being found in the jungle – the market becomes a jungle in the context in which you operate with the logic of predatory price cutting due to the fact that it is no longer an arena of social cooperation in which everyone can earn something, but rather a jungle that must be controlled, supervised and so on.
Infantile and senile industries
Both protectionist policies have disastrous effects in practice. The main disadvantage of imposing these protectionist tariffs is that it is claimed that the protected industry would subsequently develop a comparative advantage, without simultaneously generating a mechanism to remove protection, either when the comparative advantage is, in fact, obtained or when it proves to be illusory (Gray, 1973). But this is exactly where the problem can be identified: the comparative advantage cannot be discovered even if a mechanism for removing protection is created, because it has an entrepreneurial character, so it cannot be determined by a protectionist trade policy. The main key to be noticed in all of these discussions is that these terms, when the industry “returns” to a level of profitability and so on, become the prerogative of technocratic, political occupations, from outside the market. Once such an industry is created –despite the market verdict of not being chosen to support it –, all the problems that will arise after will obviously be solved not by the market criteria, but by non-economic criteria: industrial prestige, national security, employment problems and so forth.
An “entrepreneurial ecosystem” is more than a buzzword, being “the coordination of like-minded entities, individuals, or institutions to form a network to help create, grow and sustain business development” (Cain, 2012). “Understanding and sustaining the ecosystem is critical for the long-term viability of both businesses and the broader community” (Cain, 2012). In his work, “The Entrepreneurial Ecosystem: Think Biology 101”, Cain believes that the similarity with biology lies in the fact that in an entrepreneurial system, as in an ecosystem, we should not isolate individual parts, but consider instead how individual components relate to a more complex system.
The product life cycle theory (Raymond A. Vernon)
It seeks to explain seemingly abnormal patterns – in terms of comparative advantages – in international trade (successive exports and imports of the same type of product). However, in this case we can see the theoretical weakness of biological analogies generally used in economics, given the fact that the “anomalies”, which claim to be explained by the life cycle of the product, are intelligible if we remember the dynamic nature of the comparative advantage.
Although I have started in the elaboration of my argument from the premise of the inconsistency of biological analogies in economics, I cannot conclude that the obtained result has managed to confirm or refute the initial hypothesis.
On the other hand, what I have learned is that between black and white there are always many shades of grey. Paul Krugman, in particular, makes a correspondence between evolutionary theorists and economists through the medium of equilibrium and maximization models. Despite the fact that he interprets these principles in the first instance as mere “extreme and unrealistic assumptions”, he does prefer “to make sense of the world using models in which individuals maximize” and in which their “interaction can be summarized by a concept of equilibrium”, being aware of the “power of maximization-and-equilibrium to organize one’s thinking” (Krugman, 1996: 9).
Similarly, Alfred Marshall, while not rejecting the utilization of biological analogies in economics, considering that the progress of biological sciences has a strong influence on the development of economic reasoning, stands up for the conditionality of analogy usage with the establishment of a certain threshold or a precise limit that would require the subsequent abandonment of these analogies.
In addition, from Edith Penrose’s perspective, biological analogies did not qualify as an appropriate methodology in economics. Penrose argued that neo-Darwinian theories of evolution exclude deliberate and calculated behaviour that is distinctive to human action in the economic field, mentioning in particular the impossibility of manifesting human motivation. She also argues that there is no reason for biological analogies to be used in explaining the development of firms as they come from the whole family of analogies between biological organisms and social institutions, which flourished profusely in the 19th century, but which, for the most part, are no longer popular among those working in the social sciences. In support of her considerations, Penrose comes up with an original approach consisting in the economist’s analysis of the two functions of analogical reasoning, namely the explanatory value of reasoning, according to which “there must be some reason for believing that two series of events have enough in common for the explanation of one, mutatis mutandis, to provide at least a partial explanation of the other”, as well as on the educational function of metaphor according to which the similarities between two phenomena are used to add a “picturesque note” to an analysis (Penrose, 1952: 807). However, Levallois claims that many found it ironic how Penrose’s critical stance toward biological analogies contradicts her own theory of the growth of the firm, her own work being “very much consistent with evolutionary economics” (Levallois, 2011: 467), only biological economists (such as Armen Alchian and Kenneth Boulding) being the ones to expound the growth approach in any systematic form – those who consider firms to be organisms and who come to the conclusion that firms grow like organisms (Penrose, 1955). In this situation, Penrose justifies the consistency of her theory which, compared to the biological economists’ approach that “leaves no room for human motivation and conscious human decision” (Penrose 1955: 531), proposes a substitute approach which, in common with the biological variant, asserts that “a predisposition to grow is inherent in the very nature of firms, but which, in contrast, makes growth depend on human motivation – in the usual case on the businessman’s search for profits”.
