Heise, Sentiment Formation, page i
To be published in Kent McClelland and Thomas J. Fararo (eds.),
Purpose, Meaning and Action: Control Systems Theories in Sociology
(New York: Palgrave Macmillan, 2006).
Sentiment Formation in Social Interaction
David R. Heise
Indiana University
Heise, Sentiment Formation, page ii
Abstract
This chapter considers time-varying sentiments as an aspect of
affect control theory. I estimate time-varying sentiments in interactions
between Middle Eastern polities and use those sentiments to predict the
polities’ subsequent behaviors toward one another. The successful
results indicate that time-varying sentiments can be used to analyze
rapidly changing relations between interactants. The results also reveal
that international relations are substantially affective in nature,
predictable from affect control theory.
Heise, Sentiment Formation, page 1
Introduction
In the language of cybernetics, affect control theory (ACT) proposes
that sentiments about people, actions, and objects serve as reference
signals for assessing impressions produced by social events. Individuals
control impressions through their social actions, and in particular they
construct interpersonal events in such a way as to generate impressions
that confirm sentiments. In this indirect way, social interactions are
shaped by individuals’ sentiments. However, sentiments have to develop
at some point, and during the developmental period, social interaction
presumably shapes sentiments rather than vice versa.
Powers (1973, Chapter 15) deals with the formation of reference
signals by positing two kinds of switches in hierarchical control systems.
A “memory switch” determines whether or not past experiences are being
recalled to provide reference signals for assessing current perceptions. A
“perceptual switch” determines whether or not perceptual signals are
passed up the system hierarchy, thereby getting stored in memory on the
one hand and on the other hand influencing outputs of higher-order
assessments. The functioning of these two control-system switches
corresponds to the processes of assimilation and accommodation
described by Piaget (1954, pp. 397-8): “assimilation …tends to
subordinate the environment to the organism as it is, whereas
Heise, Sentiment Formation, page 2
accommodation is the source of changes and bends the organism to the
successive constraints of the environment. …. Every acquisition of
accommodation becomes material for assimilation, but assimilation
always resists new accommodations.” In control-theory terms,
assimilation occurs when the memory switch is setting reference values
from memories, and accommodation occurs when the perceptual switch
is allowing additions to memory or adjustments in what is being
remembered.
ACT ordinarily is discussed as if the social interactants’ memory
switches are providing reference signals from memory, and their
perceptual switches are blocking accommodation to social interaction.
Thereby the interactants produce sentiment-confirming social
interactions, and automatically generate the roles associated with their
identities. ACT additionally allows that particularly confounding
interactions can be escaped through reidentifications—the form of
accommodation in which replacement reference signals are recalled from
memory. Thus far, though, little ACT research deals with how individuals
accommodate to unexpected experiences by generating new sentiments
that can be used as future reference signals.
In this chapter I examine how sentiments might form and change
during sequences of dyadic social interaction. Emergence of sentiments
out of dyadic activity is not the only mechanism of sentiment formation,
because sentiments also form through the aggregation of networked
Heise, Sentiment Formation, page 3
social influences (Axelrod 1997, Chapter 7; Friedkin 1998; McClelland,
this volume; Ridgeway and Erickson 2000). Also, children’s knowledge of
basic word connotations as early as seven years of age suggests that
many cultural sentiments are obtained during the acquisition of
language (Donahoe 1961; Maltz 1963). However, time-varying sentiment
formation in dyads may be important whenever personal definitions
supplant cultural and institutionalized definitions, and interactants
operate fairly independently of a broader social network.
Longitudinal Models of Sentiment Formation
Heise (1979, pp. 61-63) formulated a weighted-average model of
sentiment formation, as an adaptation of some attitude-change
literature, especially the work of Anderson and Farkas (1973). In this
approach, a sentiment constitutes an average of all experienced
impressions of the object, perhaps weighting some impressions more
than others. For example, to represent a primacy effect, impressions
from earliest experiences are weighted maximally and later experiences
minimally, whereas a recency effect is represented by weighting the latest
impressions most and early impressions little. The averaging model of
Anderson and Farkas (1973) weights impressions from all experiences
equally, implying that the weight for each impression declines in size
over time as an individual gains more experiences with the object.
This chapter deploys the weighted-average model to examine how
sentiments develop over time. Since a primacy model denies change after
Heise, Sentiment Formation, page 4
early experiences, and the Anderson-Farkas model implies little impact
from recent events once one acquires substantial experience with an
object, neither is appropriate for studying ongoing temporal changes in
sentiments. Instead I implement a recency-effect model of sentiment
formation which averages outcomes from the last N experiences, where N
is an empirically-estimated parameter1.
Heise (1979) postulated that a sentiment is an average of past
impressions of an entity. An impression is the feeling about an entity
that is produced by an event—a feeling that emerges from prior feelings
about all of the event’s components, and from consistencies and
inconsistencies among those prior feelings. Thus, viewing a sentiment as
an average impression grounds sentiments in mental processes of a
reactive nature.
On the other hand, Lerner (1983) treated a sentiment for an
interactant as an average of ideal-sentiments in past events2. The idealsentiment for an interactant is the hypothetical sentiment that answers
the question, “What kind of actor would engage in this behavior toward
this object person”, or the question “What kind of object person befits
this behavior by this actor?” Such questions evoke interpretive thinking,
and therefore this approach views sentiment formation as a result of
mental processes of an inductive nature.
