Today the situation is very different. Tools such as ... agent-based economic computational modeling can be used to put Hamilton's views on evolutionary economic change into action so that heterodox economists can compete more successfully in the arena of public opinion. Let's get to it!
Michael J. Radzicki
This paper, in many ways, is a response to Professor Radzicki's 2003 call in this journal for intensified use of systems dynamics and computer simulation modeling to rigorously address economic change as the alteration of social and economic structures themselves and not just changes within existing structures. In this vein, this paper aims to broaden the understanding of the process of technological change and standardization through an evolutionary approach, which avoids the determinism of conventional orthodox models of technology diffusion and standardization. A model is developed to explore how change between competing technological standards can be initiated endogenously within an industry. We do this by first focusing on the characteristics of challenging technological innovations themselves. We then focus on the role of the changing preferences of agents adopting the technologies and how preference evolution can also induce technological change. Technological change at the industry level is important because it can influence the development of larger macroeconomic structures and institutions such as corporations, political regulatory structures, growth rates, and the accumulation and structure of capital. To achieve our goals we have developed an agent-based model (ABM) (1) using distributed artificial intelligence (DAI) concepts drawn from the general methodology of social simulation.
The model also helps expand our understanding of path-dependent technological evolution, building upon Arthur 1988 and 1990, David 1985, and others' conceptualizations of increasing returns to adoption and technological lock-in. Our model intends to extend W. Brian Arthur's basic formulation beyond the one-time competition between two technologies to a vision of technological change as an ongoing evolutionary succession of technological standards over an infinite time horizon. Within this framework we examine hypotheses about how change is induced. The modeling allows us to recognize an apparent paradox in the increasing returns and lock-in conceptualization, mainly the fact that while increasing returns can drive an industry toward technological standardization, their presence always holds out the possibility of the emergence of a new technological alternative which can destabilize the lock-in. Technological lock-in can only be partially understood from the fundamentally new institutionalist perspective of increasing returns to scale. Explaining longer term technological stability in industries appears to require explanations beyond the new institutional arguments about decision making. Instead, the old institutional emphasis on ceremonial and learned behaviors and the emergence of durable institutional structures have greater explanatory power. While increasing returns modeling has advanced formal understanding, it is argued that the thinking of institutional and Post Keynesian authors like Thorstein Veblen, Gunnar Myrdal, John R. Commons, Clarence Ayres, and Nicholas Kaldor are still fundamental to any complete understanding of technological lock-in and stability.
Technology Change and Diffusion Modeling
This paper begins by extending the new institutionalist approach to technological competition in the presence of increasing return to scale. The main differences between the orthodox, or neoclassical, approach to the analysis of technological change and the approach adopted here basically arises from the objections of evolutionary and institutional economists to the way in which the (aggregate) production function is used by neoclassical economists and their apparent inability to explain the processes of technological change (Nelson and Winter 1974, 1977, 1982; Dosi 1982; Dosi et al. 1988). Thus, while the neoclassical approach portrays technological change as a simple change in the information available on the relationship between the economy's inputs and outputs (Stoneman 1983; Gomulka 1990), the evolutionary approach considers technological change to be the result of a self-referential process of evolution influenced by the prevailing economic, social, and political institutions. Technology, in this sense, is embedded within a physical infrastructure and set of social institutions that can generate complex "circular and cumulative causation" and path-dependent technological adoption patterns (Myrdal 1957). Accordingly, technological development should be understood as a process of evolution in which alternative technologies compete with one another and with the pre-existing dominant technology with considerable uncertainty at the outset about who the winners will be (Nelson and Winter 1982). Given that fundamental uncertainty is intrinsic to the process of technological decision making (Davidson 1982-1983, 1991), the processes are considered nonergodic and path dependent. Furthermore, the assumption of rational maximizing behavior must be replaced by a search for profit "in the dark" through heuristic search routines that emerge through complex interactions with existing technological infrastructure and social institutions. Decision making is furthermore influenced by ceremonial behavior, heuristics, custom, habit, and myth (Veblen 1994; Nelson and Winter 1982; Commons 1931) that can condition these search routines. As a result, there is no single welfare-maximizing equilibrium but rather a plurality of outcomes or possible equilibria. We evaluate the implications of these observations in the discussion section of the paper. Finally, in evolutionary models the structure, including institutions, is often made explicit so that its role in the process of technological change can be studied (Lipsey and Carlaw 1998).
Technology diffusion can be defined as a process whereby innovations (products, processes, and/or management techniques) propagate within and between economies (Stoneman 1986). Empirical research has shown that over time, the diffusion of new technologies follows a predictable pattern, represented graphically by an S-shaped curve (Griliches 1957; Mansfield 1961; Davies 1979; Gort and Keppler 1982), which traces the rate of adoption over time (Mansfield 1961, 1968). Diffusion research has historically focused on explaining the rate and the order in which innovations are adopted. Recent research into increasing returns to scale, or positive feedback, on the dynamics of technology diffusion has received growing attention in the last decade (Arthur 1989, 1990, 1994), although the fundamental concepts are deeply rooted in institutional thinking. Arthur has written extensively (1988, 1990) about being influenced by institutional and Post Keynesian economists (including Kaldor 1981, Myrdal 1957, and David 1985) who emphasize the importance of positive feedback loops, increasing returns, and path dependency in explaining evolutionary economic behavior (Radzicki 2003).
While an S-shaped curve illustrates the rate of diffusion over time, it can also be used to describe how the performance of a technology improves relative to the effort put into its development and commercialization (Foster 1986, 96). This is illustrated in figure 1. As the figure shows, returns are not constant over the period of adoption and, after a point of inflection, the possible improvements in performance are progressively smaller (Moreau 1999, 9; Laffond et al. 1999; Loch and Huberman 1999, 12). In effect, technology improvement moves from a period of increasing returns in the lower and center segments of the S-curve to decreasing returns in the upper segment. Orthodox economic analysis tends to focus on the decreasing returns segment at the top of the curve (i.e., the long-term equilibrium), but the preceding period of increasing returns has been shown to have important impacts on the outcomes of technological competition (Arthur 1989; Schilling 1998). Ultimately, however, the increasing returns process is limited by the decreasing returns at the top of the S-curve, which provide the negative feedback that limits exponential growth as markets become saturated.
[FIGURE 1 OMITTED]
Recent work has focused on a handful of increasing returns mechanisms that include broad categories of scale, learning, network, and information economies or effects. Traditional scale economies arise from spreading large initial investments in research, development, and capital assets over increasing unit output. As greater production experience is acquired, manufacturers learn how to produce additional units more cheaply (learning by doing) (Arrow 1962). As greater experience is also acquired in the use of the technology, users' productivity increases (learning by using) (Sheshinsky 1967). Further positive externalities arise because the physical and informational networks in which technologies are embedded can grow more valuable to users as they increase in size (Katz and Shapiro 1985, 1986a, b; Farrell and Saloner 1986a, b; Economides 1996). Increased adoption furthermore lowers uncertainty and information search costs (Blackman 1999), reducing perceived risks to adoption, and can create bandwagon effects (Leibenstein 1950; Veblen 1994). These diverse increasing returns mechanisms combine to create powerful positive feedback loops that can dramatically alter technological performance as the adoption process proceeds.
Many authors have noted that these dynamics have important implications for economic outcomes (Young 1928; Arthur 1989). When increasing returns to adoption exist, the same distribution of technologies and user preferences can lead to different structures of results, depending on the initial conditions and the sequencing of...