Using Experiments to Assess the Role of Expectations in Foreign Policy Attributions
Mark Paradis
PhD Candidate
Political Science and International Relations
University of Southern California
Paper prepared for the FLACSO-ISA Conference held in Buenos Aires, Argentina from July 23-
25, 2014. I appreciate the generous financial support of the USC School of International Relations and the USC Dornsife College of Letters, Arts, and Sciences. I would also like to thank Brian
Rathbun, Joshua Kertzer, Rod Albuyeh, and Patrick James for their assistance on the project.

In the world of foreign policy making, the concept of reputation has been very important. During the Cold War, for example, an entire foreign policy was designed and foreign interventions implemented in the service of reputation. In the study of international relations and foreign policy making, there is an ongoing debate as to whether reputation actually matters. Sartori (2005) and
Walter (2009) find that an investment in reputation has a positive impact on future interactions.
Conversely, Mercer (1996) and Press (2005) find little evidence of an investment in reputation having any impact on future interactions. Using a repeated entry deterrence game to examine reputations for resolve, Tingley and Walter (2011) find that while many players invested in reputation and that this investment had an effect on the behavior of others, neither challenger nor defender typically followed the equilibrium model and several participants significantly under or over-invested in reputation. They point to differences in cognitive abilities and preferences to explain the variability.
What is required for a reputation to form? According to Mercer (1996, 45), two criteria must be met. First, the potential challenger must believe that the defender’s actions were caused by personal characteristics—such as resolve—of the defender. If the defender’s actions are believed to have been caused by forces outside of the defender’s control, then the defender should not gain a reputation. Second, the challenger must use previous behavior in predicting future behavior. If the previously identified personal characteristics of the defender are not used in forming expectations for future behavior, a reputation has not formed.
As part of an updated version of the Tingley and Walter repeated entry deterrence game, conducted in collaboration with Brian Rathbun and Joshua Kertzer, I examine the first of these criteria. Specifically, I examine the role of expectations in whether challengers make dispositional attributions for the behavior of defenders, and whether defenders make dispositional attributions
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for the behavior of challengers. In the first case, dispositional attributions would support the formation of reputation. In the second case, dispositional attributions would also support the formation of reputations. However, since they are facing different opponents each period, situational attributions are more likely to support learning. In both cases, I suggest that outcomes that conform to expectations will be more likely to produce dispositional attributions, and outcomes that do not conform to expectations will be more likely to produce situational attributions.
The paper proceeds in six stages. First, I summarize the structure of the repeated entry deterrence game and some of the equilibrium positions. Second, I provide a brief overview of attribution theory from psychology, as well as some of the applications in IR. Third, I present the two hypotheses to be tested. Fourth, the operationalization of the game into an experiment and the measures used to test the hypotheses are discussed. Fifth, I analyze the results from the study.
Finally, I conclude with a review of the findings, a discussion of some of the limitations of the paper, and some suggestions for future research.
The Game
The repeated entry-deterrence game was originally designed to look at market entry in economics.
In market entry, a large firm can try to dissuade smaller firms from entering the market by decreasing its price. While this decrease in price hurts the larger firm in the short term, the show of resolve is hoped to deter challengers. For the smaller firm, if they believe that the larger firm will fight its entry, then not challenging becomes the more profitable move. Thus in a game with a finite time period, the reputation model would expect that in the early stages, large firms should be willing to incur some costs in order to earn a reputation. Smaller firms, on the other hand, should be less likely to challenge. As time progresses, larger firms should be less likely to deter entry and
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smaller firms should be more likely to challenge. These latter predictions would only hold in a finite time period setting where the end-point is known. The applicability of the game extends beyond economics to any situation where one party may want to pay a cost to deter another party.
Situations where a reputation for resolve is thought to be important are common in international relations.
The game can be summarized as follows.
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In a single period version of the game, there are three moves: one by nature, one by the challenger, and one conditional one by the defender. To begin, nature determines by chance whether the defender is committed or uncommitted, information that is only known to the defender. The challenger then decides whether to enter the game or not. If the challenger does not enter, the period ends and payoffs are given accordingly.
If the challenger enters, the defender must choose whether or not to fight. A committed defender receives higher payoffs from fighting and an uncommitted defender receives higher payoffs from not fighting. The challenger’s payoffs are only affected by the choice of the defender and not by its type. Figure 1 shows the structure of the game and the payoffs.
