1. bookVolume 41 (2021): Issue 1 (January 2021)
Journal Details
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Format
Journal
eISSN
0226-1766
First Published
01 Jul 1977
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1 time per year
Languages
English
access type Open Access

Social Cohesion and Cooperation for Public Goods

Published Online: 05 Feb 2021
Volume & Issue: Volume 41 (2021) - Issue 1 (January 2021)
Page range: 1 - 6
Journal Details
License
Format
Journal
eISSN
0226-1766
First Published
01 Jul 1977
Publication timeframe
1 time per year
Languages
English
Abstract

A cohesive network keeps groups together and enables members to communicate about and cooperate for public goods. For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group’s network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.

Keywords

<p>Public goods pose dilemmas of collective action, which require a mechanism (or combination of complementary mechanisms) to solve. Solutions usually come in the form of selective incentives: (promises of) rewards and (threats of) punishments (<a ref-type="bibr" href="#j_connections-2019.020_ref_017">Olson, 1965</a>). In order for incentives to have the intended effect, however, individuals have to be monitored, and gossip that establishes their reputations has to be passed through the group’s network reliably (<a ref-type="bibr" href="#j_connections-2019.020_ref_018">Panchanathan and Boyd, 2004</a>; <a ref-type="bibr" href="#j_connections-2019.020_ref_012">Hilbe et al., 2018</a>). As a result of unreliable information, people who deliver selective incentives may confuse cooperators and defectors, or ostracize the former and invite the latter in. The challenge is, therefore, to establish accurate reputations, on the basis of which indirect reciprocity can become an effective mechanism. The challenge is more severe in larger groups were most people do not know one another directly (<a ref-type="bibr" href="#j_connections-2019.020_ref_022">Sommerfeld et al., 2007</a>). In contrast to smaller groups, in which people are more likely to be in direct contact and can easily monitor, and communicate with, each other, information in larger groups has to be transmitted through longer network paths (concatenations of ties) where it deteriorates with distance, shown in chain experiments (<a ref-type="bibr" href="#j_connections-2019.020_ref_007">Eriksson and Coultas, 2012</a>). Another experiment showed that without indirect reciprocity, network topology (i.e., structure) has no effect on the contributions to public goods (<a ref-type="bibr" href="#j_connections-2019.020_ref_023">Suri and Watts, 2011</a>). In real life, however, networks are important for reputations, and in a recent study of indirect reciprocity, reliable transmission of gossip was said to be “an interesting direction for future research” (<a ref-type="bibr" href="#j_connections-2019.020_ref_012">Hilbe et al., 2018</a>). In this paper, we take up the challenge, theorize redundancy of information channels, and conduct a lab experiment to compare a low-redundancy network with a high-redundancy network.</p> </sec> <sec id="j_connections-2019.020_s_002"> <div>Theory</div> <p>A century ago, Georg <a ref-type="bibr" href="#j_connections-2019.020_ref_020">Simmel (1908)</a> noticed advantages of a triad over a dyad, where a third person can mediate between two others in case of misunderstandings. In modern parlance, a direct tie between two actors is complemented by a second, indirect, connection that provides redundancy of monitoring and information transmission, such that noise and bias in one imperfect channel can, to some extent, be corrected by information through another imperfect channel. Whereas in a triad, everybody is connected by two node-independent paths to everybody else, this structural homogeneity does not hold in larger, typically clustered, groups where some people are better connected than others. How could we generalize path-redundancy to larger networks? Research on “complex contagions” (<a ref-type="bibr" href="#j_connections-2019.020_ref_004">Centola and Macy, 2007</a>; <a ref-type="bibr" href="#j_connections-2019.020_ref_011">Guilbeault et al., 2018</a>) has shown that multiple paths are important indeed, but has not measured the independence of these paths, which may overlap at some nodes before arriving at the recipient of a message. Douglas White and Frank Harary (2001) proposed to decompose the generalization challenge into the analysis of one pair of actors at a time. For each pair, one can count the number of node-independent paths between them. In inhomogeneous social networks with clusters, pairs embedded within a cluster will often turn out to be connected by more redundant paths than pairs across different clusters. A group of individuals can then be depicted as a “landscape” with mountains for more connective parts, usually dense clusters, separated by valleys in between the clusters (<a ref-type="bibr" href="#j_connections-2019.020_ref_015">Moody and White, 2003</a>). This way of looking at redundancy implies that for a given network and every sub-network therein, there is a minimum number of node-independent paths, K, instead of one value that ignores heterogeneity. By using a mathematical theorem by <a ref-type="bibr" href="#j_connections-2019.020_ref_014">Menger (1927)</a>, White and Harary showed that the minimum number of redundant paths in a (sub)network is equivalent to the minimum number of people who would have to be removed to break up the (sub)network into parts. Because these numbers of people and paths are identical, they can be unified in the notion of K-connectivity, where values of K vary across subgroups in the network (<a ref-type="bibr" href="#j_connections-2019.020_ref_024">White and Harary, 2001</a>). This conceptualization of social cohesion was the first where redundancy on behalf of information transmission was explicated as a key property.</p> <p>The prime reason why cohesive groups exist is that their members can realize public goods. To test if more node-independent paths yield higher levels of cooperation for a public good, we compare two networks in an experiment (<a ref-type="bibr" href="#j_connections-2019.020_ref_005">Chaudhuri, 2011</a>; <a ref-type="bibr" href="#j_connections-2019.020_ref_003">Camerer, 2003</a>), one with low (K = 1) and the other with high (K = 3) connectivity. When introducing some noise in the information that people get about one another, we expect that in the network with higher K-connectivity, ensuing inconsistencies can be more easily resolved, leading to higher levels of cooperation. To show that the expected difference is due to K-connectivity, we have to control for other network effects. <a ref-type="fig" href="#j_connections-2019.020_fig_001">Figure 1</a> (a) shows two networks that can be distinguished on the basis of their K-connectivity, not on the basis of other widely used network notions (<a ref-type="bibr" href="#j_connections-2019.020_ref_001">Bruggeman, 2018</a>): both networks have the same size (7), number of ties hence density (0.57), average shortest path distance (1.43), degree distribution with one central node, and both networks are 3-cores. The wheel is less clustered (0.55) than the bow-tie (0.73) but has higher K-connectivity (K = 3), whereas the bow-tie has a topological bottleneck and therefore lower K-connectivity (K = 1). Another famous network concept, k-core (<a ref-type="bibr" href="#j_connections-2019.020_ref_019">Seidman, 1983</a>), cannot perceive topological bottlenecks, and is therefore unsuited to describe redundancy. The concept of cluster has the same shortcoming. Taken together, these two networks seem to be suitable to test our expectation.</p> <figure id="j_connections-2019.020_fig_001" fig-type="figure"> <h2>Figure 1:</h2> <figCaption><p>(a) Bow-tie and wheel networks, adapted from <a ref-type="bibr" href="#j_connections-2019.020_ref_001">Bruggeman (2018)</a>. (b) Experimental outcomes over all groups (N = 19). Error bars represent standard errors of the means.