Aunger (2000) and Edmonds (2002) argue that memetics is a theory without a methodology, in imminent danger of dying from lack of novel interpretations and empirical work. Edmonds challenges memeticists to conduct empirical tests. This article presents Competing Memes Analysis, an empirical methodology that can readily be applied to significant social problems. The methodology is implemented in three steps. Step 1 identifies the organization of memes within an activity. Each activity is assumed to exhibit numerous small groups of memes where each meme within a group competes with all other memes in the group and can be combined with any meme from any other group. The succession of memes that occurs with increasing experience can be a powerful clue to identifying competing memes. Step 2 collects records of activities and codes them for the presence or absence of each meme identified in Step 1. Any activity that people acquire from each other by imitation can be readily coded for the presence or absence of competing memes. Step 3 analyzes changing frequencies of each coded meme over time or space. Models of these changes can give useful clues to suggest empirical studies that will provide important social and scientific results. Ecology’s Lotka-Volterra model of competing species illustrates the usefulness to memetics of population models.
1. The challenge to memetics
Aunger (2000) contains several arguments
that memetics is a theory without a methodology. He concludes (p. 230) “The
ultimate test—which would preempt theoretical objections—is whether
memetics can produce novel empirical work or insightful interpretations
of previous results. It has not yet done so, but must do so in the near
future. Otherwise, it is likely that memetics will be perceived to be a
misguided enterprise. The clock is ticking.“
Edmonds (2002) extends Aunger’s argument
by specifying three challenges for memetics. His first challenge is for
a conclusive case study which shows a cultural process that (a)
exhibits faithful replication where the replication process is
transferred to many sources and (b) passes through a large enough
quantity of such transfers to show adaptation resulting from verifiable
advantages and consistent with current population-genetics models. He
suggests nursery rhymes and legal phrases as appropriate test cases.
Edmonds’ second challenge is for a falsifiable theory that identifies
when a memetics interpretation is appropriate and necessary. Edmonds’
third challenge is for a credible simulation of an emergent (not “designed
in”) memetic process, analogous to “exhibiting a simulation of
the emergence of life from the interaction of chemicals.“
This paper describes methodological techniques for meeting Edmonds’
case-study and memetics-interpretation challenges. Before describing
the methodology it would be helpful to clarify a few aspects of the
memetics challenge.
1.1 General and specific memetics
A significant source of potential confusion about memetics resembles
the confusion within scientific history that occurred with Kuhn’s (1969) initial conception of “paradigm.” A
paradigm referred both to (a) “the entire constellation of beliefs,
values, techniques and so on shared by members of a given community”
and also to (b) “the concrete puzzle-solutions which…replace
explicit rules as a basis for the solution of the remaining puzzles of
normal science.” Note that the second, specific conception enables
the former general conception and thus is the deeper of the two.
Similarly, memetics is used in both a general and a specific manner.
Specifically, we have the model of actions or artifacts that can be
imitated with fidelity, fecundity and durability. Generally, we have
the conception of memes as actions or artifacts that evolve
independently of the people producing them. The specific conception
enables the general one and is the deeper. But just like there are a
host of biological studies that rely on but do not discuss evolutionary
processes, there are also a host of possible memetics studies that rely
on but do not discuss evolutionary processes. Furthermore, the methods
used in general memetics studies may be directly applicable to the
fundamental questions raised within the specific conception. This
occurs especially when studies have a methodological rather than a
theoretical focus. Thus, a vast array of ecological, geological and
genetic models depends on genetic evolution without addressing it and
many mathematical models in these fields preceded evolutionary models
by decades. The power of evolutionary thinking was due as much to the
remarkable convergence of knowledge that supported it as to its own
models. It seems likely that a future principle of the memetics of
science will be that converging knowledge is a necessary predecessor to
the dominance of any scientific system.
1.2 Edmonds’ First Challenge: The Case Study
Critical to Edmonds’ argument is that passing his challenges would
show the “usability” of memetics. Edmonds’ suggestion of nursery rhymes
and legal phrases as apt test cases was based on the assumption that
the examples were “of a limited nature about which good quality data
is available.” More ambitious studies would not be believed until
such “straightforward” examples had been established.
Case studies are certainly essential, but Edmonds’ suggested topics
can be improved. Unambitious content by no means guarantees
straightforward conduct and interpretation of any study. The failure of
attempts to program translation, transcription, and even textual search
belie the straightforwardness of any natural language task. Especially
at this early point in memetics research, the methodology and
interpretation of all studies will be questioned. The critical factor
for the future of memetics is not straightforward conduct and
interpretation. Rather, the critical factor is whether the questions
lead the scientific community to ignore or use and debate the results.
