INFLUENCE, COMMUNICATION, AND THE SURVIVAL
OF POLITICAL DISAGREEMENT AMONG CITIZENS
R. Robert
Huckfeldt, University of Indiana (huckfeld@indiana.edu)
Paul E. Johnson,
University of Kansas (pauljohn@ku.edu)
John D. Sprague,
Washington University in St. Louis John (sprague@artsci.wustl.edu)
Michael C. Craw,
University of Indiana (mcraw@indiana.edu)
August 17, 2000
Abstract
This paper combines results from survey research and simulation to address questions about the nature and impact of interpersonal political communication. The first part of the paper integrates the results of recent surveys about political discussion and the nature networks in which political ideas are discussed. While long-standing theories predict that people will not generally interact with others when their political attitudes differ, the accumulating evidence points to a different conclusion, namely that interactions of people with distinct political views occurs and, while persuasion occurs, it does not completely eliminate diversity.
The formation of discussion networks and their impact on political opinions is difficult to study empirically because most of the important variables are endogenous. Following Axelrod's suggestion that an agent-based model can be a useful tool for "thought experiments" and clarification of theory, we have used the Swarm simulation toolkit to investigate the formation of discussion networks and implications of theories about persuasion and information exchange.
Prepared
for delivery at the annual meeting of the American Political Science
Association, Washington, DC, Aug. 31-Sept. 2, 2000. The authors would like to thank Marcus Daniels of the Swarm
Development Group and Rick Riolo of the Center for the Study of Complex Systems
at the University of Michigan.
INFLUENCE, COMMUNICATION, AND THE SURVIVAL
OF POLITICAL DISAGREEMENT AMONG CITIZENS
A
central issue in the study of democratic politics is the capacity of citizens
and electorates for tolerating political disagreement. The model of a free, open, and democratic
society is one in which political issues are fully explored and political
debates are fully aired. In such a
society, citizens are open to persuasion, the social boundaries on political
viewpoints are fluid and shifting, and individuals encounter the full spectrum
of issue positions and political viewpoints.
How does this model correspond to
contemporary analyses of citizens and democratic politics? At one analytic extreme, citizens play the
role of individually autonomous actors, oblivious to the experience of
political disagreement. Individual
preferences inform individual choices, and these preferences are idiosyncratic
to particular, socially self-contained individuals. Hence, the preferences and choices of one person become irrelevant
to the preferences and choices of another, and political disagreement among
citizens becomes irrelevant to political outcomes.
At an opposite extreme, inspired
by a conformity model of social influence (Asch 1956), some analysts see
citizens as the individually powerless dupes of an irresistible social
influence process. The psychic discomfort of disagreement causes individuals to
reduce dissonance through various means (Festinger 1957). In particular, individuals adopt socially
prevalent viewpoints and, just as important, they avoid disagreement in the
first place by censuring their patterns of social interaction to create
politically homogeneous networks of political communication.
Neither of these analyses is able
to accommodate the survival of disagreement in patterns of meaningful
communication and deliberation among citizens.
In the model of the self-contained citizen, communication is not
meaningful because it is extraneous to the formulation of individual
choice. In the conformity model,
disagreement is extinguished through censured patterns of interaction and
powerful mechanisms of social influence.
The strategy of this paper is
twofold. First, we evaluate empirical
evidence regarding the survival of political disagreement among citizens within
their naturally occurring patterns of social interaction. Second, we evaluate a dynamic, agent-based
model of political persuasion to assess the mechanisms that might sustain
disagreement among citizens. In both
instances we assume that communication among citizens is politically
consequential - that citizens are politically interdependent in the
sense that they rely on one another for political information, expertise, and
guidance. The primary question becomes,
what are the conditions under which diversity of opinion is likely to be
sustained?
CONFORMITY AND THE SURVIVAL
OF POLITICAL DISAGREEMENT
In their pioneering studies of social influence among
citizens, Lazarsfeld and his colleagues (Lazarsfeld et al. 1944; Berelson et
al. 1954) argue that political communication among citizens becomes less
frequent during the period between election campaigns, and hence political
preferences tend to become individually idiosyncratic. As the frequency of political communication
increases in response to the stimulus of the election campaign, these idiosyncratic
preferences become socially visible, and individuals are correspondingly
brought into conformity with micro-environmental surroundings.
These arguments were based, at
least implicitly, on the micro-level foundations of group conformity and its effects. In the context of an election campaign, the
dynamic logic of group conformity pressures is quite compelling. Before the campaign begins, people are less
concerned about political affairs, and hence their conversations focus on
other, nonpolitical topics: baseball, gardening, etc. As long as their political preferences are socially invisible,
they are immune to conformity pressures, and hence preferences become
individually idiosyncratic. As the
campaign accelerates, so does the rate of political communication among
associates, and individual political preferences are increasingly exposed to
social scrutiny. The stage is thus set
for the introduction of conformity pressures that bring individual preferences
into line with the preferences that are dominant within networks of social
relations.
Carried to its extreme, the logic
of group conformity suggests that political disagreement should disappear
within networks of social relations.
Pressures toward conformity might drive out disagreement in several ways
(Festinger 1957; Huckfeldt and Sprague 1995).
First, the discomfort of disagreement might encourage people to modify
their patterns of social relations so as to exclude people with whom they
disagree, or in a less extreme form, to avoid political discussion with
associates who hold objectionable preferences.
Second, and partially as a consequence of discussion avoidance, people
might incorrectly perceive agreement among those with whom they actually disagree. That is, a well-documented bias exists in
which people over-estimate the extent to which others hold their own political
preferences (Huckfeldt and Sprague 1995; Fabrigar and Krosnick 1995). Finally, and perhaps most importantly,
individuals might bring their own preferences into correspondence with the
preferences that they encounter within their networks of social relations - they may be influenced by the preferences of others.
Contrary Evidence Regarding Disagreement
As compelling as the group
conformity argument may be, it suffers from one major shortcoming - disagreement is not typically extinguished within networks of
social relations, even at the end of high stimulus presidential election
campaigns. At the end of the 1984
election campaign, Huckfeldt and Sprague (1995) interviewed discussants who had
been identified by a sample of respondents from South Bend, Indiana. And at the end of the 1992 election
campaign, Huckfeldt et al. (1995) interviewed discussants who had been
identified by a nationally drawn sample of respondents. In both instances, no more than two-thirds
of the discussants held a presidential candidate preference that coincided with
the main respondent who named them.