FIGURE 1
In the repeated play version, defenders face a series of different challengers. The results of the previous periods are revealed to each challenger. Therefore, each new challenger has more information about the previous actions of the defender. This additional information can alter their beliefs about the nature of the defender producing a situation where an uncommitted defender may have an incentive to misrepresent its nature (or invest in its reputation for resolve). According to
Tingley and Walter (2011, 348-349), in equilibrium, a committed defender would always fight, an uncommitted defender’s probability of fighting would be high early in the game and decrease
1 For a more in-depth discussion of the model equilibrium predictions, see Tingley and Walter (2011, 350).
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throughout the game, a challenger adjusts its strategy based on what the defender has done, and a challenger should be most likely to try a challenge later in the game.
Attribution Theory
While attribution as a concept is old within philosophy, Fritz Heider is generally viewed as the first to study attributions in psychology (1958). His work inspired the two models that launched attribution theory to prominence: Kelley’s covariation model and Jones and Davis’ correspondence inference theory (1967; 1965; for more comprehensive reviews, see Ross and
Fletcher 1985, 73-83; Krull 2001; 211-215). In this section, I provide a brief overview of these two prominent theories and a theory that synthesizes them.
Kelley’s model examines how people determine the cause of behavior (1967; Krull 2001,
213). Kelley argues that people use three pieces of information when forming attributions: consensus information, distinctiveness information, and consistency information (Kelley 1967;
Krull 2001, 213; Ross and Fletcher 1985; 79-80). According to Kelley, by combining this information we can produce a causal explanation for an event. For example, the combination of a high level of consensus, distinctiveness, and consistency “would imply something about the relevant stimulus” (Ross and Fletcher 1985, 80).
However, the situation is not always so simple. For various reasons, including lack of information, lack of motivation, and cognitive limitations, we may not always engage in this full causal process (Krull 2001, 212). In these cases, Kelley suggests that we rely on two different causal schemas (Kelley 1972). If an observer believes that there may be multiple causes for an event, we apply the multiple-sufficient-causes-schema. With this schema, we apply a discounting principle, which states that “the role of a given cause in producing an effect is discounted if other plausible causes are present” (Kelley 1973). This principle has been demonstrated in several
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studies (see Krull 2001, 213). Conversely, the multiple-necessary-causes-schema, which does not require the discounting principle, occurs when multiple causes are believed to be jointly necessary.
Beginning with his chapter with Davis, Edward Jones and his colleagues, along with numerous other social and cognitive psychologists, sought to explain “what determines whether perceivers will draw correspondent inferences, namely, whether they will believe that an actor actually possesses the attitudes or traits (s)he overtly displays” (Trope 1998; 67; Jones and Davis
1965; Jones and McGillis 1976; Jones and Nisbett 1972; Jones 1979, 1990). Jones and Davis argued that people make correspondent inferences unless certain factors are present that suggest that a situational inference is more appropriate (Jones and Davis 1965; Hamilton 1998, 100; Krull
2001, 213). These factors include the degree of choice that an actor had and the desirability of the action. If an actor has little or no choice in their behavior, then a dispositional inference is less likely. Similarly, if there is a clear standard of behavior in a given situation, then a dispositional inference is less likely. Jones and McGillis replaced social desirability with prior expectations
(1976; Ross and Fletcher 1985, 76). Prior expectations is both broader—in the sense that it allows for multiple sources of expectations—and more precise—in the sense that it is more tangible for measurement purposes. They identify two main categories of expectations. The first of these, target based expectancies, “are derived from prior information about the specific target person in question,” whereas the second, category-based expectancies, “reflect the perceiver’s knowledge that the target person is a member of a particular category or group” (Ross and Fletcher 1985, 76).
While Jones and colleagues were the first to describe the importance of the correspondence bias, it was Lee Ross who first described its implications (1977; Gilbert 1998, 10-11). Ross described the fundamental attribution error as the tendency “to underestimate the impact of situational factors and to overestimate the role of dispositional factors in controlling behavior
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(1977, 183). Therefore, while the correspondence bias that emerges from correspondent inference theory is not necessarily an error—it is, after all, possible for a person’s personality to correspond to a behavior that was situationally determined—the fundamental attribution demonstrates that this bias can lead to errors (Krull 2001, 214-215). A full understanding of the fundamental attribution error allows us to overcome its effects (Ross 1998, 57).
