Classic Media Theory and New Methods

An Interactive Essay on Agent Based Models

(and Uses & Gratifications Theory)

Joe A. Wasserman | Department of Communication Studies, West Virginia University

@joewasserman |

A brief introduction

This essay was spawned from an observation and an interest.

The observation. Often, theories are more nuanced and complex than the research into those theories. Uses and gratifications (UG&) theory is a case in point: it includes feedback loops among media consumers and producers, processes that unfold over time, interactions among people, and both media and non-media behaviors. Most U&G research, however, is on the gratifications people seek from consuming media.

The interest. The tools and methods of complexity science present new ways to model and understand complex processes in which behavior and outcomes arise from difficult-to-predict interactions among many individual agents, including humans. These tools include agent-based modeling, which is the focus of this interactive essay.

Renewing Classic Media Theory with New Methods: A Proof-of-Concept Agent Based Model of Uses and Gratifications Theory

It has been argued that the tools of complexity science could help communication and media studies scholars study and understand communication as a dynamic process (Sherry, 2015). Complexity science provides methods for studying dynamic processes that account for the interdependence of individuals. Models and theories that involve interdependent individuals contravene the typical individual-level focus of communication and media studies (see Walter, Cody, & Ball-Rokeach, 2018). Despite the promise of complexity science, many of its tools have rather large technical barriers to entry, including a high degree of mathematics fluency that is not typically held by communication scholars. One method for investigating dynamic, complex systems stands out from the others as not requiring any mathematics whatsoever: agent-based modeling. Agent-based modeling is a promising entry point to complexity science for communication scholars not only because it does not require math modeling, but also because agent-based models are specified at the level of individual agents—and sociopsychological communication scholars are used to thinking about individuals. After describing complexity science and agent-based modeling, this article explores the suitability of uses and gratifications theory (Katz, Blumler, & Gurevitch, 1973) for agent-based modeling as a demonstration of the promise and challenges of moving from traditional approaches in communication and media studies to complexity science and agent-based modeling.

Current Communication Studies Paradigm

The dominant empirical paradigm in communication and media studies has been sociopsychological (Craig, 1999), primarily concerned with predicting and explaining cognitive, affective, and behavioral dimensions of the antecedents and consequences of communication and media consumption. Communication and media studies as a field has been predominated by positivist or post-positivist quantitative research on individuals (Walter et al., 2018). Macro-scale research on communities or societies has been rare, and research crossing micro- and macro-scales even rarer (Walter et al., 2018). In this paradigm, typical analyses involve subjecting small sets of quantitative variables measured at the individual level to statistical analyses in a regression framework.

While this paradigm has undoubtedly yielded great insights into media consumption and its effects, Sherry (2015) has argued that this paradigm has of necessity treated human communication in terms of static snapshots of one-directional causal effects. In contrast, communication (including mediated communication) is dynamic as it unfolds over time, involves the interaction of interdependent heterogeneous individuals, and involves recursive and feedback processes that contravene one-way causal models (Sherry, 2015). Furthermore, research that focuses strictly on independent individuals is limited in its ability to inform understandings of more macro-level phenomena involving aggregates of individuals. Advances in mathematics and computing have produced a cross-disciplinary scientific paradigm that holds great promise for advancing understandings of communication and media processes as temporally-unfolding, dynamic processes involving interacting, interdependent, heterogeneous agents whose actions feed back into and shape these processes: complexity science (Sherry, 2015).

Complexity Science and Complex Systems

Complexity science studies complex systems, the precise definitions of which are still debated. Even so, several conceptualizations of complex systems are informative for understanding what makes complex systems distinct from simple systems. Complex systems have been defined as “system[s] in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution…[or]…that exhibit nontrivial emergent and self-organizing behaviors” (Mitchell, 2009, p. 13).