These levels of disagreement
become even more important when we recall that they are based on dyads rather
than networks. If the probability of
dyadic disagreement within a network is .7, and if the likelihood of
disagreement is independent across the dyads within a network, then the
probability of agreement across all the relationships within a three-discussant
network drops to .73 or .34.
In other words, disagreement and heterogeneous preferences are the
rule rather than the exception within the micro-environments surrounding
individual citizens.
The pervasiveness of disagreement
within networks of social relations forces a reassessment of social conformity
as a mechanism of social influence, as well as a reconsideration of the dynamic
implications that arise due to politically interdependent citizens. In the
analysis that follows, we assess the conditions that give rise to socially
sustained disagreement. Our argument
is, quite simply, that complex patterns of communication among citizens might
sustain as well as extinguish patterns of disagreement among citizens.
EVIDENCE FROM THE 1996 INDIANAPOLIS-ST. LOUIS STUDY
The 1996 Indianapolis-St. Louis
election study was conducted by the Center for Survey Research at Indiana
University. Its primary focus is on
patterns of communication over the course of the campaign, and thus interviews
began late in February of 1996 and stopped in early January of 1997. The study includes two samples: a sample of
main respondents (N=2,174) drawn from the lists of registered voters, combined
with a one-stage snowball sample of these main respondents' discussants (N=1,475). The main respondent samples are drawn from
two study sites: (1) the Indianapolis metropolitan area defined as Marion
County, Indiana; and (2) the St. Louis metropolitan area defined as the
independent city of St. Louis combined with the surrounding (and mostly
suburban) St. Louis County, Missouri.
Our pre-election main respondent sampling plan was to complete
interviews with 40 main respondents each week before the election, equally
divided between the two study sites.
After the election, an additional 800 respondents were interviewed, once
again divided between the St. Louis and Indianapolis metropolitan areas. Discussant interviews were completed at a
rate of 30 interviews each week during the pre-election period, with an
additional 500 interviews conducted after the election. In the pre-election period, the discussant
interviews for a particular main respondent were completed within two weeks of
the main respondent interview.
Every
respondent to the survey was asked to provide the first names of not more than
5 discussion partners. A random half of
the sample was asked to name people with whom they discuss ``important
matters''; the other half was asked to name people with whom they discuss
``government, elections, and politics'' (Burt 1986; Huckfeldt et al. 1998b). At the end of the interview, we asked
the main respondents for identifying information that we might use to contact
and interview their discussants. Based
on their responses we interviewed nearly 1,475 discussants, employing a survey instrument
that was very similar to the instrument used in the main respondent
interview.
Table
1 shows the self-reported voting preferences for main respondents and
discussants, and the results closely parallel those of earlier studies. Nearly 60 percent of Clinton respondents
name discussants who report supporting Clinton, and nearly 70 percent of Dole
respondents name discussants who report supporting Dole. The level of shared preference drops off
dramatically for respondents who support Perot, but these results are
coincidental with other results for individuals who hold preferences that
constitute a minority (Huckfeldt et al. 1998a). Hence, while substantial agreement is present within these dyads,
the disagreement is far less than complete.
Do these
individuals recognize the presence of disagreement among their associates?
Either because they avoid the discussion of political topics, or because they
misperceive in effort to avoid cognitive dissonance, or because they myopically
generalize on the basis of their own preferences, it is not uncommon for
individuals to infer incorrectly that others hold their own preferences. This tendency is present among the
Indianapolis-St. Louis respondents - 66 percent of the
Clinton supporters perceive that their discussants support Clinton, and 73
percent of the Dole supporters perceive that their discussants support
Dole. Thus, while perceived levels of
agreement are higher than the actual levels, the differences are not
great. And regardless of whether we consider
the perception or reality of disagreement, it becomes clear that these
communication networks are not characterized by political homogeneity.
Other analyses of these data
demonstrate a variety of factors that enhance or inhibit political influence
among and between citizens. In
particular, the influence of any given discussant is enhanced to the extent
that other discussants within the network hold preferences that are the same as
the discussant (Huckfeldt, Johnson, and Sprague 2000). That is, the political influence of a
Democratic discussant is enhanced to the extent that the citizen is imbedded in
a communication network that is homogeneously Democratic.
Results such as these, which
suggest that minority preferences are less likely to be politically influential
within networks of communication, add to a significant body of evidence
suggesting that political minorities operate under pronounced disadvantages in
democratic politics (Miller 1956).
Other efforts show that minority preferences are less likely to be
communicated effectively, and hence less likely to be recognized, even by
fellow members of the minority (Huckfeldt et al. 1998a; Huckfeldt and Sprague
1995). In other words, the influence of the discussant's preference is weighted
by majority-minority standing within networks of social communication. Hence, the preferences of those in the
political majority count more heavily in the deliberative process than the
preferences of those in the minority.
Majoritarian Biases and the Survival of Disagreement
Regardless
of the cumulatively bleak picture for the communication and influence of
minority preferences, there is no evidence here to suggest that minorities tend
to be eliminated as part of the deliberative process (see Moscovici, Mucchi-Faina,
and Maass 1994). This is especially
striking because we are defining minority and majority preferences relative to
closely held social environments created through the communication networks of
individual citizens. Within this
context, only 36 percent of the main respondents who support Dole or Clinton
perceive that all their discussants hold the same candidate preference. In other words, a lack of political
agreement is the modal condition among our respondents, even within enduring
networks of communication and association.
This raises an important question - how is the minority able to survive?
At an aggregate level, perhaps the most important factor maintaining
political minorities is the Markov principle.
That is, a small defection rate operating on a large (majority)
population will at some point be at equilibrium with a large defection rate
operating on a small (minority) population.
Hence, minorities survive due to the stochastic logic of mathematical
equilibria.
Minorities are also more likely to
survive when the micro-environments created through networks of communication
are not closed systems. In particular,
even if Joe and Bill are reciprocally related to one another as discussants and
close friends, their micro-environments may be almost completely
independent. Hence, Joe and Bill may
hold different political preferences, and yet both may be part of a political
majority within their own respectively defined networks of political communication.
