The Handbook of Conflict Resolution (3rd ed)

by Peter T Coleman

  In contrast, social conflict can arise in social systems through network resistance, such as emanating from the stay-at-home spouse who is trying to stop the overtime. This is referred to as goal prevention role behavior in the theory (P) and is shown with dashed paths to goals. If this spouse has others in the network supporting the resistance, referred to as supportive resistance (V), it shows how other entities in the network are helping fuel forces against the goal and thus how a wider conflict can exist in the social network. This is also depicted in 41.1b with dashed paths between relevant parties. Greater goal preventing and supportive resisting in a social network is predicted to have a larger effect on thwarting the target goal pursuit. Ironically, the individuals in some of these conflict settings may not show any hostility or aggression toward one another, such as two professional athletes engaged in a tough match who share no animosity toward each other (or may even be friends). Hence, dynamic network theorizing allows for such nuanced relations, instead of relying on assumptions that goal prevention and supportive resistance behaviors always have hostile or aggressive intent.

  In other cases, interpersonal negativity, such as hostility, prejudice, and aggression, can coexist with goal prevention (e.g., a hostile competition). Dynamic network theory refers to such interpersonal linkages that contain such affect-based hostility, negativity, or prejudice as, first, system negation (N), to represent a person’s negativity toward another’s goal pursuit. Second, system reactance (R) represents a person’s negativity toward another’s resistance toward the goal pursuit. To illustrate these concepts in our example in figure 41.1b, the stay-at-home spouse shows system negation toward the working spouse’s desire for overtime (i.e., is upset about it), while the working spouse is reacting to this by showing hostile negativity back to the spouse because of the resistance.1 This example illustrates how a mutual conflict in the social network has formed in relation to the underlying goal issues about overtime work. That is, system negation has formed in conjunction with goal prevention. More broadly, research has shown that negative social network ties in general are related to increased psychological distress (Finch, Okun, Barrera, Zautra, and Reich, 1989) and lower life satisfaction (Brenner, Norvell, and Limacher, 1989), although much of this research has not unpacked how these negative links are related to underlying goal prevention and/or system negation, which we presume are often independent dimensions that may be correlated in some contexts.

  Finally, there may be other ironic effects of goal prevention and supportive resistance on conflict resolution processes. For example, these behaviors could represent constructive forms of conflict resolution when they successfully prevent others from pursuing actions that ironically inhibit them from securing other more valuable goals and outcomes, such as a negotiator getting another person to see that the counteroffer will actually result in more positive outcomes for the person than the initial offer.

  More generally, besides providing a new approach to understand social connections, dynamic network charts also allow scholars and practitioners to describe overall dynamics in social systems. For example, the network affirmation ratio shows the overall ratio of positive to negative forces involved in goal pursuits. This value was .57 in the work-family conflict in figure 41.1b, illustrating that considerable conflict exists in this system in relation to the overtime issue. It is here where we can see potentially sharp differences between traditional social network diagrams and dynamic network charts. For example, the traditional network approach (without goals) indicates that the overall ratio of positive to negative valences in figure 41.1a was .75. This represents a rather positive system and a substantively higher value than .57 in the dynamic network chart, which represents a system with considerably more conflicting forces that in some intractable contexts may accumulate over time (Coleman, 2011; Gottman and Levenson, 1992).

  How do we explain this difference? On closer inspection, we see that although the friend and stay-at-home spouse’s linkage is positive in the traditional network approach, this linkage is actually a set of behaviors that is supporting the stay-at-home spouse’s resistance to others wanting to pursue overtime. Although the friend and stay-at-home spouse have a generally positive link with each other, this link is a force working against others in the social network wanting the overtime. Hence, the traditional network approach is potentially oversimplifying the system and overestimating the positivity ratio when we look at a key issue in the system. The dynamic network chart, in contrast, can unpack how people are working with (or against) one another toward their different goals and desires, which may provide a deeper understanding of human conflict forces operating in a systemic context (Lewin, 1951).

  Finally, although not shown in figure 41.1b for simplicity, dynamic network theory also illustrates that people in peripheral roles can have an impact on goal pursuit and human conflict processes. In particular, interactants (I) are people who are interacting around others in goal pursuit but not helping, hurting, or even observing what is going on in the system. For example, a child may be interacting around the spouses but is not paying attention to their discussions about overtime at work. Such interactants can also change conflict dynamics inadvertently, such as the two spouses toning down their disagreement about overtime when the children are nearby. This interaction may also introduce additional goals into the system. For example, a child may remind the couple of their positive interdependence and love, which in turn would decrease the relative importance of the work-conflict issue. Observers (O), the last role in the theory, represent people watching, listening to, or generally observing a given goal issue or conflict between people but not helping, hurting, or interfering with the situation. For example, a cousin may have heard about the conflict between the spouses but did not take a position either way. People in peripheral roles can also be targets for change in conflict resolution strategies. For instance, the working spouse may ask the cousin to support the overtime and try to change the stay-at-home spouse’s mind about it.2

