As the CCi-FEAST conference organisers have pointed out, an emerging, knowledge-based society creates demand for evolutionary economics that consider outputs, inputs and social networks wherein peoples’ choices are dependent upon, and valuated by, the choices of others. How individuals behave within these contexts is fundamental to the nature of growth of creative industries, in whether they proceed in predictable directions, or drift upon the tides of fashion.
It is useful to model elements of creative industries as being positioned along a highly simplified spectrum, between ideas that are
Conceptualised this way, the study of creative industries can take advantage of sophisticated tools from epidemiology (2), population genetics (3) and other culture evolution models in all their variety (4). For the ‘selection’ end of the spectrum, we have a wealth of models of independent decision-makers who weigh the costs and benefits of their options, while subject to various biases of influence (5). This applies well to behaviours that serve some adaptive purpose, i.e., that
At the other end of the spectrum are behaviours that do not inherently ‘matter’, in terms of human survival, and for which there is often a large variety of options – decorative designs, musical motifs, and word forms, for example. In analogy to population genetics, these choices can be considered ‘neutral’ traits, in that what is chosen has no
Practically speaking, the random copying model does not require that people make choices without any reasons at all, but rather that the statistics of all their idiosyncratic choices, at the
Given the dichotomy – random copying versus selective decisions – often the question is where certain behaviours lie on the spectrum between them. For example, with independent, rational thinking, creative culture should converge upon the collective priorities of individuals, rather than drift constantly (11). On the other hand, random copying with occasional innovation leads our collective tastes to drift continually, in a direction that is unpredictable (12) but at a rate that is steady and predicted by the level of innovation (13). We are not meant to decide beforehand which aspects of the creative industries are subject to drift, as in fact this is what we can find out empirically, using these contrasting models for the patterns of change through time.
Academia is itself a creative industry, and academic publishing is very much subject to fashion. Ideally, science is the systematic process of testing multiple hypotheses, but in reality, it is practiced by real people, in social contexts. Academics do their research within complex collaboration networks (14), and are prone to copy ideas from one another. Indeed, new publishing pressures and the continual diversification of specialities have changed what once was a relatively compartmentalised, restricted enterprise into one where academics compete for citations and other forms of wide-reaching, academically-sanctioned ‘publicity’. In the U.K, the incentive to disseminate research has become explicit under the R.A.E. system, which scores each academic’s top four publications in terms of ‘impact factor’. Databases like the ISI Web of Knowledge have turned citation analysis not only into a science, but into a universal valuation system with metrics such as the ‘h-index’ and comprehensive citation analyses that summarize an academic’s career at the a single click. In essence, most academics are judged on the number of citations they have received. The competition now is becoming less for validity of research, and more for the volume of ‘hits’ that research receives (15), just like any other creative industry in the modern cyber-economy – how many comments a blog has received, how many tickets were sold, how many copies of a song/video were downloaded, or how many friends have linked to a MySpace page. Academic publications, as listed on journal citation databases with all the outgoing links (references cited) and incoming links (“cited by…”), are not so different from online social network pages in this respect.
Such competition for popularity drives diversification and the construction of new niches. As a result of the pressure to publish at all costs, there is now a proliferation of new journals on almost every conceivable topic (e.g.
As a consequence, trendy academic jargon tends to demonstrate the continual flux and empirical patterns of random copying (19), which implies that buzzwords do not actually matter in a meaningful, scientific sense. “Copying strips of words” was a part of human interaction that George Orwell (20) hated, but on the other hand our remarkable ability to imitate is a prerequisite for culture itself.
Diverse opinions exist as to what constitutes trendy ideas versus more meaningful research paradigms, yet there is little means of evaluating this objectively. Evolutionary theory is an ideal means to model these aspects of scientific process (21). By applying basic population genetic analogy to citations database research, we can characterize the use of modern scientific keywords in terms of a continuum between copying fashionable ideas at one extreme (akin to the neutral model of random genetic drift), and independent selective testing of hypotheses at the other (akin to selection, falsifying the neutral model).
Following the selection-random copying spectrum discussed above, we can propose two simple hypotheses for the evolution of academic vocabulary, which can be quantifiably tested for a given case study: (1) Vocabulary is
Of the two hypotheses, random copying serves as the null model against which selection might be identified by contrast. By ‘random copying’ we do not mean that the words themselves are random, as they obviously will be intelligible, but that they exist within a large set of possible keywords, none of which is inherently more useful than any other. In analogy with the neutral model of population genetics (9), randomly-copied keywords would be
The neutral model can be modelled as follows: We start with a set of
where
where
Using these predictions as the null model, we can identify selection as departures from these patterns, dependent on the kind of selection operating. In my CCi-FEAST presentation, I will present the preliminary results of this analysis. Among several cases, I test differences between subfields older versus younger, and within the physical sciences versus the social sciences. In doing so, I find some remarkable regularities.
Almost by definition, the creative industries involve the transmission of information between individuals, with the continual production of new inventions, some of which become innovations (i.e., rise to prominence). With the invention of new ideas analogous to ‘mutations,’ the process is almost ideally suited to evolutionary analysis, particularly one of evaluating the degree to which ideas are selected versus randomly copied.
The selection-fashion dichotomy is more generally acceptable in today’s world than it was previous decades when labour unions were strong, the Internet was a novelty of the US government agencies and academic publication was still done on real paper. Now, however, after rapid rise and fall of dot-com equities, YouTube videos, MySpace personalities, and throwaway books, ideas of random copying and drift are almost unavoidable. The relationship between evolutionary theory and other disciplines has also changed. If we see economics as a historical science, rather than a law-like one like physics, then evolution may be the
Since the mid-1990s, physics has changed, and started explicitly applying analyses of dynamic, historical processes of change – such as network evolution, complex adaptive systems, information cascades, sudden state changes and extreme events – toward models of social change. In the last decade, the science of interacting particles (or network nodes) has provided significant insights into modeling collective interactions in social systems, from Internet communities to pedestrian and vehicle traffic, and economic markets (23).
Nevertheless, economic study needs to focus on the flux of variation in open systems, rather than the maintenance of equilibrium in closed systems (24). Whereas variation in physics is often treated as ‘noise’, it is the essence of an evolutionary approach. The direct analogy between people and particles (or network nodes) in “social atom” models (25) are crucially dependent on the assumed rules of interaction, which often strays too far from reality (26).
The best approach, then as now, is evolution, and the tools that come with over 100 years of studying change among entities that pass on their similarities to others through time. As Daniel Dennett argued in