Individual Behavior

The focus of the model is a finite population of individuals who each must decide among dichotomous options, one of which is the status quo. Support for the alternative to the status quo is participation. Each individual is defined by internal and external motivations to support the alternative to the status quo—in other words, motivations to participate. Internal motivations encompass all motivations deriving from personal characteristics that do not depend on the participation of others. Under the assumption that there are many such characteristics that affect individuals’ decision-making, each distributed across the population in some unknown fashion, a central limit theorem suggests that the net of these will be drawn from a normal distribution. All individuals’ internal motivations are drawn at random from this distribution.

External motivations cover everything that internal motivations do not—specifically, all factors that do depend on the participation of others. In the model, these factors arise from two sources of information: one's social network and the media. The information provided by social networks is the local participation rate (i.e., rate of support for participation) among those to whom one is connected.Footnote ¹⁰ The information provided by the baseline media is the global participation rate. Biased media alter this information, as described below.

Global and local sources of information together dictate external motivations: external motivations are a linear combination of these two information sources. The media strength parameter, M_S, determines the weight on each of the information sources in the linear combination. The greater the parameter, the more weight on global information. A media strength of 1 implies that local information has no influence on one's decisions, while a media strength of 0 implies that global information has no influence on one's decisions. This modeling choice allows an easy connection to the less complex model of Siegel (Reference Siegel2009) and provides a straightforward way to vary the importance of the media's information.

Since the combined information from local and global sources increases in others’ participation, so too do external motivations. This may be due, for example, to supportive information or influence (e.g., Huckfeldt and Sprague Reference Huckfeldt and Sprague1995), increased safety-in-numbers (e.g., Kuran Reference Kuran1991), increasing shame from violating norms of fairness by shirking (e.g., Gould Reference Gould1993), or an increasing chance of being punished by the group for deviations from a norm.

Individuals simultaneously decide whether or not to participate over a series of periods; these end only when no one wants to change one's decision. Each period's decision is a simple comparison of internal and external motivations: if the sum of the two motivations exceeds a cutoff, an individual participates in that period. Consequently, the greater both local and global participation rates are, the more likely one is to participate. Similarly, increased internal motivations also make one more likely to participate.

The first period begins with a random realization of individuals’ internal motivations and all individuals not participating. Early participants are driven to participate by high internal motivations. Subsequent participants are drawn in by a combination of their internal motivations and others’ participation. Some will never participate under any circumstances, perhaps because they derive a great deal of power, prestige, or money from the status quo. Though individuals are free to stop participating at any time, in practice they never do so since external motivations are monotonic in others’ participation.Footnote ¹¹ This implies that aggregate participation weakly increases with period. At some point no new people begin to participate and the model reaches its steady-state outcome. Analysis focuses on this steady state. In particular, I focus on the steady-state, or equilibrium, aggregate level of participation in the population. This is aggregate support for the non-status-quo option.

For each realization of individual motivations one of two steady-state outcomes can occur: either there is a cascade that results in nearly complete participation, or there is not a cascade and only a lower level of participation is reached. For a given population size and given internal motivation distribution parameters, one of three classes of outcome can occur in the model (Siegel Reference Siegel2009). Since these are related to the distribution of internal motivations, I call each a motivation class. First, there can be frequent cascades; this occurs in the strong motivation class. Second, there can be few cascades; this occurs in the weak motivation class. Third, cascades can happen in some realizations but not others; this occurs in the intermediate motivation class. More frequent cascades translate directly to greater average aggregate participation.

Within each motivation class, average aggregate participation is similar. More important, the impact of network structure and the media on aggregate participation is the same in each class as well. This means that analysis can focus on differences across classes. In performing this analysis, I find that the effect of the interaction between network structure and the media is identical in form in both the weak and intermediate motivation classes, but differs in the strong class. I thus only vary internal motivations across the strong and the intermediate motivation classes in the results that follow, but all results for the latter class also apply directly to the weak class.Footnote ¹²

Social Networks

Social networks detail the set of other individuals who directly influence one's behavior. While there are a tremendous number of potential networks one could explore, I focus on a typology of qualitative network structures that mirror commonly observed empirical networks (Siegel Reference Siegel2009). I choose this focus to help make the analysis broadly useful: comparatively little data are necessary to discern network type, allowing scholars with limited network data to apply the model's insights into the interaction of media and networks to their empirical cases. Figure 1 provides a visualization of the four network types I use: the Small World, the Village (or Clique), the Opinion Leader, and the Hierarchical network. All ties in these networks are assumed symmetric: anyone you influence also influences you.Footnote ¹³ Network structure is set at the time internal motivations are distributed and is assumed constant as participation spreads.Footnote ¹⁴ However, I explicitly vary network structure in the analysis. Unless stated otherwise, the distribution of net internal motivations is uncorrelated with the position of individuals in the network.

