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Comparative Theories of the Evolution of Technology

Evolution of technology is a stepwise advancement of a complex system of artifact, driven by interactions with sub-systems and other technological systems, considering technical choices, technical requirements, and science advances, which generate
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  C Comparative Theories of theEvolution of Technology Mario CocciaCNR   –   National Research Council of Italy,Torino, ItalyYale University, New Haven, CT, USA Synonyms  Nature of technology; Technological advances; Technological change; Technological evolution; Technological progress Definition Evolution of technology is a stepwise advance-ment of a complex system of artifact, driven byinteractions with sub-systems and other techno-logical systems, considering technical choices,technical requirements, and science advances,which generate new and/or improved products or  processes for use or consumption to satisfyincreasing needs of people and/or to solve com- plex problems in society. Introduction The evolution of technology plays an important role for the economic and social change of societies and the competitive advantage of   fi rmsand nations (Arthur  2009; Basalla 1988; Bryan et al. 2007; Coccia 2018a, b, 2019a, b, c; Hosler  1994). In order to explain main theories of theevolution of technology, it is important to clarifythe concept of evolution and of technology.Firstly, evolution is a stepwise andcomprehen-sive development of a complex system in natureand/or in society (cf. Coccia 2019e).Secondly, technology is a complex systemof artifact that is composed of more than oneentity or sub-system and a relationship that holds between each entity and at least one other entityinthe system (Coccia 2018a, 2019a). Technology is selected and adapted in society to satisfy needs,achieve goals, and/or solve problems of people,institutions, and nations (Coccia 2005b, 2006, 2012, 2015b, 2017b, 2019a; Coccia and Wang, 2015). Another important concept is the interac-tion between technologies: an interrelationship of information/resources/energy and of other physi-cal/chemical phenomena in interrelated complexsystems of artifacts for reciprocal adaptations inmarkets and society (Coccia 2019a). In this con-text, the coevolution of technologies is the © Springer Nature Switzerland AG 2019A. Farazmand (ed.),  Global Encyclopedia of Public Administration, Public Policy, and Governance ,  evolution of reciprocal adaptations in a complexsystem, supporting the reciprocal enhancement of technologies ’  growth rate and innovations(Coccia 2019a).Moreover, any technology is  not   independent from the behavior of other technologies (Coccia2018a, b). Sahal (1981), analyzing the patterns of  technological innovation, argues that  “ evolution . . .  pertains to the very structure andfunction of the object (p. 64)  . . . . involves a pro-cess of equilibrium governed by the internaldynamics of the object system (p. 69). ”  (cf.Coccia 2005b, 2006, 2012, 2014b, 2016) Kauffman and Macready (1995, p. 26, srcinalemphasis) state that   “ Technological evolution,like biological evolution, can be considered asearch across a space of possibilities on complex,multipeaked  ‘ fi tness, ’ ‘ ef  fi ciency, ’  or   ‘ cost  ’  land-scapes. ”  Kauffman and Macready (1995, p. 27and p. 42) also point out that evolution, biologicalor technological, is actually a story of coevolu-tion. In particular, the evolution of technology paves the way for other technologies in a processthat Kauffman has called  “ expanding the adjacent  possible. ”  Tria et al. (2014) suggest a model, based on a generalization of Polya ’ s urn, that pre-dicts statistical laws for the rate at which noveltieshappen (e.g., Heaps ’  law describes the number of distinct words in a document as a function of thedocument length, so-called type-token relation),as well as signatures of the process by which onenovelty sets the stage for another (i.e., technolog-ical evolution). In this research  fi eld, Iacopiniet al. (2018) describe the occurrence of noveltiesas a (noncausal) network exploration process(an edge-reinforcing random walk) showing theappearance of Heaps ’ s law, whereas the model byMazzolini et al. (2018, p. 8) shows that the causalrelationships between individual componentsencoded in the network affect the trend of Heaps ’ s law and thus the probability of   fi ndingnew components in a dependency cone added to arealization (cf. Vespignani 2009).In general, technological evolution can beexplained in economics and management withtwo different approaches (Fig. 1): •  Traditional theories are based on processes of competitive substitution of a new technologyfor the old one and a competition between predator and prey technologies in markets. •  New theoriesbased on a multimode interaction between technologies (Coccia 2018a, 2019a; Pistorius and Utterback  1997; Sandén andHillman 2011; Utterback et al. 2019). A main theoretical framework in this new researchstream isthe theory oftechnological parasitism by Coccia (2019a). THEORIES OF THE EVOLUTION OF TECHNOLOGY THEORIES BASED ON COMPETITION NEWTHEORIESBASED ON AMULTI-MODE INTERACTION BETWEEN TECHNOLOGIES  The viewpoint of technological substitution and competition between technologies  Model of Fisher and Pry  Predator-prey approach  The approach by Utterback and other scholars  Theory of the technological parasitism and virus-technologies Comparative Theories of the Evolution of Technology, Fig. 1  Theories of the evolution of technology 2 Comparative Theories of the Evolution of Technology  Theories of the Evolution of Technologies Based on CompetitionBetween New and EstablishedTechnologies The Viewpoint