research and development

Introduction

There is a growing urgency among sex researchers to bridge the overwhelming gap between two dominant approaches to understanding sexuality – the positivist, empirical approach and the social constructionist approach (Bancroft, 2000). While on the one hand, it is undeniable that such fundamental biological processes as genetics, hormones, and biological development play important roles in the expression of sexuality at every level, it is also undeniable, on the other hand, that a culture’s discourse around sexuality also influences sexual expression in ways that are not necessarily amenable to quantifiable, empirical study. Although it is generally acknowledged that there is some kind of interaction between individual biology and culture, these two strains of research have not yet found a shared language which mutually satisfies the two camps, and no methodologically useful way of modeling this interaction has been widely accepted among sex researchers. If we hope to understand sexuality in all its complexity, we must account for both sides of the story. The positivists must acknowledge as legitimate and incorporate into their research the important cultural factors that influence sexuality; in parallel, the social constructionists must acknowledge that basic genetic and biological processes affect cultural expressions of gender and sex.

Sex research needs simplified models of sexuality that are valuable insofar as they help create positive change (Bancroft, 2000). Dynamical systems theory may be a possible solution. Already utilized in fields as diverse as developmental psychology (cf. Thelen and Smith, 1994), artificial intelligence and philosophy of mind (Clark, 1998), meteorology (Lorenz 1963), anthropology (Bateson, 1980), and curriculum development (Doll, 1993), dynamical systems theory (DST) differs from both the traditional medical approach and the critical social constructionist approach in its theoretical assumptions and, more significantly, in its methodological approach to studying its subject. Dynamical systems theory may provide a practical, coherent way to model sexual development, identity, and culture in a way that accounts for the complex interactions between individual biology and psychology with social forces, accounting for both individual variability as well as social-level characteristics.

Dynamical systems theory has been used with increasing frequency in social psychology and related fields, though its original use was in physical sciences (Lewin, 1943). Today its dominant use in the social sciences is in the realm of cognitive science and developmental psychology, where it is used to explain motor development, cognition, and perception (cf. Thelen and Smith, 1994). There is also a growing agenda in social psychology to model social interaction using dynamic models (cf. Nowak and Vallacher, 1998).

Two lines of research in sexuality currently use dynamical systems theory. First, Rodgers (2000) is using an epidemiology-inspired non-linear model in studying sexual debut and pregnancy in adolescents. Second, an emerging application of the theory is in the development of gender identity – a research agenda newly adopted by Fausto-Sterling (2003), examining the development of gender in infants to one year olds. This promises to be a highly useful agenda which will establish a precedent in the fields of sex and gender research.
In this paper, I discuss the theoretical gap left between the positivists and the social constructionists. I then offer initial and tentative ideas about a dynamical systems framework which has the potential to bridge the gap. I give a basic overview of dynamical systems theory, including the goals of a dynamical systems research agenda, and essential constructs and principles that govern the structure of dynamical systems. I then offer historical and philosophical justification for applying the theory to health behavior, followed by three possible strains of sex research which might usefully apply dynamical systems. Next, I describe some potential shortcomings of DST. Finally, I discuss the research and practical implications that dynamical systems may have on the field of sex research. I rely heavily on the work of Bancroft – a psychiatrist by training – (2000) and colleagues, Clark – a philosopher of mind – (1998), Nowak and Vallacher – social psychologists – (1998; 1994), and Thelen and Smith – developmental psychologists – (1994, 2000). The interdisciplinarity of my sources illustrates the complexity and adaptability of dynamical models.

Dominant Theories in Sex Research
In this section, I will review the two dominant camps in current sex research, here referred to as positivism and social constructionism. For each, I will give a brief overview of the main characteristics of the approach, followed by a summary of their strengths and their weaknesses as epistemologies of sexuality. I will conclude with a brief overview of the problems each has had in past sex research and a discussion about what issues researchers might face without an adequate solution to the problems presented by each – in short, the need for an interactionist account that is simple, plausible, and yet falsifiable.

Positivism
Positivism is a dominant camp which relies on observation, induction, and experimentation to test or refute hypotheses based on the outcome of experimentation (Glaz, Rimer, and Lewis, 2002). As the most common and historically most relied on scientific approach, the positivist approach forms the foundation for the vast majority of modern science – indeed modern science is defined by its reliance on empiricism to deduce truth. Positivism is good at investigation, at asking answerable questions and offering methods for answering them that appear objective. Methods used by positivists include what we commonly understand as the “scientific method” – proposing a theory and then testing it empirically. Any question not answerable by these methods is meaningless for the positivists. Where it falls short is in its assumption that measurement is an accurate reflection of the truth and in its emphasis on the individual, rather than the social or cultural. Fields typified by the positivist approach include biology, chemistry, medicine, and much of psychology.