Several simple network structures,
as well as their implications for the survival of disagreement, are considered
in Figure 1. Individuals are
represented as ovals, discussant relationships as connecting lines, and the
presence of a particular political preference as the presence or absence of
shading in the oval. In Part A of the
figure, each individual is connected to each of three other individuals in a
self-contained network of relations. In
such a situation, disagreement is quite likely to disappear, and only the
heroic individual is likely to sustain an unpopular belief. In contrast, Part B of Figure 1 shows two
sub-networks of four individuals each, where every individual is connected to
every other individual within the sub-network.
In addition, one individual within each sub-network is connected to one
individual in the other sub-network, thereby providing a bridge that spans a
structural hole between sub-networks (Burt 1992). In this setting, while agreement is likely to be dominant within
each of the sub-networks, disagreement will be socially sustained
between the individuals who bridge this particular type of structural hole.
How important are such networks to
the survival of disagreement? One way
to address this question is by examining the networks of both main
respondents and their discussants.
The interview with the discussants included the same network name
generator that was employed in the interview with the main respondents. Thus we are able to compare (1) the main
respondent's perception regarding the political composition of the main
respondent's network with (2) the discussant's perception regarding the
political composition of the discussant's network. Guided by Part B of Figure 1, we are particularly interested in
the composition of the residual networks - the networks that
remain when the two members of the dyad are removed.[1] Two questions
arise. First, how closely related is
the political composition of the discussant's residual network to the political
composition of the main respondent's residual network? Second, does this relationship depend on the
existence of political agreement or disagreement between the main respondent
and the interviewed discussant?
In Part A of Table 2, the
percentage of the discussant's residual network supporting Clinton is regressed
on the percentage of the main respondent's residual network supporting
Clinton. The regression is estimated
twice - once for all dyads in which interviewed discussants
and main respondents each name at least two discussants, and a second time for
all dyads in which the main respondent and the discussant each identify more
than two discussants. In both instances
we see a positive slope with a large t-value and a small R2. In short, the political composition of the
main respondent's residual network generally resembles the political
composition of the discussant's residual network.
These simple regressions are
repeated in Parts B and C of Table 2, first for main respondents and
interviewed discussants reporting the same candidate preferences, and then for
main respondents and interviewed discussants reporting different candidate
preferences. For the agreeable dyads,
we see an enhanced relationship in the form of a larger regression slope, as
well as a larger coefficient t-value and an enhanced R2. In contrast, for the disagreeable dyads, we
see a reversed slope of smaller absolute value with a nearly non-existent R2,
but with a coefficient t-value that supports the presence of a discernible
negative relationship.
What do these results
suggest? Agreement within dyads is
typically sustained by larger networks of communication that simultaneously
support the preferences of both individuals within the dyad. In contrast, we see at least some evidence
to suggest that disagreement is also socially sustained, but by politically divergent
networks that serve to pull the two members of the dyad in politically opposite
directions. At the very least,
disagreement within dyads is characterized by political independence between
the two participants' larger networks of association and communication.
In summary, the survival of
disagreement within dyads is profitably seen within larger patterns of
association and communication. The
logic of social influence creates a bias in favor of majority sentiment,
thereby making it difficult for disagreement to be sustained. Indeed, to the extent that networks of
communication and influence constitute closed social cells, characterized by
high rates of interaction within the network but very little interaction beyond
the network, we would expect to see an absence of disagreement among and
between associates. Indeed, the
survival of disagreement depends on the permeability of communication networks
crated by "weak" social ties (Granovetter 1974) and the bridging of
structural holes (Burt 1992). At the
same time that these ties lead to the dissemination of new information
(Huckfeldt et al. 1995), they also bring together individuals who hold
politically divergent preferences, thereby sustaining patterns of interaction
that produce political disagreement.
Where has this review and analysis
led? Two tentative conclusions seem
warranted:
1. Disagreement is more likely to
survive to the extent that networks of political communication are
characterized by low density levels - to the extent that
associates do not share identical sets of associates. To the extent that the friends of your friends are not
necessarily your friends as well, a situation is created in which disagreement
as well as agreement might be socially sustained.
2. These low density networks
expose individuals to higher levels of disagreement in their closely held
communication networks. And the
potential for such disagreement to be sustained is further enhanced by a
mechanism of influence that places disproportionate weight on preferences that
are widely held. For example, you and
your coworker are more likely to sustain your disagreement if your remaining
friends share your political preferences and her remaining friends share her
political preferences.
How are we to evaluate the
implications of these conclusions? We
certainly do not possess the data that would be needed for a full evaluation of
political homogeneity and diversity among and between the networks of
communication within which citizens are imbedded. Indeed, such a body of information is, as a practical matter,
quite nearly inconceivable. And hence
our efforts point in a different analytic direction.
AGENT BASED MODELS OF SOCIAL INFLUENCE
In
the remainder of this paper, we pursue a modeling strategy inspired by Axelrod
in his analysis of cultural dissemination (1997). Axelrod constructed an agent-based model which explored the
emergent properties of small scales social interaction. Axelrod's model conceptualizes interactions
among agents in the following way. A
square grid of agents, described as villages, is created. Each village has a culture, represented by
an array such as (0,1,2,1,4), where each “trait” is randomly assigned at the
outset. Each element of the array is
called a “feature.” These represent
cultural issue dimensions or topics.
The
Axelrod simulation proceeds along these lines.
Each village is conceived of as a unitary actor. An agent (village) is randomly selected and
given the opportunity to interact with a randomly chosen neighbor. The set of neighbors is a truncated von
Neumann neighborhood. Except for agents
on the edge of the grid, the neighbors are found on the on the east, west,
north, and south borders. Cells that
lie on the outside boundaries are only allowed to look into the grid for
neighbors (in other words, Axelrod does not employ a model in which the space
“wraps around” to form a torus on which agents are situated (in contrast, see
Epstein and Axtell, 1996). After a
random neighbor is selected, an interaction occurs with probability equal to
the similarity of the traits of the two agents. If the interaction occurs, then an issue on which the two
disagree is selected at random and the agent's opinion on the issue is changed
to match the other. Hence, influence
automatically follows whenever interaction occurs.