As should already be evident, the covariation model and correspondent inference theory are addressing two related, but separate, questions (Krull 2001, 213). The covariation model is a model of causal attribution. It looks at, for example, how American leaders explained Soviet behavior. Did they point to the Soviet leaders as evil communists or did they focus on the constraining nature of the balance of power? On the other hand, correspondent inference theory is primarily a theory of how we learn about dispositions from behavior. Scholars working in this field might examine how American leaders inferred that Soviets were aggressive from Soviet military posturing or from Soviet nuclear buildup. While this may seem like a small distinction, evidence from cognitive psychology suggest that correspondent inferences and causal attributions are part of distinct cognitive processes (for a review, see Krull 2001, 213-214). Therefore, instead of viewing the covariation model and correspondent inference theory as competing theories, some scholars suggest that we need to provide better analytical clarity on these concepts in order to advance research (for example, see Krull 2001; Hamilton 1998).
Krull argues that the fundamental attribution error should be divided into dispositionalism and the correspondence bias (2001, 214-215). Both dispositionalism and the correspondence bias are supported by extensive research (for a review of the literature on dispositionalism, see Jones
1979; 1990; Nisbett and Ross 1980; Ross 1977). The correspondence bias is viewed as the “most robust and ubiquitous finding in the domain of interpersonal perception (Jones 1990, 164).
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Dispositionalism would refer to our preference for dispositional attributions (Krull, 214-215).
Dispositionalism says nothing about whether the weight assigned to dispositional factors is appropriate. The correspondence bias refers to our tendency to draw inferences about personality from action, even when there are situational factors that can explain the action. As was previously mentioned, the correspondence bias need not be an error. Since these are distinct systems, it is possible to make a dispositional inference while arguing that the behavior was caused by the situation.
Several scholars have proposed models that seek to fit these two different processes together (Gilbert 1989; Gilbert, Pelham and Krull 1988; Trope 1986; 1998; Hamilton 1988; 1998).
Hamilton’s model appears to be the most consistent with evidence, and will form the foundation of the paper’s hypotheses. Hamilton adds more depth to Jones and McGillis’ (1976) and Trope’s
(1986) use of expectations. For Hamilton, expectations become the key factor in separating between the inferential and causal mechanisms (1998, 104). Three important research developments form the basis of his argument (105).
First, research shows that people spend a greater amount of time processing unexpected information (for example, see Bargh and Thein 1985; Hemsley and Marmurek 1982; Stern, Marns,
Millar and Cole 1984). From these findings, we would expect American leaders to spend a greater amount of time processing North Korean conciliatory actions than oppositional ones. Similarly,
American leaders would spend more time processing oppositional British actions than friendly ones.
Second, when people receive unexpected information, they search their memory for information about the target. On the other hand, they do not engage in any additional searches when information conforms to expectation (for example, see Sherman and Hamilton 1994). Using
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the same examples as above, American leaders would engage in a greater search of their memory for information about North Korean and British leaders when they act in a friendly and unfriendly manner respectively. They would not need to search their memory if North Korean and British leaders acted in an aggressive and conciliatory manner respectively.
Finally, when they encounter unexpected information, people engage in attributional thinking to determine why the behavior deviated from their expectations (for example, see Clary and Tesser 1983; Hamilton 1988; Hastie 1984; Schoeneman, van Uchelen, Stonebrink and Check
1986). Conversely, attributional thinking does not automatically occur when encountering expected information. According to these findings, we would expect that Americans would not try to explain friendly British behavior, but would try to explain friendly North Korean behavior.
Hamilton concludes that behavior that conforms to expectations is processed in a manner that is “more spontaneous and less analytic, more automatic and less controlled, more heuristic and less systematic” than when behavior is unexpected (1998, 106). In other words, when faced with expectancy-consistent behavior, people automatically make inferences about the person’s disposition from their behavior. This inference will form part of their future expectations for the actor. It is only when faced with expectancy-inconsistent behavior that people engage in more analytic, causal thinking.
It is important not to overstate the separation between the two processes. There is an important way in which inferences can influence attributions (Hamilton 1998, 107). If a person is asked to explain a behavior in the future, the correspondent inference that they made at the time of the event can bias their thinking in favor of a dispositional attribution. This may be the mechanism underlying findings in support of dispositionalism. For example, when Iranian leaders act in an oppositional manner in negotiations, something that American leaders likely expect, American
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leaders likely draw an inference of Iranians as oppositional or irrational, but do not engage in attributional thinking to explain this behavior. However, if a policy response is later needed, the inference of Iranian as oppositional or irrational will bias the causal thinking in favor of explaining the event in terms of the Iranian leadership’s disposition.