I will implement both hypotheses about the source of sentiments
in this chapter. Sentiments will be estimated as typical impressions by
Heise, Sentiment Formation, page 5
averaging transient impressions of each interactant in recent interactive
events. Sentiments will be estimated as typical ideal-sentiments by
averaging reidentifications of each interactant in the same events. The
relative value of the two conceptualizations of sentiments will be
assessed by seeing which does the better job of predicting future
interactive events.
To summarize, this chapter will address the following questions.
•
Are some forms of social interaction governed by time-varying
sentiments, with the time variations in sentiments being functions of
past events in the interaction?
•
If so, does a recency-effect averaging model specify how information
from past events is incorporated into current sentiments?
Specifically, does the following model generate meaningful
sentiments?
S0 =
1 −N
∑ rt
N t = −1
where S0 stands for the sentiment associated with an interactant at
time 0, r stands for a mental response to the interactant that occurs
as a result of an event, t is an index of events over past times, and N
is the number of events that are averaged.
•
What is the best kind of mental response to average in order to define
sentiments—impressions of an interactant, or inferred ideal-
Heise, Sentiment Formation, page 6
sentiments for the interactant? Which basis is most predictive in
accounting for future social interaction?
International Interactions
Interactions among individuals are ubiquitous but largely
unchronicled, and therefore they are not readily available for research.
Meanwhile, interactions among nations are reported, analyzed, and
recorded in print on a daily basis in great detail. Consequently, and
ironically, the most available data on long-term social interactions
involve macro-actors engaging in collective activities.
Can the abundant data regarding interactions among nations be
analyzed with ACT, even though ACT focuses explicitly on processes
occurring in individuals’ minds? Two lines of reasoning support an
affirmative answer. One argument is that international activity is the
social interaction of nations’ representatives—presidents, ministers,
military officers, and other authorities—employing their nation’s
diplomatic corps, bureaucracies, and militaries as instruments of their
individual macroactions toward one another (Heise and Durig 1997).
Another argument is that citizens of a country respond symbolically to
events involving macroactors in the same ways as they do to events
involving individuals (Irwin 2003; 2004), and as a result they form
expectations about appropriate behavioral responses that every citizen—
authority or not—seeks to implement. A companion article (Heise and
Heise, Sentiment Formation, page 7
Lerner Forthcoming) develops these arguments in detail, so here I merely
note that such arguments are available and justify exploratory analyses
of international interactions with ACT.
Data
The events analyzed in this chapter are international interactions
recorded in Azar’s (1980; 1993b) Conflict and Peace Data Bank—
COPDAB. This is a large database (431,263 events) for the years 19481978 built on reports from 71 worldwide sources and giving excellent
coverage to international interactions in the Middle East.
I focus on 1971-78 interactions involving Israel, Egypt, Syria, and
the Palestinian Liberation Organization (PLO). Whereas relations among
some Middle Eastern states were highly institutionalized in the 1970s
(Heise and Lerner Forthcoming), other international relationships were
unsettled. Figure 1, showing the goodness-badness of actions from Egypt
to Israel and from Israel to Egypt, illustrates the instability that existed
in some Middle East relationships during the 1970s. Sentiments about
Egypt and Israel that might explain actions in 1973 during the Ramadan
(Yom Kippur) War would not be explanatory for the much more positive
actions in 1977-78. Thus, sentiments must have been time-varying if
affect control was a factor in the interaction of Egypt and Israel during
this period. Similarly, Egypt’s relations with Syria turned notably more
conflictual during this time period, and Israel’s relations with Syria
became notably less conflictual. While the PLO had fairly stable relations
Heise, Sentiment Formation, page 8
with these three nations during this period, I include the PLO’s relations
in my analyses because the PLO was an emerging political entity at that
time, having been recognized by the United Nations as the representative
of the Palestinian people in 1974.
Figure 1 about here
To examine sentiment formation regarding the actors in
international events, we must have quantitative measurements of
sentiments associated with international behaviors. Azar and Lerner
(1981) obtained sentiment measurements of 125 international behaviors
from 29 professionals in international relations (academics, consulting
firm specialists, State Department employees). They measured
sentiments with three semantic differential scales in order to assess the
three standard dimensions of sentiment variation—Evaluation, Potency,
and Activity—EPA.
Each scale had nine rating positions with adverbial anchors
("infinitely," "extremely," "quite," and "slightly," on either side of
"neutral" or "neither" in the middle.) The poles of the scales were
defined by …: evaluation—good vs. bad; potency—strong vs. weak;
activity—fast vs. slow. … The responses were numerically
interpreted along a non-interval, metric scale known to be
associated with the adjective modifiers which were used on the
forms.” (Azar and Lerner 1981, pp. 363-364)
Heise, Sentiment Formation, page 9
The metric interpretations of scale ratings were obtained from
Heise (1978). The most cooperative action was Merging voluntarily into
one nation state, rated on the average as quite good (2.4), quite powerful
(1.6), and quite active (1.9); and the most conflictual action was Conduct
campaigns of genocide, rated as extremely bad, quite powerful, and quite
active (-3.4 2.0 2.2).
The COPDAB database describes international behaviors in a more
detailed manner than the 125 behavior descriptions specified by Azar
and Lerner (1981). Table 1 provides some examples of COPDAP
descriptions, focusing on a sequence of interaction that occurred
between Israel and the PLO in August-September of 1975. The topmost
event in the table, occurring on August 31, is specified by COPDAP as
follows: Israel is the Actor, PLO is the Target of action, “clash” is the basic
Activity, “Israel + PLO forces” is the Issue-Area; and the event has a Scale
Value of 14. COPDAB’s trained raters assigned a Scale Value to each
event by coding event descriptions from the source periodicals onto one
of 15 scale points.