The potential for use and debate, rather than obscurity, varies
directly with the author’s skill, the author’s community support and
the significance of the topic. Furthermore, the dynamics of the
transmission of memes will be affected by the importance of the topic.
Observing natural memetic transmission and adaptation is likely to be
far more difficult for small niche topics, like those Edmonds
suggested, than for topics that have more urgent and widespread social
content. A primary goal of this paper is to show that although the
quick-fix case study does not exist, there are methods that make the
consequential studies no more difficult than those with minimal
importance.
1.3 Edmonds’ Second Challenge: The
falsifiable theory of when to use memetics
In his challenge for a falsifiable theory of when to use memetics,
Edmonds rightly argues for a biological advantage of large brains
independent to the hosting of memes. The issue of deciding when to
apply memetics arguments, however, might be better conceived as
methodological than theoretical. Though a theory might be easier to
apply to ongoing social change, researchers would benefit more from
operational definitions. Once several operational definitions have
become established, the common elements would point to the needed
theory.
The ideal, operational definition would involve direct observation
of imitation. Short of that ideal, indirect studies could use
situations involving developmental or historical change that (a)
minimize the influence of intra-individual processes and (b) allow for
a high level of opportunity for imitation. One way of precluding
intra-individual processes is to study only a single record per person.
A high level of opportunity for imitation occurs by examining
situations that contain a high probability of frequent contact between
persons being studied.
2. Competing Memes Analysis
Competing Memes Analysis is a method that makes studies of
consequential topics no more difficult than those with minimal
importance. It is a three step process that
(1) organizes activities into groups of competing memes
that often emerge in developmental successions,
(2) codes records of activities for the presence or absence of each
meme, and
(3) constructs models of the changing frequencies of the memes.
The methodology has been constructed through studies of the
ontogenetic development of drawing (Dirlam, 1996,
contains a preliminary analysis), writing (Dirlam,
1982), and the historical development of research methods in
developmental psychology (Dirlam, Gamble, and
Lloyd, 1999). The purpose of the drawing and writing studies was to
design objective pedagogical alternatives to educational testing that
could be used with natural products of classroom activities. The
purpose of the study of research methods was to determine whether the
analytical models that fit the ontogeny of drawing and writing also fit
historical development.
2.1. Creating multidimensional classifiers from successions of memes
The first step in the Competing Memes Analysis of an activity
is to create multidimensional classifiers of competing memes (Dirlam, 1980). Each activity is assumed to
exhibit numerous small groups of memes each of which comprises a
dimension. A group of memes is a dimension when each meme within the
group competes with all other memes in it and can be combined with any
meme from any other group (see Appendix A for
an example). The succession of memes that occurs with increasing
experience can be a powerful clue to identifying competing groups. A
memetic classifier for an activity consists of several dimensions of
memes.
Memetic classifiers can be readily constructed by anyone with modest
expertise in a topic. A straight forward approach is to observe
differences between people performing the activity who have various
levels of experience. When one approach to the activity is commonly
replaced by another, the two approaches are competing memes. Sometimes
the later appearing memes contain earlier appearing ones. In any case,
since the memes can be defined to be mutually exclusive, each
succession becomes a dimension.
Dimensions can and should be defined as exhaustive by generalizing
to includeall other cases in the definition of one meme within
each dimension. Thus, every record of an activity being investigated is
assigned to one meme within each group of competing memes. When an
emerging meme becomes frequent enough to separate from a generalized
meme, it will be necessary to redefine and recode the dimension.
Analysis of large collections of records might also reveal that some
memes are so rare that they should be logically combined with others.
Experts in an activity can help to construct definitions of each
meme so that coders can reliably distinguish which is being used. For
most important activities, there is theoretical literature that
suggests a rich variety of memes. For example, more than half of the
dimensions of competing memes used in the study of developmental
research methods mentioned above were based on concepts from Danziger (1990). The memetic classifier for the study
is presented in Appendix A. The study involved
coding 912 articles written from 1930 to 1992 for the presence or
absence of each meme in the classifier.
The number of dimensions in a competing memes analysis is a matter
of researcher choice and depends on the state of knowledge in the
subject at the time of the study. It should be noted, however, that
gathering and examining records is usually much harder than coding
them. Therefore, studies with several dimensions require considerably
less resources per dimension than single-dimension studies. The memes
themselves are like fractals—they can apply to content as fine-grained
as words, lines and study locations or as general as complete
discourses, complete drawings and complete research articles.