When faced with expectancy-inconsistent behavior, the more analytic thinking is likely to provide causal arguments that focus on situational factors (Hamilton 1988; 1998, 108-109;
Crocker, Hannah and Weber 1983). This is not surprising given the unexpected nature of the events. For example, according to this argument, if North Korea were suddenly to renounce all claims on nuclear weapons, American leaders would be more likely to look for situational explanations than to conclude that North Korea’s leadership has a better disposition than previously believed. However, even when faced with unexpected behavior, dispositional attributions remain possible (for a review, see Vonk 1994). The dominance of situational attributions when faced with unexpected information seems to contradict the strong findings in favor of dispositionalism. However, two factors can explain the strength of the evidence in favor of dispositionalism. First, as was just mentioned, dispositional attributions are sometimes the result of unexpected behavior. Second, and more importantly, most behaviors will be viewed as consistent with expectations (Hamilton 1998, 108; Trope 1986). If a person’s expectations have any validity, then they should generally not be surprised by events. Moreover, when it is ambiguous as to whether a behavior conforms to expectations, expectations can bias the interpretation of the event.
To conclude, this section has reviewed the evolution of attribution theory in social psychology. Evidence suggests that there are two processes that we use when we encounter the behavior of others. When the behavior of others does not surprise us, we update our view of their
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personality based on the nature of the action. Since this updating only occurs when we are not surprised, our views of others tend to be very stable. When we are surprised by the behavior of others, we seek to explain the behavior. We generally point to situational factors in these cases.
These findings will form the foundation of a theory of how leaders, media, and the public understand foreign policy events.
Attribution Theory in IR
Numerous reviews of political psychology and IR point to the applicability of attribution theory to the study of world politics (for example, see Jervis 1976; Tetlock 1998; Vertzberger
1990; Houghton 2009). In describing the relevance of attribution theory to foreign policy analysis,
Larson writes that:
Specifically, attribution theories seek to discover the principles of “naïve epistemology,” the rules and procedures laymen use in gathering and interpreting data, as opposed to those associated with the model of scientific epistemology. How do people know what is real and, more important, how do they know that they know? This suggests that the concepts and hypotheses of attribution theory might be useful for understanding foreign policymaking because it is impossible for policymakers to satisfy the requirements of the scientific model in their attempts to explain and interpret international events, they must resort to principles of “naïve epistemology’ in making judgments about other states (1985, 35).
In this section, I provide a brief overview of some of the applications of attribution theory to the study of IR.
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A common area where attribution theory has been applied to IR has been the Arab-Israeli conflict. Using students from the United States and multiple Middle Eastern states, Rosenberg and
Wolfsfeld use a complicated model of attribution to “explicate how the nature of an individual’s relationship to a group and its activity will determine his perception of the causes of that group’s activity” (1977, 75). They argue that the observer’s affiliation with the actor, the successfulness of
2 We focus our review on research that examines attributions for the behavior of others. Therefore, research by Hirshberg (1993) on American self-image is beyond the scope of this review.
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the action, and the perceived morality of the action will influence the form of attribution.
Heradstveit (1979) conducts a number of interviews with elites to assess the psychological barriers to peace in the Arab-Israeli conflict. He applies a standard model and contends that an opponent’s bad behavior is explained using dispositional factors and their good behavior is explained using situational factors.
Other studies have focused on attributions in the Cold War. Larson (1985) and Heradstveit and Bonham (1986) used attribution theory to look at the origins of containment in the Cold War and to examine American and Norwegian attributions for Norwegian and Soviet actions during a naval incident in 1978 respectively. Heradstveit and Bonham found that Norwegian officials were more likely to make situational attributions in explaining Soviet actions than their own actions.
The US, a Norwegian ally, made dispositional attributions for positive Norwegian actions and situational attributions for Soviet actions. While the American attributions for Soviet actions may seem counterintuitive, they argue that American officials empathized with the Soviets as a superpower in their dealings with a smaller power.
Using both psychological and sociological theories of attribution, Montiel and Macapagal
(2006) argue that in asymmetric conflicts, the dominant group will point to dispositional causes, whereas the marginalized group points to structural factors. They test these hypotheses by examining Christian and Muslim explanations of the Mindanao conflict in the Philippines.