</p></figCaption> <img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_connections-2019.020_fig_001.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220809T110621Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=dc86c87fe9ffdabae013a1d3aeacf47237a9f4eaef0b7f10dc3618cb1419d0c7" class="mw-100"></img> </figure> <p>An earlier experiment showed that when individuals receive inconsistent gossips about someone from different sources, they tend to believe the majority (<a ref-type="bibr" href="#j_connections-2019.020_ref_021">Sommerfeld et al., 2008</a>). If the level of noise is not extreme and people have no incentive to manipulate strategically, the majority view will usually be right. This implies for our experiment where people can either contribute or free ride that an error in one channel can be corrected by true information from two other channels, if there are. We, therefore, expect that the wheel network, where everyone has three independent channels, will help the participants to better reduce noise than the bow-tie network, and that therefore contributions to the public good will be significantly higher in the wheel. This expectation is the hypothesis we test experimentally.</p> </sec> <sec id="j_connections-2019.020_s_003"> <div>Experimental design</div> <p>The experiment took place at the ELSE lab at Utrecht University, the Netherlands. Subjects for the experiment were students recruited at the Utrecht University through the online recruitment system ORSEE (<a ref-type="bibr" href="#j_connections-2019.020_ref_010">Greiner, 2015</a>). Experimental sessions consisted of one, two or three groups (networks), depending on the availability of subjects. Upon arrival, subjects were randomly assigned to groups and seated behind computer screens with separators preventing them from looking at each other’s screens and at each other. They stayed in the same network topology for the entire session, and made all of their decisions via a computer interface that prevented them from identifying their fellow group members. There were 133 subjects in 11 bow-tie groups and 8 wheel groups, hence <italic>N</italic> = 19 at the group level. Subjects were given instructions on paper that they applied in three practice rounds before the experiment started. After the three practice rounds, groups where reshuffled, with the exception of two sessions that consisted of only one group each. Each round had three stages:</p> <p>First, from an initial endowment of 10 points (at an exchange rate of 0.7 to the Euro) subjects could decide to contribute 1 or 0 points, being informed that the sum total of contributions would be multiplied by <italic>r</italic> = 1.6 and then distributed equally among all seven group members. Formally, given <italic>N</italic> = 7 participants (<a ref-type="fig" href="#j_connections-2019.020_fig_001">Figure 1a</a>) and <italic>n</italic> < <italic>N</italic> contributors without the focal individual, the latter faces a choice to defect and get a payoff <italic>P</italic><sub>D</sub> = rn/<italic>N</italic>, or to cooperate and get <italic>P</italic><sub>C</sub> = r(<italic>n</italic> + 1)/<italic>N</italic> − 1, at a cost of 1 unit. Consequently, only if there are at least <italic>n</italic> = 4 cooperators it makes sense for the focal individual to contribute herself.</p> <p>Second, the subjects were shown the contributions of their network neighbors (direct contacts), being informed that there was a 1/12 chance (given 12 ties) that information about someone’s contribution was wrong—our implementation of noise. Subjects could then gossip about their neighbors’ (lack of) contributions by clicking thumb up or down, visible to their neighbors except the gossipees.</p> <p>Third, they were shown the gossips from their neighbors, and could propose a monetary punishment of 1 point for one of them, being informed that the computer would implement it by majority vote at no cost. The reason for cost-free punishment is to avoid that costly punishment blurs the network effect we are after. Because gossip and punishment proposals were hidden for, respectively, gossipees and punished individuals, the confounding effect of revenge against particular individuals was precluded. Furthermore, the subjects were informed that their total payoff could never become negative. At the end of the experiment, the points earned by the subjects were converted to euros and paid out discretely in cash.</p> </sec> <sec id="j_connections-2019.020_s_004"> <div>Results</div> <p><a ref-type="fig" href="#j_connections-2019.020_fig_001">Figure 1</a>(b) shows the aggregate results of the contributions in each of the two network topologies. While contributions were overall slightly higher in the wheel, the overall difference is not significant. Only in the final round, contributions are significantly higher in the wheel than in the bow-tie (<italic>t</italic>-test with <italic>N</italic> = 19, difference = 1.85, <italic>p</italic> = 0.016), suggesting that the wheel network may be more robust against the end game-effect than the bow-tie network, although we did not hypothesize this to happen. Overall, however, we conclude that our hypothesis that cooperation is higher in the wheel than in the bow-tie network is not supported.</p> <p>The main difference between the two networks is in the central node and others’ dependence on it. Rather than an overall network effect, as we hypothesized, it might be the case that the outcome is mainly due to the central node in each network, which we did not anticipate. We, therefore, also examine whether contributions of the central actor differ between the wheel and the bow-tie network. We furthermore examine whether it is the case that the more often a subject observes consistently positive information about another subject, the more likely they will contribute in the next round, and the more often a subject observes inconsistent information about another subject (both positive and negative), the less likely they will contribute in the next round.</p> <p>To these ends, we estimate a multilevel mixed-effects logistic regression model with individual contributions per round as the dependent variable. As independent variables, we include the network position of the subject (central or not); the network topology; the round number; the number of alters about whom the subject saw inconsistent gossips in the previous round; the number of alters about whom the subject saw two positive gossips in the previous round; and, the total proportion of contributions by network neighbors in the previous round. We furthermore include a coefficient for the cross-level interaction between network topology and network position, along with a random slope for network position.</p> <p>The results of this analysis (<a ref-type="table" href="#j_connections-2019.020_tab_001">Table 1</a>) do not provide evidence that the network position, network topology, or the consistency of the information available to the subject had any impact on her contributions. The only significant effect is that of the increasing round number.</p> <table-wrap id="j_connections-2019.020_tab_001" position="float"> <label>Table 1.</label> <caption><p>Multilevel mixed-effects logistic regression model of individual contributions per round on network topology, network position, and information available to the subject. <italic>N</italic>(level 1) = 133; <italic>N</italic>(level 2) = 19.</p></caption> <table frame="hsides"> <colgroup span="1"> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> </colgroup> <thead> <tr> <th align="left" rowspan="1" colspan="1"/> <th align="center" rowspan="1" colspan="1">Coeff.</th> <th align="center" rowspan="1" colspan="1">St. Err</th> <th align="center" rowspan="1" colspan="1"><italic>z</italic></th> </tr> </thead> <tbody> <tr> <td align="left" rowspan="1" colspan="1">Central</td> <td align="center" rowspan="1" colspan="1">−0.050</td> <td align="center" rowspan="1" colspan="1">0.479</td> <td align="center" rowspan="1" colspan="1">−0.104</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Topology: wheel</td> <td align="center" rowspan="1" colspan="1">0.391</td> <td align="center" rowspan="1" colspan="1">0.506</td> <td align="center" rowspan="1" colspan="1">0.773</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Central in wheel</td> <td align="center" rowspan="1" colspan="1">0.472</td> <td align="center" rowspan="1" colspan="1">0.783</td> <td align="center" rowspan="1" colspan="1">0.602</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Round nr.