As an example of these concepts, consider the simulation in Figure 1, a toy model of flocking dynamics that is prototypical of simulations in complexity science. In this model, the components (or agents) are represented by triangular shapes that move about a two-dimensional plane that can be thought of as abstractions of birds. No central control guides the behavior of these bird agents. Instead, each bird individually observes its local environment and adjusts its behavior based on a set of simple rules. Specifically, each bird (1) turns away from birds that are too close, (2) turns so that it faces in the same direction as nearby birds, and (3) turns toward nearby birds that are not too close. After running for a brief period of time, these three simple rules produce complex collective behavior (i.e., nontrivial emergent or self-organizing behaviors): flocks of birds that travel together in the same direction. Emergence, which occurs when “simple rules produce complex [macroscopic] behavior in hard-to-predict ways” (Mitchell, 2009, p. 13), is a hallmark of complex systems. Crucially, this emergent behavior is self-organized, in that the “behavior arises without an internal or external controller or leader” (Mitchell, 2009, p. 13). There is no bird leader or conductor arranging the groups into flocks. Adding a small number of additional rules can produce more complex behaviors, such as flocking in vee patterns (see Wilkerson-Jerde, Stonedahl, & Wilensky, 2009).

Figure 1. Flocking model in NetLogo (Wilensky, 1998a). To run the simulation, click “setup” and then “go.”

More recently, complex systems have been defined as those that “display behavior across a wide range of scales” (Allen, Stacey, & Bar-Yam, 2017, sec. 1 para. 5) or in which “the elements act neither fully independently nor fully coherently, and the separation of scales breaks down” (Bar-Yam, 2017, para. 11). Scale is defined as “the number of entities or units acting in concert” (Allen et al., 2017, sec. 3 para. 1). Again using the flocking model in Figure 1 as an example, the elements or entities are birds. The scale of this model ranges from each bird individually (micro scale) to all of the birds in combination (macro scale). If they were fully independent, the movement of each would be unrelated to each other. If they were fully coherent, the movement of each would be identical. In the flocking model, separation of scales breaks down because the system is not sufficiently characterized either by describing the individual behavior of birds (micro scale) or by averaging over the movement of all of the birds combined (macro scale). Both micro and macro descriptions of Figure 1 miss an important characteristic of the system: the emergence of flocks. Even when only individual birds are of interest, their behavior is partially interdependent on the behavior of other birds, and thus cannot be fully explained by examining individual birds in isolation.

Many aspects of human communication and media usage appear to be complex systems (Sherry, 2015), in that they involve agents (i.e., humans) who act partly interdependently and partly coherently, such that separation of scales breaks down. Communication processes are central to the construction and maintenance of aggregates of humans including groups, firms, societies, and nation-states. None of these aggregates are fully described by each of the individuals comprising them or by some global average of those individuals. Instead, the heterogeneity of individuals, their relationships, and their communication produce emergent characteristics of the aggregate. In turn, these emergent phenomena influence the behaviors of individuals.

Examples of human communication phenomena in which separation of scales break down abound. The diffusion of innovations throughout some group involve heterogeneity throughout the population as different individuals adopt an innovation (or not) at different times, are variously influenced depending on their social and communication networks, and different individuals hold greater or lesser influence (Rogers, 1983). The competitiveness of individuals and firms is related to their position in a structure of relationships between individuals and firms (Burt, 1995). Media tastes and consumption practices are structured at a group-level (Gans, 1974). More recently, the proliferation of Internet networks have called to scholars’ attention to communication and information networks connecting individuals (Castells, 2010). Only networks in which each individual is equally connected with every other individual would be fully coherent, and only non-networks of entirely unconnected individuals would be fully interdependent. More realistic network structures lie somewhere in between (e.g., Watts & Strogatz, 1998), including both connected and unconnected individuals. These network structures are typical of complex systems that have network structures. Before turning to a detailed consideration of applying a complexity perspective to a single media theory, a promising complexity science method for communication and media scholars is described: agent-based modeling.

