The term ‘‘social dynamics’’ is used in a wide variety of contexts that vary in level from the societal to the individual and in approach from qualitative (verbal) to quantitative (mathematical). For example, on the societal level, one can point to Sorokin’s ([1937–1941] 1957) qualitative approach in Social and Cultural Dynamics. At the other extreme, though also at the global level, there are works such as Forrester’s (1971) mathematical, computer-oriented approach in World Dynamics and the statistical, empirical approaches found in Ramirez et al.’s (1997) study of the adoption of women’s suffrage throughout the world and Frank et al.’s (1997) research on the spread and development of a world environmental regime. On the individual level, examples of qualitative approaches include Hareven’s (1982) Family Time and Industrial Time and the relevant chapters in Bertaux and Thompson’s Pathways to Social Class (1997). Also on the individual level, there are mathematical approaches such as White’s Chains of Opportunity (1970) and statistical, empirical approaches such as Zhou et al.’s (1996, 1997) studies of stratification dynamics in China. Studies that combine qualitative and quantitative approaches are rare. A classic example is Elder’s Children of the Great Depression (1974). Because of the great diversity in substance and approach, one cannot identify a single line of cumulative research on social dynamics. Instead, there are distinct, loosely related developments that arise in several contexts.
This article has five main sections. The first describes the three main sociological contexts for studies of social dynamics and summarizes their contributions to cumulative sociological research. Since the term ‘‘social dynamics’’ invariably implies a focus on change over time in a social entity, it is closely related to the term ‘‘social change.’’ Some of the key differences between the two terms are discussed in the second section. The third section summarizes reasons for studies of social dynamics in general and the formulation of dynamic models in particular. The fourth section explains the fundamental differences between dynamic models and other types of models. The fifth section reviews the main variations in the types of dynamic models that sociologists have used.
In one context, social scientists refer to the dynamics of a phenomenon, meaning that they focus on how it changes over time. In this traditional usage, the emphasis is primarily on a substantive social phenomenon, and research progress depends on acquiring a deeper theoretical understanding and expanding empirical knowledge about that phenomenon. Topics vary, for example, from ‘‘group dynamics’’ (social interactions among the members of a small group over time) to the ‘‘dynamics of development’’ (change from a traditional rural society to a modern urban industrial society and then to a postindustrial society that belongs to a global system). It is hard to identify substantive commonalities across disparate topic areas except ones of the most abstract sort, for example, that social change is universal but varies in speed. Despite the limited number of substantive generalizations about social dynamics, the study of social dynamics is theoretically and methodologically helpful for reasons that are summarized in the third section.
In a second context that is more typical in recent work, researchers refer to a dynamic model of a phenomenon, meaning that their goal is to formulate, test, or explore the consequences of a set of mathematical assumptions or a computer algorithm that is intended to mimic the behavior of the phenomenon of interest. For example, researchers may use a model of population growth and decline in a society; a model of foundings, reorganizations, divestments, mergers, and failures in businesses or other organizations; or a model of the diffusion of an innovation through a population (e.g., the adoption of a new social policy by governments or a new contraceptive by women). Despite the substantive diversity, the formal properties of dynamic models of different phenomena are often similar. This similarity has fostered cumulative progress in studies of social dynamics because a model developed for one topic may be transferable to another topic after only minor modifications of its formal properties. For example, the notion that growth rates are ‘‘densitydependent’’ (depend on population size) arose first in dynamic models of population growth, with the main rationale being that a growing population increases competition among members of the system and depletes environmental resources, eventually leading to a lower rate of population growth. Later this notion was applied to explorations of dynamic models of the formation and survival of unions, businesses, and other kinds of organizations and how those processes depend on the structure of competition (Hannan and Carroll 1992; Carroll and Hannan 2000).
In a third context, authors use a dynamic analysis of empirical data on a phenomenon, meaning some form of temporal (longitudinal) analysis of data pertaining to different points in time. Since dynamic analyses are based on dynamic models, work done in the second and third contexts has close parallels. Typically, however, focus on dynamic models implies a greater emphasis on the model itself, whereas a focus on dynamic analyses indicates a greater stress on the problems of estimating and testing the model as well as the resulting substantive empirical findings. Advance made in methods for the dynamic analysis of one social phenomenon often can be used in dynamic analyses of other phenomena. This also has facilitated cumulative progress in research on social dynamics. For example, Tuma et al.’s (1979) proposal for dynamic analysis of event histories, which originally was applied to data on marriage formation and dissolution, subsequently has been applied to dynamic analysis of data on occupational and geographic mobility, organizational mergers and fail ures, changes in political regimes, the adoption of governmental policies, and many other social phenomena.