</td> <td align="center" rowspan="1" colspan="1">−0.223*</td> <td align="center" rowspan="1" colspan="1">0.093</td> <td align="center" rowspan="1" colspan="1">−2.385</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Inconsistent gossip</td> <td align="center" rowspan="1" colspan="1">−0.167</td> <td align="center" rowspan="1" colspan="1">0.195</td> <td align="center" rowspan="1" colspan="1">−0.860</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Two thumbs up</td> <td align="center" rowspan="1" colspan="1">−0.200</td> <td align="center" rowspan="1" colspan="1">0.369</td> <td align="center" rowspan="1" colspan="1">−0.543</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Tot. contrib.</td> <td align="center" rowspan="1" colspan="1">0.373</td> <td align="center" rowspan="1" colspan="1">1.012</td> <td align="center" rowspan="1" colspan="1">0.369</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Gossip tot. pos.</td> <td align="center" rowspan="1" colspan="1">0.296</td> <td align="center" rowspan="1" colspan="1">0.555</td> <td align="center" rowspan="1" colspan="1">0.532</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Gossip tot. neg.</td> <td align="center" rowspan="1" colspan="1">0.150</td> <td align="center" rowspan="1" colspan="1">0.402</td> <td align="center" rowspan="1" colspan="1">0.374</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Cons.</td> <td align="center" rowspan="1" colspan="1">2.605</td> <td align="center" rowspan="1" colspan="1">2.103</td> <td align="center" rowspan="1" colspan="1">1.238</td> </tr> </tbody> </table> <table-wrap-foot> <fn id="j_connections-2019.020_tab_001_fn_001" symbol="1"><p>Note: *<italic>p</italic> < 0.05.</p> </fn> </table-wrap-foot> </table-wrap> </sec> <sec id="j_connections-2019.020_s_005"> <div>Discussion</div> <p>We tested the hypothesis that under noisy conditions, redundancy in the form of multiple node-independent network paths makes it possible to reduce noise and to cooperate at higher levels. This was not supported by the experiment. To control for other network effects, the two networks were similar, but thereby perhaps too similar to make their difference significant. It might also be the case that a higher level of noise would have shown their difference more clearly.</p> <p>One could argue that on top of redundancy, a proper concept of cohesion should also take distance into account, but in our small networks, distances are small and the average distances are identical. One candidate for such an alternative concept that has, to our knowledge, not yet been described in the literature, relies on the notion of nexuses. On top of node-independent paths, there may be nexuses between these paths, which can re-enforce messages from a sender (S) to a recipient (R), illustrated in <a ref-type="fig" href="#j_connections-2019.020_fig_002">Figure 2</a> both networks have the same K-connectivity (K = 2) but network (a) features nexuses, with significant positive effects on the reliability and accuracy of information transmission, shown in an experiment (<a ref-type="bibr" href="#j_connections-2019.020_ref_007">Eriksson and Coultas, 2012</a>).</p> <figure id="j_connections-2019.020_fig_002" fig-type="figure"> <h2>Figure 2:</h2> <figCaption><p>Two 2-connective networks, (a) with nexuses and (b) without nexuses.</p></figCaption> <img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_connections-2019.020_fig_002.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220809T110621Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=679da59a402fff6d5447361c8e87659be6b08a8cda2eba4497854decbf130e69" class="mw-100"></img> </figure> <p>There is a network measure that does take redundancy, distance, and nexuses into account: algebraic connectivity. To calculate this measure, one row-normalizes the adjacency matrix, as in models of social influence (<a ref-type="bibr" href="#j_connections-2019.020_ref_009">Friedkin and Johnsen, 2011</a>), and transforms it into a Laplacian matrix by putting 1 everywhere on the diagonal, provided that everyone has at least one tie, and a minus sign everywhere else (<a ref-type="bibr" href="#j_connections-2019.020_ref_006">Chung, 1997</a>). The second smallest eigenvalue of the Laplacian is called algebraic connectivity (<a ref-type="bibr" href="#j_connections-2019.020_ref_008">Fiedler, 1973</a>). It is higher if there are more node-independent paths (<italic>λ</italic><sub>2</sub> = 2/3 wheel; <italic>λ</italic><sub>2</sub> = 1/3 bow-tie), consistent with K-connectivity, hence it varies across sub-graphs, just like K-connectivity. Furthermore, it is lower for longer average distances, which neither K-connectivity nor k-core is responsive to. Information transmission deteriorates with distance, certainly off-line, and it’s therefore important to have a measure that takes distance into account. Algebraic connectivity distinguishes nexuses, and equals <italic>λ</italic><sub>2</sub> = 2/3 for the network with nexuses in <a ref-type="fig" href="#j_connections-2019.020_fig_002">Figure 2</a> and <italic>λ</italic><sub>2</sub> = 1/2 for the other one. Last but not least, it predicts how quickly consensus is achieved (<a ref-type="bibr" href="#j_connections-2019.020_ref_016">Olfati-Saber and Murray, 2004</a>), consistent with experimental outcomes (<a ref-type="bibr" href="#j_connections-2019.020_ref_013">Judd et al., 2010</a>). Consensus is important in groups to decide which public goods to realize and how.</p> <p>In our experiment, the positive effect of the multiple paths in the wheel might have been nullified by the positive effect of the additional nexus in each sub-network of the bow-tie. If for social cohesion, algebraic connectivity is a significantly better measure than K-connectivity remains a question for future research. Whereas in all likelihood, social cohesion, measured in one way or another, is important for cooperation, we note that the effect is nonmonotonic. High cohesion overburdens group members with social pressure, decreases innovation, and may strengthen rather than dampen false information (<a ref-type="bibr" href="#j_connections-2019.020_ref_002">Burt, 2008</a>). At too low cohesion, in contrast, groups fall apart. The sweet spot must be somewhere in between the extremes, which poses a challenge for future studies to discover.</p> </sec> </div></div></div></div><div id="pane-4" class="SeriesTab_card__26XnC SeriesTab_tab-pane__3pc7y card tab-pane" role="tabpanel" aria-labelledby="tab-4"><div class="SeriesTab_card-header__1DTAS card-header d-md-none pl-0" role="tab" id="heading-4"><h4 class="mb-0"><a data-toggle="collapse" href="#collapse-4" data-parent="#content" aria-expanded="false" aria-controls="collapse-4" style="padding:24px 0">Figures & Tables<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="chevron-down" class="svg-inline--fa fa-chevron-down fa-w-14 " role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></a></h4></div><div id="collapse-4" class="SeriesTab_seriesTabCollapse__2csiF collapse" role="tabpanel" aria-labelledby="heading-4" data-parent="#content"><div class="SeriesTab_series-tab-body__1tZ1H SeriesTab_card-body__31JEh card-body Article_figures-tables__2SC5X"><figure><h4 class="mb-4">Figure 1:</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220809T110621Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=dc86c87fe9ffdabae013a1d3aeacf47237a9f4eaef0b7f10dc3618cb1419d0c7" alt="(a) Bow-tie and wheel networks, adapted from Bruggeman (2018). (b) Experimental outcomes over all groups (N = 19). Error bars represent standard errors of the means." class="mw-100"/><figcaption class="fw-500">(a) Bow-tie and wheel networks, adapted from Bruggeman (2018). (b) Experimental outcomes over all groups (N = 19). Error bars represent standard errors of the means.