In an innovative study that uses the appraisal tendency theory, Small, Lerner, and Fischhoff
(2006) analyzed the effects of emotional primes on causal explanations of 9/11. They found that those who were “reflecting on their anger generated more causal attributions than did those reflecting on their sadness, even though angry and sad participants wrote equally long responses.
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These findings support the hypothesis that activation of anger evokes more attributional thought than does activation of sadness” (294). The study is restricted to the response of Americans.
Finally, I have already briefly discussed Mercer’s work on attributions (1996). Instead of expectations, he uses social desirability to form predictions about whether a dispositional or situational attribution will be made.
Hypotheses
To examine when the first criteria for the formation of a reputation during a deterrence game is met—that is, when dispositional attributions are made—we ask participants to identify their expected outcome and to explain the actual outcome. In order to determine whether both challengers and defenders are able to draw reputational inferences from interactions, the question is asked of both challengers and defenders. As was previously noted, since defenders face different challengers each round, the reliance on reputations would be sub-optimal in this situation. Instead, defenders should be trying to learn as much as possible about the situation. Nonetheless, for both the challengers and defenders, I hypothesize that:
H1: when the outcome conforms to expectations, there will be a greater weight placed on dispositional attributions;
H2 : when the outcome does not conform to expectations, there will be a greater weight placed on situational factors.
In both cases, I expect that the relationship will hold regardless of the size of the expected outcome, the size of the actual outcome, and the direction of the discrepancy. Therefore, for situations where outcomes do conform to expectations, dispositional attributions should be more likely regardless of whether the outcome was higher or lower than expected. Similarly, I expect that there will be a larger weight placed on situational factors when the actual outcome is higher or lower than expected. The content of the attribution may differ based on the direction—that is, a
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focus on different aspects of the situation—but the focus will be on external, rather than internal, factors.
Methods
Participants were undergraduate students from a large private university on the west coast. They were paid a show-up bonus of $10 and an additional sum that was determined by their choices and by chance (on average, approximately $3.50). A session lasted approximately one hour and included two surveys and the game. One survey was taken before the game and the other after the game. The order of the surveys was randomized. The surveys included both demographic questions and psychometric tests.
The participants had the following demographic characteristics: 23.3 years was the average age; 53.5% were female; 41.1% identified as Caucasian and 43.8% as Asian; and 4.78 was the average on the 7-point conservative to liberal scale. The sample is noticeably younger and more female than the population of political elites. The difference in ideology depends on the ideology of the government of the day.
Participants were randomly divided between challengers (first movers) and defenders
(second movers). At the end of the first round, the roles were reversed. Two rounds—one in each role—were played. Each round consisted of eight periods. Participants are told that they will be facing a different opponent each round and that they will be unaware of the exact identity of their opponent. Defenders are divided between type 1 and type 2 defenders. The defender’s type is not known to the challenger.
At the beginning of each period, the first mover is asked whether or not they want to challenge. In order to avoid any biasing language, they are asked whether they would like to choose to move to A1 or A2 on the decision tree. The defender is simultaneously asked whether or not
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they would fight if challenged (move to B1 or B2 on the decision tree). While in some ways it would be better to only ask the defender what they would like to do when they are actually challenged, this would sacrifice a great deal of data.
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At the beginning of each period, the first mover receives information about the second mover’s previous history. In the first period, no information is given because there has not yet been any history. The history only shows the decisions of the second mover’s previous behavior for the periods in which they were challenged. In the periods in which they were not challenged, the decision that they made remains private. This poses an interesting challenge for first movers when they are trying to identify the second mover’s type. If the second mover has been lucky (or skilled) enough to avoid challenges in the past, then the first movers don’t actually gain much information from the histories, other than useless information about other challengers.
We made two important change to the game. First, we do not vary the number of periods in the game. This is more in line with the economics version of the game. Since Tingley and Walter have already examined the effects of varying the length of the game, there was no need, with a limited subject pool, to lose power to maintain that treatment. Second, instead of playing against actual other participants, we had all participants paly against a pre-programmed computer. This computer opponent was programmed to play the equilibrium strategy. Participants were unaware of this change. A full description of the reasons for this change are beyond the scope of this paper and it does not alter the hypotheses or analysis.