Social Constructionism
Bancroft (2000) refers to this camp as “post-modern,” (336) a fitting conceptualization of this theoretical perspective borne from the work of postmodern philosophers like Foucault (1972) and Derrida (1981). Social constructionism relies on the process of discovery as the source of knowledge (Glanz, Rimer, and Lewis, 2002). An essential element of the epistemology of social constructionism is the inability to separate the perspective of the observer (or researcher) from that which he is observing. Methods used in social constructionist research include qualitative analysis of in-depth interviews, ethnography, and critical readings of scientific and cultural work. Reduction or decontextualization of these data renders them meaningless for social constructionists. In terms of its shortcomings, as Richard Parker put it so succinctly, “If one takes a social constructionist position, how does one account for the possibility that individuals make choices?” (Bancroft, 2000 p. 314). Social constructionism is the theory of the social, and at its logical end it denies the agency of the individual to make decisions independent of the social discourse. Without some breakdown of reality into smaller chunks, researchers find themselves equipped with a one-to-one map of reality, an unmanageable account which describes rather than predicts. Fields typified by the social constructionist approach include anthropology, some sociology, and educational ethnography. The humanities, such as comparative literature and history, also often take a social constructionist stance in their critiques of culture, politics, and science.

The Need for an Interactionist Account
It should be noted that both camps generally acknowledge that each has a claim to some of the truth and hardly any researcher would deny that a great deal of interaction takes place, but neither paradigm is equipped to manage the questions posed by the other, and neither can account fully for the data already at hand. As a single example, a study of men in Scotland and in China found at their responses to injections of androgens did not differ on the biological measurement – namely nocturnal penile tumescence – but did differ on the psychological assessment of mood and level of sexual interest (Bancroft, 2000). If all human biology is essentially the same, how can a positivist, biomedical account of the data explain the more subjective response of the two groups? And if sexuality is cultural, how can the social constructivist account adequately explain the similarity in biological response, given the difference in psychological response?

Given the shortcomings of both paradigms, what must an interactionist account do? It must create a uniform language which social constructivists and logical empiricists can share, a language which can explain not only the influence of pre-natal hormones on the development of sexual orientation in American culture, but also the influence of American culture on the sexual identity of Navaho people living on reservations and those living in cities or rural areas. That is a tall order. To finish the quote from Richard Parker:
I see it in some ways as the social constructionist’s dilemma: If one takes a social constructionist position, how does one account for the possibility that individuals make choices? It’s because they operate in a field that does give them options, so we really have to pay a good deal of attention to that.” (Bancroft, 2000 p. 314)

An interactionist theory must model the field in which the individual has choice, but not infinite choice. It must represent the constraints on individual choice, and it must represent individual variability to account for different people in the same situation making different choices. It must represent relationships – between an individual and her culture, between more than one culture, between an individual and his own biology – in a systematic, concrete, and falsifiable way, to satisfy the empiricists. Yet it must not reduce or decontextualize data and human behavior, in order to satisfy the social constructionists. Given the complexity of the real world, how can a researcher manage these interactions theoretically in order to make them accessible to investigation?

What is Dynamical Systems Theory?
Dynamical systems theory is not a theory about human behavior or any other single type of complex system: it is a theory about systems generally, which can be applied to any system whose behavior is determined by interactions among difference and differential equations. It is “an area of mathematics used to describe the behavior of complex systems by employing differential and difference equations” (Eliasmith, 2003). As such, DST enthusiasts sometimes present the theory as an overarching metatheory of everything –for example, one biologist who applies dynamical systems theory to the global carbon cycle describes his research interest as “he role of life in the Earth system” (Volk, 2003). DST critics make the same claim and point to it as a failing in the theory – it accounts for too much. Like evolution, it is difficult to disprove and may therefore lose its usefulness insofar as it is virtually unfalsifiable. I would posit that there is some potential ground for both claims. With applications as diverse as physics, developmental psychology, and curriculum design, it is hard to argue that any aspect of scientific endeavor about the natural world – of which humans are a part – cannot be modeled effectively in a dynamical approach. Hence, sexual researchers may establish that sexual behavior at any particular level may be modeled with difference or differential equations, just as evolution, population and predator-prey ratios, and infants learning to walk may be modeled thus. Dynamical systems is about interaction, and sex research is currently torn in its need for a coherent account of interaction.

What, if any, aspect of sexuality, is a dynamical system? Before I answer, I want to distinguish between real versus mathematical dynamical systems. A “real” dynamical system is anything that exists in space which changes over time (Guinti, 1995). Given that definition, all aspects of sexuality are dynamic systems, from sexual dimorphism to the social construction of sexuality. A mathematical dynamical system is a mathematical representation of the change process a real system undergoes (Guinti, 1995). The model, unlike the real system, is constant, representing the change, rather than practicing change. The model consists of time, the set of all possible states in which the system might, at any given time exist, and “a set of functions… which tell us the state of the system at any instant…” (Guinti, 1995 p. 551). This distinction begs the question: what disciplines of sexuality (given that they are dynamical systems), if any, can be represented mathematically this way?