2.2. Collecting and coding records
The second step in Competing Memes Analysis is to
systematically collect and code records of memes. In order to permit
the construction of testable models, the records should be drawn from a
corpus that contains a meaningful point in time or space for all
samples. For example, time points for the studies of drawing and
writing were the age of each participant and time points for the
developmental research study were the publication years of the
articles. Studying the spread of memes from their spatial points of
origin should also generate rich, useful results.
Drawings are self-contained records of the actions of those who made
them. Similarly, writing samples record the actions of writers and
research reports record the actions of researchers. Audio and video
tapes are also records of memetic activity. Because memes are imitated,
any user of a meme and many non-users can identify uses of it by
others. This means that discrete samples can be reliably coded for the
presence or absence of a meme. If a ten-dimensional system has been
defined (as in section 2.1 above), each record
will contain ten memes.
One clear implication of memetics is that humans are uniquely
capable of identifying memes. The only rival species, apes and parrots,
are readily surpassed in speed, variety and complexity of imitation by
preschool children. Examining the diversity of memes in the three
studies that have used Competing Memes Analysis reveals that the
complexity or importance of memes did not affect our ability to
identify them. Hence, consequential studies are no more difficult than
those with minimal importance.
2.3. Modeling frequency changes over time or
space
Once records have been coded, it is possible to count the uses of
memes in situations with important consequences, whether they be
economic (e.g., the use of a new product), social (e.g., the spread of
juvenile crime), political (e.g., the repetition of a candidate’s
message), educational (e.g., the way students and programs are
evaluated) or scientific (e.g., the spread of a methodology). For
example in the study of developmental research methods, the model
projected that the growth of difference statistics was so fast that if
a competitor did not emerge in the next few decades, data analysis
would consume so much of available resources (i.e. social acceptance)
that it would implode (see Figure 1). During the
1930-1992 study period, data modeling was too rare to analyze
separately from difference statistics. But since it is a valued and
slow-growing alternative, it is a possible solution to the collapse of
the analysis dimension.
Figure 1. Best fitting Lotka-Volterra model for
competing data analysis memes.
Memetics skeptics would most readily accept studies that
operationally define their memetic nature by setting up situations for
observing imitation. However, imitation can be inferred rather than
directly observed, and can be interpreted broadly as reproduction. This
opens up the potential to use archival data (e.g. research reports,
crime incident reports, corporate audits and so forth). A dramatic
memetics success would occur if memes were discovered that effectively
competed with such practices as crime among low income juveniles, self
and community destructiveness among religious fundamentalists and fraud
among corporate CEOs faced with poor results. Key evidence for
successful competition would come from changing frequencies of memes
over time or space.
Beyond identifying memes, Competing Memes Analysis may
involve a search for
(1) the path of succession from one meme to the next,
(2) the resources required,
(3) the growth rate and
(4) the competitive strength of such practices.
Results of these types of studies may help us to understand such
issues as
(a) how memes that are seen as
“weeds” contribute to general survival and
(b) how the harmful effects of overgrowth of
such memes can be controlled.
An example of (a) would be marketing
processes that make it possible to establish products in extremely
competitive markets, but permit monopoly-like overgrowth in
noncompetitive markets. Examples of(b)would be
finding competitors to the emergence of monopolies ranging from
antitrust laws to innovations that disrupt them. The problems solved
while undertaking a rich variety of such studies could provide the sort
of clarity needed to meet Edmonds’ challenge for a falsifiable theory
of when to use memetics.
Definitive answers to questions about resources,
growth rate and competitive strength may be difficult to obtain without
detailed experimental studies. However, once hundreds of records of
memes have been systematically collected from a population, placed
within a meaningful distribution of time or space, coded and counted,
it is possible to develop models of the frequency changes. Such models
can reveal characteristics of memes that are difficult to observe
directly. They also make detailed, testable predictions.
A model that influenced the competing memes
analysis, proposed here, is the Lotka-Volterra model of species
competition (see Appendix B). This model
describes the population of species that compete within an ecosystem as
depending on four parameters: the initial population, the maximum
sustainable population, the growth rate, and the competitive strength.
Given these parameter values, at least four life cycles of memes can be
identified (see Appendix A). Rapid growth
rate is high enough to create chaotic fluctuations in a noncompetitive
environment. Data analysis modeled in Figure 1 is
an example. High competitive strength reduces the rapid growth of
competitors to moderate levels or less.
|
Life cycles |
Parameter values |
||
|
Initial prevalence |
Growth rate |
Competitive strength |
|
|
Default |
High |
Near zero |
Near zero |
|
Niche |
Low |
Slow |
High |
|
Pioneering |
Low |
Rapid |
Low |
|
Dominant |
Low |
Moderate |
High |
Table 1. Lotka-Volterra values for four memetic
life cycles.