Antonelli, C. (2001). The microeconomics of technological systems. Oxford University Press, USA.
Barker, D. (2012). Slime mold cities. Environment and Planning B: Planning and Design, 39(2), 262-286.
Boschma, R., and Martin, R. (2007). Editorial: Constructing an evolutionary economic geography. Journal of Economic Geography, 7(5), 537–548.
Cain, C. (2012). The entrepreneurial ecosystem: think biology 101. The Small Business Advocate, 31(4), 6.
Edey, M. (2009). The global financial crisis and its effects. Economic Papers: A Journal of Applied Economics and Policy, 28(3), 186-195.
Elliott, J. E. (1980). Marx and Schumpeter on capitalism’s creative destruction: A comparative restatement. The Quarterly Journal of Economics, 95(1), 45-68.
Evenson, R. E. (2000). How far away is Africa? Technological spillovers to agriculture and productivity. American Journal of Agricultural Economics, 82(3), 743-749.
Gallup, J. L., and Sachs, J. D. (2000). Agriculture, climate, and technology: why are the tropics falling behind? American Journal of Agricultural Economics, 82(3), 731-737.
Gray, H. P. (1973). Senile industry protection: A proposal. Southern Economic Journal, 569-574.
Krugman, P. (1996). The self-organising economy. Cambridge, Mass., and Oxford: Blackwell Publishers.
May, C. T. (1999). Nonlinear pricing: theory and applications (Vol. 65). John Wiley and Sons.
Krugman, P. (1998). A slime mold model of city formation. Topics in Public Economics, 15-32.
Levallois, C. (2011). Why were biological analogies in economics “a bad bhing”? Edith Penrose’s Battles against social Darwinism and McCarthyism. Science in Context, 24(4), 465-485.
Markusen A (1996). Sticky places in slippery space: A typology of industrial districts. Economic
Geography 72: 293–313.
Martin, R., and Sunley, P. (2007). Complexity thinking and evolutionary economic geography. Journal of Economic Geography, 7(5), 573-601.
Martin, R., and Sunley, P. (2015). Towards a developmental turn in evolutionary economic geography? Regional Studies, 49(5), 712-732.
May, C. T. (1999). Nonlinear pricing: theory and applications (Vol. 65). John Wiley and Sons.
McKay, A., and Thorbecke, E. (Eds.). (2015). Economic growth and poverty reduction in sub-Saharan Africa: current and emerging issues. Oxford University Press.
Mises, L. V. (2002). Acţiunea umană. Un tratat de teorie economică. Institutul Ludwig von Mises România.
Nelson, R., and Winter, S. (1982). An evolutionary theory of economic change. University of Illinois at Urbana-Champaign’s Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship. Cambridge, MA: Belknap Press of Harvard University.
Nelson, R. R. (1995). Recent evolutionary theorizing about economic change. Journal of Economic Literature, 33(1), 48-90.
Penrose, E. T. (1952). Biological analogies in the theory of the firm. The American Economic Review, 42(5), 804-819.
Penrose, E. T. (1955). Limits to the growth and size of firms. The American Economic Review, 45(2), 531-543.
Shumpeter, J. A. (1942). Capitalism, socialism and democracy. New York, NY: Harper and Brothers).
Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., ... and Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science, 327(5964), 439-442.
Viano, F. L. (2005). Cognitive models and enterprise: Veblen revisited. Guidi, M.E.L. and Parisi, D. (Eds), The changing firm: contributions from the history of economic though. Milan: Franco Angeli, 227-59.
Witt, U. (1994). Evolutionary economics. In The Elgar Companion to Austrian Economics. Edward Elgar Publishing.
Witt, U. (2003). The evolving economy. essays on the evolutionary approach to economics. Cheltenham: Edward Elgar.
Witt, U. (2006). Evolutionary economics. Papers on Economics and Evolution, No. 0605, Max Planck Institute of Economics, Evolutionary Economics Group, Jena.