FIGURE 1.

Network Typology

The Small World network (Watts Reference Watts1999) captures life in cities and suburbs. Individuals’ local networks overlap significantly, so that people connected to each other are likely to share other connections as well. Yet each person also may have a number of “weak ties” (Granovetter Reference Granovetter1973), defined here as connections that link socially distant people. This gives individuals a chance to influence people outside their own clusters, allowing for the swift spreading of information across the network. These networks can form when tight-knit groups of friends or family disperse upon, for example, transition to college or forced migration.

The Village network is meant to mimic villages, small towns, and cliques, in which everyone knows everyone else within the social unit. In addition, individuals may have weak ties to people in other villages; local elders who regularly meet in multivillage conclaves might possess these. Such individuals are assumed to be few, and so paths of influence are strongly clustered within social units.

In these two networks individuals have roughly equal connectivity, and thus may be thought of as possessing approximately equal levels of social influence. Each of these networks has two parameters: one dictating average connectivity—the average number of people to whom one is connected—and one dictating the frequency of weak ties. I find that the effect of the media in these nonelite networks depends on, in order of importance: (1) average connectivity, and (2) whether the number of weak ties is suboptimal, optimal, or greater than optimal in encouraging participation.Footnote ¹⁵ I thus vary both connectivity and the optimality of weak ties in subsequent analysis.

I also consider two elite networks in which individuals have unequal connectivity. In the first, the Opinion Leader network, most people have one or two connections, while a few have many. Simple versions of this network have also been termed “stars” or “wheels” (e.g., Gould Reference Gould1993). Networks of this form have both elites and followers. The elites have many more connections than the followers and so have much more influence. This sort of network might be premised on expertise of the elites, or arise naturally from individuals’ preferring to befriend already popular people. In addition to the case in which internal motivations are distributed uncorrelated with number of connections, for these networks I also analyze cases in which elites have uniformly high (positive correlation) or uniformly low (negative correlation) internal motivations. I refer to both correlations as the case of unified elites.

The Hierarchy is the second elite network, and is a standard organizational structure that is common in bureaucracies as well as many social and religious groups. Hierarchies expand outward in width from the top, with each individual connected to one superior and a set number of subordinates. Individuals also may be connected to others in their same organizational level to capture bonds formed from close work. The power of elites within the Hierarchy lies in their privileged placement at its top, rather than in their absolute number of connections; a too-wide (or too-narrow) Hierarchy can actually diminish the influence of the elites. I analyze cases in which elites have uniformly high (positive correlation) or uniformly low (negative correlation) internal motivations, as well as cases in which internal motivations are uncorrelated with position in the network.

The Hierarchy has two parameters, while the Opinion Leader network has one. One parameter in both networks translates into the level of influence of the elites, which is a function of the number of elites and the structure of their connections. The second parameter in the Hierarchy translates into the level of influence of the followers, which is significant when there are many within-level connections. I find that the effect of the media in these elite networks depends on, in order of importance: (1) the correlation of motivations with network positions, (2) the level of influence of their elites, and (3) the level of influence of their followers (only for the Hierarchy). I thus vary all three in subsequent analysis.

Media

As I discuss the model of the media at length in the previous section, here I only connect the verbal description there with the details provided in this section. A media outlet is any source of information that exists outside of the social network, is accessible by all within the network, and influences individuals’ external motivations. Each media outlet is defined by two parameters: its strength, M_S, and its bias, M_B. There may be more than one outlet, and each one may have different values of these parameters.

The baseline media (comprising any number of unbiased outlets) provide information about the global participation rate, i.e., the aggregate rate of participation across the population.Footnote ¹⁶ As noted above, the strength of the media, M_S, determines how much each individual weights the media's information relative to that arising from one's social network. I analyze the baseline media in the next section by exploring the simultaneous variation of media strength, network properties, and motivation class.