Studying Complexity with Agent-Based Models

Agent-based models (ABMs; see Wilensky & Rand, 2015) are a promising type of dynamic simulation for merging complexity science with communication and media studies. In ABMs, agents are simulated entities whose behaviors are determined by algorithms that are typically executed by a computer (Wilensky & Rand, 2015). In order to ‘translate’ a verbal theory to an ABM, it is necessary to specify the theory’s propositions as a set of algorithms for agents’ behavior. ABMs have several advantages for communication and media scholars over equation-based approaches to complexity:

  • ABMs are defined at the level of individuals’ behaviors, which aligns with the often individual-level focus of communication and media scholarship;
  • ABMs are able to identify individual-level behaviors that plausibly produce emergent phenomena at meso- and macro-levels; and
  • the process of developing an ABM can identify gaps in knowledge and theoretical specification that can drive future research and theory development.

The aforementioned flocking model in Figure 1 is an example of an ABM. Building ABMs requires several kinds of definitions:

  • defining the types of agents in a model and their properties;
  • defining the possible behaviors of the agents in a model;
  • defining the environment of the agents, which are typically spatial or network-based;
  • defining the interactions among agents, between agents and their environments, and among environmental elements;
  • defining agents’ cognition, which combines agent properties, their behaviors, and their environments: how agents respond to their properties and environments, maximize utility functions, pursue goals, adapt, learn and/or evolve (see Wilensky & Rand, 2015).

The contexts in which creating an ABM is most beneficial map well to many phenomena of interest to media and communication researchers. ABM is most beneficial when:

  • a situation contains a moderate number of agents, roughly on the order of tens to millions;
  • agents are heterogeneous, i.e., they do not all have identical properties or behaviors;
  • agents interact with each other;
  • agents interact with their environments; and
  • processes of interest have temporal dynamics, i.e., they occur over time (see Wilensky & Rand, 2015).

Many communication and media phenomena would seem to correspond with these criteria. Outside of modeling dyads, small groups, and entire countries (or larger), most communication and media practices involve somewhere between 10 and several millions of individuals, such as social media usage (Ahern, 2017). Individuals have different characteristics that influence how they behave, such as the influence of individuals’ partisanship on hostile media perceptions (Vallone & Ross, 1985). Humans interact with each other, often via communication. Humans interact with their environments, for example consuming media in the pursuit of goals (Katz et al., 1973). Finally, human processes unfold over time.

ABMs have been used to model human phenomena, ranging from early, simple toy models (for an accessible review, see Rauch, 2002) to more recent highly detailed ABMs that incorporate real-world data (see Waldrop, 2018). For example, consider the ABM in Figure 2. In this ABM, which was based on Schelling’s work on segregation (e.g., Schelling, 1971), each colored square is an agent representing a household in a neighborhood. The “%-similar-wanted” slider determines the minimum percent of same-colored neighbors each household desires. If that number is not reached, the household will be unhappy (represented by an “X”) and move to a random open space. At first, on average, households are randomly positioned and are bordered by about 50% same-colored households, which is displayed on the “Percent Similar” graph. This value can be thought of as a metric of the amount of segregation among neighborhoods. Like many complex systems, this ABM is sensitive to initial starting conditions and exhibits non-linearities in which small differences can lead to dramatically different outcomes. For values of “%-similar-wanted” less than or equal to 25%, percent similar barely increases over 50%. However, with “%-similar-wanted” at 26%, percent similar usually reaches 70% or more. In other words, a small increase in preferences for similar neighbors yields dramatically greater segregation.

Figure 2. Segregation model in NetLogo (Wilensky, 1997).