At least two research agendae are already using dynamical systems. Rodgers (2000), Rodgers and Rowe (1993), and Rodgers and Buster (1998) applied an epidemiological mathematical model to sociosexual behavior, a model called Epidemic Models of the Onset of Social Activities (EMOSA). While some express concern about the language of the model, which relies on epidemiological terminology of infection and contagions, the mathematical model powerfully account for sexual onset and risk for pregnancy (Rodgers and Buster, 1998). Use of non-linear dynamic modeling in epidemiology is traditional, and this application of the model to social, rather than biological, functioning takes a step toward representing humans at the behavioral and social level. The other research agenda is the exploration of the development of gender in infants (Fausto-Sterling, 2003). This research is in its preliminary stages and has produced no data yet. But I will propose here that DST can be used to model sexuality at every level, from the genetic and hormonal to inter-culture interactions.
In order to explore the extent of the possibility of using this system in the interdisciplinary study of sexuality, in this section I will identify six major goals of dynamical systems it relates to human development and behavior. Next, I will define several essential concepts which are important to the theory. Then I will discuss how these constructs are operationalized within a research agenda, in order to build and test a model.

Goals of DST Modeling
Thelen and Smith (1994) describe 6 majors goals of a dynamic theoretical ground for human development, which are equally relevant to sexuality and provide a good starting point for discussing what DST can do for sex research that we have not yet been able to do with other conceptual frameworks. These 6 goals are:
1. To understand the origins of novelty
2. To reconcile global regularities with local variability, complexity, and context-specificity.
3. To integrate developmental data at many levels of explanation.
4. To provide a biologically plausible yet non-reductionist account of the development of behavior.
5. To understand how local processes lead to global outcomes.
6. To establish a theoretical basis for generating and interpreting empirical research. (Thelen and Smith, 19994, p. xviii)

“Novelty” is the emergence of a new behavior. In the context of human development, this includes things like learning to walk or produce language. In sexuality, novel behavior may be sexual debut, infection with HIV, first homosexual behavior, or starting contraceptive use.

Global regularities are those aspects of human behavior that seem universal – all people learn to walk and talk. In the context of sexuality, quite simply, all living people are have genitals and nervous systems. Local variability is a reference to individual differences inside those global regularities – people learn to speak different languages depending on their environment. In sexuality, many people are attracted to people of the opposite sex, some to people of the same sex, and others to both.

Integrating developmental data at many levels of explanation refers to the experimental and theoretical process of modeling and explaining the available data about human development. I would transfer this goal to sexuality and set the goal of modeling and explaining the available data about human sexuality – for example the difference between male and female sexual responsiveness. This is a complex problem which no model has yet resolved. Dynamical systems theory may be able to clarify it.

Potentially the most important goal of DST applied to human development, establishing a biologically plausible yet non-reductionist explanation for human behavior is key to wedding the positivists to the social constructionists in a single language which satisfies both. The biological plausibility establishes a basic, undeniable connection with the physical world. The non-reductionistic nature of the explanation avoids the social constructionist critique of positivism that it bases generalizations about humans on deprived stimuli, overly deterministic, heuristically questionable theoretical approaches and conclusions.

To understand how local processes lead to global outcomes in sex research may take many forms, from the emergence of a homosexual identity from a particular combination of culture with pre-natal hormone levels to the emergence of dominant sexual groups with minority groups oppressed and subjugated. Dynamical systems is equipped readily to cope with the biological processes involved in sexuality. It will need a great deal of careful operationalization for the more social aspects, as we will see.

Key Concepts
Systems are components interacting in an organized way (Nowak and Vallacher, 1998). Closed systems consist of components interacting in the absence of external influence. Open systems consist of components interacting both internally (see “intrinsic dynamics” below) as well as in response to the “suprasystem,” or to external influences. In the context of sexuality, researchers may analyze many different levels of analysis, from genetics to cultures. Both can be conceptualized (and, I will argue, operationalized) as systems.

Dynamics refers to the nature of change (Nowak and Vallacher, 1998). Sexual science is largely concerned with change processes – from the process of sexual anatomical differentiation in vitro to the mutual interaction and influence among cultures. Dynamic systems in this sense are characterized by several qualities, including intrinsic dynamics, emergence, and equifinality, which will be elaborated below. These characteristics form a system which is deterministic but not predictive, a characteristic which may be problematic in the social sciences, as discussed later in this paper.