Axelrod
made a number of observations on the basis of his model, the most striking
being that, over the long run, there is not likely to be very much cultural
diversity. While the tendency toward
homogeneity is greater for some parameter settings than others, it is powerful
in all cases.
When
diversity survives in the Axelrod model, it is a diversity of the most extreme
sort. Different cultural clumps are
completely homogeneous and totally isolated from one another. If a village interacts, it interacts with
villages that are identical to it. As
Axelrod shows, separate groups do not form in some conditions, but they are
more likely to form if the number of traits per feature is high. Under those conditions, two agents are less
like to have anything in common and so they never interact. He shows that the number of clusters
decreases as the number of features increases, and the number of clusters
increases as the number of traits increases.
Axelrod's
conclusion poses a challenge for the current modeling exercise. If we are to formulate a useful model of
political communication within small networks of citizens, we do not want the
major implication of the model to be that diversity is unlikely to exist. One solution is to create agents who are
individually resistant to environmental influences, but that is not the route
explored here. Rather the emphasis is
on developing a more intricate understanding of the formation of networks and
the formulation of public opinion. Using
an Axelrod-style model as a baseline, our own analysis turns elsewhere to
consider the consequences of several other, newly introduced, model
features.
The model is implemented in
Objective-C using the Swarm Simulation Toolkit (Minor, et. Al, 1996; we used
version 2.1.4.2000-07-26). Swarm is
currently being supported by the Swarm Development Group, a nonprofit
membership organization (http://www.swarm.org). The modeling project we describe here introduces a raft of
variables that can be inspected, including the basics like the size of the
grid, the number of features and traits, the scheduling of agent actions, and
so forth. The substantively important
additions concern the processes through which others are sought out for
discussion and opinions are adjusted.
In Appendix 1, we present a summary of the features of the simulation as
it currently stands. In addition to
introducing a number of system and individual level parameters, we also have
introduced summary measures for the diversity of opinion (entropy) as well as
measures of individual perceptions of diversity. These are discussed below (see also, Johnson 1999).
We have pursued this agent-based approach as an alternative to cellular automata. Projects by Latane, Nowak, and Liu (1994) and Nowak and Lewenstein (1996) have used a cellular model in which cells are subjected to influence of varying degree from neighbors to demonstrate some interesting emergent phenomena. The agent-based model can incorporate the strengths of that approach, but it can add a variety of new features, perhaps most importantly the movement of agents within and across the grid and the development of individually distinct logics that govern network development.
1. A Baseline Model
Our baseline model is designed to
replicate (nearly exactly) the results of Axelrod while building a structure
for further study and comparison. In
the computer model, each agent in the model is conceived of as a separate
“citizen” object, which has the ability to move about, initiate interactions,
and adjust its opinions. The baseline
model is a restricted version, since the agents are distributed evenly over a
10 by 10 grid and they are fixed in positions.
In these models we describe in this paper, we have set the number of
features at five and the number of traits at 3.
Our model is designed to
incorporate agent movement. We have done
so with "dynamic scheduling" (see Johnson and Lancaster, 2000:
Chapter 9.6). Swarm is a discrete event
simulator, meaning that time is broken-up into small time steps. We have structured our model so that each
agent plans its activities over the course of a “day”, which is a predetermined
(in this case, 10) number of time steps.
At the beginning of each day, the agents are randomly sorted and each is
told to schedule its movements throughout the day and to select (at random) a
time during the day at which to initiate an interaction. In the baseline model, the agents are not
allowed to move. In this baseline
model, the agent looks for a discussion candidate in the way that Axelrod
described, i.e., a discussion candidate is chosen at random from the neighborhood
and interaction occurs with probability equal to the similarity of the two
agents. (If one sets the day to length
1, and selects only one agent for an interaction per day, then this model is
identical to the original Axelrod model.)
When an agent finds a discussant, then the agent will copy one feature
on which the two differ from the discussant.
As
the simulation proceeds, the agents are keeping records about the others they
have encountered. They note, first,
what fraction of the discussion candidates they encounter agree with them about
a randomly chosen feature (when they find such a common feature, we call them
"acquaintances" because an interaction will follow). Among the people selected for interaction,
the agent makes note of the proportion of features on which it agrees with the
discussant (the degree of "harmony"), and it also notes if the
agent's features are identical to its own.
Each agent uses a 20 period moving average to tally these
observations. We can aggregate these
individual perceptions by calculating various summary statistics.
The
baseline model produces a pattern of interaction which is consistent with the
original Axelrod results. A graph
depicting three summary measures calculated from one run of the model is presented
in Figure 2. The most obvious feature
of Figure 2 is that all three measures converge to unity. First, the "acquainted" line
indicates the average proportion of random encounters that produce
interaction. A higher acquaintance rate
reflects a higher level of shared preferences among individuals a
neighborhood. As time goes by, more and
more neighbors find themselves open to interaction with a randomly chosen
neighbor. Second, the
"harmonious" line indicates the level of agreement between people who
interact. It reflects perceptions within
networks of interaction, which indicate that the chances
of disagreeing about any particular issue are diminished over time. Finally, the "identical" line
indicates average of individual perceptions of the extent to which the people
that they are identical to the people with whom they interact. Here again, the focus is on those agents
engaged in interaction, and particularly the proportion of interacting agents
who hold identical positions on all five issues. Not only are people open to more interaction, but also those
interactions are increasingly likely to result in total agreement between the
agents.
This
particular run is not significantly different from the others we conducted with
these parameter values. We set the
model so that it would terminate the simulation if no opinion change was
observed for 10 consecutive days, or 100 time steps. The average number of steps to termination is 8972.9, and in each
of the 100 runs, all diversity was eliminated.
Entropy, an index of diversity across the population of opinion, drops
to 0 in all cases.
Quite
clearly, this model does lead to the same outcome as the Axelrod model. Equally clearly, does not correspond to our
empirical observations. Uniformity and
a lack of disagreement are not standard features of the political landscape,
and our objective in this analysis is to consider several changes in the
specification of the model that would yield a more believable world.