The COPDAB scales for international and domestic events
are each comprised of 15 points or partitions. In the international
scale, point 1 is the value given to the most cooperative events
between two nations (e.g., nations A and B voluntarily unite into
one nation-state). A scale value of 15 represents the most
Heise, Sentiment Formation, page 10
conflictive events between two or more nations (e.g., total war).
(Azar 1993a, p. 25)
The codebook for scaling events offered examples of events in each
partition, the examples being the 125 events that were studied later by
Azar and Lerner (1981).
Azar and Lerner (1981, p. 374) examined the relation between the
three EPA dimensions and the international and domestic versions of the
cooperation-conflict scale. “In both analyses the three semantic
dimensions were able to explain a very large proportion of the variation
in the cooperativeness or conflictiveness of the events. The three
semantic dimensions accounted for 74 percent of the variance on the
international scale and 88 percent of the variance on the domestic scale.
In both analyses evaluation proved to be the strongest and Potency the
weakest determinant of the scale values.”
Table 1 about here
Table 1 shows an EPA profile for each event, but these are not
given in COPDAP. The EPA profiles are derivatives obtained through a
procedure developed by Lerner (1983, pp. 88-89). The Lerner procedure
combines 194 of COPDAB’s Activity verbs with COPDAB’s 15 Scale
Values—e.g., “clash, with scale value 13”, “clash, with scale value 14”,
“announce, with scale value 2”. “announce, with scale value 13”. Lerner
linked events in each such classification to the most similar behaviors in
the Azar and Lerner (1981) list, then obtained an average EPA profile for
Heise, Sentiment Formation, page 11
the linked events. Thereby he obtained an EPA profile for Activity-byScale-Value combinations (more details are provided in Heise and Lerner
Forthcoming).
I linked the thousands of events in my analysis to the EPA profiles
by using 431 computer-implemented rules from Lerner (1983). For
example, three of the rules for processing events with Activity “clash”
were:
•
If (scale_value=13 & activity="CLASH" ) then E=-1.1 and P=1.0 and
A=1.4.
•
If (scale_value=14 & activity="CLASH" ) then E=-1.3 and P=0.6 and
A=1.4.
•
If (scale_value=15 & activity="CLASH" ) then E=-2.6 and P=2.3 and
A=2.7.
Behaviors that still were without an EPA profile because their
Activities were not among the 194 used in the classification system were
assigned the mean EPA values of all successfully-coded behaviors with
the same Scale Value. The final result was a dictionary of EPA profiles for
813 international behaviors defined in terms of combinations of scale
values and activities or in terms of scale values alone.
If a nation engaged in multiple behaviors toward another nation on
a single day, then I averaged the sentiment measures for the separate
behaviors to get a single EPA profile for the day’s activities.
Heise, Sentiment Formation, page 12
ACT Analyses
Affect control analyses were conducted with program Interact
(Schneider and Heise 1995), a Java applet available on the World Wide
Web (Heise 1997). The procedure within Interact was as follows.
1. Name the interactants Israel, Egypt, Syria, and PLO.
2. Import the dictionary of 813 international behaviors discussed
above. Each entry consists of a behavior designator and the
behavior’s numerical EPA profile.
3. Import the list of 3,077 events involving the four interactants
during the years 1971-78. The list was partitioned into six
segments corresponding to dyads, and events were ordered
sequentially within each segment. Each event was specified by its
actor, target (object), and behavior designator. Events occurring on
the same day (e.g., Israel doing something to Egypt, and Egypt
doing something to Israel) were tagged for treatment as
simultaneous events.
4. Obtain the Interact implementation of each event in sequence. In
particular:
a. Compute current sentiments about the actor and object from
outcomes of past events. Sentiments are set to zero for the
first event in each sequence. Thereafter, they are the mean of
impressions or of reidentifications produced in prior events,
Heise, Sentiment Formation, page 13
averaging over N prior events. N was set to 1, 2, 4, 8, 16, 32,
or 64 in different runs.
b. Retrieve the impressions of the interactants generated in the
last event and use these as the basis for computing the new
impressions generated by the current event. Impressions are
estimated in ACT via empirically-derived impressionformation equations.
i. Set prior impressions to zero in the first event of each
sequence.
ii. Obtain impressions in simultaneous events separately,
then average to provide an overall outcome impression.
c. Compute an ideal EPA profile for the actor in the current
event to answer the question, What kind of actor would do
this act to this object? Compute an ideal EPA profile for the
object to answer the question, Who befits this act by this
actor? Ideal sentiments are estimated in ACT via
reidentification equations, which are mathematically-derived
from impression-formation equations under the assumption
that people try to confirm sentiments. These ideal EPA
profiles are used to compute sentiments in future events, if
sentiments are based on reidentifications. (In the case of
simultaneous events, reidentifications are conducted
separately for each event and averaged for future usages.)
Heise, Sentiment Formation, page 14
d. Compute the ideal behavior of the actor toward the object for
the current event, on the basis of the current sentiments and
the impressions generated by the last event. This is the
predicted behavior that is correlated with the observed
behavior at the same point in time in order to conduct
empirical tests.
Results
Interact analyses were conducted of 1971-78 dyadic interactions
for Israel-PLO, Israel-Egypt, Israel-Syria, PLO-Egypt, PLO-Syria, and
Egypt-Syria. Within each set of dyadic analyses, a predicted-behavior
EPA for the current event was computed from time-varying sentiments
and from impressions generated by the prior event. Figure 2 presents the
correlations between predicted EPAs and observed EPAs, pooling data
from all six dyadic analyses.