A Complexity Approach toward Uses and Gratifications Theory

This section explores the suitability of a classic media theory, uses and gratifications (U&G), for agent-based modeling as a demonstration of the promise and challenges of moving from traditional approaches in communication and media studies to complexity science and agent-based modeling. U&G is “concerned with: (1) the social and psychological origins of (2) needs, which generate (3) expectations of (4) the mass media or other sources, which lead to (5) differential patterns of media exposure (or engagement in other activities), resulting in (6) need gratifications and (7) other consequences, perhaps mostly unintended ones” (Katz et al., 1973, p. 510). The majority of U&G research has focused on (a) typologies of media gratifications, or the expected need satisfaction to be experienced via media usage, and (b) the relationship between those expectations and differential patterns of media consumption (Rubin, 2009). U&G research has also addressed social, psychological, and contextual influences on media consumption, as well as how media usage motivations influence media effects (Rubin, 2009). However, nearly all U&G research has neglected the reciprocal manner in which the consequences of media usage might reciprocally influence individuals’ attitudes, behaviors, and social relationships—and thereby the very social and environmental contexts that are thought to produce needs in the first place. U&G theory and research has not been immune to critique (for a review, see Ruggiero, 2000), including that it is overly individualistic. While this critique may be fair for the majority of U&G research, U&G theorizing includes supra-individual elements. Thus, individual-focused U&G research has not fully captured all of the dimensions of U&G theory.

Rosengren’s (1974) formulation of the U&G paradigm (Figure 3) provides one of the most nuanced treatments of U&G theory that includes supra-individual elements. For the most part, advances in U&G have involved more precisely specifying the nature of and relationships among theoretical constructs, rather than substantially altering the proposed theory (see Rubin, 2009). It specified a process in which individuals’ needs produce perceived problems and solutions to those problems, which in turn produce motives for consuming media and engaging other behaviors. These motives drive media consumption that yield gratifications (or non-gratifications) related to prior needs. Media consumption, other behavior, and gratification/non-gratification in turn influence individuals’ characteristics as well as social structures. Crucially, social structures and individual characteristics were specified as influencing all of these processes, thus incorporating feedback loops and scale-crossing phenomena into the model.

Figure 3. Graphical depiction of uses and gratifications, adapted from Rosengren (1974).

Rosengren’s (1974) formulation of U&G appears to describe a complex system. Individuals’ behaviors influence their environments, which in turn influence their behavior. Although these environments were under-theorized by Rosengren (1974), they can be reconceptualized as emergent properties of aggregates of individuals. Thus, U&G could involve partially independent actors whose behaviors also exhibit a degree of coherence. In other words, U&G involves phenomena in which separation of micro and macro scales breaks down.

U&G appears to meet all five criteria for benefitting from agent-based modeling. First, U&G involves a moderate number of agents (media consumers). Second, individuals are heterogeneous, having different personal characteristics. Third, individuals’ interactions with each other influence the process described by Rosengren (1974) and potentially gratify perceived needs. Fourth, individuals interact with their environments when they consume media and their environments influence the U&G process. Finally, these processes occur over time. Indeed, Slater’s (2007) reinforcing spirals model of the mutual, reciprocal influences of media usage and beliefs, attitudes, and behaviors can be interpreted as a cognate of U&G. Although Slater (2007) represented reinforcing spirals as a cross-lagged path model, he explicitly referred to general systems theory (Bertalanffy, 2015), an antecedent of complexity science, and feedback loops, a common element of complex systems. What, then, might it look like to construct an ABM based on U&G? The following description of converting a verbally-described theory into a fairly simplistic ABM (Figure 4) will highlight some of the choices necessary to make during this process, the high level of specificity required for modeling, and the challenges involved.

Figure 4. Social gratifications agent-based model in NetLogo.

Defining the Agents and Their Properties

The basic unit of U&G is an individual human. In the ABM in Figure 4, each individual is represented as a square. U&G proposes a large variety of properties an individual could have. Only individuals’ characteristics that are either (a) relevant to the ABM or (b) outcomes of interest should be included in the model. Drawing directly from Rosengren (1974), some candidate characteristics include needs, perceived problems, perceived solutions to problems, motivations for action, history of past media usage and other behavior, and the gratification or non-gratification associated with those past behaviors. In this model, individuals only have one property: their current level of social need, which is modeled as a continuous variable that can range from zero to one. Gratifications related to social interaction have been one of the most commonly-identified in gratification typologies (Sundar & Limperos, 2013). Indeed, the need for social relatedness has been identified as a basic, universal psychological need (Deci & Ryan, 2000). Instead of explicitly modeling the antecedents of needs in this model, origins of needs are modeled as a random variable such that at each time point, or tick, individuals’ social need increases between zero and a maximum value set by the “need-increase-max” slider (Figure 4).