2. The Impact of Self-Selection
What if
people are not so selective in political interaction? We have investigated that question by relaxing the self-selection
assumption by incorporating the early work of Coleman (1964: chap. 16). In his effort to relax baseline assumptions
of random mixing in patterns of interaction within populations, Coleman
introduced a parameter that allowed individuals to interact even when the
parameters which governed the model would ordinarily dictate otherwise. Rather than ignore (with certainty) a person
that is different from oneself in every respect, the Coleman model introduces
the possibility of interaction between these different sorts of people. Perhaps the homogenizing influences which
drive the Axelrod model can be abated if individuals are not completely
isolated from others with which they disagree about everything.
We
have adapted Coleman's logic to the current context. As in the Axelrod model, the agent chooses a candidate at random
from the neighborhood. The candidate
will be accepted with probability equal to the similarity of the agents. However, if that discussant is rejected,
then with a given probability (which we call the Coleman parameter), an
interaction occurs. Hence, as the
Coleman parameter grows larger, the diversity of interaction increases. If the interaction does not take place, the
individual repeats the search process, until an interaction partner is located
or ten efforts have been made.
Why would political communication
take place with politically disagreeable individuals? Perhaps the individual failed to recognize the absence of
political agreement, or the individual does not care much about politics or
political issues, or the individual enjoys political argumentation, or the
individuals simply like the same baseball team, or the two individuals work
together and political discussion is unavoidable. The point is that selection is an imperfect, stochastic mechanism
with systematic slippage, and by adding the Coleman parameter we are able to
examine the consequences of relaxing self-selection.
In
Figure 3a, we illustrate one simulation of the model with the Coleman parameter
set to .2, meaning that, if one were to reject a discussion candidate on the
first pass, there is a .2 probability that an interaction will occur anyway. The random numbers used in this simulation
are the same as the baseline model, so the difference between these two runs
results solely from the introduction of the Coleman logic. The conclusion from Figure 3a is that
reducing the level of self-selection serves to produce a more rapid
convergence to political homogeneity.
That is, the presence of self-selection serves to sustain
disagreement. People who avoid
interaction with politically disagreeable encounters are acting to sustain
their own cluster of beliefs. The
attenuation of self-selection does not change the fact that, over the long
haul, disagreement disappears. But the
preservation of these small clusters tends to delay the process of political
homogenization.
In
Figures 3b and 3c the Coleman coefficient is increased to .5 and .8
respectively. We present these graphs,
partly for completeness, but also to illustrate something important about
simulation research. Claims about the
impact of parameter changes should be made on the basis of many runs, rather
than a few. On the basis of these
graphs, one would suspect that raising the Coleman coefficient would delay the
eventual homogenization of opinion.
These models begin with conditions that are exactly the same—the same
individual opinions—as the models we have illustrated in Figures 2, and 3a, and
so that is a reasonable conclusion.
However, the overall pattern is in the opposite direction. The duration of simulations with the Coleman
parameter equal to .5 is greater in 45 of 100 simulations. The average number of periods until the
model terminates is 8040.8, 6776.4, and 5842.5, when the Coleman parameter is
.2, .5, and .8, respectively. The
distribution of outcomes is shown in Figure 4,and it supports the contention
that exposing agents to political interactions with others who are completely
different can shorten the survival of political diversity.
In
summary, relaxing the self-selection effect does not transform the results of
the model. Indeed, the attenuation of
self-selection only serves to accelerate convergence toward a politically
homogeneous outcome.
3. The Impact of Geographic Dispersal
The model variations we have
considered thus far are geocentric in their assumptions and organization. In other words, the encounters that produce
opportunities for social interaction are all spatially organized. None of the agents have the ability to form
associations with individuals who are located beyond the four cells that are
contiguous to their own. This is, of
course, an imperfect and perhaps misleading abstraction. The modern citizen sleeps in one
neighborhood, works in another, plays softball in a third, and goes to church
somewhere else. Indeed, modern communication
and transportation technologies may serve to minimize the importance of
geographically defined proximity.
We accommodate geographic
dispersal by incorporating the possibility of movement between grids. For ease of discussion we refer to these as
work grids, but they might be church grids, or softball grids, or even bowling
grids. There can be any number of home
grids and work grids in the model. In
the specific examples below, the five work grids (size 5 x 5) are smaller than
the home grid (size 10 x 10). The
agents are assigned coordinates in the work grids in a completely random way,
so the work grids can have several agents in a single cell, and there can be
empty cells as well. (Multiple of
occupancy of cells is allowed by a subclass of Swarm's Grid2d that we have
created). The important point is that
interaction in these workplaces is completely independent of geographic
location in the home grid. Each
individual is randomly assigned to one and only one position on one of the work
grids, and hence the work grids provide a formal representation for
geographically dispersed networks of social interaction.
The allocation of time during the
day makes use of the scheduling scheme described above. Each agent begins the day in a
"home" grid (this allows us to conduct a daily survey of their
experience at the start of the day).
Each day has 10 periods, and during the first period agents are told to
schedule their activities during the remaining 9 time periods. No interactions take place during that
scheduling period. Agents first
schedule their movement from home to work.
Then they can schedule themselves to interact at any time step during
the day, except when they are “in transit” from one grid to another. The number of time steps spent at home is
governed by an individual trait called “home duration.” This variable is set when the model begins
by adding 1 to a draw from a Binomial distribution B(9,h), meaning 9
"trials" with probability of success equal to h. If h=0.5, then most agents spend “about
half” of their time at home, while some spend significantly more and some spend
less. If “home duration” is 1, then the
agent is in the home grid only in the first time step of the day, and then
moves to the work grid. Conversely, an
agent whose home duration is 10 will never go to work, and will thus never have
direct exposure to that grid’s occupants.
At some randomly chosen time step during the day, possibly at home or at
work, the agent will initiate an interaction.
The interaction can occur only with other agents who happen to be in
that grid at that time, and thus a source of heterogeneity is created. Since the work grids may have more than one
citizen in each spot, a second sort of heterogeneity is created, because agents
who look “up” might not always find the same discussant. Hence, the difference between the baseline,
as depicted in Figure 2, and this new scenario, as in Figure 5a, results from
greater heterogeneity of exposure.