Biased media act in a simple manner: they add their bias, M_B, to the aggregate participation rate and provide that information instead.Footnote ¹⁷ This biased information is used by individuals in the same manner as is unbiased information. Both types of bias, polling bias and advocacy, are implemented identically. The only difference is that I keep polling bias relatively small, whereas media advocates for or against participation have bias sufficient to set their information to 1 or 0, respectively, for all global participation rates.

In the fifth section I analyze two cases of biased media. First I consider one biased media outlet, and then two biased media outlets on opposite sides of the issue. I call L the one biased toward participation and R the one biased toward the status quo. To accommodate this I add another parameter, M_L, which dictates the relative strength of the proparticipation L outlet. The net information from two biased media outlets is a linear combination of the biased outlets’ information, with M_L the weight on L's information. The net information is combined with one's local information as for an unbiased outlet.

In my analysis I simultaneously vary media strength, network properties, media bias, and, for two outlets, the strength of the L outlet. Though I keep my analysis to two biased outlets, it can easily be extended to multiple biased outlets with the addition of parameters dictating their relative strengths.

This analysis assumes that the media have identical strength for all people, regardless of their personal characteristics. This assumption is not likely to hold when media are obviously biased. Consequently, in the sixth section I allow for selection into biased media and repeat my analysis under this new assumption. How selection works depends on the number of media outlets. With one biased media outlet, those with high (low) internal motivations raise (lower) their individual values of media strength in the presence of a proparticipation (antiparticipation) media outlet. With two media outlets, those with high (low) internal motivations increase (decrease) the weight on L's information and decrease (increase) the weight on R's information.

Analysis relies on simulation to overcome the problem of intractability. In this section I have detailed the progression of each realization of the model, from the distribution of internal motivations and creation of the network to the attainment of the steady state. However, as the realizations of both individual motivations and network connections involve random components, a single realization is insufficient. Thus, for each set of parameters I compute 200 realizations and take the mean of all steady-state outcomes. I report mean aggregate participation in the next three sections. The seventh section summarizes all results of the model, discusses measurement issues, and derives and discusses related testable hypotheses. Readers may find it useful to read that section before the three prior results sections, particularly if their focus is on substantive applications of the model.

Media Strength—Uncorrelated Networks

In this section I consider baseline media that provide unbiased information equal to the global rate of participation. I vary media strength: the level of influence the media have relative to social networks. I first consider uncorrelated networks: nonelite networks and elite networks in which elites are not unified.

Figure 2 displays four plots of the mean equilibrium participation rate against the strength of the media for uncorrelated networks. Each plot illustrates a different combination of network type and motivation class. Lines within each plot show representative values of network parameters. First examine Figures 2A and 2B, which correspond to participation within Small World networks in the intermediate and strong motivation classes, respectively. One difference is striking. In Figure 2A, increasing the strength of the media has either a negative or a nonmonotonic effect on equilibrium participation. In Figure 2B, increasing media strength solely increases participation.

FIGURE 2.

Motivations and Optimal Media Strength in Uncorrelated Networks

To understand this difference, consider the role played by unbiased media. By making known the global participation rate, the media present to individuals a fuller picture of the population. Since people want to participate more if their fellows do, this more complete picture can have different effects depending on both network structure and the distribution of motivations in the population (motivation class). In the strong motivation class there are more highly motivated people, and hence more early participants. The media make these individuals known to those who might otherwise only know about local nonparticipants; thus the media act as a spur. This turns out to be a robust regularity. For all network types, in all network parameterizations, increasing the strength of the media in the strong class increases aggregate participation. Only the rate and amount of increase vary with network type and parameterization. Further, the rate of increase in the strong class is generally faster in the same circumstances in which increasing media strength in the weak or intermediate classes most increases participation. Given this, we will consider only the intermediate class in what follows.Footnote ¹⁸

In contrast, in the intermediate class (and in the weak as well), the media's role is far more conditional on network structure. The optimal level of media strength—that which maximizes participation—is no longer always at M_S = 1; now increasing the strength of the media can diminish participation over a significant range of media strength. Compare Figures 2A, 2C, and 2D, corresponding to Small World, Village, and Opinion Leader networks, respectively, all in the intermediate class and with uncorrelated motivations. For the Small World network only when connectivity is low and weak ties suboptimal can the media have any positive effect on participation, and even here only when the media are less than one-fourth as influential as social networks. In the Village network the media increase participation for some strengths, but begin to be a hindrance well before they are as influential as social networks. In the Opinion Leader network the media are more helpful, particularly when leaders are not as influential.

