JoM-EMIT LogoEdmonds, B. (2005). The revealed poverty of the gene-meme analogy – why memetics per se has failed to produce substantive results.
Journal of Memetics - Evolutionary Models of Information Transmission
, 9.

The revealed poverty of the gene-meme analogy –
why memetics per se has failed to produce substantive results

Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

Over two years ago in this journal I challenged the memetics community to meet three challenges (Edmonds 2002).  My prediction was that if these were not substantially met that memetics as a distinct approach would not survive.  Despite some attempts to do this these challenges were not met, at least not within the terms I had proposed.  Now JoM‑EMIT in its current form has ceased due to a lack of quality submissions.  Whilst I do not claim that the former caused the later, I do claim that the failure to answer those challenges was indicative of the poverty of the memetics project resulting in a lack of demonstrable progress which, in turn, has meant that it has failed to interest other academics.

Here I distinguish between what might be called the "broad" and the "narrow" approaches to memetics.  The former, broad, approach involves modelling communication or other social phenomena using approaches which are evolutionary in structure.  Work within this approach is often done without appealing to "memes" or "memetics" since it can be easily accommodated within other frameworks.  In other words, it does not require an analogy with genetics.  The later, narrow, sense involves a closer analogy between genes and memes – not necessarily 100% direct, but sufficiently direct so as to justify the epithet of "memetics".  What has failed is the narrow approach – that of memetics.  Work continues within the broad approach, albeit under other names, and in other journals.

I claim that the underlying reason memetics has failed is that it has not provided any extra explanatory or predictive power beyond that available without the gene-meme analogy.  Thus whilst the idea of memes has retained its attractiveness for some in terms of a framework for thinking about phenomena, it has not provided any "added value" it terms of providing new understanding of phenomena.  The fact that some who wear the theoretical spectacles (Kuhn 1969) of memetics insist of redescribing a host of phenomena in these terms despite the lack of substantive results merely confirms other academics' opinion of the approach.  The ability to think of some phenomena in a particular way (or describe it using a certain framework), does not mean that the phenomena has those properties in any significant sense.

Academics who seek to study memetics in serious ways have suffered in the respect that they are often confused with those on the penumbra for whom memetics is a fad.  However, this mistake is grounded in an element of truth.  The study of memetics has been characterised by theoretical discussion of extreme abstraction and over ambition.  Thus for example, before any evidence is available or detailed causal models constructed, attempts have been made to "explain" some immensely complex phenomena such as religion in general [note 1] or consciousness.  This sort of discussion shifts any study of memetics from the realm of science to that of a philosophy and, on the whole, this philosophy has adopted the subsumption tactic (Hull 2001), seeking to generalise explanation rather than been productive of essentially new insights.

In the broader sense it would be extremely surprising if there were no social processes with evolutionary aspects.  However, the intricacy of social phenomena means that understanding the effects of different mechanisms in systems of multiply interacting actors requires a finer tool than that of discursive analogy.  Thus unravelling some of the conditions for observed social processes (which may have evolutionary aspects) requires such tools as agent-based computational simulation which can track long-chains of intricate interactions.  This approach also allows for a wide variety of different mechanisms and processes to be explored and not merely those amenable to description via biologically-rooted analogies.   Of course, there is a successful community of social simulators who study, among other things, evolutionary models of information transmission [note 2].  Similarly there is work in computer science, applying evolutionary ideas to computational processes and work in theoretical biology studying non-genetic evolutionary processes.  Thus this wider work will continue as subsets of other projects, but not under the discredited label of memetics.

To illustrate how the memetics bandwagon may have peaked I used ISI's large citation index ( and Google Scholar ( to estimate the number of papers that mention the word memetics.  I picked "memetics" rather than "meme" (which has a longer history) because of the existence of the common French word "meme" (as in le meme chose).  I subtracted those papers about "memetic algorithms" because these are not about memes in any meaningful way [note 3].

Graph showing the number of paper that mention the word "memetics" has peaked

Figure 1. Number of papers mentioning "memetic*" (but not "memetic algorithm*") each year according to Google Scholar (numGS, pink circles) and on the ISI's citation index (numWOS, blue circles).  Lines are 6th degree fitted polynomial trend lines of the respected series.

The fact is that the closer work has been to the core of memetics, the less successful it has been.  The central core, the meme-gene analogy, has not been a wellspring of models and studies which have provided "explanatory leverage" upon observed phenomena.  Rather, it has been a short-lived fad whose effect has been to obscure more than it has been to enlighten.  I am afraid that memetics, as an identifiable discipline, will not be widely missed.


  1. Here I want to distinguish woolly explanations of religion in general from the more careful study of the phylogeny of particular institutions such as in (Lord and Price 2001).
  2. The success of the Journal of Artificial Societies and Social Simulation ( contrasts markedly with that of JoM-EMIT.
  3. "Memetic Algorithms" are Genetic algorithms but where hill-climbing is used to locally optimise solutions.  No spread of solutions is involved (unlike a few other evolutionary computation approaches which I have included).


Edmonds, B. (2002). Three Challenges for the Survival of Memetics. Journal of Memetics - Evolutionary Models of Information Transmission, 6. <http://jom‑>

Hull, D. (2001) In Search of Epistomological Warrent. In Cecilia Heyes and David Hull (eds) Selection Theory and Social Construction: the evolutionary naturalistic epistemology of Donald T. Campbell. State University of New York Press, Albany, NY, 2001, 155-167.

Kuhn, T. (1969) The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

Lord, A. and Price, I (2001). Reconstruction of organisational phylogeny from memetic similarity analysis: Proof of feasibility. Journal of Memetics - Evolutionary Models of Information Transmission, 5. <http://jom‑>

© JoM-EMIT 2005

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