</figcaption></figure><figure><h4 class="mb-4">Figure 2:</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220809T110621Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=679da59a402fff6d5447361c8e87659be6b08a8cda2eba4497854decbf130e69" alt="Two 2-connective networks, (a) with nexuses and (b) without nexuses." class="mw-100"/><figcaption class="fw-500">Two 2-connective networks, (a) with nexuses and (b) without nexuses.</figcaption></figure><h4 class="mb-4 mt-4">Multilevel mixed-effects logistic regression model of individual contributions per round on network topology, network position, and information available to the subject. N(level 1) = 133; N(level 2) = 19.</h4><table frame="hsides"> <colgroup span="1"> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> <col align="left" width="1*" span="1"/> </colgroup> <thead> <tr> <th align="left" rowspan="1" colspan="1"/> <th align="center" rowspan="1" colspan="1">Coeff.</th> <th align="center" rowspan="1" colspan="1">St. Err</th> <th align="center" rowspan="1" colspan="1"><italic>z</italic></th> </tr> </thead> <tbody> <tr> <td align="left" rowspan="1" colspan="1">Central</td> <td align="center" rowspan="1" colspan="1">−0.050</td> <td align="center" rowspan="1" colspan="1">0.479</td> <td align="center" rowspan="1" colspan="1">−0.104</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Topology: wheel</td> <td align="center" rowspan="1" colspan="1">0.391</td> <td align="center" rowspan="1" colspan="1">0.506</td> <td align="center" rowspan="1" colspan="1">0.773</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Central in wheel</td> <td align="center" rowspan="1" colspan="1">0.472</td> <td align="center" rowspan="1" colspan="1">0.783</td> <td align="center" rowspan="1" colspan="1">0.602</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Round nr.</td> <td align="center" rowspan="1" colspan="1">−0.223*</td> <td align="center" rowspan="1" colspan="1">0.093</td> <td align="center" rowspan="1" colspan="1">−2.385</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Inconsistent gossip</td> <td align="center" rowspan="1" colspan="1">−0.167</td> <td align="center" rowspan="1" colspan="1">0.195</td> <td align="center" rowspan="1" colspan="1">−0.860</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Two thumbs up</td> <td align="center" rowspan="1" colspan="1">−0.200</td> <td align="center" rowspan="1" colspan="1">0.369</td> <td align="center" rowspan="1" colspan="1">−0.543</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Tot. contrib.</td> <td align="center" rowspan="1" colspan="1">0.373</td> <td align="center" rowspan="1" colspan="1">1.012</td> <td align="center" rowspan="1" colspan="1">0.369</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Gossip tot. pos.</td> <td align="center" rowspan="1" colspan="1">0.296</td> <td align="center" rowspan="1" colspan="1">0.555</td> <td align="center" rowspan="1" colspan="1">0.532</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Gossip tot. neg.</td> <td align="center" rowspan="1" colspan="1">0.150</td> <td align="center" rowspan="1" colspan="1">0.402</td> <td align="center" rowspan="1" colspan="1">0.374</td> </tr> <tr> <td align="left" rowspan="1" colspan="1">Cons.</td> <td align="center" rowspan="1" colspan="1">2.605</td> <td align="center" rowspan="1" colspan="1">2.103</td> <td align="center" rowspan="1" colspan="1">1.238</td> </tr> </tbody> </table></div></div></div><div id="reference" class="SeriesTab_card__26XnC SeriesTab_tab-pane__3pc7y card tab-pane" role="tabpanel" aria-labelledby="tab-5"><div class="SeriesTab_card-header__1DTAS card-header d-md-none pl-0" role="tab" id="heading-5"><h4 class="mb-0"><a data-toggle="collapse" href="#collapse-5" data-parent="#content" aria-expanded="false" aria-controls="collapse-5" style="padding:24px 0">References<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="chevron-down" class="svg-inline--fa fa-chevron-down fa-w-14 " role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></a></h4></div><div id="collapse-5" class="SeriesTab_seriesTabCollapse__2csiF collapse" role="tabpanel" aria-labelledby="heading-5" data-parent="#content"><div class="SeriesTab_series-tab-body__1tZ1H SeriesTab_card-body__31JEh card-body"><p class="Article_refData__1fofs"><span class="Article_d-block__2MPqH"><ref id="j_connections-2019.020_ref_001"> <mixed-citation>Bruggeman, J. 2018. 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The journal seeks to publish significant work from any domain that is relevant to social network applications and methods. Review articles that critically review and synthesize a body of published research and commentaries or short papers in response to previous articles published in the journal are also welcome. As a new feature, we will now publish network datasets and instruments (which will be available on the website) and accompanied by a short article (not to exceed 2,500 words) describing the data. Authors who wish to submit a commentary, book review, network image or data set should first e-mail the editors with a brief description. \u003c/P\u003e \u003cP\u003eTo publish in Connections, authors \u003cU\u003eare not required to pay an article processing charge (APC).\u003c/U\u003e \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eSubmitting Manuscripts\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eAuthors are required to submit manuscripts online using the manuscript management system available here: \u003cA href=\"https://www.editorialmanager.com/connections\"\u003ehttps://www.editorialmanager.com/connections\u003c/A\u003e.\u003cBR\u003eFeedback from the editor and reviewers will be sent to the corresponding author within three months after receipt. Revised or resubmitted manuscripts should include a detailed explanation of how the author has dealt with each of the reviewer's and Editor's comments. For questions or concerns about the submission process, authors should contact the editor at \u003cA href=\"mailto:editorconnections@gmail.com\"\u003eeditorconnections@gmail.com\u003c/A\u003e. \u003c/P\u003e \u003cP\u003eManuscripts must be in MS Word or WordPerfect format and should not exceed 40 pages including tables, figures and references. Manuscripts should be arranged in the following order: title page, acknowledgments, abstract, text, references, appendices, and figure legends. Format and style of manuscript and references should conform to the conventions specified in the latest edition of Publication Manual of the American Psychological Association. Include author's contact information in the title page. Abstracts should be limited to 250 words. Please embed all images, tables and figures in the document. If you have a large figure, you may also send it as a separate file. A figure and its legend should be sufficiently informative that the results can be understood without reference to the text. \u003c/P\u003e \u003cP\u003eUpon acceptance of a manuscript, Authors will be requested to sign \u003cA href=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/CONNECTIONS/Open_Access_Agreement.pdf\"\u003eOpen Access Agreement\u003c/A\u003e prior to publication. \u003c/P\u003e \u003cP\u003eAll of the articles will be blindly reviewed. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eData Exchange Network\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eThe new DEN feature is to meet the goal of providing citable references for datasets and instruments. Submissions must include an electronic version of the network dataset and/or instrument and a short article (not to exceed 2,500 words) describing the data being submitted. These articles need not be as detailed as a full codebook, but should provide enough information that other researchers may appropriately use the data or measures. Additionally, the article should contain any information about the context from which the data were collected that may be relevant to others for appropriately using the data. All materials submitted for the DEN will be peer-reviewed to ensure the utility and usability of the data/instrument. Data should be submitted in the most generic format possible (preferably in Excel). Accepted DEN contributions must be described fully and clearly and any threats to validity should be made transparent. \u003c/P\u003e"},{"type":"editorial","language":"English","textformat":null,"content":"\u003cP\u003e\u003cSTRONG\u003eEditor-in-Chief\u003c/STRONG\u003e\u003cBR\u003eProf. Dan Halgin - University of Kentucky, USA\u003cBR\u003eProf. Kerstin Sailer - The Bartlett School of Architecture, University College London, UK \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eEditorial Board\u003c/STRONG\u003e\u003cBR\u003eKate Coronges - Executive Director of the Network Science Institute (NetSI), Northeastern University, USA\u003cBR\u003eKayla de la Haye - Associate Professor of Preventive Medicine, University of Southern California, USA\u003cBR\u003eJana Diesner - Associate Professor at the School of Information Sciences (the iSchool), University of Illinois at Urbana-Champaign, USA\u003cBR\u003ePatrick Doreian - Professor Emeritus, Department of Sociology, University of Pittsburgh, USA\u003cBR\u003eBetina Hollstein - Professor of sociology at the SOCIUM - Research Center on Inequality and Social Policy at University of Bremen\u003cBR\u003eMark Lubell - Professor, Department of Environmental Science and Policy, University of California, Davis, USA\u003cBR\u003eAlexandra Marin - Assistant Professor, Department of Sociology, University of Toronto, Canada\u003cBR\u003eLiesbeth Mercken - Assistant Professor, Care and Public Health Research Institute, Department of Health Promotion, Maastricht University, The Netherlands\u003cBR\u003eSusie Pak - Associate Professor, History, St. John´s University, USA\u003cBR\u003eJuergen Pfeffer - Professor of Computational Social Science \u0026amp; Big Data Bavarian, School of Public Policy, Technical University of Munich, Germany\u003cBR\u003eDavid Schaefer - UCI School of Social Sciences\u003cBR\u003eOlivier Walther - Assistant Professor, University of Florida \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eContact\u003c/STRONG\u003e\u003cBR\u003e\u003cA href=\"mailto:connections@insna.org\"\u003econnections@insna.org\u003c/A\u003e \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003ePublisher\u003c/STRONG\u003e\u003cBR\u003eDe Gruyter Poland\u003cBR\u003eBogumiła Zuga 32A Str.\u003cBR\u003e01-811 Warsaw, Poland\u003cBR\u003eT: +48 22 701 50 15 \u003c/P\u003e"},{"type":"advantages","language":"English","textformat":null,"content":"\u003cP\u003e\u003cSTRONG\u003e\u003cEM\u003eConnections\u003c/EM\u003e\u003c/STRONG\u003e publishes original empirical, theoretical, and methodological articles, as well as critical reviews dealing with applications of social network analysis. From volume 2018 the journal is published in a \u003cU\u003econtinous format\u003c/U\u003e. \u003c/P\u003e \u003cP\u003eThe research spans many disciplines and domains including: Anthropology, Sociology, Psychology, Communication, Economics, Organizational Behavior, Knowledge Management, Marketing, Social Psychology, Mathematics, Public Health, Medicine, Computer Science, and Policy. \u003c/P\u003e \u003cP\u003eAs an official journal of the \u003cA href=\"https://www.insna.org/\"\u003eInternational Network for Social Network Analysis\u003c/A\u003e, the emphasis of the publication is to reflect the ever-growing and continually expanding community of scholars using network analytic techniques. Connections also provides an outlet for sharing news about social network concepts and techniques and new tools for research. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eRejection rate\u003c/STRONG\u003e: 60% \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eOpen Access Policy\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eThis journal provides immediate open access to its content under the \u003cA href=\"https://creativecommons.org/licenses/by/4.0/\"\u003eCreative Commons CC BY 4.0 license\u003c/A\u003e on the principle that making research freely available to the public supports a greater global exchange of knowledge. Under the \u003cA href=\"https://creativecommons.org/licenses/by/4.0/\"\u003eCreative Commons CC BY 4.0 license\u003c/A\u003e users are free to share (copy and redistribute the material in any medium or format) and adapt the work (remix, transform, and build upon the material for any purpose, even commercially) if the contribution was properly attributed and all of the changes indicated. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eABOUT SOCIETY\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003e\u003cA href=\"https://www.insna.org/\"\u003eInternational Network for Social Network Analysis\u003c/A\u003e is the professional association for researchers interested in social network analysis. The association is a non-profit organization incorporated in the state of Delaware and founded by Barry Wellman in 1977. \u003c/P\u003e \u003cP\u003eINSNA was founded on the premise that the behavior and lives of social entities are affected by their position in the overall social structure. By examining the etiology and consequences of structural forms overall, of the location of entities within these structures, and of the formation and dynamics of ties that make up these structures, INSNA hopes to learn about the parts of behavior that are uniquely social. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eArchiving\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eSciendo archives the contents of this journal in \u003cA href=\"https://www.portico.org/\"\u003ePortico\u003c/A\u003e - digital long-term preservation service of scholarly books, journals and collections. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003ePlagiarism Policy\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eThe editorial board is participating in a growing community of \u003cA href=\"https://www.crossref.org/services/similarity-check/\"\u003eSimilarity Check System's\u003c/A\u003e users in order to ensure that the content published is original and trustworthy. 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For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group’s network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.\u003c/p\u003e\n\u003c/abstract\u003e"}],"figures":[{"label":"Figure 1:","caption":"(a) Bow-tie and wheel networks, adapted from Bruggeman (2018). (b) Experimental outcomes over all groups (N = 19). Error bars represent standard errors of the means.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220809T110621Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=dc86c87fe9ffdabae013a1d3aeacf47237a9f4eaef0b7f10dc3618cb1419d0c7"},{"label":"Figure 2:","caption":"Two 2-connective networks, (a) with nexuses and (b) without nexuses.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220809T110621Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=679da59a402fff6d5447361c8e87659be6b08a8cda2eba4497854decbf130e69"}],"tableContent":{"Multilevel mixed-effects logistic regression model of individual contributions per round on network topology, network position, and information available to the subject. N(level 1) = 133; N(level 2) = 19.":"\u003ctable frame=\"hsides\"\u003e\n\u003ccolgroup span=\"1\"\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\" rowspan=\"1\" colspan=\"1\"/\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003eCoeff.\u003c/th\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003eSt. Err\u003c/th\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e\u003citalic\u003ez\u003c/italic\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCentral\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.050\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.479\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.104\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTopology: wheel\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.391\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.506\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.773\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCentral in wheel\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.472\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.783\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.602\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eRound nr.