Within the intermediate class, aggregate participation depends on an often delicate trade-off between encouraging enclaves of participation and allowing for the spread of participation out of these enclaves (Siegel Reference Siegel2009). In the absence of the media, only network neighbors matter. An individual whose neighbors are highly internally motivated may be spurred to participate, even though few apart from these neighbors are participating. Enclaves such as this encourage participation, which can then spread to others. Media that reach all individuals heavily favor participatory spread at the expense of local encouragement. Strong media can inform individuals with participating neighbors about others’ lack of participation. Suddenly participation doesn't look as good to those individuals, and the enclave doesn't form. In the aggregate, decisions like this can result in a spoiler role for the media, in which too much media influence hinders the encouraging role of the social network.

The question, then, is when do the media spur participation, and when do they act as spoilers? In general, the answer is conditional on network type and structure. Thus, in the aggregate, we cannot simply say that the media substitute for or complement social networks, and cannot aggregate individual-level influences on behavior. However, some generalities hold. Specifically, the poorer a particular network configuration is at spreading behavior, the more helpful the media are, and the more media strength can be increased before it starts to diminish participation. Uncorrelated Opinion Leader networks typically are less good at spreading participation than uncorrelated Hierarchies or Villages, which are less good than Small World networks. All else equal, then, we should expect the media's ability to spur participation to decline by network type in this same pattern.

Media Strength—Unified Elite Networks

Thus far we have discussed the media's influence in the context of networks in which elites are not unified in support for an option. Figure 3 extends these results to unified elite networks. Figures 3A and 3B show equilibrium participation rates as a function of media strength when elites in Opinion Leader networks are positively and negatively correlated, respectively. Figures 3C and 3D display the same for Hierarchies. As in uncorrelated networks, we see that nonmonotonic responses to media strength are the norm here. Only in the negatively correlated Opinion Leader network are the media purely a spur to participation.

FIGURE 3.

Optimal Media Strength in Unified Elite Networks

There is a difference, though. For nearly all parameterizations of unified elite networks, the level of media strength necessary for the media to have a noticeable effect on aggregate participation is higher than in uncorrelated networks. In many cases the media do not begin to play a role until they are equally as influential as social networks, and in some cases the media must be fifty times more influential than social networks before they even begin to alter participation levels. In such cases, elites unified in encouraging mass participation are able to drive aggregate behavior as long as they receive a modicum of attention from their followers. If the media are less influential in individual decision-making than social networks (Beck et al. Reference Beck, Dalton, Greene and Huckfeldt2002), then the media will rarely have any effect at all when elites are unified in support for either option.

One can see why this occurs by considering a simple “star” Opinion Leader network, in which the central opinion leader is participating and there are many followers. Each follower's neighborhood only consists of a single participating elite in this case. In this case, even media information weighted nine times more than that from this elite still results in external motivations great enough to allow for the immediate participation of a substantial range of individuals.Footnote ¹⁹ From these early movers, participation quickly spreads. Unified elites send a similarly unified signal to their followers, echoing this simple star example.

This result should not be understated. The power of unified elites in networks is such that an external source of accurate information is unlikely to play a role in mass behavior, calling into question the utility of external propaganda within these types of societies. If leaders are sufficiently influential and all desire some behavior on the part of their followers, there is virtually nothing a realistic media outlet can do by passing along accurate information about the population, even if the outlet is trustworthy.

In fact, not only can the media seemingly do little to stop proparticipation unified elites from getting their way, it can unwittingly aid them when the structure of the network prevents the elites from reaching the entire population with their influence. When opinion leaders are too few or a Hierarchy is too wide, the population can become fragmented. Clusters split from the leadership are insufficiently tied to participants, resulting in their low external motivations. In such cases the media encourage participation by effectively connecting the clusters. The media and networks act in synergy here: first the people in power get a subset of the population to participate, and then news of this subset is passed on to those not so favorably connected, spurring them on. This cycle continues until participation is near total. The truth, in this case, is effectively manipulated by elites to overcome limitations in their network of influence.