Phase Space or State Space is “a visual means of representing the state of a particular system; phase-space being the entire range of values that are possible within a particular system” (Puddifoot, 2000 p. 85). A phase space is a picture or mathematical representation of the system. It is the assessment of phase spaces that methodologically differentiates a dynamical systems approach to social science from the standard statistical approaches.

Attractors are phase spaces toward which a dynamical system will tend (Puddifoot, 2000; Clark, 1998). They can be understood as two general types – “classical” attractors, drawn directly from mechanical models, and “strange” attractors, which are characterized by a systems tendency toward a state (or “basin of attraction” [Clark, 1998 p. 100]) which it never quite reaches, but rather circles around.

Trajectories are the paths possible through the state space, which are determined by the interaction of the systems components and the attractors of the field (Clark 1998).

Degrees of Freedom are the scope of the states possible within the phase space (Clark, 1998). These are not infinite, but rather constrained by the real parameters that govern the system and the subsequent dynamics of the components’ interactions (interdeterminacy) (Thelen and Smith, 1994). Degrees of freedom is a particularly important concept in the modeling of a dynamic system in order to make it represent the real system, rather than acting merely as a conceptual framework or heuristic.

Bifurcations are phase transitions, where the system shifts to a different trajectory when the control parameters cross a threshold (Thelen and Smith, 1994). If we modeled human sexual response dynamically, we would likely find that orgasm represents a bifurcation in the dynamic system of sexual arousal, where the system behaves in a qualitatively different way when neuromuscular tension associated with arousal crosses a threshold.

Intrinsic Dynamics – This refers to changes that occur within the system in the absence of external influence (Clark, 1998). A key characteristic of intrinsic dynamics in complex systems is that from minute alterations in the parameters of the systems can arise large-scale changes (Doll, 1993). The classic expression of this is the so-called “butterfly effect,” postulated by Lorenz (196x), that a butterfly flapping its wings in Mexico may lead to a tornado in Texas.

Periodicity refers to the cyclic structure of some systems (CITE). Periodicity is observable in many human behaviors such as the sleep-wake cycle, hormonal fluctuations in women across their menstrual cycles, and, arguably, in sexual response.

Equifinality is the capacity for systems to arrive at the same end via different means (Fausto-Sterling, 2003). This is a key characteristic of complex systems because it is their robustness in the face of perturbation which illustrates their internal structure(Thelen and Smith, 1994). Philosophically, it presents issues which social constructionists may find problematic, as I will discuss later.

Perturbation is interference with the functioning of the system from a source external to the system.
At times of transition, when attractors are not strongly coherent, small changes in the organism, the task, or the environment can lead to profound reorganizations…. ven relatively minor and sometimes seemingly unconnected manipulations have a major impact on behavior. Before the transition, and after the behavior is well-established, these same factors do not disrupt ongoing performance.” (Thelen and Smith, 1994, p. 87)
Perturbation is a key tool in developing and testing a dynamic model because it allows the researcher to set the initial state (perturb the system) and then watch it run (internal dynamics) (Thelen and Smith, 1994).

Emergence, also known as self-organization, is a pattern of behavior that both arises from and influences the internal functioning from the system (Clark, 1998). To put it another way, according to Nowak (1998):
Rather than being imposed on the system from above or from outside the system altogether, the higher order structures emerge from the internal workings of the system itself. In this process, the system loses degrees of freedom, and the state of the system may be described by a small number of variables. Ironically, then, complex systems can sometimes be described by fewer variables than can relatively simple systems. (53)

Emergence, then, is one primary principle of dynamical systems. It is also a primary differentiating factor from conventional health behavior theories. Dominant theories such as Health Belief Model and Transtheoretical Model are componential, describing behavior as arising from controlled variables that are immediately available within the theoretical framework for “twiddling” (Clark 1998, p. 99) in order to change the structure of the behavior. Emergent properties of a system cannot be altered directly; rather one must change an element within the system and determine if that change results in the change in emergence one sought. However, dynamic systems as a rule have two characteristics which make that twiddling difficult. First, systems tend toward equilibrium, or balance, and some systems are extremely robust and will tolerate a great deal of alteration before a change happens. Second, as mentioned above, minute changes in parameters can give rise to large changes. The metaphor for this characteristic is Lorenz’s butterfly metaphor: if a butterfly flaps its wings in Brazil, it may eventually give rise to a hurricane in Texas (Doll, 1993).

There are a few additional characteristics of dynamic systems worth noting: chaotic systems are deterministic, but non-predictive. These systems are orderly in their disorder – that is to say, although we could map all the potential positions of the system, and we can follow its trajectory, we cannot predict with certainty the state of the system at any given time.

Thus, a “dynamical system” is an organization of parts, wherein all elements behave according to fundamental rules, from which cooperation emerges self-organized behavior. By formulating simple rules followed by multiple variables within a system, dynamical systems accounts for emergent system-level properties that do not exist in any single element of the system.