Defining Agents’ Behaviors

Agent behaviors can be fully deterministic or involve randomness. Important agent behaviors to include in an ABM of U&G include at minimum the selection of media to consume. Drawing directly from Rosengren (1974), media consumption behavior can be characterized in terms of time spent and types of media consumed. In this simple model, distinctions are not made among different kinds of media. Instead, agents have only two possible behaviors: either consume media or interact socially. Thus, this model includes both media consumption and non-media behavior that are relevant to gratifying a single need (see Rosengren, 1974). The ability of media consumption to gratify individuals’ social needs is determined by the “media-social-grat” slider (Figure 4), which can range from zero to one. Similarly, the ability of social interaction to gratify individuals’ social needs is influenced by the “ix-social-grat” slider (Figure 4), which ranges from .01 to one. The rules determining how agents choose whether to consume media or interact socially is described below in “Defining Interactions and Cognition.”

Defining Agents’ Environments

Environments in an ABM take spatial or network forms. In this model, agents’ environment is spatial: their four immediately adjacent orthogonal neighbors. So that all agents have the same number of neighbors, the top and bottom edges of the world wrap to connect to each other, as do the left and right edges. In this way, agents’ environments are each other. Collectively, this grid of agents could be considered a simulated microcosmic society.

Defining Interactions and Cognition

Like agent behaviors, interactions can be deterministic or include randomness. Agents’ interactions can occur within themselves, with their environment, and/or with other agents. Using “cognition” to describe what agents in an ABM do can be metaphorical, as it describes processes that are not necessarily mental. Cognition simply refers to the manner in which agents decide what actions to perform. Aside from potentially selecting media to consume from their environments, how individuals interact with each other and how they interact with their environments are the most underspecified part of Rosengren’s (1974) model of U&G. In this ABM, agent cognition is primarily concerned with the decision whether to consume media or socially interact on a given tick. To make this decision, agents compare (a) the amount of social need gratification they would obtain by consuming media to (b) the amount of social need gratification they expect to obtain by interacting socially assuming that none of their neighbors decide to consume media. Because the latter is not always an accurate assumption, agents sometimes overestimate the amount of social need gratification they will obtain via social interaction.

After making this decision, agents who decided to consume media reduce their social need by the value determined by the “media-social-grat” slider (see Figure 4). In this ABM, agents interact with each other when they choose to socially interact. The amount by which social interaction reduces an agent’s social need is determined by a combination of their four neighbors’ availability and the “ix-social-grat” slider (see Figure 4). Agents’ availability for those consuming media is 0, and for all others is equal to their current social need, reflecting an assumption that those with greater social needs will be willing to devote more time to social interaction. Gratification obtained via social interaction is the sum of neighbors’ availability times the value of the “ix-social-grat” slider.