  The Network Rippling of Emotions

  Dynamic network theory also proposes how emotions and hostilities spread in networks through the network rippling of emotions process (Westaby, 2012), which is important in explaining how relational conflicts can start. This process illustrates how goal achievement or goal progress (or a lack thereof) affects the spreading of emotions to specific people in social networks. To illustrate, when goal strivers achieve their goals or desires, they are expected, not surprisingly, to feel positive emotions about the success. But if these goal strivers also had system supporters (even among those in out-groups), these supporters would be expected to have positive emotional reactions in regard to the goal striver’s success, such as a parent feeling good about his or her child’s winning an award in a heated competition. Here, one can see that emotions in the network are contingent on the goal and are directed in systematic ways.

  In contrast, if goal preventers, supportive resistors, or system negators exist in the same system defined around a given goal issue, they would be expected to experience negative emotions such as frustration, envy, or jealousy when others are achieving the goals they wanted to resist. For example, both the child who lost the heated competition and his or her parents would likely feel negative emotions and target some of this negativity and frustration toward those on the winning side. It is here where interpersonal negative links now exist. This hostility may be direct, such as confronting the other family, or indirect, such as gossiping malevolently about them. Such triggers can also set the stage for potentially longer-enduring or even intractable conflicts between people (with negative attractor states) unless these negative orientations can be destabilized or reconciled (Vallacher, Coleman, Nowak, and Bui-Wrzosinska, 2010).

  The theory also delineates that generalized conflicts can exist among entities in social networks even when there is no previous interaction or direct goal prevention. For example, some people may have preconceived stereotypes and prejudices about other
s, based on their group classification or identity, even though they have never interacted before. Or people may believe negative things they hear about others through third-party gossiping—even things that may be groundless in fact. In these cases, individuals and groups can learn and develop system negation, distrust, and prejudice without having been involved in one another’s lives.

  CONFLICT RESOLUTION STRATEGIES IN SOCIAL NETWORKS

  In this section, we illustrate how practitioners could use dynamic network concepts to portray conflicts and use such information for facilitating change. In the traditional social network approach, a practitioner would often enter network data into a computer program (such as through an adjacency matrix) to understand how people are positively or negatively linked to one another, as illustrated in figure 41.1a. Computer programs then provide visuals that allow practitioners to see all the linkages between entities. Although visually interesting, the data can be overwhelming to understand when the network becomes large, with many boxes and numerous lines. Fortunately, as an alternative, researchers or practitioners could also focus on calculating statistics about each of the entities (i.e., an egocentric analysis) to gauge their presumed importance in the system, especially their level of centrality (Balkundi and Kilduff, 2006). A traditional network approach often assumes that the entities that are most central are critical to target in an intervention.

  While this makes intuitive sense at first glance, there may be some difficulties in implementation when compared to other approaches. To illustrate, in figure 41.1a, the working spouse is most central in this simple network because he or she has the most connected linkages. Thus, a traditional network approach could suggest that this is the primary entity to target with the intervention. However, such an approach, void of other motivational orientations in the conflict, may not steer an intervention effectively. For example, according to dynamic network theory, an intervention must include both the working spouse and stay-at-home spouse because they are not only negatively reacting to one another (N and R), they are both equally and directly involved in their goal-related conflict about working overtime (G and P). The theory therefore provides additional guidance to understand key motivational orientations in networks, which can be used to inform interventions.

  NETWORK CONFLICT WORKSHEET

  Another concern that practitioners face when using social network diagrams and even fully detailed dynamic network charts is the laborious nature of using these methods in practice. Fortunately, because dynamic network theory can capture critical motivational parameters involved in network conflicts, it can provide another parsimonious approach, which we introduce in this chapter. Specifically, figure 41.2 presents a network conflict worksheet that may be helpful for gaining a system perspective about how multiple social network entities are motivationally involved in a network conflict. This initial formulation, which needs further refinement, could provide practitioners with a basic method of conflict analysis from a network perspective. This tool could also be used in conjunction with other tools of assessment, such as those assessing the parties’ values, interests, and objectives. To process the worksheet, researchers or practitioners could use the worksheet to collect information confidentially from individuals in the network. Or the worksheet could be used to facilitate group discussions by initially breaking the groups into different sides of the conflict to minimize overt conflict between parties and then later bringing the sides together to develop a deeper understanding of the complexities of the conflict once a professional facilitator processing the information deems it safe and ethically appropriate. That is, researchers or practitioners should be very careful about the way in which identities, general descriptors, or pseudonyms are used in discussions, reports, or data presentations based on ethically appropriate choices for information sharing. In sensitive settings, it may be prudent for mediators, for example, to use the information collected confidentially (or anonymously) from individuals using the worksheet to help understand the social context and brainstorm solutions, but not to share how individuals responded to the worksheet in public.