To test the hypotheses about attributions, both challengers and defenders were asked a set of questions about expectations and attributions during one period in each role. For challengers, the questions were randomly presented in either the first or seventh round. For defenders, the
3 To compensate for the decrease in experimental realism, we insert delays in the game after each player has submitted their choices. This way, it provides a better simulation of sequential moves.
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questions were randomly presented in either the first or fourth period. The randomization of the timing of the presentation was done in order to ensure that the attribution questions did not have any confounding impact on any of the other tests being conducted on the data from the game.
To assess the participant’s expectations for the outcome of the period, participants were asked “how much do you expect to earn in this period” immediately before choosing their move.
They were able to choose from any of the three possible outcomes. For challengers, the possible outcomes were 80, 95, and 150 experiment points (EP). For defenders, the possible outcomes were
70, 160, and 300 EP.
After being shown the result from the period, participants were asked to answer the following questions.
1.
In a few lines, please describe what you believe to be the main cause(s) of the outcome.
2.
To what extent were the following factors important in producing the outcome? a.
Personal characteristics of the first (second) mover b.
Personal characteristics of yourself c.
Aspects of the situation (environment, structure)
Question 2 was a 7-point scale ranging from ‘not at all important’ to ‘extremely important.
Results and Analysis
In this section, I provide the results of the expectation and attribution questions asked of the participants, as well as analysis of any connection between the two. I examine challengers and defenders in turn. For each, I begin with some summary statistics of their play. This will help to show how the participants played, as well as identify some potential limitations in the data. For example, some of the outcomes were far rarer than others. I then examine whether there is a difference between the attributions made based on their expectations. I use two primary methods for this analysis. First, I use boxplots to graphically represent the structure of the data. It can help to identify some differences that normal statistical techniques can miss. They will also help to
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show whether the differences between the groups are in the hypothesized direction. Second, I use one-way ANOVA to examine the difference between the means. I also report the results of an
ANOVA using Welch’s test. This is important because, as the boxplots will reveal, the variance was not equal in many cases.
For challengers, the possible outcomes were 80, 95, and 150 EP. In total, there were 207 participants who completed the expectations and outcomes sections of the study. The outcome was
80EP 132 times (63.8%). Of those 132 times, expectations were met 23 times (17.4%). The outcome was 95EP 48 times (91.7%). Of those, the outcome agreed with expectations 44 times
(91.7%). Finally, there were 27 participants who received 150EP. In 16 of those cases (59.3%), the results met expectations. In total, 40.1% of outcomes fit expectations.
Of the 159 times that participants chose to challenge (76.8%), they expected to earn 150EP
125 times (78.6%). Without a detailed analysis of the data, it is difficult to draw conclusions about whether this statistics fit with the equilibrium predictions. However, since roughly 50% of participants received the attribution questions in the first period and that you should never challenge in the first period, many of these participants were not following the rational strategy.
For the other 50%, the rationality of their decision depends on the history of the defender. We also see that a large percentage of those participants who challenged expected that they would receive
150EP. Given that they challenged, this is perhaps not surprising. However, it does raise two questions. First, why did such a large percentage of them expect to receive 150EP? For long term, and sometimes short term reasons, defenders have an incentive not to give the challenger 150EP, particularly in early periods. Yet over 50% of all participants expected to earn 150EP when challenging. Second, for those who expected to get 80EP from a challenge, why did they choose to challenge? There is no strategic reason for this choice other than, perhaps, a relative gains
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rationale where they wanted to avoid the other player receiving 300EP. This type of thinking was not incentivized in the game, but it remains a possibility.
Tests of the impact of the conformity of expectations on dispositional attributions reveal moderate to high support for H1 . Figures 2-4 are the boxplots for these relationships.
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Figure 2 shows the results for all of the outcomes, with 0 referring to non-conformity and 1 referring to conformity. The higher importance of the other is evident for those whose expectations were met.
Similarly, the ANOVA showed a significant effect on attribution (F(1,205)=6.72, p<0.01). The results of Welch’s test were significant at the same level.
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FIGURES 2-4
Figures 3 and 4 show the difference between the groups when the outcome was 150EP and
80EP respectively. The x-axis labels of 1, 2, and 3 identify the expected outcome, with 1 referring to 80EP, 2 to 95EP, and 3 to 150EP. Figure 3 provides some moderate evidence of a difference.
At minimum, there is a much higher degree of variance for those who had an unexpected outcome.