The data points at the far left of the graph in Figure 2 show
correlations obtained when only the most recent happening enters into
formation of sentiments. In this case, the current event theoretically is
constructed to confirm sentiments established by the previous event. The
next column of points presents correlations obtained when sentiments
average outcomes from the two most recent events. The third column of
data points gives correlations obtained when sentiments are obtained by
averaging over four events, the fourth column over eight events, the fifth
Heise, Sentiment Formation, page 15
column over sixteen events, the sixth column over thirty-two events, and
the rightmost column of data points shows correlations obtained when
sentiments are obtained by averaging outcomes from the sixty-four most
recent prior events.
Figure 2 about here
Lines with circle points display correlations obtained when
sentiments are estimated from EPA impressions produced in prior
events. That is, the EPA impressions of actor and object generated in
each event are averaged over a set of recent events to determine the
current sentiment associated with each interactant. Lines with triangle
points display correlations obtained when sentiments are estimated from
reidentification EPA profiles. In this case, each prior event is analyzed to
obtain EPA profiles characterizing an actor who would perform the
action, and an object who would befit the action, and these EPA profiles
are averaged over a set of recent events to determine the current
sentiment for each interactant. Lines with square points show how EPA
measurements of current behaviors correlate with the average EPA of
behaviors in preceding events, regardless of who is actor and object,
these stability correlations serving as baselines for evaluating the
correlations from ACT analyses.
Solid connecting lines in Figure 2 connect correlations between
Evaluations of predicted behavior and Evaluations of observed behaviors.
Dashed connecting lines link correlations between Potencies of predicted
Heise, Sentiment Formation, page 16
and observed behaviors. Dotted connecting lines link correlations
between Activities of predicted and observed behaviors.
Consider results from analyses in which sentiments are based on
recent impressions. In these cases, we compute the EPA profile for the
impression produced of an interactant in each recent event, average the
impression profiles to estimate the sentiment currently associated with
that interactant, and use the thusly-computed sentiments for both
interactants along with impressions produced by the last event to predict
the most sentiment-reinforcing behavior for the actor in the current
event. In such analyses, correlations peak when sentiments are derived
by averaging EPA profiles over the last one or two events. When
sentiments are based on just the prior event, the correlation between
predicted and observed Evaluation scores for behavior is 0.52; the
correlation between predicted and observed Activity scores is 0.40; and
the correlation between predicted and observed Potency scores is 0.09.
When sentiments are based on the last two events, the correlation
between predicted and observed Evaluation scores for behavior is 0.52;
the correlation between predicted and observed Activity scores is 0.42;
and the correlation between predicted and observed Potency scores is
0.05. I will assume that the sentiment is obtained from the last two
events.
Now consider results from analyses in which sentiments are based
on reidentifications. In these cases, we compute the ideal EPA profile for
Heise, Sentiment Formation, page 17
an interactant’s participation as actor or object in each recent event,
average the ideal profiles to estimate the sentiment currently associated
with that interactant, and use the thusly-computed sentiments for both
interactants along with impressions produced by the last event to predict
the most sentiment-reinforcing behavior for the actor in the current
event. In such analyses, correlations between predictions and observed
actions peak when sentiments are derived by averaging EPA profiles over
the last four events. Specifically, when sentiments are based on the prior
four events, the correlation between predicted and observed Evaluation
scores for behavior is 0.52; and the correlation between predicted and
observed Activity scores is 0.40. Correlation on the Potency dimension is
essentially non-existent at 0.01.
Finally consider results when behaviors are predicted from a
moving average of past actions. These correlations peak when the moving
average is based on the last eight behaviors by the given actor. Averaging
over eight prior events, the correlation between predicted and observed
Evaluation scores is 0.59; the correlation between predicted and
observed Activity scores is 0.49; and the correlation between predicted
and observed Potency scores is 0.37.
Reidentifications Versus Impressions
Surprisingly, the correlation results do not favor one or another
mental basis for sentiment formation. On the Evaluation and Activity
dimensions, the maximum predicted-versus-observed-behavior
Heise, Sentiment Formation, page 18
correlations for the reidentification basis (0.52 and 0.40) are nearly the
same as for the impression basis (0.52, 0.42). On the Potency dimension
the impression basis gives a higher correlation (0.09 versus 0.01), but
neither correlation is big enough to serve as validating evidence. These
results suggest that time-varying sentiments can be estimated equally
well from reidentifications or from impressions.
However, EPA profiles in ACT theoretically must do more than
correlate with observed phenomena. The profiles also are supposed to
index identities, behaviors, emotions, traits, and settings. For example, a
predicted EPA profile for behavior is supposed to be the actual profile of a
behavior that is enacted, within a margin of error. This requirement
eliminates the impression basis, because the impression basis yields
sentiments about interactants that are too attenuated to predict realistic
EPA profiles for behaviors.
The standard deviations of behaviors predicted from impressionbased sentiments are 0.51, 0.37, and 0.37 on Evaluation, Activity, and
Potency, respectively. The corresponding standard deviations of actually
observed behaviors are 1.29, 0.50, and 0.42. Thus, on the Evaluation
dimension, the variability of behavior predictions based on impressions
is forty percent of the variability of observed behaviors; on the Activity
dimension the variability of predicted behaviors is seventy-four percent of
variability of observed behaviors, and on the Potency dimension the
figure is eighty-eight percent. In contrast, the standard deviations of
Heise, Sentiment Formation, page 19
behaviors predicted from reidentification-based sentiments are 0.97,
0.42, and 0.40 on Evaluation, Activity, and Potency. In this case, the
variability of predicted behavior evaluations is seventy-five percent of the
variability of observed behavior evaluations; on the Activity dimension
the variability of predicted behaviors is eighty-four percent of variability
of observed behaviors, and on the Potency dimension the figure is ninetyfive percent. Thus, behavior EPAs predicted from reidentification-based
sentiments are substantially closer to the EPA values of actually
occurring behaviors.