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.223*\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.093\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−2.385\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eInconsistent gossip\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.167\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.195\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.860\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTwo thumbs up\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.200\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.369\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.543\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTot. contrib.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.373\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e1.012\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.369\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eGossip tot. pos.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.296\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.555\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.532\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eGossip tot. neg.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.150\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.402\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.374\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCons.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e2.605\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e2.103\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e1.238\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},"tables":null,"articleContent":"\n\u003cdiv\u003e\n\u003csec id=\"j_connections-2019.020_s_001\"\u003e\n\u003ctitle/\u003e\n\u003cp\u003ePublic goods pose dilemmas of collective action, which require a mechanism (or combination of complementary mechanisms) to solve. Solutions usually come in the form of selective incentives: (promises of) rewards and (threats of) punishments (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_017\"\u003eOlson, 1965\u003c/a\u003e). In order for incentives to have the intended effect, however, individuals have to be monitored, and gossip that establishes their reputations has to be passed through the group’s network reliably (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_018\"\u003ePanchanathan and Boyd, 2004\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_012\"\u003eHilbe et al., 2018\u003c/a\u003e). As a result of unreliable information, people who deliver selective incentives may confuse cooperators and defectors, or ostracize the former and invite the latter in. The challenge is, therefore, to establish accurate reputations, on the basis of which indirect reciprocity can become an effective mechanism. The challenge is more severe in larger groups were most people do not know one another directly (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_022\"\u003eSommerfeld et al., 2007\u003c/a\u003e). In contrast to smaller groups, in which people are more likely to be in direct contact and can easily monitor, and communicate with, each other, information in larger groups has to be transmitted through longer network paths (concatenations of ties) where it deteriorates with distance, shown in chain experiments (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_007\"\u003eEriksson and Coultas, 2012\u003c/a\u003e). Another experiment showed that without indirect reciprocity, network topology (i.e., structure) has no effect on the contributions to public goods (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_023\"\u003eSuri and Watts, 2011\u003c/a\u003e). In real life, however, networks are important for reputations, and in a recent study of indirect reciprocity, reliable transmission of gossip was said to be “an interesting direction for future research” (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_012\"\u003eHilbe et al., 2018\u003c/a\u003e). In this paper, we take up the challenge, theorize redundancy of information channels, and conduct a lab experiment to compare a low-redundancy network with a high-redundancy network.\u003c/p\u003e\n\u003c/sec\u003e\n\u003csec id=\"j_connections-2019.020_s_002\"\u003e\n\u003cdiv\u003eTheory\u003c/div\u003e\n\u003cp\u003eA century ago, Georg \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_020\"\u003eSimmel (1908)\u003c/a\u003e noticed advantages of a triad over a dyad, where a third person can mediate between two others in case of misunderstandings. In modern parlance, a direct tie between two actors is complemented by a second, indirect, connection that provides redundancy of monitoring and information transmission, such that noise and bias in one imperfect channel can, to some extent, be corrected by information through another imperfect channel. Whereas in a triad, everybody is connected by two node-independent paths to everybody else, this structural homogeneity does not hold in larger, typically clustered, groups where some people are better connected than others. How could we generalize path-redundancy to larger networks? Research on “complex contagions” (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_004\"\u003eCentola and Macy, 2007\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_011\"\u003eGuilbeault et al., 2018\u003c/a\u003e) has shown that multiple paths are important indeed, but has not measured the independence of these paths, which may overlap at some nodes before arriving at the recipient of a message. Douglas White and Frank Harary (2001) proposed to decompose the generalization challenge into the analysis of one pair of actors at a time. For each pair, one can count the number of node-independent paths between them. In inhomogeneous social networks with clusters, pairs embedded within a cluster will often turn out to be connected by more redundant paths than pairs across different clusters. A group of individuals can then be depicted as a “landscape” with mountains for more connective parts, usually dense clusters, separated by valleys in between the clusters (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_015\"\u003eMoody and White, 2003\u003c/a\u003e). This way of looking at redundancy implies that for a given network and every sub-network therein, there is a minimum number of node-independent paths, K, instead of one value that ignores heterogeneity. By using a mathematical theorem by \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_014\"\u003eMenger (1927)\u003c/a\u003e, White and Harary showed that the minimum number of redundant paths in a (sub)network is equivalent to the minimum number of people who would have to be removed to break up the (sub)network into parts. Because these numbers of people and paths are identical, they can be unified in the notion of K-connectivity, where values of K vary across subgroups in the network (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_024\"\u003eWhite and Harary, 2001\u003c/a\u003e). This conceptualization of social cohesion was the first where redundancy on behalf of information transmission was explicated as a key property.