One Media Outlet—Polling Bias

I begin exploring the effect of biased media by looking at polling bias with a single media outlet. Here the media's information is biased by some relatively small amount. Figures 4 and 5 each display six plots of participation against media bias, conditional on media strength. (The bar running along the top of each plot indicates the level of media strength for that plot.) Though I will focus on bias in the range of ±3%, the horizontal axes take bias out to ±10%. In all cases, bias acts as expected: positive bias increases participation and negative bias decreases it. For more interesting results, consider Figure 4 first, which illustrates participation in Village networks. The upper-left plot uses M_S = 0.32, for a media outlet roughly half as influential as one's network, within the range suggested by data (Beck et al. Reference Beck, Dalton, Greene and Huckfeldt2002). At this strength the lines corresponding to different network parameterizations almost converge, and do converge at even higher strengths. Consequently, sufficiently strong biased media render most differences between network parameterizations irrelevant for predicting participation.

FIGURE 4.

Polling Bias in Nonelite Networks

FIGURE 5.

Polling Bias in Elite Networks

The magnitude of the effect of bias is striking at this level of strength. Consider the lower connectivity, suboptimal weak tie network parameterization. With no bias, its mean equilibrium participation rate is 65%. With each additional 1% of negative bias this drops to 57%, then 51%, then 39%. Even low levels of polling bias can thus substantially decrease participation. By the same token, a positive bias of only 3% takes participation to 88%. Polling bias is therefore a potent determinant of aggregate outcomes, and a substantial threat to the proper functioning of democratic governments. For example, a swing in bias from −3% to +3% (in this noncompetitive media environment) alters aggregate participation (e.g., turnout or support for one of two candidates) by 49%, enough to swing most elections. Because the level of bias remains small, the media outlet can occasion this swing without necessarily lying to the public: error is within ±3%, as stated in the fine print.

The power of media bias stems from its action early in the spread of participation, when aggregate participation is still small. Positive bias subtly shifts the playing field, increasing the number of individuals willing to participate early. These early participants encourage others via both social networks and the media, increasing aggregate participation. Then, this new level of global participation is again inflated, leading to yet more participation, and so on until a cascade of participation occurs. Negative bias acts in reverse, hiding the activities of the highly motivated and so preventing the initiation of any cascade. The plots in the bottom row of Figure 4 indicate that even weaker media outlets can effectively use bias, by virtue of its role in path-dependent behavior (Page Reference Page2006).Footnote ²⁰

What can limit the damage arising from biased media? One answer, explored below, is competing media. A second answer, shown in Figure 5, is unified elites. Figure 5 mirrors Figure 4, except in an Opinion Leader network. The central line displays the uncorrelated case under high leader influence as a baseline. We see again that media bias is powerful in uncorrelated networks: a +3% bias increases participation from 43% to 61% when M_S = 0.48, as seen in the bottom right plot. Look, however, at the other two lines, which correspond to a negatively and a positively correlated Opinion Leader network, again under high leader influence. At this same level of media strength, bias only begins to have an effect when it is large, and even then the effect is comparatively small. Only when the media outlet has more influence than one's own social network do lower levels of bias begin to diminish the power of elites. Unified social elites are thus a solution to a biased media outlet, though in a sense one is merely replacing one monolithic influence for another.

One Media Outlet—Advocacy

Polling bias can occur under the radar, given that it is a slightly altered informative message. This might be desirable for biased media outlets looking to maintain widespread trust. Advocacy, however, is apparent to all. Media outlets such as the old partisan papers, Fox News, or MSNBC, or many Internet news sources openly advocate for a particular option and so do not hide their biases. Positively biased advocates come out for participation, while negatively biased advocates argue for the status quo. Figure 6 displays two plots of participation against media strength. The top plot is of three parameterizations of a Small World Network, the bottom of the positively and negatively correlated cases of an Opinion Leader network under high leader influence. Each line corresponds to one network parameterization; the upper (lower) portion of a line represents positive (negative) bias.

FIGURE 6.

Advocacy (upper (lower) Portion of Each Line Reflects Positive (negative) bias)

The top plot illustrates the incredible effectiveness of advocacy. Even low-strength media—which may be all one can expect given open bias—is sufficient to drive participation much higher or much lower, dominating any role of network parameterization. This is again due to the path-dependent nature of the process. The manner in which the media's information affects external motivations, and, therefore, the decision to participate, implies that advocacy functions in much the same way as uniformly increasing (or decreasing) the internal motivations of the population. This has a strong positive (or negative) effect on participation levels, since it significantly affects the number of early movers whose behavior is translated into increased (or decreased) aggregate participation in the network.