The attenuated predictions from impression-based sentiments
arise from attenuation in the estimated sentiments themselves. Standard
deviations of sentiments estimated from impressions are 0.55 on
Evaluation, 0.24 on Activity, 0.41 on Potency. Standard deviations for
sentiments estimated from reidentifications are uniformly larger: 1.13 on
Evaluation, 0.42 on Activity, 0.57 on Potency.
Henceforth, I presume that sentiments emerge from
reidentification processes rather than from impression-formation
processes.
Moving-Average Versus ACT
Table 2 shows that averaged sentiments for the last few behaviors
in the interaction correlate well with current behavior sentiments. This
replicates an established empirical generalization, that past behavior is
typically a good predictor of current behavior (Wiggins, Wiggins and
Heise, Sentiment Formation, page 20
Vander Zanden 1994, pp. 246-7). A theoretical basis is that norms
control behavior, so individuals select behaviors to fit norms and to
repair deviations from norms (McClelland 2003).
ACT explicates the norm model by specifying causal linkages
among behavior sentiments at different times.
First, sentiments about interactants produce behavioral continuity.
The time-varying sentiment3 toward an interactant emerges by asking
what kind of actor would have done a behavior, or what kind of object
would befit a behavior, and roughly speaking, good behaviors generate
good interactants, bad behaviors make bad interactants, bustling
behaviors create active interactants, reserved behaviors yield passive
interactants. Each interactant then tries to confirm these sentiments.
For example, good and bustling behaviors in the past make an
interactant seem vigorous, disposing the interactant to more good and
bustling behaviors to confirm the vigorous identity. In this way,
sentiments about interactants extend past behaviors into future
behaviors.
Impression formations constitute a second factor. As long as
transient impressions are similar to stable sentiments, behaviors of the
same character keep emerging in an interaction. When inappropriate
behavior interrupts the interaction, unusual transient impressions
motivate and shape compensating behaviors. For example, two vigorous
nations ordinarily confirm their nation sentiments through affiliating
Heise, Sentiment Formation, page 21
behaviors like conferring on problems of mutual interest. However, a
postponement of a visit by a head-of-state creates impressions of both
interactants as being incongruously unhelpful, unimportant, and
unenergetic. These impressions motivate a repair like developing an
economic pact together, after which the two return to their usual
affiliating behaviors. Thus, as transient impressions are controlled by
sentiments, behavior disturbances are balanced by somewhat opposite
compensations in behavior, maintaining a normative average in
interactants’ behaviors.
ACT explicates the relation between past behavior and current
behavior. Why then does a moving average of past behavior predict
current behavior somewhat better than ACT? I suppose this happens
because the complex computations required to implement the ACT model
introduce inaccuracies. In particular, the model for deriving sentiments
from behaviors is probably too simple, and perhaps there are errors in
the empirically-derived impression-formation equations, which enter into
all ACT computations.
Notwithstanding such errors, the extensive non-linearities in ACT
behavior predictions offer nuances that are not predicted from the
moving average of past behavior. Table 2 reports results of an analysis
showing this: ACT predictions offer predictability beyond that provided
by the moving average of past behavior.
Heise, Sentiment Formation, page 22
The first numeric column in Table 2 gives the standardized
regression coefficients (betas) that are obtained when the Evaluation,
Potency, or Activity of current behaviors is regressed on ACT’s prediction
of the behavior EPA along with the moving averages of behavior EPAs.
The moving averages always are associated with the largest betas, but
some of the betas associated with ACT predictions also are notable.
Table 2 about here
Instead of interpreting individual betas which assess the impact of
one predictor when other predictors hypothetically are constant, I
combined the betas into sheaf coefficients (Heise 1972). Sheaf coefficients
show effects from a set of predictors, rather than from just one predictor,
when predictors outside the set are held constant. The sheaf coefficients
appearing in the second numeric column of Table 2 show the net
predictability obtained from ACT’s predictions, as compared to
predictability obtained from moving-averages. Tests of statistical
significance given in the last two columns of Table 2 indicate that ACT
predictions always offer a significant enhancement to predictions based
on moving averages.
So moving averages of past behaviors in an inter-nation dyad have
an edge over ACT predictions in predicting current behavior in the dyad,
presumably because of still-undiscovered errors in implementing ACT in
this application. On the other hand, ACT explains why behavior stability
exists, and combining ACT’s predictions with moving average
Heise, Sentiment Formation, page 23
extrapolations yields more predictability than is obtained from moving
averages alone.
Potency
ACT’s predicted behavior Potency fails to predict observed behavior
Potency even though predictable variance is present, since the Potency of
current behaviors correlate moderately with moving averages of past
behavior potencies.
One possible reason for ACT’s failure in this case is that
impression-formation equations for Potency are less well established
than Evaluation and Activity equations. Heise (2000) discussed the
matter.
Overall, Smith-Lovin (1988) found considerable crosscultural similarity in the equations predicting assessment of an
actor's goodness and activity. The universality of core processes
affecting evaluation of actors also has been confirmed in Canada
by MacKinnon (1985/1988/1998) and in Japan by Smith,
Matsuno, and Umino (1994).