The intuition that guides the
development of this model is that exposing agents to interaction with agents
with different backgrounds can ameliorate the forces which homogenize opinion
in the home neighborhood. This
intuition is not valid, however. Recall
that the average number of time steps to convergence in the baseline model is
8,972.9, but the average for this model is 6,275. A comparison between Figure 5a and Figure 2 illustrates this
phenomenon. Figure 5a shows time paths
that converge toward a homogeneous equilibrium over the long haul. The observed path to convergence appears
different, however, for the first 2000 time periods. Figure 5a shows that the initial level of political diversity is
much higher with geographically dispersed opportunities for social interaction.
What happens when people spend all
their time at in the work grids? The
results we obtained mirror the results for multi-agent cells presented in
Johnson (1999). In Figure 5b, the
home-grid probability is set to zero, which means that all opportunities for
interaction occur at the work grid.
Perhaps not surprisingly, the pattern of convergence is similar to that
of the baseline model, except in this case the rate of convergence is much
quicker. The average number of time
steps is 1024.5, about one-eight of the baseline model. This happens for several reasons, but we
believe the two most important are the smaller size of the work grid (5x5) as
well as the possibility of open cells that act as “firewalls” isolating agents
from each other.
In summary, our imposition of
geographically dispersed social interaction does not alter the outcome of the
baseline model in any fundamental sense.
The end result continues to be a politically homogeneous community that
is devoid of disagreement and diverse political opinions. More analysis is clearly warranted,
however. In particular, a natural next
step is to produce initially skewed distributions of opinion in several
alternative home grids that are socially segregated from one another, combined
with non-geographically based work grids that combine individuals from all the
alternative home grids. This would
produce an opportunity to study the maintenance of political divergence between
politically disparate communities, and the consequences of cross-cutting
institutions on the survival of political diversity between and among
geographically based communities (see Fuchs 1955).
4. Separating Persuasion from Interaction
The
baseline model conflates interaction with persuasion. Every time agents who differ interact, one feature is copied from
one to the other. Agents are wholly
indiscriminate in their adoption of opposing points of view. For many purposes, this is perhaps a wholly
adequate model. If you need information
regarding web sites for vacation alternatives, you might indeed seek out
information from people with travel interests similar to your own and take
whatever information they provide.
In
contrast, the value of political information taken through social interaction
is problematic. Even if you acquire
information from a generally trustworthy individual suggesting that George W.
Bush is just another rich fraternity kid who would make a terrible president,
you might want to evaluate the worth of that information. The important point is that communicated
information does not necessarily translate into influence, and in this sense
the influence of even effectively communicated information is quite
problematic.
How
do people evaluate the worth and credibility of political information? What makes for political information on the
part of a communicated opinion or preference?
Indeed, a range of factors could be considered: the clarity with which
individuals communicate, the imputed expertise of political discussants, and
more. In this analysis we build on
earlier work (Huckfeldt, Johnson, and Sprague, 2000) to focus on the incidence
of opinions within networks of political communication.
If
you think that George W. Bush is high quality presidential material, and one of
your friends tells you that George Bush is just another rich fraternity kid,
how might you respond? According to the
baseline model you would simply change your opinion, but an alternative
strategic response is to contextualize the information provided by the
discussant relative to information provided by other discussants. Hence if you like Bush, but your friend Joe
dislikes him, you might take account of other opinions about his
capabilities. If all your other
information sources suggest that he is a good guy, you might downgrade the
credibility that you place on Joe's opinion.
On the other hand, if all your other information sources agree with Joe,
you might reconsider your own opinion on the matter (see McPhee 1963).
In
summary, we are suggesting that the influence of particular opinions held by
particular discussants is proportional to the incidence of these opinions
within larger networks of opinion. If
all my friends and I believe that Bush is an excellent presidential candidate,
then I am highly unlikely to change my mind when someone tells me
otherwise. If, on the other hand, I
increasingly receive reports that Bush is just another rich fraternity kid,
then it becomes more difficult to dismiss the opinion, and I am more likely to
revise my opinion. Any single piece of
information is seen within the context of all the information that is available. The social influence of any single
interaction ceases to be determinate, and the agent becomes an evaluator of
information received through a successive process of social interaction.
In
our final model, we consider the consequences of this model, which, for lack of
a better term, we are calling the "friends from the network"
model. The discussants are selected in
the same manner as previous models, but agents keep records on the contacts
they have experienced and use those records when formulating their response to
new points of view. The current scheme
is somewhat coarse, but it captures the essence we are trying to capture. Each time an agent interacts, it counts the
number of features it holds in common with the other. When an interaction occurs, the agent polls the people that it
agrees with on more than one-half of the issues, and if more than one-half of
those “friends” agree with the new point of view, it is adopted. Thus, new ideas or novel preferences should
take longer to catch on, and individual agents should be less susceptible to
persuasion. What are the results?
As
Figure 6 shows, when the influence of an opinion is proportional to its
incidence within an individual's network of contacts, diversity is maintained
within both the larger population and within networks of political
communication. First, the level of
acquaintanceship is lower than in the previous models, reflecting the fact that
the opinions of the agents are not so homogeneous. People are regularly put in contact with others with whom they
disagree. Second, only a relatively
small proportion of networks are composed of dyads with identical
preferences. Finally, the average
proportional agreement with any discussion partner (harmony) is only slightly
above .6. This can be interpreted as
the probability that two discussants will agree on any particular issue, and
hence the level of agreement in Figure 6 roughly parallels the earlier
empirical results.
We
hasten to add that this figure is very much representative of the 100 runs we
performed with these settings. The
number of steps averaged 732.1 with a standard deviation of 148. However, the averages (and standard
deviations) of the acquainted, harmony, and identical variables were .44
(0.03), .36 (0.05), and .6 (0.04).
Entropy is not zero at the end of any of the runs.
What
do these results suggest? Much more
work clearly remains to be done, and we have only begun to address the complex
political processes that yield sustained disagreement and diverse preferences
in democratic politics. But these first
results point to the importance of separating the communication of
information from the persuasiveness of information. Even effectively communicated messages may
lack influence, and this analysis points to the importance of interdependent
citizens as discriminating consumers of political information.