Modeling a System
These constructs, by themselves, provide a useful metaphor for describing human social systems – one can imagine organizations which function according to these principles and assign certain characteristics as “emergent properties” of those systems. However, a greater potential power for DST, and its greatest distinction for other interactionist models and from social constructionist uses of dynamical inspired metaphor, is the actual process of modeling systems in order to quantify them. DST is not mere metaphor; it is not simply a framework in which hang descriptions of interactions. It can be used as a concrete, specific formulation for studying, understanding, and potentially changing systems in more complex ways than social science currently does.

The process of generating a model of a system is relatively straightforward on the surface. In order to generate a model of a system, there are essentially four steps, per Thelen and Smith, 1997):

1. Identify the elements which change within the system – the statistical model’s equivalent would be the dependent variables.
2. Hypothesize principles which govern the interaction of the elements
3. Express these principles as difference or differential equations
4. Define “plausible parameters” for the system (Thelen and Smith, 1997, p. 578)

Once the equations have been fit with parameters, the equations generate a trajectory within the phase space, which the modeler can compare with preexisting data and with empirical evidence about the behavior being modeled. If the trajectory matches the data, then the model presumably matches the real system informing the observed behavior. If the trajectory does not match the data, the modeler may alter the parameters until she finds a match (Thelen and Smith, 1997). The practice of establishing the state of a field at a given time follows Lewin’s foundational work (1943).

However, a characteristic of complex, dynamic systems is the emergence of large changes due to minute characteristics of the initial parameters. The metaphor for this characteristic is Lorenz’s butterfly metaphor: if a butterfly flaps its wings in Brazil, it may eventually give rise to a hurricane in Texas (Doll, 1993). This becomes problematic in the measurement of complex systems because the assessment of what elements are significant in the system, what types of interaction they have, and what mathematical model best represents those interactions must be extremely precise. It also becomes the primary mode of testing the model, by way of perturbing the system in order to generate a precisely understood initial state; from that initial state, the researcher lets the system run and notes its behavior. She then gives the system a different initial state and lets it run, noting if the system is stable – i.e., it returns to a given state – or unstable – i.e., returns to a different state under different conditions (Thelen and Smith, 1994).

The output of these models differs substantially from that of traditional models. Rather than generating statistical models, which predict future behavior of an individual or groups based on statistical representation of a population samples’ behavior, these models create a multidimensional phase space which defines all the possible states of the system at any given time – the state space. Its validation lies in the ability of the system to reproduce reality, rather than in its “validity,” as measured statistically. Particularly in modeling motor or biological processes, the model is essentially a direct representation of the system. In this, DST can be remarkably powerful. However, this becomes problematic, as I discuss below, in terms of the operationalization of complex social-level behaviors.

Historical and Philosophical Justification for Applying DST to Sexuality
How do these goals, concepts, and methods for modeling systems help resolve the problems related to both positivism and social constructionism? To review, the primary problem with these two approaches was their failure to bridge both biological, essentialist elements of sexuality to social, cultural, and interpersonal elements of sexuality. In this section of the paper, I put forward two propositions:
o I propose that dynamical systems is a historically and philosophically justified step in the progression of sexual science, incorporating the scientific roots of postmodernism into the social sciences.
o I propose further that a dynamical systems resolves this problem by modeling both the biological and the social in one uniform, unifying language that is appropriate for both.

Dynamical systems is a historically and philosophically justified.
To begin with, there is no logical reason to suppose that human beings follow different rules than any other element in the universe. The separation of mind from body – that is human being’s minds from the environment – dates at least to Descartes (1641/1996), who fundamentally divided human existence into these two categories and impacted the structure of studying humans. This philosophy of dualism has dominated positivist, logical empiricist science for the last 400 years. More recent work in anthropology and philosophy advocates reuniting mind and body, conceptually acknowledging the interrelatedness and complexity of individual human beings interacting with each other and their environment (e.g., Bateson, 1980; Clark, 1995). This shift represents a fundamental change in epistemology, from rationalist epistemology to “recursive” or “ecological” epistemology (Bateson, 1980), which unites the thinker to the subject of his thoughts, each reciprocally affecting the other. Following Heidegger and his “Dasein” (“being there”) (1927/1962), this different epistemology acknowledges that mind and identity cannot be separated in any meaningful way from body, environment, and experience. Thus our study of humans must be informed more complexly by the nature of a human’s interaction with the world, the structure and meaning of “mind” or “consciousness,” and how body and mind mediate as a unit the individual’s navigation through the world.

