How and why do technologies spread when and where they do? What are the implications and consequences for the structure, wealth, and management of human organizations? Social and industrial evolutionary processes follow a sequence of replacements or substitutions: new ideas for old, new labor patterns for old, new technologies for old. The diffusion of new technologies follows common patterns that guide the selection of technologies and their rate of adoption in the human environment.
We are all accustomed to the idea of growth to a limit, for example, the number of people becoming ill in an epidemic that grows rapidly before eventually reaching a plateau. In fact, observers have recorded thousands of examples of such S-shaped growth in settings as diverse as animal populations, energy and transport infrastructures, language acquisition, and technological performance. Typically, the variable representing a population or system development and diffusion (species population, plant height, engine power, microchip capacity) grows exponentially at the outset. However, such systems whether natural or man-made cannot sustain exponential growth indefinitely. Negative feedback mechanisms or signals from the environment slow the growth, producing the S-shaped curve.
For a single growth process, a single sigmoidal curve is often a useful model. In niches or markets in which several populations or technologies compete, the growth and decline of each entry also often exhibit logistic behavior. This behavior depends on interactions among the competitors. Namely, if a technology's market share grows, it comes at the cost of shares of others. This process is well-described by the so-called "logistic substitution model."
The logistic model also finds applications in demography, notably to model transitions in fertility. For example, in 2009 PHE researchers conducted a demographic study projecting population for the globe as well as individual countries in the year 2050 based on a logistic model of fertility. See 'Implosions, explosions: Population projections to 2050 based simply on a logistic model of fertility'
To offer guidance for considering the diffusion of technologies that affect the human environment, the PHE explores the factors determining when socio-technical diffusion succeeds and how its speed and extent change over time. As part of this effort to advance and ease analyses of logistic behavior in time-series data, the PHE has developed the "LogletLab" software package to fit logistic curves and apply the logistic substitution model to single as well as multiple time-series data sets.
Click here for the LogletLab page
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