of Technological Substitutionand Competition Between Technologies The adoption and diffusion of anewtechnologyisassociated with the nature of some comparableolder technology in use. When comparable tech-nologies do exist, each technology tends to affect the behavior of the other. In fact, the evolution of technology does not take place in isolation, but it is a process of actual substitution of new technol-ogy for the old one. In this context, an establishedtechnologycanimprovewhenconfrontedwiththe prospect of being substituted by a new technol-ogy. Pistorius and Utterback (1997) argue that emerging technologies often substitute for moremature technologies. In general, the interaction between technologies is typically referred to ascompetition between new and old technology.As a matter of fact, Pistorius and Utterback (1997, p. 72) claim:  “ Pure competition, where anemerging technology has a negative in fl uence onthe growth ofa mature technology,and the maturetechnology has a negative in fl uence on the growthof the emerging technology. ”  Overall, then, acompetition is often embodied in substitutes, andPorter (1980) considers substitutes as one of theforces in his model of industrial competition for competitive advantage of   fi rms and nations (cf.Calabrese et al., 2005; Coccia 2005a, 2015b, 2017b, 2018c, 2018d, 2019d; Coccia and Wang 2015). Model of Fisher and Pry Fisher and Pry (1971, p. 75) argue that technolog-ical evolution consists of substituting a new tech-nology for the old one, such as the substitution of coal for wood, hydrocarbons for coal, etc. Fisher and Pry (1971) modeled the evolution of anew product or process becoming a substitute for a prior one and they plotted the substitution datain the form of   f   /(1   f   ) as a function of time onsemilog paper,  fi tting a straight line through theresultingpoints  –  where  f   isthemarketshareoftheemerging product or process in question versustime (cf. Utterback et al. 2019, p. 2). Fisher andPry (1971, p. 88) state that   “ The speed with whicha substitution takes place is not a simple measureof the pace of technical advance  . . . . It is, rather ameasure of the unbalance in these factors betweenthe competitive elements of the substitution. ” Predator-Prey Approach Farrell (1993a, b) used a model based on Lotka-Volterra equations to examine pure compe-tition between various technologies, such asnylon versus rayon tire cords, telephone versustelegraph usage, etc. In this context, the interac-tion between technologies can generate a predator-prey relation, where one technologyenhances the growth rate of the other, but thesecond inhibits the growth rate of the  fi rst (Pistorius and Utterback  1997, p. 74). In fact,a predator-prey relationship can exist between anemerging technology and a mature technology,whereemergingtechnologyentersanichemarket.Inthiscase,emergingtechnologycanbene fi tfromthe presence of mature technology. At the sametime,emergingtechnology mayreduce themarket share of mature technology. Overall, then, a predator-prey interaction has an emerging tech-nology in the role of predator and the maturetechnology as prey. However, it is also possibleto visualize a situation where a mature technologyis predator and emerging technology is prey(Pistorius and Utterback  1997, p. 78). Utterback et al. (2019) show this type of predator-prey rela-tion in a speci fi c period between plywood andoriented strand board (OSB) technology (OSB isa composite of oriented and layered strands, peeled from widely available smaller trees). New Theories of the Evolution of Technologies based on MultimodeInteractions The Viewpoint by Utterback and OtherScholars Utterback et al. (2019) suggest to abandon theidea that technology and innovation originateonly in pure competition between new andestablished artifacts. These scholars argue that the growth of one technology will often stimulatethe growth of other technologies, calling this Comparative Theories of the Evolution of Technology 3  interaction as  symbiotic competition  (Utterback et al. 2019).As a matter of fact, there are many caseswhere technologies interact in a relationshipthat is not of competition in the strict sense of the word. In this context, Pistorius and Utterback (1997, p. 72ff) suggest different interactionsamong technologies in analogy with biology.Sandén and Hillman (2011, p. 407) also proposesix technological interactions, using a similaritywith the interaction of species, i.e., neutralism,commensalism, amensalism, symbiosis, compe-tition, and parasitism-predation into one cate-gory. Coccia (2018a) suggests a matrix to showhow these different relationships between tech-nologies evolve over time (Fig. 2). Pistorius andUtterback (1997, p. 67) argue that a multimodeinteraction between technologies provides amuch richer theoretical framework for technol-ogy analysis. Theory of Technological Parasitism and VirusTechnologies Technological parasitism by Coccia (2018a,2019a) is a  new  theory to explain the evolutionof technology in society considering the interac-tion between technologies that generates thecoevolution of a host-parasite complex systemof artifacts. The theoretical backgroundof this theory is based on a  “ GeneralizedDarwinism ”  (Hodgson and Knudsen 2006) for framing a broad analogy between technologiesand evolutionary ecology of parasites that pro-vides a logical structure of scienti fi c inquiry(cf. Coccia 2018a, 2019a). Basalla (1988) suggested that the evolution of technology can  Strong  Parasitism/predation    B  e  n  e   f   i   t   t  o   T  e  c   h  n  o   l  o  g   i  e  s   T         j    f  r  o  m   i  n   t  e  r  a  c   t   i  o  n  w   i   t   h   T         i Symbiosis    Strong  Mutualism  Strong  Commen-salism + +++Benefit to Technologies T i  from interaction with T  j  +  Neutral Competition Commen-salism   Amensalism ParasitismPredation – 0  Evolution of a Complex System of Technology S(Ti,Tj) – +0  Parasitism/  PredationStrong  Parasitism/  PredationCommensal ismStrong Commen- salismStrong Mutualism  Amensalism Mutualism Comparative Theories of the Evolution of Technol-ogy, Fig. 2  Types of relationships between technologiesand evolutionary pathways in a complex system.