Figure 4 provides much weaker support. Given that we can largely ignore those who expected an outcome of 95, there does not appear to be much difference between the two groups. In both of these cases, the ANOVA did not reveal any statistically significant results.
Tests of the impact of the conformity of expectations on situational attributions reveal no support for H2 . Figures 5-7 are the boxplots for this relationship. Figure 5 reveals the results for all outcomes. Contrary to H2 , those who expected the outcome argued that the situation was far more important than those who did not expect the outcome. The ANOVA showed a significant effect (F(1,204)=12.77, p<0.01). Welch’s test also found a significant effect.
4 I omit the results for the attributions made when the outcome was 95. Since the participants had full control over the outcome, it is unclear what was going on with the participants who expected a different outcome.
5 The full tables of results are in Appendix A.
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FIGURES 5-7
Figures 6 and 7 similarly do not show support for the hypothesis, but they are less contradictory than figure 5 would suggest. In both cases, there does not appear to be any difference across groups. Not surprisingly, the ANOVA failed to find any significant effects. This raises a question about what is driving the results for the combined sample. Figure 8 suggests that it was those who correctly expected to earn 95EP who are driving the results. Given that those participants had full control over their outcome, it may be that we need to look at the self-serving attribution bias in order to explain these results.
FIGURE 8
For the challengers, there appears to be moderate to strong support for H1 . Those who expected the outcome did appear to be more likely to make a dispositional attribution. More work is required to unpack some of the results for individual outcomes. Conversely, the results fail to support H 2 . At best, there appears to be no difference between the groups. It may even be possible that there is an effect, but that it is in the opposite direction than hypothesized. However, the selfserving bias may explain some of these findings. Future research is required.
For the defenders, there were 209 participants who completed the section, with 36.8% of outcomes fitting with expectations. There were three possible outcomes: 70, 160, and 300EP. The outcome was 70EP 11 times (5.3%). Of those, 2 out of 11 outcomes fit expectations (18.2%). It is interesting to note that 2 of the 9 who received an unexpected outcome predicted that they would receive 160. The difference between an outcome of 70 and 160 is entirely within the control of the defender. It is likely that those 2 participants did not fully understand the game. With an N of only
11, I will not be able to do any analysis on this outcome. They will, however, be included in the general model. For those who received 160EP, the outcome fit the expectations of 8 of 27
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participants (30.0%). Of the 19 who did not receive the expected outcome, all of them expected to receive 300 (that is, not to be challenged). Finally, for those receiving 300EP, 68 of 171 participants predicted this outcome (40.4%).We see that many of these participants overweighted the probability of being challenged, particularly in the first period. In summary, the descriptive results show that the majority of participants did not expect the received outcome and 300EP was, by far, the most common outcome. The results also show that there was a relatively low investment in reputation among those who were challenged.
Tests of the impact of the conformity of expectations on dispositional attributions reveal no support for H1 . Figures 9-11 are the boxplots for these relationships. While the distribution is noticeably focused around higher scores on the attribution measure when the outcome was expected when the outcome was 300EP and in the full model, the results of the ANOVA and
Welch’s test do not identify a significant effect of receiving the expected outcome. Moreover, when the outcome is 160EP, the trend identified in the boxplot does not fit with the hypothesis.
However, again in this case, the statistical tests fail to find any effect.
FIGURES 9-11
When examining the impact of expectations on situational attributions, the evidence does not provide any support for H2 . Figures 12-14 present the boxplots for these relationships. While the boxplots for the entire model provide some visual support for higher situational attributions when the outcome is unexpected, the statistical tests do not find any statistically significant effect.
When the outcome is 300, both the boxplots and statistical tests suggest that there is no effect.
When the outcome is 160, the boxplots suggest that there is a higher variance when the outcome is expected, with a tendency towards higher results when the outcome is unexpected. However, the statistical tests do not identify any significant effects.
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FIGURES 12-14
For the defenders, there is little to no support for the two hypotheses. Some of the boxplots suggest that there may be some support for the hypotheses regarding situational attributions, but the ANOVA and Welch’s test do not allow for similar optimism. Given that participants were playing against different opponents each period and that the reputation of a single player has no real impact on the game, it is perhaps not surprising that I fail to find significant effects for defenders. It is possible that traditional attributional mechanisms are not followed under these circumstances. Future research will need to place a greater emphasis on understanding these situations.