Summary and Conclusion
A
substantial body of evidence has accumulated regarding the distribution of
preferences within citizens' networks of political communication. Contrary to a great deal of conventional
wisdom, these networks are not safe havens from political
disagreement. Quite to the contrary, it
would appear that disagreement is the modal condition among citizens - most citizens experience disagreement and divergent political
preferences within these networks.
Indeed, this conclusion is based on the closely held, self-reported
relationships of the citizens themselves, and on the most visible of
contemporary political choices - support for a particular
presidential candidate.
Hence
the question becomes, what is the nature of the dynamic process that sustains
disagreement among citizens? A number
of different analytic strategies can be used to address this problem. In the current paper we employ an
agent-based model of social interaction and political influence. The model shows that political diversity is
maintained for longer periods of time to the extent that individuals are able
to seclude themselves from disagreement by self-selecting their political
discussants to create politically censured clusters of like-minded
individuals. Over the long haul, even
this strategy loses out, ultimately giving way to a process of political
homogenization that creates uniformity in political preferences across the
population.
It is
important to remember that, in the real world, transient responses may be more
important than long-term equilibrium responses. This is doubly true in political processes that are constantly
being bombarded by stochastic events - Monica Lewinsky,
fund raisers at Buddhist temples, unforeseen reactions to vice presidential
candidates, and the like. In such a
stochastic world, initial conditions are continually being reset. Hence the transient, short-term response may
end up being most important, and the speed of recovery to equilibrium becomes
especially crucial.
In
this context, a number of factors related to the social structure of
interaction appear to affect the speed of the homogenization process. First, to the extent that self-selection is
attenuated, convergence toward homogenization is accelerated. Second, to the extent that a set of
geographically dispersed institutions and structures create a network of
communication that overlaps the geographic organization of social life,
convergence toward homogeneity is further accelerated. This latter result is wholly in keeping with
classic studies of contagion (Bartholomew 1967), and with more recent studies
of the communication consequences that arise due to structural holes (Burt
1992) and weak ties (Granovetter 1974) within networks of communication.
As
long as persuasion is the inevitable consequence of interaction within discrete
dyads, the elimination of political diversity and disagreement may be a
foregone conclusion, at least over the long haul. In contrast, a far different outcome emerges when we redesign the
model to make persuasion within dyads the problematic and less than automatic
consequence of interaction across an individual's entire network of
contacts. Based on earlier empirical
results, we conceive the probability of persuasion as a function of an
opinion's incidence within an individual's network of relationships. That is, individuals are less likely to be
persuaded by opinions that win only limited support among the participants
within their communication networks.
Indeed, this model of persuasion serves to maintain diversity and
disagreement both in the short run and over the long haul.
In
many ways this is a surprising outcome.
The model of influence we are describing rewards majority opinion at the
same time that it punishes the political minority, but it produces an aggregate
outcome in which the minority does not disappear. The potential of this mechanism for maintaining political
disagreement is that the influence of majorities and minorities are defined
according to the distribution of opinion within closely held micro-environments
of political communication. Hence,
people are able to resist divergent viewpoints within the network because every
opinion is filtered through every other opinion.
Finally,
the power of the mechanism we are describing is wholly dependent on the low
levels of network density that are built into the model. If the network densities were high - if networks were wholly self-contained so that all members shared the
same interaction partners - then disagreement would
disappear even though diverse preferences would be sustained in the larger
environment. That is, no one would ever
encounter diverse preferences because every particular network is wholly
self-contained and entirely homogeneous.
In contrast, low network densities, combined with influence that is
predicated on the incidence of particular opinions within networks, serve to
sustain political diversity in the larger environment as well as the experience
of disagreement within citizens' closely held networks of political
communication.
Appendix 1
Brief summary of model features
Structural Features.
1. Toroidal World or Square World. (runtime "wrap-around" toggle)
2. Multiple
Scheduling Options (compile-time preprocessor flags and runtime options). Axelrod "random one-at-a-time"
simulation or Knight's tour through all agents (optionally, in random order)
during each "day". A
"day" is an adjustable number of time steps. CPP flag "NO_MASTER_SCHEDULE"
compiles in fully autonomous dynamic scheduling by individual agents. Otherwise, all agents place their actions
onto a single master schedule, in which actions at any particular time step may
be randomized.
3. Multiple
neighborhoods "home grids" in which people live and multiple
"work grids" where they might interact. All grid sizes can be adjusted at runtime.
4. Agent movement and access. An agent's location at any time is
controlled by individual movement parameters.
After the "home duration" is completed, the agent removes
itself from that neighborhood and takes its position in the work
environment. As currently designed,
each agent initiates an interaction once per day, and the probability of
initiating an interaction in a grid is proportional to the total number of time
steps per day spent there. At the
beginning of each day, each agent finds itself at home, and it then can
schedule itself to go to the other environments and to initiate an interaction
at some point during the day.
Behavioral Features:
The model separates agent actions into two groups
of interchangeable modules,
"discussant selection" and "opinion adjustment)
1. Discussant selection: a method fulfilling this role
returns either another agent or a missing value.
A.
Axelrod's Selection method: randomly choose a neighbor from the von Neumann
neighborhood, then choose that person with probability equal to the proportion
of identical features between the two agents.
B.
Parochialism variant: Axelrod's method, except that when there are multiple
people per cell, choose a discussant from within own cell with probability
"parochialism" and otherwise select randomly from each of the 4
neighbors.
C.
Coleman variant: Same initial selection of discussion candidate as model
A. The candidate is selected for
discussion with probability
D.
Selective exposure models: Agents build a list of others by random sampling
from own cell and neighbor cells, then choose the one they expect to be most
"agreeable" as discussant.
Variants on this theme explore assumptions about strangers and the way
agents keep records on each other.
2. Opinion adjustment: response to an interaction.
A.
Axelrod method: find a feature on which the discussant differs from the agent
who initiates interaction. Copy that
feature from the discussant. (Runtime variants in our model allow the
possibility that either agent or both may adjust)
B.
Social network model: find a feature on which the discussant differs, then
consider adopting his opinion if a sufficiently high proportion of
"friends" supports that opinion.
(Runtime variables can adjust the criteria for considering someone a
friend and whether or not friends are polled or rather a recollection of their opinions
is used).
