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Efficiencies Not all parameters are needed: Extensive use of defaults Set according to the Host Requirement RFCs 1122, 1123 Host assumes the default value for anything not contained in the DHCPACK message. DHCP makes use of the Flags field to work around the chicken-and-the-egg problem of IP address and ARP responses.
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perceptions, and behavior toward in-group versus out-group members that can ultimately create a self-ful lling prophecy. Initial in-group favoritism can also provide a foundation for embracing more negative intergroup feelings and beliefs that result from intrapersonal, cultural, economic, and political factors. In the next section we describe alternative, and ultimately complementary, theoretical approaches to intergroup con ict and integration. Perspectives on Intergroup Relations and Con ict In general, research on social con ict, harmony, and integration has adopted one of two perspectives, one with an emphasis on the functional relations between groups and the other on the role of collective identities. Functional Relations Between Groups Theories based on functional relations often point to competition and consequent perceived threat as a fundamental cause of intergroup prejudice and con ict. Realistic group con ict theory (Campbell, 1965; Sherif, 1966), for example, posits that perceived group competition for resources produces efforts to reduce the access of other groups to the resources. This process was illustrated in classic work by Muzafer Sherif and his colleagues (Sherif et al., 1961). In 1954 Sherif and his colleagues conducted a eld study on intergroup con ict in an area adjacent to Robbers Cave State Park in Oklahoma. In this study 22 12-year-old boys attending summer camp were randomly assigned to two groups (who subsequently named themselves Eagles and Rattlers). Over a period of weeks they became aware of the other group s existence, engaged in a series of competitive activities that generated overt intergroup con ict, and ultimately participated in a series of cooperative activities designed to ameliorate con ict and bias. To permit time for group formation (e.g., norms and a leadership structure), the two groups were kept completely apart for one week. During the second week the investigators introduced competitive relations between the groups in the form of repeated competitive athletic activities centering around tug-of-war, baseball, and touch football, with the winning group receiving prizes. As expected, the introduction of competitive activities generated derogatory stereotypes and con ict among these groups. These boys, however, did not simply show in-group favoritism as we frequently see in laboratory studies. Rather, there was genuine hostility between these groups. Each group conducted raids on the other s cabins that resulted in the destruction and theft of property. The boys carried sticks, baseball bats, and socks lled with rocks as potential weapons. Fist ghts broke out between
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Figure 7.26 Bar charts by cluster for a set of ag variables: cluster 3 appears to be the most promising cluster.
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Part III More Complex Interfacing
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In the sections that follow, I briefly describe how to perform some common toolbar modifications manually, using the Customize dialog box.
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Create a second keyframe where you want the animation to end, then select the ending frame (immediately to the left of the second keyframe on the Timeline). Do any of the following to modify the instance, group, or text block in the ending frame:
DV-hop. A drawback of Sum-dist is that range errors accumulate when distance information is propagated over multiple hops. This cumulative error becomes signi cant for large networks with few anchors (long paths) and/or poor ranging hardware. A robust alternative is to use topological information by counting the number of hops instead of summing the (erroneous) ranges. This approach was named DV-hop by Niculescu and Nath [16], and Hop-TERRAIN by Savarese et al. [22]. The DV-Hop propagation method is the most basic scheme, and it rst employs a classic distance vector exchange so that all nodes in the network discover distances, in hops, to the landmarks. Essentially, DV-hop consists of two ood waves. After the rst wave, which is similar to Sum-dist, nodes have obtained the position and minimum hop count to at least ood-limit anchors. The second calibration wave is needed to convert hop counts into distances such that nodes can compute a position. This conversion consists of multiplying the hop count by an average hop distance. Whenever an anchor A1 infers the position of another anchor A2 during the rst wave, it computes the distance between them, and divides that by the number of hops to derive the average hop distance between A1 and A2 . When calibrating, an anchor takes all remote anchors into account that it is aware of. Nodes forward (broadcast) calibration messages only from the rst anchor that calibrates them, which reduces the total number of messages in the network. Euclidean. A drawback of DV-hop is that it does not work for highly irregular network topologies, where the variance in actual hop distances is very large. Niculescu and Nath [16] have proposed another method, named Euclidean, which is based on the local geometry of the nodes around an anchor. Again anchors initiate a ood, but forwarding the distance is more complicated than in the previous cases. When a node has received messages from two neighbors that know their distance to the anchor, and to each other, it can calculate the distance to the anchor. Figure 14.6 shows a node X that has two neighbors n1 and n2 with distance estimates (a and b) to an anchor. Together with the known ranges c, d, and e, there are two possible values (r1 and r2 ) for the distance of the node to the anchor. Niculescu describes two methods to determine which, if any, distance to use. The neighbor vote method can be applied if there exists a third neighbor n3 that has a distance estimate to the anchor and that is connected to either n1 or n2 . Replacing n2 (or n1 ) by n3 will again yield a pair of distance estimates. The correct distance is part of both pairs, and is selected by a simple vote. Of course, more neighbors can be included to make the selection more accurate.
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