On the other hand, Smith-Lovin (1988) reported interesting
differences in how Arabic speakers assess an actor's potency, as
compared to English speakers. Subjects in the U.S.A. and Ireland
(and also it turns out in Canada and Japan) feel that actors are
especially powerful when they engage in potent actions, while the
powerfulness of the object of action has little impact. For Arabs,
Heise, Sentiment Formation, page 24
though, resorting to strong actions implies that an actor is
powerless. Meanwhile, object potency influences how potent an
actor seems: engaging powerful others makes an actor seem more
potent, and acting on weak others costs an actor potency. SmithLovin forwent speculation about this difference because of
methodological limitations in the studies she considered, but it is
interesting to employ the technology of affect control theory in
order to work out what such a difference could mean for
international understanding.
Here is an example. Among people in the non-Arabic world it
is reasonable for a master nation like the U.S.A. to overwhelm a
villain nation, like Libya or Iraq, and doing so would make the
winners feel satisfied and think of themselves as shrewd, while
people in the overwhelmed villain nation should feel shocked, and
should start being more agreeable. However, this may not be how
the same event plays for Arabs, even if the Arabs accept the same
identifications of winner and villain nations. For Arabs, a winner
overwhelming a villain is feeling anxiety, and such an actor would
have to be labeled as cowardly, whereas the object of action will be
viewed as brave, even though shocked by the action.
Large studies still are not available to determine whether Arabs
process Potency in a different manner than Westerners. However, a
substantial equation study was conducted in Japan (Smith, Matsuno
Heise, Sentiment Formation, page 25
and Umino 1994), and it produced Potency equations with some
differences from Western equations, aside from similarity in potent
behaviors implying potent actors. Indeed, using the Japanese equations
instead of U.S.A. equations in the simulations with reidentification-based
sentiments results in a slight increase in the correlation between
predicted and observed behavior Potency, from 0.01 to 0.11.
ACT’s ability to predict potencies of international behaviors
perhaps can be improved by attention to other methodological issues as
well, such as improving the EPA measurements of international
behaviors. In this study, EPA profiles for international behaviors were
inferred from combinations of Activity and Scale Value in the COPDAP
data set. Direct ratings of observed international behaviors would provide
more reliable EPA profiles and most likely would increase the explainable
variance on all three EPA dimensions. Shrodt (2001, p. 9) reported that
about 4,000 verbs and verb phrases are sufficient to capture most
political behavior, and obtaining direct EPA ratings of 4,000 verbs is a
feasible project, though substantial.
Discussion
ACT proposes that interactants’ sentiments about themselves and
about social behaviors are the reference signals for assessing
impressions of themselves and of social behaviors, as produced in social
interaction. In this chapter I employed an expanded notion of social
Heise, Sentiment Formation, page 26
interactant to include nation-state macroactors, and an expanded notion
of social behavior to include international-behaviors, or macroactions.
The analyses show that ACT predicts the goodness and liveliness of
international behaviors moderately well when sentiments associated with
polities are allowed to be time-varying. A companion article (Heise and
Lerner Forthcoming) indicates that interactions between established
Middle Eastern polities in stable relationships are similarly predictable,
using time-constant sentiments. Together these works suggest that
international interactions can be analyzed with ACT, warranting further
investigations of ACT applications in macrosociology.
Empirical results obtained in this study indicate that time-varying
sentiments about political entities emerge from remarkably little history.
Only the last four events contribute to sentiments that help predict a
polity’s next action. In fact, knowledge of only the last eight events or so
is useful for predicting future events from a moving average of past
events. However, these results are for polities that were shifting between
conflict and cooperation during the 1970s or that were just emerging (the
PLO). In contrast, when consideration is restricted to dyads with stable,
institutionalized relations, current behavior can be predicted within the
ACT model using sentiments derived from behaviors that occurred years
prior (Heise and Lerner Forthcoming). In other words, the behaviors of
polities stabilize when cultural and institutionalized definitions of
interactants take over, and the interactants operate within the influence
Heise, Sentiment Formation, page 27
of a broader social network. Presumably the stability emerges when
multiple nations in the international community—not only those in the
dyadic relationship—work to maintain the polities’ identities, thereby
creating a firm, resilient international control system.
Having established the need for time-varying sentiments in the
macrosociological domain begs the question of whether time-varying
sentiments are operative in individual-level encounters as well. A
reasonable supposition is that time-varying sentiments do arise in noninstitutionalized social interaction. If so, intimacy relationships would
seem to be a prime area for ACT analyses using time-varying sentiments.
Example
I end by demonstrating the lens that ACT offers for understanding
international interaction, focusing on actual events that occurred
between Israel and the PLO between August 31, 1975, and September
16, 1975, as listed in Table 1. The computations summarized below were
conducted with program Interact.
Assuming that sentiments are derived from reidentifications in the
last four events, the events directly4 impinging on the PLO’s action of
September 16, 1975, begin with the August 31 clashes of Israel and the
PLO—events that are simultaneous and thereby counted as one event for
the purpose of computations. Relevant events continue with the
September 2 attack by the PLO, the September 3 arrest by Israel, and the
simultaneous actions of Israel and the PLO on September 4.
Heise, Sentiment Formation, page 28
Averaging reidentifications in these events produces sentiments as
follows:
•
EPA profile for the PLO: -0.9, -0.4, 0.8;
•
EPA profile for Israel: -1.2, -0.3, 0.5.
Transient impressions that are relevant to the PLO’s action of
September 16, 1975, are computed from the two events on September 4.
