Research Article: A case against the trickle-down effect in technology ecosystems

Date Published: June 13, 2019

Publisher: Public Library of Science

Author(s): Guanglu Zhang, Douglas Allaire, Venkatesh Shankar, Daniel A. McAdams, Denis Horvath.


Technology evolution describes a change in a technology performance over time. The modeling of technology evolution is crucial for designers, entrepreneurs, and government officials to set reasonable R&D targets, invest in promising technology, and develop effective incentive policies. Scientists and engineers have developed several mathematical functions such as logistic function and exponential function (Moore’s Law) to model technology evolution. However, these models focus on how a technology evolves in isolation and do not consider how the technology interacts with other technologies. Here, we extend the Lotka-Volterra equations from community ecology to model a technology ecosystem with system, component, and fundamental layers. We model the technology ecosystem of passenger aircraft using the Lotka-Volterra equations. The results show limited trickle-down effect in the technology ecosystem, where we refer to the impact from an upper layer technology to a lower layer technology as a trickle-down effect. The limited trickle-down effect suggests that the advance of the system technology (passenger aircraft) is not able to automatically promote the performance of the component technology (turbofan aero-engine) and the fundamental technology (engine blade superalloy) that constitute the system. Our research warns that it may not be effective to maintain the prosperity of a technology ecosystem through government incentives on system technologies only. Decision makers should consider supporting the innovations of key component or fundamental technologies.

Partial Text

Technology evolution describes a change in a technology performance over time. The modeling and fundamental understanding of technology evolution is crucial for designers, entrepreneurs, and government officials. Among other uses, such understanding informs decision makers as they set reasonable R&D targets, invest in promising technology, and develop effective incentive policies [1–3]. Scientists and engineers have developed mathematical functions to model technology evolution [4–6]. For example, Moore’s Law is an exponential function that is widely used in the semiconductor industry to model the change in microprocessor transistor count over time [7]. These mathematical functions, such as Moore’s Law and logistic function, model a single technology’s evolution in isolation from those of related technologies. However, many technologies are part of a technology ecosystem [8, 9], comprising system technology, component technology, and fundamental technology layers. For example, a smartphone, a microprocessor, and lithographic technology represent technologies that live in the smartphone technology ecosystem’s system, component, and fundamental layers, respectively. Existing technology evolution models do not generally consider the interaction among the technologies in a technology ecosystem.

Vito Volterra introduced the Lotka-Volterra equations in the early 20th century to model the population changes of sharks and fishes in the Adriatic Sea. The mathematical form of the Lotka-Volterra equations have been modified and successfully applied in community ecology and demography during the last century [14, 15].

We build a simplified hierarchical ecosystem for passenger aircraft as shown in Fig 2A to test the trickle-down effect and understand other technology interactions in the ecosystem. For this study, we only include one technology in each layer of the technology ecosystem. Here, passenger aircraft is the system technology, turbofan aero-engine is the component technology, and engine blade superalloy is the fundamental technology. The grey and white arrows in Fig 2A denote the interactions among these three technologies in the ecosystem. In this ecosystem, the Lotka-Volterra ecosystem model has three equations. We choose a major performance metric for each technology and collect historical data from 1960 to 2010. We fit the Lotka-Volterra ecosystem model to the data and estimate the values of parameters A, B, and C in the model using least squares data fitting [24]. The best estimated parameter values derived through least squares data fitting minimize the sum of squared residuals, where a residual is the difference between an observed technology performance and the fitted technology performance provided by the Lotka-Volterra ecosystem model. Fig 2B shows the model fitting results. The parameter C in each equation corresponds to an arrow in Fig 2A. The value of parameter C represents the extent of impact from one technology on the other technology. We find a significant impact only from the turbofan aero-engine (component technology) on the passenger aircraft (system technology), represented by the 0.256xy term in the Lotka-Volterra equation for the system technology. The values of parameter C in the other two equations are small enough to be zero (i.e., 2.22⋅10−14, 2.22⋅10−14, and 2.13⋅10−13). These small values indicate limited technology interactions. We interpret these results to reveal a limited trickle-down effect, an isolated component technology, and a fundamental technology barrier in the passenger aircraft technology ecosystem.




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