Conclusion
For a reputation to be formed, an observer must believe that the observed behavior was caused by personal characteristics of the actor and that these characteristics will influence future behavior. In this paper, I sought to identify under what circumstances dispositional attributions are made. Based on research in social psychology, I argued that expectations are the most important factor in determining whether a dispositional or situational attribution will be made. Specifically, when explaining an expected outcome, dispositional attributions are made, whereas when explaining unexpected outcomes, situational attributions are made. These hypotheses were tested using a repeated entry deterrence game. Participants, both challengers and defenders, were asked to predict the outcome of an interaction and to subsequently explain the outcome. For challengers—for whom the identification of a reputation is most important—I found moderate support for a greater focus on dispositional factors when outcome are expected. However, there was no support for a greater focus on situational factors when outcomes are unexpected. For defenders—for whom
20

understanding the structure of the game is most important—I found no support for the first hypothesis and weak support for the second hypothesis.
Moving forward, a few steps should be taken. First, given the number of groups necessary for the analysis, increasing the number of participants would be beneficial. Second, combining net attribution scores—dispositional – situational attribution scores---may be useful to identify whether expectations affect whether one form of attribution is relatively more important. Finally, while the repeated entry deterrence game has a great number of uses, these hypotheses should be tested using an actual experiment, preferably one using less abstract and complicated tasks.
21

Figures
Figure 2.
Dispositional Attributions vs. Conformity of Expectation to Outcome Across all
Outcomes
Figure 3.
Dispositional Attributions vs. Conformity of Expectation to Outcome for Outcome of
150EP
22

Figure 4.
Dispositional Attributions vs. Conformity of Expectation to Outcome for Outcomes of
80EP
Figure 5.
Situational Attributions vs. Conformity of Expectation to Outcome Across all Outcomes
23

Figure 6.
Situational Attributions vs. Conformity of Expectation to Outcome for Outcome of
150EP
Figure 7.
Situational Attribution vs. Conformity of Expectation to Outcome for Outcomes of 80EP
24

Figure 8.
Situational Attributions vs. Conformity of Expectation to Outcome for Outcomes of
95EP
Figure 9.
Dispositional Attributions vs. Conformity of Expectation to Outcome Across all
Outcomes
25

Figure 10.
Dispositional Attributions vs. Conformity of Expectation to Outcome for Outcome of
300EP
Figure 11 . Dispositional Attributions vs. Conformity of Expectations to Outcome for Outcome of
160 EP
26

Figure 12.
Situational Attributions vs. Conformity of Expectation to Outcome Across all
Outcomes
Figure 13.
Situational Attributions vs. Conformity of Expectation to Outcome for Outcome of
300EP
27

Figure 14 . Situational Attributions vs. Conformity of Expectations to Outcome for Outcome of
160 EP
28

Appendix A
Challengers
Table A.1 Importance of Others
One-way ANOVA
All outcomes
150EP
F(1,205)=6.72, p=0.01
F(1,25)=0.60, p=0.45
95EP
80EP
F(1,46)=2.90, p=0.10
F(1,130)=0.69, p=0.41
Table A.2 Importance of Situation
All outcomes
150EP
95EP
80EP
One-way ANOVA
F(1,204)=12.77 , p=0.00
F(1,25)=0.018, p=0.89
F(1,46)=0.86, p=0.36
F(1,129)=0.029, p=0.86
Defenders
Figure A.3 Importance of Others
All outcomes
300EP
160EP
One-way ANOVA
F(1,205)=0.36, p=0.55
F(1,167)=1.63, p=0.20
F(1,25)=0.67, p=0.42
Figure A.4 Importance of Situation
All outcomes
300EP
160EP
One-way ANOVA
F(1,205)=0.011, p=0.91
F(1,167)=0.29, p=0.59
F(1,25)=0.41, p=0.53
Welch’s one-way ANOVA
F=6.38, p=0.01
F=0.53, p=0.48
F=2.41, p=0.33
Insufficient obs.
Welch’s one-way ANOVA
F=11.94, p=0.00
F=0.0129, p=0.89
F=8.37, p=0.12
Insufficient obs.
Welch’s one-way ANOVA
F=0.013, p=0.91
F=0.78, p=0.46
F=0.42, p-0.53
Welch’s one-way ANOVA
F=0.38, p=0.54
F=0.16, p=0.86
F=0.34, p=0.57
29

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