The historical antecedents of dynamical systems reach beyond the turn of the twentieth century. Lewin’s 1935 exploration of field theory in social science established the foundation for dynamical systems research. Noted by Thelen and Smith (1994) as a prime innovator in the field of dynamics applied to psychology, Kurt Lewin interpreted psychological research in the first half of the twentieth century in terms of field theory, then most commonly used in philosophy and physics (cf., Lewin 1940).

Given these historical and philosophical roots, dynamical systems represents an alternative epistemology, based not on the dualism represented by the dichotomy between the positivists and the social constructivists, but on an integration of body, mind, and world. In the very equations which define dynamic interactions, environmental factors and organismic factors interact interdependently, bringing a new and potentially powerful method for modeling behavior of systems, human and otherwise, biological and social, microscopic and macroscopic.

Dynamical systems models the biological and the social in one uniform, unifying language.
Dynamical systems consist of multiples components interacting in an organized way which gives rise to emergent phenomena. To explain emergent phenomena requires an approach that has two characteristics:
1. “[It must be] well suited to modeling both organismic and environmental parameters” (Clark, 1998, p. 113). Emergence happens from the interaction of multiple layers of organism with environment and mind. Due to the multidimensional nature of sexuality, the individual responding to and interacting with his environment, our field requires a theory that can account both for variations within the organic creature, as well as the social and ecological events which surround him. Thus this rule reflects what is necessary to describe emergence and what is necessary to describe sexual phenomena.
2. “[It must] model them both in a uniform vocabulary” (Clark, 1998, p 113). Under the essential principle of parsimony, a single language to describe these excruciatingly complex behaviors brings much needed clarity to a theory which might otherwise marry multiple theories together in a mangled approximation of complete explanation.

DST offers these two primary principles (Clark, 1998) which are potentially extremely useful in understanding sex. The positivists and the social constructions have critically responded to each other, but their language has been completely different, based on their different epistemological approaches to the subject of sexuality. While the positivists have couched their research in dualistic, reductionist, empirical terms, social constructionists have discussed sexuality in terms of dominant discourses, social processes, and post-modern conceptualizations of meaning embedded in culture.

A dynamical systems approach is not necessarily an absolute departure from more traditional positivist theoretical bases, nor from typical constructionist perspectives. I propose that an emergent, dynamical explanation provides a likely match with measurable phenomena in the realm of sexual behavior. Unlike componential explanations, dynamical systems, as an emergence-oriented theory, can provide a manageable yet rich representation of judgment, choice, action, and belief precisely because it acknowledges and accounts for complexity, the usual purview of the social constructionists. Indeed, it may provide the most powerful theoretical background for determining an individual’s state space within a given system, and thus may suggest the most effective mode of intervention to create positive change in that person’s health state.
Dynamical systems weds the two by accounting for essentialist functions like biology, anatomy, and physiology, and their interactions with environmental forces such as social processes and the discourses which surround the organism. It involves both in a single, functional language. A more complete account of precisely how this language works is outside the scope of this paper, but further work in the mathematical details of dynamical systems modeling will elucidate the exact nature of how this method captures the interaction of both (Clark, 1998; Thelen and Smith, 1994; Thelen and Smith, 1997).

Possibilities and Precedents for Using DST
So far I have used the term “sex research” unclearly. My reason for that is the basic lack of clarity of the term, in the sense that sex research is dizzyingly interdisciplinary. While Fausto-Sterling (2003) is developing a gender development research program based in close parallel with the motor development research of Thelen and Smith (1994), social psychologists around the world are building research on the possibility of modeling human social interactions dynamically (Nowak and Vallacher, 1998). This is a young, immature, unstable field with many questions remaining unanswered, but the metaphors are powerful and persuasive; and the logical consistency of identical models for both social and biological behavior is strong (see above). Thus in this section I argue for the intriguing and appealing possibility that this single framework can be used in many different kinds of sex research. For the sake of simplicity, I will present here two possible areas of research. The first is the agenda already taken on by Fausto-Sterling, creating a dynamical systems model of gender development in infants to one year olds. The second is an adaptation of the Kinsey Institute’s Dual Control model of sexual response to a dynamical systems framework. For each, I will examine the possibilities of dynamical systems, in the context of social psychological and biological research that sets precedents for similar research agendas.
Fausto-Sterling (2003) has begun the initial phases of establishing the phase space for the development of gender in infants. Her inspiration is the developmental work of Thelen and Smith (1994), whose dynamical approach to motor development in infants has established precedents for applying dynamical systems to developmental psychology. Fausto-Sterling’s work is extremely new and has not yet generated independent data, but the current status of the work is the massive assessment of gender development data, finding what we know definitely, what findings need replication, and where we have utter gaps in our knowledge. By framing gender development as a dynamical system, Fausto-Sterling provides a practical, suitably complex system for examining what has been a key area of dispute between the positivists (who typically argue for an essentialist origin of gender) and the social constructionists (who typically argue for a socially constructed origin of gender). Because dynamical systems includes in its functional elements both relevant aspects of the organism and relevant aspects of the environment, it provides a bridge for calculating the nature of the coupling of multiple layers of interaction.