Two differences distinguish advocacy from polling bias: (1) Its greater bias implies that it alters participation levels more strongly and requires less media strength to be effective. (2) As seen in the bottom plot, an influential media advocate is able to partially overcome the power of social elites. While an advocate in the presence of unified elites requires more strength for its influence to be effective than it does in the absence of unified elites, advocacy still plays a role at a level of media strength that makes media less influential than social networks. Consequently, even social elites are not proof against an openly biased media outlet, as long as it is sufficiently influential. Given this, I now turn to the other means by which a biased media outlet might be neutralized: countervailing media.

Multiple Media Outlets

We have seen that bias is powerful. So powerful, in fact, that one would expect to observe additional biased media outlets whenever there were no institutional constraints prohibiting them. Competition for attention will often lead them to take opposite positions, and here I explore one such case. I assume two oppositely biased outlets, L and R, with L (R) positively (negatively) biased.Footnote ²¹ However, the analysis extends in a straightforward fashion to more than two outlets of varied bias, at the cost of additional parameters representing the relative influence of the outlets.

Consider polling bias first. Here each media outlet sends information equal to the global participation rate plus its bias. This implies that the net information from the media is equal to the global participation rate plus a linear combination of each outlet's bias. This linear combination is the net polling bias.Footnote ²² This is equivalent to a single biased media outlet employing the net polling bias. Given this, the results described in and around Figures 4 and 5 already apply to the case of multiple media sources employing polling bias.Footnote ²³ For outlets of equal strength, if L's bias exceeds R's the net media information drives participation higher; if R's exceeds L's the net information drives participation lower. If we assume that biased sources will seek the highest level of bias they are able to slip by their audiences and that individuals tolerate the same level of bias from each outlet, the media outlets’ biases will cancel out, providing accurate net information. However, if L (R) had greater strength than R (L), or L (R) could maintain a similar level of strength with greater bias than could R (L), then the net message would drive participation higher (lower). In other words, the media outlet better able to maintain its strength despite bias has the advantage.

Thus, multiple media outlets can, under some conditions, be a panacea for polling bias. What about advocacy? Because advocates effectively ignore the global participation rate, the net media information is now just a linear combination of 1 (full participation) and 0 (no participation). Since the relative strength, M_L, multiplies the former, the net information is just M_L.Footnote ²⁴ In general, the relative strength of the proparticipation outlet will not be equal to the global participation rate, so two advocates—even equally balanced ones—will not see their influences cancel out. Instead, they are equivalent to one outlet sending intermediate information between 0 and 1.

Figure 7 illustrates the effect of intermediate information. As usual, the vertical axis reports equilibrium participation rates; the horizontal axis now displays the relative strength of L (M_L). As in Figure 6, the top plots illustrate participation given three representative parameterizations of a Small World network, while the bottom illustrate participation given elites with positively and negatively correlated motivations. The middle two plots (B and E) look at a lower level of media strength (M_S = 0.2), the top-right and bottom-right plots (C and F) look at a higher level of media strength (M_S = 0.6), and the top-left and bottom-left plots (A and D) provide results without media (M_S = 0) as a baseline.

FIGURE 7.

Advocacy—Two Opposing Media Sources

The most important commonality in the plots is that there is an asymmetry between the positive and the negative outlet. In all cases, the net effect of two opposing outlets with equal strength (M_L = 0.5) is an increase in participation over the baseline. It is easier, and so requires a weaker media outlet, to spur participation (or to drive support away from the status quo) than it is to quell participation (or maintain the status quo). In a democracy, one would therefore expect it to be easier for a biased media to agitate contrary to the incumbent government than in support of it, coloring what media strategies would be effective by the incumbent government and the positioning of successful media outlets.

The reason for this asymmetry is that intermediate information has two effects. First, early in the spread of participation, when global participation is still low, intermediate information encourages more people to participate. Second, when participation is widespread, it discourages people from participating. The first effect is more powerful than the second because of the path-dependent nature of the process: the first ensures that there will be more participants in each person's social network early in the process, allowing network interactions to spread participation. Once participation is widespread any discouragement will have a limited effect, since it also has to overcome behavior in one's social network. There will be similar, though more involved, logic when we discuss selection into media in the next section.

There is a more subtle thing to note as well in Figure 7. Without unified elites intermediate information is effective; however, unified elites can more easily limit the effect of intermediate information than they could the stronger bias of a single advocate. Thus, unified elites facing influential and biased media would do well to encourage the growth of a countervailing media outlet. However, as we see in Figure 7F, were the media to gain in strength, two equally strong media outlets would diminish the ability of elites to dictate aggregate outcomes, so elites must be cautious against this development.