References
Asch, S.E. 1956. Studies on
Independence and Conformity: A Minority of One Against a Unanimous Majority.
Psychological Monographs 70: 416.
Berelson, Bernard R., Paul F. Lazarsfeld, and
William N. McPhee. 1954. Voting: A Study of Opinion Formation in a Presidential
Election. Chicago: University of Chicago Press.
Burt, Ronald S. 1992. Structural
Holes. Cambridge, MA: Harvard University Press.
Fabrigar, Leandre R. and Jon A. Krosnick. 1995. ``Attitude
Importance and the False Consensus Effect." Personality and Social
Psychology Bulletin 21:468-479.
Festinger, Leon. 1957. A Theory of
Cognitive Dissonance. Palo Alto, California: Stanford University Press.
Fuchs, Political Lawrence H. 1955.
"American Jews and the Presidential Vote," American Science Review
49:385-401.
Granovetter,
Mark. 1973. "The Strength of Weak Ties," American Journal of
Sociology 78: 1360-80.
Huckfeldt, Robert, Paul Johnson,
and John Sprague. 2000. "Political
Environments, Political Dynamics, and the Survival of Disagreement."
Working paper.
Huckfeldt,
Robert, Paul Allen Beck, Russell J. Dalton, Jeffrey Levine, and William Morgan.
1998a. "Ambiguity, Distorted Messages, and Nested Environmental Effects on
Political Communication," Journal of Politics 60: 996-1030.
Huckfeldt, Robert, Jeffrey Levine,
William Morgan, and John Sprague. 1998b. ``Election Campaigns, Social
Communication, and the Accessibility of Perceived Discussant Preference,"
Political Behavior 20: 263-294.
Huckfeldt,
Robert, Paul A. Beck, R. Dalton, and Jeffrey Levine. 1995. ``Political
Environments, Cohesive Social Groups, and the Communication of Public
Opinion,'' American Journal of Political Science 39: 1025-1054.
Huckfeldt, Robert and John
Sprague. 1995. Citizens, Politics, and Social Communication. New York:
Cambridge University Press.
Johnson, Paul E. 1999. Protests, Elections, and
Other Forms of Political Contagion.
Paper presented at the Annual Meeting of the American Political Science
Association, Atlanta, GA.
Johnson, Paul E. and Alex
Lancaster. 2000. Swarm User Guide. URL:
http://www.santafe.edu/projects/swarm/swarmdocs/userbook/userbook.html.
Lazarsfeld, Paul, Bernard Berelson, and Hazel
Gaudet. 1948. The People's Choice. New York: Columbia University Press.
Latane, Bibb, Andrzej Nowak, and James H. Liu.
1994. Measuring emergent social phenomena: dynamism, polarization, and
clustering as order parameters of dynamic social systems. Behavioral Science,
39:1-24.
McPhee, William N. , with Robert B. Smith and
Jack Ferguson. 1963. "A Theory of Informal Social Influence." In
William N. McPhee, Formal Theories of Mass Behavior. New York: Free.
Miller, Warren. 1956. "One Party Politics
and the Voter," American Political Science Review 50: 707-725.
Minar, Nelson, Roger Burkhart, Christopher
Langton, and Manor Askenazi. 1996. The Swarm Simulation System: A Toolkit for
Building Multi-Agent Simulations. Technical Report 96-04-2, Santa Fe Institute,
Santa Fe, New Mexico.
Moscovici, Serge, Angelica
Mucchi-Faina, and Anne Maas (eds.). 1994. Minority Influence. Chicago: Nelson
Hall.
Nowak,
Andrzej, and Maciej Lewenstein, "Modeling Social Change with Cellular
Automata," in R. Hegselmann et al., eds. Modeling and Simulation in the Social Sciences from a
Philosophy of Science Point of View.
Amsterdam: Kluwer, pp. 249-285.
Table 1. Discussant's candidate vote preference by main respondent's
candidate vote preference in 1996 Indianapolis-St. Louis study.
Discussant's Main Respondent's Self-Reported Preference
Self-Reported
Preference Clinton Dole Perot Total
Clinton 59.7 20.5 38.6 39.5
Dole 25.0 68.6 33.3 46.7
Perot 2.6 2.3 14.0 3.0
Don't Know, 12.7 8.6 14.0 10.8
No vote
Total 573 605 57 1235
Table 2. The proportion of
the respondent's network perceived (by the
respondent) to support Clinton regressed on the proportion
of
the discussant's network perceived (by the discussant) to
support
Clinton, conditional on whether the main respondent
perceives
agreement within the respondent-discussant dyad.
number of discussants named by both the
respondent and the discussant:
2 or more more than 2
A. ALL DYADS
constant
.31 .29
(20.07) (15.48)
slope
.19 .27
(6.20) (6.78)
R2
.04 .07
s.e. .38 .35
N
1006 640
B. AGREEING DYADS
constant
.26 .22
(13.46) (9.61)
slope .36 .47
(9.23) (9.78)
R2
.12 .19
s.e.
.37 .34
N
605 401
C. DISAGREEING DYADS
constant
.39 .42
(16.03) (14.24)
slope
-.08 -.15
(1.73)
(2.34)
R2
.01 .02
s.e.
.36 .34
N
378 223
Note: In constructing perceived network support
for Clinton, the
interviewed discussant is extracted from the respondent's
network,
and the respondent is extracted from the interviewed
discussant's
network. Hence, the resulting measures index the political
composition of the residual microenvironments absent the
preferences
of the particular dyad.
Figure 1. Patterns of social connection and implications for electoral change.
A. Conformity and the
socially heroic holdout.
B. Socially sustained
disagreement.
Figure 2. Baseline model
of communication and influence.
Figure 3a. Baseline
model with Coleman parameter = .2.
Figure 3b. Baseline
model with Coleman parameter = .5.
Figure 3c. Baseline
model with Coleman parameter set to .8.
Figure
4: Histograms summarizing the duration of simulations and the effect of the
Coleman parameter 
Figure 5a. Multi-grid
model with 1 :home grid and 5 work grids, h = 0.5.
Figure 5b. Multi-grid
model with 1 home grid and 5 work grids, h = 0.0.
Figure 6. "Friends
from the Network" model.