Social Dynamics Versus Social Change
Although the terms ‘‘social dynamics’’ and ‘‘social change’’ both indicate a focus on change over time, they are used in different circumstances. Social dynamics has a more precise meaning.
First, social dynamics usually presumes change within a social system. That system may consist of similar entities (e.g., members of a family, families in a neighborhood, nations in the world) or disparate entities (e.g., different types of actors in a political or economic system) or various attributes of a single social entity (e.g., an individual’s education, occupational prestige, and income or a business firm’s age, size, and structure). The system usually is regarded as bounded, allowing the rest of the world to be ignored for purposes of explanation.
Whether the system consists of actors or variables, the term ‘‘system’’ presumes interdependence and typically involves feedback. Thus, action by one entity in the system leads to counteraction by another entity. For example, managers of a firm may counter a strike by workers by acquiescing to the workers’ demands, outwaiting them, or hiring nonunion laborers. Alternatively, change in one variable in the system leads to an opposing or reinforcing change in one or more other variables. For example, an increase in educational level is followed by an increase in prestige and then an increase in income. Changes resulting from interdependent forces and feedback effects within the system are called endogenous changes.
There also may be exogenous changes, that is, unexplained (perhaps random) changes that influence change within the system under study but whose causes originate outside that system. For example, in analyses of interaction between a husband and wife, changes in the economy and society in which the couple lives usually are treated as exogenous changes that affect the couple’s behavior, but the societal-level changes themselves are not explained.
Because of interdependent forces and feedback effects as well as possible exogenous changes, social dynamics typically implies a concern with complex changes. Simple linear changes or straightforward extrapolations of previous trends are rarely of primary interest.
Second, social dynamics connotes social changes that have a regular pattern. That pattern may be one of growth (e.g., economic expansion, growth of a population), decline (e.g., rural depopulation, the extinction of a cultural trait), cyclical change (e.g., boom and bust in the business cycle), a distinctive but nonetheless recurring transition (e.g., ethnic succession in neighborhoods, societal modernization, the demographic transition from high mortality and fertility to low mortality and fertility), or simply a drift in a particular direction (e.g., the slow but accelerating spread of a social belief or practice through a population).
Third, social dynamics usually implies a degree of predictability: Social change not only can be comprehended in terms of post hoc reasons but also can be explicitly modeled. The model, whether it consists of verbal statements or mathematical equations or computer instructions, involves a set of assumptions or propositions that permit fundamental patterns of change to be deduced. In contrast, although a unique historical event may foster social change, its uniqueness makes successful prediction impossible. One challenge in studies of social dynamics is therefore to convert phenomena that are unique on one level to ones that are representative and therefore predictable on another level. Thus, what some regard as a unique historical event, others see as an example of a regular pattern of change. For example, to a historian, the Russian Revolution of 1917 is a unique event, whereas a sociologist may regard it as exemplifying a response to changes in underlying social conditions. Thus, while recognizing many distinctive factors, Skocpol (1979) argues that similar patterns of causes underlie the dramatic political and social transformations that historians call the French, Russian, and Chinese revolutions.
Fourth, the term ‘‘social dynamics’’ is used more commonly than is the term ‘‘social change’’ when regularity in patterns of change is associated with some kind of equilibrium (steady state or homeostasis), that is, when feedback effects are such that small deviations from equilibrium lead to compensating effects that cause equilibrium to be restored. For example, in the United States, the distribution of family income (the share of total income received by different families) was remarkably stable throughout the twentieth century despite tremendous growth in population and economic output and social upheavals such as the civil rights and women’s liberation movements. This stability suggests that the process governing the allocation of family income was nearly in equilibrium. The term ‘‘social change,’’ especially change seen as part of a unique historical process, usually is associated with change from one distinctive situation to another, very different situation. It implies the antithesis of social equilibrium. The way in which the social status of women and minorities has changed during the twentieth century exemplifies social disequilibrium.