Adapting the Dual Control Model of sexual response, developed at the Kinsey Institute (Bancroft and Janssen, 2000) poses a possible application of DST immediately available in sex research. The dual control model, in brief, proposes that a mechanism in the central nervous system has inhibitory and excitatory capability, and that individuals vary in their “excitability” and “inhibition.” The inhibitory process is divided theoretically into two processes, one based on fear of performance failure and another based on fear of consequences from the sexual arousal. People who are high on the first are prone to sexual dysfunction, and people who are low on the second are prone to sexual risk-taking. Currently the model is assessed with a survey, on which participants receive a score for each of the three variables. This is useful for assessing an individual’s proneness to risk-taking (which can lead to negative health consequences) or to sexual dysfunction (which can lead to negative emotional situations, relationship issues, and inability to reproduce).

To adapt this theory to a dynamical model, we would probably need three equations, one for each function within the central nervous system, interacting dynamically over time. In order to build a dynamic model, we must identify the elements which function within the system, hypothesize the principles which govern the interaction of the elements, express these principles as difference or differential equations, and then define plausible parameters for the system. The elements which function within the system are the body and mind of the individual, plus any external stimulus involved in his arousal. The principles which govern the interaction of those elements, per Bancroft and Janssen (2000) are the central excitation and inhibition of arousal. To express these functions as difference or differential equations is outside the scope of this paper, but I would suggest a model similar to that found in Thelen, Schoner, Scheier, and Smith (2000), which coupled a motor field with a perceptual field and relied on the nature of the coupling between these two. Reasonable parameters for the fields would be defined by the legitimate boundaries of human capacity for excitation and inhibition (i.e., it is not plausible for a possible parameter to allow for continuous, perpetual orgasm). With a hypothesized mathematical system, with three equations simultaneously solving for the same variable over time, we can collect data from individuals who go through a process of sexual arousal and perturb the system in ways that explore what attractors may alter the functioning of the system at any possible state in the phase space.

Problems with DST
There seems little argument with the claim that sexuality is dynamic, involving mutually influencing systems, both organic and social (if one can legitimately distinguish social systems as “not organic”), from which arise behaviors which are, in theory, at least broadly predictable. Operationalizing this conception in terms of dynamical systems theory, however, is far from simple. Neither the positivists nor the social constructionists would feel fully comfortable with dynamical systems, and it is important to delineate where DST falls short, from each of those perspectives.

DST and the Positivists
Critiques of dynamical systems theory from the positivist perspective generally take three forms. First, there is the possibility that some systems cannot generate predictions, which flies in the face of positivism. Second, there is the difficulty in operationalizing complex concepts like “social process.” And third, there are mathematical hurdles to be crossed in transferring knowledge from a statistical standard to a dynamical systems model.

Positivism asserts not only that reality can be tested and measured, but that from those measurements we can derive predictions. Dynamical modeling, although it certainly assumes that mathematical representation can accurately illustrate a system, generates non-predictive models in extremely complex systems (like the weather) (Doll, 1993). I would respond to this criticism by pointing out that if a system is genuinely chaotic, it is a Sisyphusian endeavor for researchers to attempt to formulate predictive models. Better we should know what is unpredictable than never admit that the system has, indeed, beaten our best science.

Another criticism from the positivists generates from the difficulty in operationalizing the elements of the system. Puddifoot (2000) focuses on the difficulty of operationalizing the concept of “social process,” a fundamental problem with which any dynamic model of a social system much content. This critique is a good example of the painstaking care social psychological, sociological, anthropological, and other social science disciplines must take in operationalizing concepts which otherwise would seem well-established within their discipline. Here the social constructionists will be useful in their analytical critiques of the positivists construction of terminology within different experimental frameworks.
A positivist critique of the EMOSA (Stoolmiller, 1998) focused on the statistical aspects of the model.

DST and the Social Constructionists
Dynamic modeling is fundamentally more positivist than social constructionist insofar as it (1) posits the possibility of empirically validating a pre-constructed theory and (2) assumes it can accurately represent mathematically a system which exists in space and time. Yet it matches the social constructionist agenda closer than purely statistical representations of a theory because it is explicitly a mathematical representation of change, of relationship, and it is not necessarily predictive (however deterministic) in extremely complex systems.