\u003c/p\u003e\n\u003cp\u003eThe prime reason why cohesive groups exist is that their members can realize public goods. To test if more node-independent paths yield higher levels of cooperation for a public good, we compare two networks in an experiment (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_005\"\u003eChaudhuri, 2011\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_003\"\u003eCamerer, 2003\u003c/a\u003e), one with low (K = 1) and the other with high (K = 3) connectivity. When introducing some noise in the information that people get about one another, we expect that in the network with higher K-connectivity, ensuing inconsistencies can be more easily resolved, leading to higher levels of cooperation. To show that the expected difference is due to K-connectivity, we have to control for other network effects. \u003ca ref-type=\"fig\" href=\"#j_connections-2019.020_fig_001\"\u003eFigure 1\u003c/a\u003e (a) shows two networks that can be distinguished on the basis of their K-connectivity, not on the basis of other widely used network notions (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_001\"\u003eBruggeman, 2018\u003c/a\u003e): both networks have the same size (7), number of ties hence density (0.57), average shortest path distance (1.43), degree distribution with one central node, and both networks are 3-cores. The wheel is less clustered (0.55) than the bow-tie (0.73) but has higher K-connectivity (K = 3), whereas the bow-tie has a topological bottleneck and therefore lower K-connectivity (K = 1). Another famous network concept, k-core (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_019\"\u003eSeidman, 1983\u003c/a\u003e), cannot perceive topological bottlenecks, and is therefore unsuited to describe redundancy. The concept of cluster has the same shortcoming. Taken together, these two networks seem to be suitable to test our expectation.\u003c/p\u003e\n\u003cfigure id=\"j_connections-2019.020_fig_001\" fig-type=\"figure\"\u003e\n\u003ch2\u003eFigure 1:\u003c/h2\u003e\n\u003cfigCaption\u003e\u003cp\u003e(a) Bow-tie and wheel networks, adapted from \u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_001\"\u003eBruggeman (2018)\u003c/a\u003e. (b) Experimental outcomes over all groups (N = 19). Error bars represent standard errors of the means.\u003c/p\u003e\u003c/figCaption\u003e\n\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_connections-2019.020_fig_001.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220809T110621Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=dc86c87fe9ffdabae013a1d3aeacf47237a9f4eaef0b7f10dc3618cb1419d0c7\" class=\"mw-100\"\u003e\u003c/img\u003e\n\u003c/figure\u003e\n\u003cp\u003eAn earlier experiment showed that when individuals receive inconsistent gossips about someone from different sources, they tend to believe the majority (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_021\"\u003eSommerfeld et al., 2008\u003c/a\u003e). If the level of noise is not extreme and people have no incentive to manipulate strategically, the majority view will usually be right. This implies for our experiment where people can either contribute or free ride that an error in one channel can be corrected by true information from two other channels, if there are. We, therefore, expect that the wheel network, where everyone has three independent channels, will help the participants to better reduce noise than the bow-tie network, and that therefore contributions to the public good will be significantly higher in the wheel. This expectation is the hypothesis we test experimentally.\u003c/p\u003e\n\u003c/sec\u003e\n\u003csec id=\"j_connections-2019.020_s_003\"\u003e\n\u003cdiv\u003eExperimental design\u003c/div\u003e\n\u003cp\u003eThe experiment took place at the ELSE lab at Utrecht University, the Netherlands. Subjects for the experiment were students recruited at the Utrecht University through the online recruitment system ORSEE (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_010\"\u003eGreiner, 2015\u003c/a\u003e). Experimental sessions consisted of one, two or three groups (networks), depending on the availability of subjects. Upon arrival, subjects were randomly assigned to groups and seated behind computer screens with separators preventing them from looking at each other’s screens and at each other. They stayed in the same network topology for the entire session, and made all of their decisions via a computer interface that prevented them from identifying their fellow group members. There were 133 subjects in 11 bow-tie groups and 8 wheel groups, hence \u003citalic\u003eN\u003c/italic\u003e = 19 at the group level. Subjects were given instructions on paper that they applied in three practice rounds before the experiment started. After the three practice rounds, groups where reshuffled, with the exception of two sessions that consisted of only one group each. Each round had three stages:\u003c/p\u003e\n\u003cp\u003eFirst, from an initial endowment of 10 points (at an exchange rate of 0.7 to the Euro) subjects could decide to contribute 1 or 0 points, being informed that the sum total of contributions would be multiplied by \u003citalic\u003er\u003c/italic\u003e = 1.6 and then distributed equally among all seven group members. Formally, given \u003citalic\u003eN\u003c/italic\u003e = 7 participants (\u003ca ref-type=\"fig\" href=\"#j_connections-2019.020_fig_001\"\u003eFigure 1a\u003c/a\u003e) and \u003citalic\u003en\u003c/italic\u003e \u0026lt; \u003citalic\u003eN\u003c/italic\u003e contributors without the focal individual, the latter faces a choice to defect and get a payoff \u003citalic\u003eP\u003c/italic\u003e\u003csub\u003eD\u003c/sub\u003e = rn/\u003citalic\u003eN\u003c/italic\u003e, or to cooperate and get \u003citalic\u003eP\u003c/italic\u003e\u003csub\u003eC\u003c/sub\u003e = r(\u003citalic\u003en\u003c/italic\u003e + 1)/\u003citalic\u003eN\u003c/italic\u003e − 1, at a cost of 1 unit. Consequently, only if there are at least \u003citalic\u003en\u003c/italic\u003e = 4 cooperators it makes sense for the focal individual to contribute herself.\u003c/p\u003e\n\u003cp\u003eSecond, the subjects were shown the contributions of their network neighbors (direct contacts), being informed that there was a 1/12 chance (given 12 ties) that information about someone’s contribution was wrong—our implementation of noise. Subjects could then gossip about their neighbors’ (lack of) contributions by clicking thumb up or down, visible to their neighbors except the gossipees.\u003c/p\u003e\n\u003cp\u003eThird, they were shown the gossips from their neighbors, and could propose a monetary punishment of 1 point for one of them, being informed that the computer would implement it by majority vote at no cost. The reason for cost-free punishment is to avoid that costly punishment blurs the network effect we are after. Because gossip and punishment proposals were hidden for, respectively, gossipees and punished individuals, the confounding effect of revenge against particular individuals was precluded. Furthermore, the subjects were informed that their total payoff could never become negative. At the end of the experiment, the points earned by the subjects were converted to euros and paid out discretely in cash.\u003c/p\u003e\n\u003c/sec\u003e\n\u003csec id=\"j_connections-2019.020_s_004\"\u003e\n\u003cdiv\u003eResults\u003c/div\u003e\n\u003cp\u003e\u003ca ref-type=\"fig\" href=\"#j_connections-2019.020_fig_001\"\u003eFigure 1\u003c/a\u003e(b) shows the aggregate results of the contributions in each of the two network topologies. While contributions were overall slightly higher in the wheel, the overall difference is not significant. Only in the final round, contributions are significantly higher in the wheel than in the bow-tie (\u003citalic\u003et\u003c/italic\u003e-test with \u003citalic\u003eN\u003c/italic\u003e = 19, difference = 1.85, \u003citalic\u003ep\u003c/italic\u003e = 0.016), suggesting that the wheel network may be more robust against the end game-effect than the bow-tie network, although we did not hypothesize this to happen. Overall, however, we conclude that our hypothesis that cooperation is higher in the wheel than in the bow-tie network is not supported.\u003c/p\u003e\n\u003cp\u003eThe main difference between the two networks is in the central node and others’ dependence on it. Rather than an overall network effect, as we hypothesized, it might be the case that the outcome is mainly due to the central node in each network, which we did not anticipate. We, therefore, also examine whether contributions of the central actor differ between the wheel and the bow-tie network. We furthermore examine whether it is the case that the more often a subject observes consistently positive information about another subject, the more likely they will contribute in the next round, and the more often a subject observes inconsistent information about another subject (both positive and negative), the less likely they will contribute in the next round.