•
EPA profile for the impression that the PLO generates of itself by
its own action: -0.6, -0.8, 0.4; impression of PLO from Israel’s
action on the same day: -1.0, 0.1, 1.0; average: -0.8, -0.4, 0.7.
•
EPA profile for the impression that the Israel generates of itself
by its own action: -1.3, 0.6, 1.1; impression of Israel from PLO’s
action on the same day: -0.9, -0.3, 0.6; average: -1.1, 0.2, 0.9.
Given these fundamentals and transients, the ideal EPA profile for
the next behavior of the PLO toward Israel is -0.8, -0.1, 0.9. That is, a
behavior with this EPA profile transforms existing impressions into new
impressions that best correspond to sentiments about the two polities at
that point in time. The actual behavior—the PLO’s September 16
denouncement of an Israeli agreement—had an EPA profile of -1.0, 0.0,
1.4. The predicted-behavior and actual-behavior EPA profiles are very
similar, as reflected in an Euclidean distance5 between the two profiles of
0.34.
Heise, Sentiment Formation, page 29
Affect and Rationality
Theoretically, the EPA profile for an actor’s actual behavior should
be close to the ideal EPA computed via ACT. However, ACT does not
propose that the closest behavior is selected automatically. Rather,
common sense and rationality prevail in choosing from the several
behaviors that are most affectively appropriate. To see this, consider the
ten behaviors from the Azar-Lerner (1981) list6 that are closest to the
PLO’s ideal behavior toward Israel on September 16, 1975.
1.
refusing a protest note (Euclidean distance, 0.22)
2.
objecting to explanations of goals positions, etc. (Euclidean
distance, 0.24)
3.
condemning strongly specific actions and policies (Euclidean
distance, 0.24)
4.
denouncing leaders, system or ideology (Euclidean distance, 0.30)
5.
refusing to support foreign military allies (Euclidean distance,
0.34)
6.
terminating major agreements (Euclidean distance, 0.36)
7.
failing to reach an agreement (Euclidean distance, 0.48)
8.
denying support (Euclidean distance, 0.52)
9.
imposing economic sanctions (Euclidean distance, 0.56)
10. refusing participation in meetings or summits (Euclidean
distance, 0.57)
Heise, Sentiment Formation, page 30
Condemning Israel’s recent agreement with the United Arab
Republic about disengagement was the behavior that the PLO
consummated, and that is the third best behavior for the PLO in the list
above. The first- and second-best behaviors were impossible because
their pre-conditions were unfulfilled. That is, refusing a protest note was
the action that would have been most affectively appropriate for the PLO
toward Israel at that moment in history, but that action was not possible
because there was no protest note to refuse. Similarly, Israel’s lack of
recent explanations of its goals and positions precluded the possibility of
objecting to them—the second best behavior.
Before deciding on the condemnation of Israel’s recent action, the
PLO leaders might well have considered and rejected some of the other
affectively-appropriate acts in the list above. Some actions, like imposing
economic sanctions, had to be dismissed because they were not within
the PLO’s power. Other actions, such as refusing support to Israel’s allies
like the U.S.A., might have been considered and dismissed because of
their potential for stressing other relationships. These instances
illustrate that ordinary kinds of rational decision-making apply in
selecting one out of several affectively appropriate actions.
On the other hand, ACT posits that many conceivable actions—
both benevolent and malevolent—never are given any serious
consideration by an actor because they are so far beyond the bounds of
affective appropriateness. For example, below are the ten behaviors in
Heise, Sentiment Formation, page 31
the Azar-Lerner (1981) list that are furthest from the ideal behavior for
the PLO toward Israel on September 16, 1975.
1.
use of atomic or nuclear weapons (Euclidean distance, 17.84)
2.
establishing joint program to raise the global quality of life
(Euclidean distance, 14.92)
3.
merging voluntarily into one nation state (Euclidean distance,
14.13)
4.
conduct campaigns of genocide (Euclidean distance, 12.86)
5.
invasion of territory (Euclidean distance, 12.73)
6.
full scale air, naval or land battles (Euclidean distance, 12.24)
7.
massive bombing of civilian areas (Euclidean distance, 11.87)
8.
giving industrial, cultural, educational assistance (Euclidean
distance, 11.61)
9.
joint programs and plans to initiate and pursue disarmament
(Euclidean distance, 10.67)
10. giving disaster relief (Euclidean distance, 10.48)
Some of the actions can be dismissed because prerequisite
occurrences were lacking (like a disaster to activate the giving of disaster
relief). Other actions were not within the PLO’s power at that point in
time (like using nuclear weapons).
However, other of the actions were feasible, maybe even rational by
some analysis, but morally out of the question. For example, initiating
disarmament plans in the midst of hostilities with Israel would have been
Heise, Sentiment Formation, page 32
unthinkable for PLO leaders—a loss of heart that would betray their
movement. A genocidal pogrom against Jews was equally unthinkable as
a horror disproportionate to the level of belligerency existing between the
PLO and Israel.
Rational decision making operates at the same time as affective
control of interaction, but it operates within the domain of affectively
appropriate actions. Actions that are well outside the bounds of affective
appropriateness are morally taboo and may never even be considered in
rational deliberations.
Rational-choice theorists have expressed concern about the lack of
a “moral dimension” in game-theoretical calculations (e.g., Axelrod 1997,
p. 40), and Axelrod (1997) developed an elaborated game with sanctions
to address the problem. However, to bring affectively inappropriate
actions within its purview, rational-choice theory probably would have to
allow that such actions have a huge overall cost of engaging in them—
prohibitive negative utilities that dwarf the positive utilities of plausible
actions. Additionally rational-choice theory would have to deal routinely
with time-varying utilities, since the affective appropriateness of actions
varies as a function of impressions and sentiments created by recent
events.