(  Note:  The notions of positive, negative, and neutral ben-e fi t to technologies T i  and T  j   in a complex system S frommutual interaction are represented with the following sym- bols of logic: +,  , 0 (zero); ++ is a strong positive bene fi t to technologies T i  and T  j   in S from long-run mutualsymbiotic interaction, i.e., coevolution of T i  and T  j   in S, 8 i = 1, . . . , n; 8  j  = 1, . . . , m .Thicksolidarrowsindicatethe probable evolutionary route of interactive technologiesin a complex system  S  : the possibility for parasitic-virustechnologies to become commensals, mutualists, and sym- biotic; thin arrows show other possible evolutionary path-ways of technologies T i  and T  j   during the interaction in acomplex system  S   of artifact.) 4 Comparative Theories of the Evolution of Technology   pro fi tably be seen as analogous to biological evo-lution.Technologicalevolution,alongside biolog-ical evolution, displays radiations, stasis,extinctions, and novelty (Solé et al. 2013).The crux of the theory of technological para-sitism is rooted in the evolutionary ecology of  parasites, and since the concept of parasite isuncommon in economics of technology, it is use-ful to clarify it. In the evolutionary ecology, par-asites (from Greek   para = near;  sitos = food) areany life form  fi nding their ecological niche inanother living system (host). Parasites have arange of traits that evolve to locate in availablehosts, to survive and disperse among hosts, and toreproduce and persist. Coccia (2018a, 2019a) argues that technologies can have a behavior similar to parasites because technologies cannot survive and develop as independent systems per se, but they can function and evolve in societiesif they are associated with other host or master technologies, such as audio headphones, wirelessspeakers, software apps, etc. that function  if and only if    they are associated with host or master electronic devices, such as smartphone, radioreceiver, television, etc. In particular, a parasitictechnology  P   in a host or master technology  H   is atechnology that during its life cycle is able tointeract and adapt into the complex system of   H  ,generating coevolutionary processes to satisfyhuman needs and desires and/or solve problemsin society. Parasitic technologies are often sub-systems embedded within and primarily func-tional in the ecological system of host   or   master technologies. A technology can be a parasite of different hosts  or   master technologies, as well as atechnology can be a host or master of different  parasitic technologies (e.g., mobile devices arehost of software applications, headphones,Bluetooth technology, and other parasitictechnologies, cf. Coccia 2018a). In general,manytechnologiesdonotfunctionasindependent systems, but   de facto  they depend, as parasites, onother technologies (hosts  or   masters) to form acomplex system of parts that interact in a non-simple way. This behavior of technologies can begeneralized with the  theorem of not independenceof any technology  (Coccia 2018b): the long-run behavior and evolution of any technological inno-vationT i isnotindependentfromthebehaviorandevolution of the other technological innovationsT  j   ( 8 i = 1,  . . . ,  n  and  j  = 1,  . . . ,  m ).Inparticular,parasitictechnologiescan becon-sidered speci fi cally as  virus technologies  becausethey have the characteristics of obliged parasites,as they depend on a host   or   master for most of their technological functions and developmental processes. Some virus technologies are ableto function only to a speci fi c host (e.g., dieselfuel as virus technology can be used only incompression-ignition engines as host technolo-gies), while others are able to function on manyhost technologies (e.g., electrical energy as virustechnology can be used for many appliancesof different scale). Moreover, parasitic-virus tech-nologies can be de fi ned and classi fi ed on the basisof the technological host in which they adapt,and their evolution in the form of different gener-ationsisduetointeraction withhosts.Moreover,atechnology can be seen as a parasite or host,depending on the scale of analysis. Smartphoneis host of many parasitic technologies, e.g.Bluetooth technology, but it can be also seenas a parasite of satellite technology for somefunctions, communication and transmission of information.This theory of technological parasitism pro- poses a model to analyze the interaction betweena host   or   master technology (  H   system) and a parasitic-virus technology (  P   sub-system).The logarithmic form of the model (Coccia2019a) is a simple linear relationship:log  P  ¼ log a þ  B log  H   þ u t   –   P   =  evolutionary advances of parasitic-virustechnology, e.g., fuel consumption ef  fi ciencyin horsepower hours indicates the technologi-cal advances of engine for farm tractor   –   log  a = constant   –   H   =  evolutionary advances of host   or   master technology, e.g., total mechanical ef  fi ciency of farm tractor   –   u t  = error term  B  is the evolutionary coef  fi cient of growth that measures the evolution of technology  P   (parasite)in relation to  H   (host   or   master technology). Comparative Theories of the Evolution of Technology 5
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