Agent-Based Model Validation and Experimentation

Beyond a single ABM in isolation, ABMs become more valuable when they are used to test and generate hypotheses in combination with empirical research. Although this model is simplistic, it can be compared to empirical research on the real-world phenomena it is intended to represent as well as to competing models representing similar phenomena but with different assumptions. This comparison process entails model validation and experimentation. Models should be validated both on their face and empirically (Rand & Rust, 2011; Wilensky & Rand, 2015). Face validation occurs at the micro level, to ensure that agents and their behaviors correspond to real-world individuals and their behaviors, and at the macro level, to ensure that the patterns of behavior correspond with real-world processes. The specifics of both micro and macro behaviors in this ABM depend on the values of model parameters set by sliders. Generally, as both “need-increase-max” and “ix-social-grat” increase, agents are more likely to decide to socially interact over consuming media. As “media-social-grat” increases, agents are more likely decide to consume media instead. At most parameters, agents’ social need and the decision whether to consume media or socially interact are fairly homogeneous over time. At low values of “media-social-grat” and “ix-social-grat” and moderate values of “need-increase-max,” however, a checkerboard pattern emerges in which every other agent maintains a consistently high (or low) level of social need. In this arrangement, those with consistently low social need almost always decide to socially interact, while their neighbors with consistently high social need usually socially interact but sometimes decide to consume media. This checkerboard pattern emerges from agents’ initially random social need, as the neighbors of those with high social need are able to gratify all (or almost all) of their initial social need—thus “starving” their own neighbors of social gratifications due to subsequent low availability. This emergent pattern could be validated against observations of groups’ social interaction dynamics.

Empirical validation, which involves comparing data to an ABM, involves both empirical input validation to ensure the inputs to the model (e.g., frequency of events, values of parameters, influence of parameters on each other; see MacLaren et al., 2018) correspond to their real-world values and empirical output validation to evaluate the degree to which emergent model behavior corresponds with real-world trends or real-world data. Model validation and testing can involve both qualitative and quantitative data. Validated models can then be used in simulation experiments that manipulate model parameters or even which rules are included in the model. Quantitative data about overall model states and individual agents at each time point or at the end of the simulation can be exported from model runs for subsequent statistical and network analysis. This ABM includes several quantitative visualizations. Depending on the selection in the “Patch-display” menu, individual agents visually display either (a) their current social need, (b) their average social need over the duration of the simulation, or (c) the consistency of their decision to either consume media or socially interact. Furthermore, three graphs depict (a) the number of agents who decided to socially interact or consume media on each tick, (b) the mean and standard deviation of all agents’ social need at each tick, and (c) the mean and standard deviation of all agents’ variability in their decision to socially interact or consume media over the duration of the simulation.

Toward an Agent-Based Perspective on Complexity in Media and Communication Studies

Converting a verbally-described theory, such as U&G (Rosengren, 1974), into an ABM involves making many decisions, sometimes requiring a selection among competing plausible modeling strategies. At other points in the modeling process, theory and research may not exist at the fine-grained level required by programming an ABM. Encountering a theory and research gap is both a challenge and an opportunity. While the modeling decision may be somewhat arbitrary in the short-term, these gaps also identify areas for research and theory-building that would be necessary for a complete understanding of the phenomenon at issue.

Although agent-based modeling is currently far from the mainstream in communication and media studies, an ABM based on the reinforcing spirals model (see Slater, 2007) was recently published in the discipline’s flagship journal (Song & Boomgaarden, 2017). Unlike the ABM of U&G developed here, however, this ABM focused on political attitudes, partisan selective media exposure, and interpersonal influence on attitudes. Thus, they began with a basic voting ABM (Wilensky, 1998b; see Figure 5).

Figure 5. Voting model in NetLogo on which Song and Boomgaarden (2017) was based.

Agent-based modeling is a promising approach for investigating communication and media phenomena that occur across scales and over time. Interest in agent-based is growing across social sciences, including social psychology (Eberlen, Scholz, & Gagliolo, 2017b; Jackson, Rand, Lewis, Norton, & Gray, 2017) and economics (Delli Gatti, Fagioli, Gallegati, Richiardi, & Russo, 2018). Thus, resources for getting started in ABM are becoming increasingly available, including free or inexpensive online courses (see Centola, n.d.; “Complexity Explorer,” n.d.; Rand, n.d.) and a compendium of resources for getting started (Eberlen, Scholz, & Gagliolo, 2017a).


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Wilensky, U. (1998b). NetLogo Voting model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved from

Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.

Wilkerson-Jerde, M., Stonedahl, F., & Wilensky, U. (2009). NetLogo Flocking Vee Formations model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved from

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