Heise, Sentiment Formation, page 33
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Heise, Sentiment Formation, page 37
1
Out of infinite possible weighting patterns, I choose weights of 1/N for the
prior N observations, and zero for all other observations; in other words I average
the prior N observations. This two-value weighting scheme approximates more
complex weighting patterns giving relatively more weight to recent observations
and less to earlier observations. As a test, I constructed 18,000 observations of a
random variable, R, then created a variable, W, that is constituted relatively
strongly of recent values of R and relatively less of more remote values of R, as
defined by the formula (0.9)tR-t, for t equals 1 to 15. A variable averaging the last
five observations of W correlates 0.82 with W, and a variable averaging the last
ten observations of W correlates 0.92 with W. Thus the two-value weighting
scheme does approximately retrieve a weighting pattern that gives gradually
more weight to more recent observations.
2
To clarify terminology: A fundamental affective meaning is the affective
meaning of an entity that serves as a reference for individual experience,
controlling impressions of the entity that are produced by events. Sentiment is
synonymous with fundamental affective meaning. An ideal-sentiment is the
hypothetical reference signal for an entity that best explains how the entity
helped shape a given event.
3
A parallel account applies for time-constant sentiments: these tend to
produce the same kind of behavior currently as they produced in the past.
Heise, Sentiment Formation, page 38
4
Relevant events indirectly go further back in time because the
impressions and reidentifications on August 31 depend on outcomes of earlier
events.
5
The Euclidean distance between profile E1P1A1 and profile E2P2A2 is the
square root of ((E1-E2)2 + (P1-P2)2 + (A1-A2)2).
6
I use the Azar-Lerner behaviors for their readable content, as opposed to
the behaviors used in the simulation which are defined in terms of an Activity and
Scale Value—e.g., “Clash, 14”—or only a Scale Value.
Table 1. Illustrative Events From the COPDAP Data Set
Note. EPA stands for “Behavior’s affective profile on the dimensions of Evaluation,
Potency, and Activity.”
Date
Actor-
Behavior
Target
August 31,
Israel-
Clash (Israel + PLO forces), Scale Value = 14. EPA = -1.3,
1975
PLO
0.6, 1.4
August 31,
PLO-
Clash (Israel + PLO forces), Scale Value = 14. EPA = -1.3,
1975
Israel
0.6, 1.4
September 2,
PLO-
Attack (Israel position on Mount Hermon), Scale Value =
1975
Israel
14. EPA = -1.7, 1.0, 1.6
September 3,
Israel-
Arrest (suspected PLO terrorists), Scale Value = 11. EPA =
1975
PLO
-1.9, 0.6, 1.2
September 4,
Israel-
Attempt (to land near Saida - driven off), Scale Value = 12.
1975
PLO
EPA = -1.3, 1.1, 1.4
September 4,
PLO-
Clash (Israel + PLO in Lebanon), Scale Value = 14. EPA =
1975
Israel
-1.3, 0.6, 1.4
PLO-
Denounce (recent United Arab Republic-Israel
Israel
disengagement agreement), Scale Value = 10. EPA = -1.0,
September 16,
1975
0.0, 1.4
Table 2. Partitioning of Explained Variance in Evaluation, Potency, and
Activity of Observed Behaviors
Beta
Sheaf Coefficient
Fa
Probability
Prediction From:
Predicting Observed Behavior Evaluation
ACT Predicted Behaviorb
Evaluation
0.192
Potency
0.019
0.208
20.433
0.000
0.498
86.840
0.000
Activity
-0.015
Moving Averagec
Evaluation
0.474
Potency
-0.027
Activity
-0.019
Predicting Observed Behavior Potency
ACT Predicted Behaviorb
Evaluation
0.104
Potency
-0.014
0.055
3.421
0.017
0.335
91.231
0.000
Activity
0.104
Moving Averagec
Evaluation
0.021
Potency
0.250
Activity
0.122
Predicting Observed Behavior Activity
ACT Predicted Behavior
b
Evaluation
-0.028
Potency
-0.015
Activity
0.076
0.101
4.078
0.007
Moving Averagec
Evaluation
-0.174
Potency
0.070
0.441
103.314
0.000
Activity
0.244
a
Degrees of freedom are 3 / 2,980.
b
ACT Predicted Behavior, Using Interactant Sentiments Based on Reidentifications From the
Prior Four Events
c
Moving Average of Behavior Evaluation, Potency, or Activity In the Prior Eight Events
Evaluations of Actions Between Egypt and Israel,
Smoothed in a Moving Average Over 64 Events
Evaluation
1
0
Egypt-Israel
Israel-Egypt
-1
78
19
7
19 6
77
19
75
19
74
19
73
19
19
72
-2
Figure 1. Changing Behavior Evaluations in the Eygpt-Israel Dyad
Correlations of Predicted Behavior With Observed Behavior.
Abbreviations—E: Evaluation; P: Potency; A: Activity.
0.600
0.500
Correlation
0.400
Reidentification, E
Impression, E
Moving Average, E
Reidentification, P
Impression, P
Moving Average, P
Reidentification, A
Impression, A
Moving Average, A
0.300
0.200
0.100
0.000
1
2
4
8
16
32
64
-0.100
Averaging Base
Figure 2. Correlations Between Predicted Versus Observed Behavior, Where Predictions Are Based on
Reidentification Averaging, Impression Averaging, or Moving Averages of Prior Behavior, and Averaging is
Conducted Over Different Numbers of Prior Events