Once such vital statistics of a group of memes have been worked out,
scientifically significant and socially valuable experiments can be
conducted to determine the factors that influence the long-term
vitality of particular memes. For example, revisiting the species
analogy, we note that pioneering species are often those that adapt to
the presence of many predators by growing very rapidly. If such species
find an environment where the predators are absent, their populations
can grow so rapidly that they consume too much for the environment to
sustain them. The formerly adaptive high growth results in a local
extinction. It takes little imagination to realize what powerful social
consequences would occur when the analogous process operates
mimetically. Pioneering growth may be as essential to responding to
social threats or establishing new products as it is to establish
species in predatory environments. But planners need to be aware of the
social or economic danger that these new responses or products pose if
their memetic competitors were to suddenly vanish.
In conclusion, Competing Memes Analysis provides memetics with a method for conducting precise studies of memetic processes found in any human activity. It can be summarized as a 3 step process. Step 1identifies the organization of memes within an activity. Each activity is assumed to exhibit numerous small groups of memes where each meme within a group competes with all other memes in the group and can be combined with any meme from any other group. The succession of memes that occurs with increasing experience can be a powerful clue to identifying competing groups. Step 2
collects records of activities and codes them for the presence or
absence of each meme identified in Step 1. Step 3 analyzes
changing frequencies of each coded meme over time or space. Models of
these changes can give useful clues to suggest empirical studies that
will provide important social and scientific results.
Appendix A: Memetic system for the
historical development of developmental research
|
DIMENSIONS |
Life cycles (see Table 1 for explanation of life cycles) |
|||
|
Default |
Niche |
Pioneering |
Dominant |
|
|
DEPENDENT VARIABLES |
Limited |
Summed |
|
Categorized |
|
DATA ANALYSIS |
|
Descriptive
Statistics like counts, means or correlations |
Difference |
|
|
DESIGNS |
One Session |
Micro-longitudinal |
Longitudinal |
|
|
AGE |
Single Age Group |
Dependent
Variable Using age as a measure of how long it takes to develop |
|
Multiple Age |
|
SOCIAL CONTEXT |
|
Significant Other Alone or With Test |
Test Alone or |
|
|
LOCATION |
Unspecified |
School, Home, Other, Lab, or Multiple | |
|
|
BACKGROUND |
|
|
Interdisciplinary |
Disciplinary |
|
APPLICATIONS |
Researchers |
|
|
Others |
Appendix B: Lotka-Volterra competition
Lotka (1925) and Volterra (1926) independently formulated
competition between species in an ecosystem. The basic idea is an
expansion of Verhulst’s logistic law of a century earlier, which in
turn built on Malthus’ concept of exponential growth, where the
population at a particular moment in time equals the population at the
preceding moment multiplied times one plus the growth rate.
Populations do not grow beyond the available resources, but rather
either stabilize at an equilibrium that depends on the resources or
collapse after exhausting the resources. Letting k denote the
equilibrium population, Verhulst corrected Malthus’ notion by reducing
the growth rate as the population approached the equilibrium.
When the population x equals the equilibrium population k,
the growth rate will be 0. The result for slow or moderate growth rates
is the well known S-curve that reaches an asymptote at the equilibrium.
Also, this is the equation that produces wild fluctuations with very
high growth rates. Its age, simplicity and practical usefulness have
made it a favorite of chaos theorists.
Lotka and Volterra further refined Verhulst’s equation by reasoning
that a competing species would further reduce the growth rate. They
argued that the effect of each competing species would depend on its
population, y, multiplied by a characteristic competitive
strength factor, c. Of course, each competing species would
reduce the population of the target species.
Since, the model is commonly applied to discreet breeding cycles, it
is easy to develop a spreadsheet program where the populations at a
particular point of time are found in a single row and are used to
calculate the populations in the successive moment in the following
row. Further details of applying the model to developmental and
historical data can be found in Dirlam, Gamble, and Lloyd (1999).
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Acknowledgments
Most of the integrating work for this paper was accomplished in
1997-1998 while I was a James McKeen Cattell Fellow at the Laboratory
of Comparative Human Cognition of the University of California San
Diego. I would like to thank Michael Cole for comments on earlier
versions of this manuscript and for his extensive advice and counsel
over the last seven years of this project. Kurt Danziger, James
Moffett, and Jerry Balzano also provided important theoretical insights
on the road to refining Competing Memes Analysis. The readability and
usefulness of this article was greatly improved by the careful
editorial commentary of Martin De Jong. Paul Marsden also offered
numerous very useful suggestions. Especially, the treatment of
pioneering growth was more balanced as a result of his involvement.