Studies of social dynamics do not necessarily assume the existence of an equilibrium. This point is made clear by studies of the dynamics of economic growth, which often envision a process of never-ending expansion and improvement. Similarly, some dynamic processes imply not a steadystate condition but continual oscillation between conditions. A classic sociological example is Pareto’s (1935) analysis of the circulation of elites.
Fifth, the term ‘‘social dynamics’’ is almost always used in situations in which there is an interest in the process of change: the step-by-step sequence of causes and effects and the way in which intermediary changes unfold. It is rarely used when only a simple before–after comparison of the condition of the system is the object of interest. Instead, when authors use the term ‘‘social dynamics,’’ there is usually a sense that the details and sequencing of changes are important because changes are contingent: If the sequence had been interrupted or altered at an intermediary point, the final outcome might have been different. For example, models of social protest often recognize that the state’s response to protests may range from peaceful conciliation to violent suppression. The nature of the state’s response is an important contingency because it affects the likelihood, timing, and character of future protests.
Sometimes the sequence of changes occurs on the level of the system as a whole rather than on the level of individual members. For example, in a simple model of population growth, individuallevel changes are very elementary: birth followed by death, with the timing of the two being the only question. On the population level, the addition and loss of individuals over time represent a sequence of changes even though on the individual level there may be few, if any intermediary changes and thus little sense of a sequence of causes and effects.
Reasoning For Studying Social Dynamics
What motivates sociological interest in social dynamics in general and dynamic models in particular? The most potent reason is the long-standing interest of sociologists in social change, coupled with an increasing recognition that a tremendous amount of scientific leverage can be gained from identifying regularities in patterns of change and then formulating sociological theories that explain them, that is, from studying social dynamics and not just unique historical events. Leverage comes not only from the increased richness of theories of social dynamics but also from greater methodological power in discriminating among competing explanations, as is indicated in more detail below.
As observers of the great social transformations of the nineteenth century, the founders of modern sociology (e.g., Marx, Spencer, and Weber) were keenly interested in social change. However, in the middle of the twentieth century, when structural functionalism and Parsonian thought were dominant, social change was regarded as a minor subfield of sociology. Interest in social change was renewed after a reawakened recognition of social conflict and the concomitant criticism of the assumption of social equilibrium in structural functional theories.
An accelerating pace of global change has added to this interest (Sassen 1988; Boli and Thomas 1997). Rapid growth of the world’s population; high levels of mobility of people, goods, and capital between and within nations; the transformation of agricultural societies to industrial and postindustrial societies; social upheavals ranging from strikes, to social protests, to revolutions, to wars; the creation of new organizational forms (e.g., holding companies, multinational corporations, international organizations); fundamental transformations of political regimes, including the failure of communist governments; steady increases in the number and volume of new technological innovations; depletion of natural resources; extinction of plant and animal species; and changes in climate induced by human activities are only a few of the world-level changes that make it virtually impossible for sociologists who study largescale social systems not to be interested in social change.
Although some scholars view many of these changes as historically unique, the concrete social and economic problems that result from them motivate attempts to find regular patterns and predict future changes, in short, to develop dynamic models of societal and global changes. Consider the massive changes in eastern European nations that began in the late 1980s. From the late 1940s to the late 1980s, those nations were governed by totalitarian polities and had commandtype socialist economies. Now most of them appear to be headed in the direction of market-type capitalist economies and democratic polities. The intellectual challenge, as well as a major problem for policymakers, is to develop a theory of the transition from one to the other, that is, a theory of the dynamics of the social change that is expected to occur. The fact that no satisfactory theory existed when the transition began was apparent to the general public as well as to social scientists. It points to the practical as well as scholarly value of studying social dynamics.
Sociologists who study micro-level phenomena (individuals and families) also cannot ignore social change. The life course of individuals in modern societies has a typical sequence of activities associated with aging (e.g., birth, day care, school, work, marriage, child rearing, retirement, death) that commands considerable attention by sociologists. Historical changes in family patterns (e.g., increases in premarital cohabitation, delays in marriage, changes in husband–wife roles, increases in divorce, baby booms and baby busts, increasing institutionalization of the elderly) also put social change at the forefront of the attention of sociologists who study the family. These subjects are perhaps more easily viewed in terms of social dynamics than are ones pertaining to global and societal changes because similar patterns across individuals and families are more readily apparent.