  Figure 41.2 Network Conflict Worksheet

  Before describing the worksheet, there are important considerations to keep in mind. At first glance, it would appear that the worksheet forces the practitioner to limit the conflict analysis to a dichotomy of side 1 versus side 2.3 Although such a dichotomy may help map how people see some parties in the system, the worksheet goes beyond this. That is, the analysis allows researchers or practitioners to identify the various parties involved in conflicts beyond those taking sides. Furthermore, to maintain simplicity, the worksheet does not directly detail larger systemic-level forces acting on the conflict situation, which could be addressed in discussion, such as the effects of new policies, laws, or environmental pressures imposed on a system. Hence, the worksheet should be used with complementary assessments and processes whenever possible to assess a given conflict.

  To complete this basic version of the worksheet, a researcher or practitioner would ask participants (individually or in group discussions) to start at the top of the page and work their way down for each category of questions.4 For example, participants would first respond to, “What’s the conflict about?” (box 1). Answers may also reveal the types of issues, interests, needs, procedural concerns, substantive disagreements, worldviews, psychological needs, and so forth that are involved in the conflict. Then participants would be asked to indicate which parties they perceive to be directly versus indirectly involved in side 1 of the conflict (boxes 2 and 4). Then perceptions about entities involved on the other side of the conflict would be assessed (boxes 3 and 5). Conceptually the upper part of the worksheet illustrates entities who are deeply involved in the network conflict. The lower part of the worksheet illustrates entities exclusively involved in more peripheral roles in the system, such as those who are observing or are neutral in the conflict but are not involved.5 Once the information is collected individually or discussed in groups, researchers or practitioners can use the worksheet in various ways to learn about the system and then potentially to intervene.

  Describing the System

  Worksheet information can be beneficial in describing a variety of system characteristics. First, it can gauge the group’s overall level of confidence about the roles they perceive in the system. To calculate this, one would simply count the number of question marks in the worksheet or calculate the certainty ratio (i.e., [number of question marks/total number of entities in the system]−1). Second, researchers or practitioners could use information from the worksheet to measure the level of agreement, common ground, or disagreement between the parties about the nature of the conflict itself (box 1). If there are inconsistencies, practitioners could attempt to generate a clearer and more agreed-on understanding of the conflict among the parties or encourage the parties to appreciate how other views of the conflict could have legitimately formed in an effort to build more compassion and understanding in the system, akin to classic methods of conflict resolution (Deutsch, 1977; Pruitt and Kim, 2004).

  Third, the worksheet approach could be used to explore interesting clues about dynamic network intelligence (DNI) in the network (Westaby, 2012). DNI represents how accurate people are in their perceptions about who plays what roles in the system. For example, if Jane on side 1 sees Joe as a direct actor on side 2 of the conflict (e.g., a goal preventer), but Joe sincerely does not place himself in that role at all, and instead views himself as an observer in box 8, such feedback to Jane may help alleviate her anger and system reactance toward Joe. In this case, Jane’s initial DNI was low, and this could be a significant psychological contributor to the conflict that is generating her negativity toward Joe. Moreover, if some people are placed in multiple boxes at the same time, they may be playing various sides of the conflict or are acting in ways perceived to be ambiguous by people monitoring the actions in the system. In a conflict situation where low DNI has been identified, the facilitators would i
deally try to help members of the network navigate how perceptions are mapping onto reality (or not) in the social context, akin to how counselors apply principles in cognitive behavioral therapy.

  Fourth, one can gauge a network hostility ratio in the system (the negators and reactors) by calculating the number of people who have checked names in side 1 and side 2 boxes divided by the total number of people named in those boxes. As this ratio approaches 1, it suggests an extremely contentious or escalated conflict, which would require a more urgent and directed intervention strategy. An implicit goal for those trying to resolve the conflict is to reduce this ratio so that anger does not transform into physical aggression. Finally, one can also gauge a conflict motivation ratio, which shows the overall balance of people motivated on side 1 versus side 2 of the conflict (the number of people in box 2 and 4 divided by total number of people in boxes 2, 3, 4, and 5). When this ratio approaches 1, it suggests that side 1 is dominating the network. When it approaches 0, it suggests that side 2 is dominating. When it is near .5, it suggests an even split of motivation on both sides. To gather this information, researchers or practitioners could create a network conflict scorecard to portray the variety of statistics in the broader system.