Another aspect which matches it more with social constructionist work is its non-impoverishment of data. Dynamical systems want to represent human behavior at every level in all its complexity, without reducing it to the interaction among heuristics; dynamical interaction is interaction among real concepts in space and time, not hypothetical constructs.
Social constructionist criticism of EMOSA (see Bancroft, 2000) focuses not on the methods but on the labels and language used in the operationalization of the concepts. Presented at a conference on the role of theory in sex research (Rodgers, 2000), many critiques of this work focused on the language used (“virgins and nonvirgins” rather than “intercourse experienced or inexperienced” or “sexual debut”, etc) and the utility of measuring a binary such as pre- and post-first intercourse (Bancroft, 2000 pp. 273-8). I believe these comments missed the primary innovation of this work, which is an initial attempt to apply a mathematical model that is widely used in many other fields to an area of sex research which has not only otherwise eluded quantification, but also been stuffed into predictive, regressive statistical models which are theoretically inappropriate to the behavior, as I will discuss below.

DST, Causation, and Prediction
The structure of complex systems does not allow for basic causal theories of the behavior of that system. Because large changes may emerge from miniscule characteristics of the start-state of a system, and because interactions over time interact complexly, it is impossible to point to a single factor or even a group of factors which are necessarily causes of any given sexual characteristic or behavior. In a dynamical systems framework, sex researchers can only indicate that a group of factors interacting over time produced a particular state, and that the nature of the coupling of different parameters determines outcome. Also, as discussed in the History and Philosophy section of this paper, DST is not useful for prediction of behavior because highly complex systems are, by their very nature, non-predictive. If we take the weather as an illustrative example, we can determine within a range of time what possible conditions are likely to arise, but we cannot predict with certainty any given outcome. Because a key function of sex research is the prediction of, say, sexual risk taking or the outcome of a therapeutic intervention, we have a fundamental conflict in epistemology if these models turn out to be chaotic. However, we will not know whether or not those systems are chaotic (and thus unpredictable) unless we attempt to model them dynamically.

Conclusion
In this paper, I have attempted to examine dynamical systems theory as a potential new way of interrogating sexuality, from the genetic and hormonal level, to the psychological and cultural level. What I have not done is provided a specific agenda for sex researchers, nor have I delved in depth to mathematical methods of modeling of systems and the philosophical and practical implications of modeling not by standard statistical methods but with difference and differential equations. As a beginning doctoral student, I lack the knowledge to make any claims regarding mathematical modeling of any kind. But this is an area in great need of elaboration in the field of sex research if we are to adopt these methods appropriately. Further elaboration of types of attractor models which might best represent male and female development, sexual response, and identity, and the reciprocal influence of these on social dynamics remain unexplored.

Sexual response is a single aspect of sexuality as it exists in human organisms, and it arises from the complex interplay of chromosomes, hormones, social conditioning, and other factors. In order to understand sexual response per se, we must also understand sex and gender development and the social regulation of sexuality. DST has the potential to assist in these pursuits as well; as discussed, Fausto-Sterling (2003) has begun a research agenda to apply DST to the study of gender development in infants to one-year-olds. It may also be helpful in understanding the emergence of sexual identity: for example, researchers exploring basic hormonal differences in vitro and in infancy that seem correlated with homosexual desires or identity may be able to build a complex, dynamic model of development which illustrates how these minute chemical differences, functioning within the intrinsic dynamics of the system, may give rise to behavioral-level differences. In short, I have no doubt that the addition of a dynamical modeling of sexuality to the already vastly multidisciplinary field of sex research armamentarium will help bring cohesion and clarity to the basic contradictions and complexities in the data.

The theory is problematic in several ways, not the least of which is the unfamiliarity of the mathematical models used to represent the system quantitatively. Few sex researchers are prepared to perform and evaluate nonlinear differential equations; the profession’s emphasis on statistics has brought about a field full of professional and amateur statisticians, not connoisseurs of calculus. Beyond that, it requires minute data on the processes underlying health behavior: in order for the theory to account accurately for health behavior, it must incorporate precise data on the phenomenon it wishes to predict. In this way, dynamical systems demands a quality and quantity of research not yet available in this young field.
Yet the model offers a compellingly complete perspective on sexuality, perhaps the most thorough and comprehensive theory of human behavior. With such potential power, it is difficult to reject the model, despite the obstacles between the current state of health behavior research and the theory’s application in our work. As the field develops and we accumulate a more thorough-going picture of human behavior around health, the theory will grow increasingly explanatory, allowing us to design interventions that account not only for the variables we can measure directly, but also for the mechanisms underlying those variables.

Human behavior’s complexity challenges our best understanding. Social scientists have begun using dynamical systems to present parsimonious yet thorough explanations for emergent complexity in individual behavior and social organization. Sexual health behavior exemplifies the complexity of human behavior: with competing biological, interpersonal, social, and political agendas, the behavior we exhibit is a hybrid, a composite of all these competing factors. A dynamical systems model can potentially represent all these dimensions and the nature of their interaction.


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