Sociologists who study behavior in small groups were among the earliest to express an interest in social dynamics. This interest received a major boost from Bales’s Interaction Process Analysis (1950). Game theorists, who attempt to explain the moves and countermoves of actors in highly structured situations, also exemplify a concern with social dynamics in small groups, though they, much more than Bales and his intellectual descendants, concentrate on formal models and deemphasize hypothesis testing and empirical results (see Shubik 1982).
There are also metatheoretical and methodological reasons for studying social dynamics even when the primary intellectual concern is with statics, that is, with relationships among actors or variables at a single point in time.
First, studies of relationships at a single point in time implicitly assume a steady state or equilibrium. Otherwise relationships at a given time point must be transitory and in the process of changing, a situation that would degrade their potential contribution to enduring sociological knowledge. A steady state may or may not exist. If it does not exist, one needs to study social dynamics to understand relationships at a point in time. If a steady state does exist, much can be learned by studying social dynamics that cannot be learned easily by studying relationships at a single point in time. For one thing, two theories may imply the same relationship among variables at a given point in time but imply different time paths of change. In that case, a study of social dynamics can differentiate between them, whereas a study of the steady state cannot. For another thing, a theory of relationships at a point in time is invariably the special case of one or more theories of change over time, and the latter theories almost always have a richer set of implications than do the former. This means that in general there are more ways to test theories of social dynamics than to test theories of social statics.
The Nature of Dynamics Model
As was noted earlier, developments in dynamic models (and derivative developments in methods of dynamic analysis) are the major commonality in sociological studies of social dynamics. To understand the main features of dynamic models, it is important to differentiate them from other types of models.
The most basic distinction is between static and dynamic models. Static models describe relationships among social actors in a system or among the attributes of a social entity at a given point in time. As was noted earlier, they implicitly assume a steady state or equilibrium, a phenomenon that is about as common in nature as a vacuum. In contrast, dynamic models describe the process or sequence of changes among actors in a social system or among the attributes of a social system.
Dynamic models also can be contrasted with comparative static models, which are especially common in economic analyses and analyses of social experiments. Although both deal with change over time, they differ in an important way. The process of change leading from conditions at the earlier time point to conditions at the later time point is fundamental to a dynamic model. In contrast, the change process is ignored in a comparative static model, which resembles a black box that relates conditions at one point in time to conditions at a later time.
To illustrate this distinction, consider alternative ways of explaining a son’s occupational prestige. In a comparative static model, the son’s prestige may be related to his father’s socioeconomic status and his own education without any attention being paid to the mechanisms and processes that lead from those background conditions to the son’s condition as an adult. In a dynamic model, the father’s socioeconomic status and the son’s education may be seen as giving access to certain entry-level jobs, which in turn provide opportunities for further career mobility, leading to jobs with varying levels of prestige. In a dynamic model, the timing and sequence of job shifts are of concern, not just the son’s initial condition (i.e., his father’s social status and his own education). In summary, dynamic models are used to explain not only why the later condition of a phenomenon differs from its earlier condition but also how a sequence of changes leads from one condition to the other.
Types of Dynamic Models
Different types of dynamic models are distinguished on the basis of a variety of formal properties. One basic distinction seems to be whether the components of the system are social actors or the attributes of a social entity. In the former case, dynamic models of the behavior of social actors are developed: Actor A does X, in response actor B does Y, then actor A does Z, and so on. Although much of game theory is not concerned with dynamic models, some of it formulates precisely these kinds of models. In the latter kind of dynamic model, values of variables describing the social entity are related to one another. Ecological theories of organizational survival utilize these models, for example, relating the degree of environmental variability and the degree of specialization of various types of social organizations in the environment to the survival of these types (Hannan and Freeman 1987). The distinction between systems of actors and systems of variables is not as important as it may seem at first because the behaviors of actors usually can be translated into variables.
A more basic and important distinction is whether time is discrete or continuous. Most empirical phenomena can change at any moment, and this leads one to expect that time should be treated as continuous in most dynamic models. In fact, time more often is treated as discrete for two main reasons. First, the empirical data used to test a dynamic model usually measure time at only a few discrete points. Some researchers then find it convenient to build a dynamic model of the data rather than model the underlying social process. Second, some researchers consider discrete-time models to be simpler and believe that little information is lost from approximating truly continuous-time processes with discrete-time models. If the discrete time points in the data are sufficiently numerous and close together, the approximation is almost always satisfactory. If they are not, important intermediary steps in the process are likely to be ignored, possibly resulting in misleading conclusions. Whether discrete-time models are simpler than continuous-time models is less clear. To some extent, it is a matter of a researcher’s taste and training.
Another key distinction concerns whether the variables that describe the social system are discrete, metric, or a mixture of the two. Discrete variables have a finite set of values; for example, political regimes may be categorized into a small number of basic types. Metric variables have a continuum of values; for example, a person’s income and occupational prestige usually are treated as continuous variables. In fact, in both instances, the number of values is finite but is so large that treating the values as continuous may be convenient and is often fairly realistic. Age is a continuous variable, but measurements of it are always discrete (e.g., to the nearest year, month, or day).
A distinction also may be made in the way in which variables in a system change over time. By their nature, discrete variables can change only in jumps. For example, there may be a sudden change from a military political regime to a multiparty government. Metric variables often are regarded as changing gradually. For example, a firm’s profits may be treated as shifting upward or downward by small increments. In fact, metric variables also may change in jumps. For example, income may fall from a high value to nearly zero when a family’s main breadwinners lose their jobs.
Another important distinction is whether the change process is treated as deterministic or stochastic (having a random component). There is a broad consensus that stochastic models are almost always more realistic. Few social changes occur in a strictly determined fashion, and those which do change deterministically are rarely sociologically interesting. Nevertheless, deterministic models can be useful when the solution of realistic stochastic models presents severe technical problems. Those formidable problems tend to occur when there is a high degree of interdependence in the social system (e.g., in models of the diffusion of an innovation) and when both time and outcomes are treated as continuous.
Whether time is treated as discrete or continuous, models of changes in discrete variables are invariably stochastic because changes by jumps almost dictate reference to probabilities. By contrast, continuous-time models of changes in metric variables are typically deterministic.
Some progress has been made in developing empirically estimable stochastic models of heterogeneity in the spread of a social practice in the presence of interdependent influences in a social system (Strang and Tuma 1993). Davis and Greve (1997) provide an intriguing application of this modeling approach to the use of ‘‘poison pills’’ and ‘‘golden parachutes’’ among firms.
Formal dynamic models are of two main types. In one type, the model consists of a set of mathematical equations that relate some elements of the system to other elements. In the other type, the model consists of a set of computer instructions that relate inputs of various variables and/or actors at one time to outputs at a later time. The computer instructions in fact represent mathematical equations that are so complex that they cannot be solved in practice without the aid of a computer. Still, it is convenient to think of computer models as very complicated mathematical models.
A clear introduction to both discrete-time and continuous-time deterministic models of metric variables can be found in Baumol’s Economic Dynamics (1951), which also introduces several economic theories of potential interest to sociologists, including theories of wages and profits in firms and economic growth. For a discussion of deterministic models of change in metric variables, see Doreian and Hummon (1976).
Two of the early classic discussions of stochastic models of change in discrete variables are Coleman’s Introduction to Mathematical Sociology (1964) and Bartholomew’s Stochastic Models for Social Processes (1973). Tuma and Hannan’s Social Dynamics: Models and Methods (1984) discusses both deterministic and stochastic models of change in metric variables in continuous time as well as continuous-time stochastic models of change in discrete variables. This work also discusses metatheoretical and methodological reasons for studying social dynamics, applies dynamic models to a variety of different sociological problems, and provides an extensive bibliography pertaining to models and methods used in studying social dynamics. It also contains a comprehensive introduction to event history analysis.
Sociologists, whether studying whole societies or small groups, have had a long-standing and farreaching interest in social change. Traditional approaches focused on specific substantive phenomena and, especially in macro-level studies, often stressed unique historical occurrences rather than common dimensions underlying patterns of change. Recent studies of social dynamics usually focus on what is regular and predictable about social change and the social mechanisms that generate a sequence of contingent changes. Often they embed ideas about change in dynamic models and test them in dynamic analyses of over-time data. This approach has been especially valuable in fostering cumulative research.
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