\u003c/p\u003e\n\u003cp\u003eTo these ends, we estimate a multilevel mixed-effects logistic regression model with individual contributions per round as the dependent variable. As independent variables, we include the network position of the subject (central or not); the network topology; the round number; the number of alters about whom the subject saw inconsistent gossips in the previous round; the number of alters about whom the subject saw two positive gossips in the previous round; and, the total proportion of contributions by network neighbors in the previous round. We furthermore include a coefficient for the cross-level interaction between network topology and network position, along with a random slope for network position.\u003c/p\u003e\n\u003cp\u003eThe results of this analysis (\u003ca ref-type=\"table\" href=\"#j_connections-2019.020_tab_001\"\u003eTable 1\u003c/a\u003e) do not provide evidence that the network position, network topology, or the consistency of the information available to the subject had any impact on her contributions. The only significant effect is that of the increasing round number.\u003c/p\u003e\n\u003ctable-wrap id=\"j_connections-2019.020_tab_001\" position=\"float\"\u003e\n\u003clabel\u003eTable 1.\u003c/label\u003e\n\u003ccaption\u003e\u003cp\u003eMultilevel mixed-effects logistic regression model of individual contributions per round on network topology, network position, and information available to the subject. \u003citalic\u003eN\u003c/italic\u003e(level 1) = 133; \u003citalic\u003eN\u003c/italic\u003e(level 2) = 19.\u003c/p\u003e\u003c/caption\u003e\n\u003ctable frame=\"hsides\"\u003e\n\u003ccolgroup span=\"1\"\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003ccol align=\"left\" width=\"1*\" span=\"1\"/\u003e\n\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\" rowspan=\"1\" colspan=\"1\"/\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003eCoeff.\u003c/th\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003eSt. Err\u003c/th\u003e\n\u003cth align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e\u003citalic\u003ez\u003c/italic\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCentral\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.050\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.479\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.104\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTopology: wheel\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.391\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.506\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.773\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCentral in wheel\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.472\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.783\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.602\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eRound nr.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.223*\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.093\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−2.385\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eInconsistent gossip\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.167\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.195\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.860\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTwo thumbs up\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.200\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.369\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e−0.543\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eTot. contrib.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.373\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e1.012\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.369\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eGossip tot. pos.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.296\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.555\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.532\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eGossip tot. neg.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.150\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.402\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e0.374\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\" rowspan=\"1\" colspan=\"1\"\u003eCons.\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e2.605\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e2.103\u003c/td\u003e\n\u003ctd align=\"center\" rowspan=\"1\" colspan=\"1\"\u003e1.238\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable-wrap-foot\u003e\n\u003cfn id=\"j_connections-2019.020_tab_001_fn_001\" symbol=\"1\"\u003e\u003cp\u003eNote: *\u003citalic\u003ep\u003c/italic\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003c/fn\u003e\n\u003c/table-wrap-foot\u003e\n\u003c/table-wrap\u003e\n\u003c/sec\u003e\n\u003csec id=\"j_connections-2019.020_s_005\"\u003e\n\u003cdiv\u003eDiscussion\u003c/div\u003e\n\u003cp\u003eWe tested the hypothesis that under noisy conditions, redundancy in the form of multiple node-independent network paths makes it possible to reduce noise and to cooperate at higher levels. This was not supported by the experiment. To control for other network effects, the two networks were similar, but thereby perhaps too similar to make their difference significant. It might also be the case that a higher level of noise would have shown their difference more clearly.\u003c/p\u003e\n\u003cp\u003eOne could argue that on top of redundancy, a proper concept of cohesion should also take distance into account, but in our small networks, distances are small and the average distances are identical. One candidate for such an alternative concept that has, to our knowledge, not yet been described in the literature, relies on the notion of nexuses. On top of node-independent paths, there may be nexuses between these paths, which can re-enforce messages from a sender (S) to a recipient (R), illustrated in \u003ca ref-type=\"fig\" href=\"#j_connections-2019.020_fig_002\"\u003eFigure 2\u003c/a\u003e both networks have the same K-connectivity (K = 2) but network (a) features nexuses, with significant positive effects on the reliability and accuracy of information transmission, shown in an experiment (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_007\"\u003eEriksson and Coultas, 2012\u003c/a\u003e).\u003c/p\u003e\n\u003cfigure id=\"j_connections-2019.020_fig_002\" fig-type=\"figure\"\u003e\n\u003ch2\u003eFigure 2:\u003c/h2\u003e\n\u003cfigCaption\u003e\u003cp\u003eTwo 2-connective networks, (a) with nexuses and (b) without nexuses.\u003c/p\u003e\u003c/figCaption\u003e\n\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_connections-2019.020_fig_002.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/62098ef6b4e3964d85867b55/j_connections-2019.020_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220809T110621Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220809%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=679da59a402fff6d5447361c8e87659be6b08a8cda2eba4497854decbf130e69\" class=\"mw-100\"\u003e\u003c/img\u003e\n\u003c/figure\u003e\n\u003cp\u003eThere is a network measure that does take redundancy, distance, and nexuses into account: algebraic connectivity. To calculate this measure, one row-normalizes the adjacency matrix, as in models of social influence (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_009\"\u003eFriedkin and Johnsen, 2011\u003c/a\u003e), and transforms it into a Laplacian matrix by putting 1 everywhere on the diagonal, provided that everyone has at least one tie, and a minus sign everywhere else (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_006\"\u003eChung, 1997\u003c/a\u003e). The second smallest eigenvalue of the Laplacian is called algebraic connectivity (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_008\"\u003eFiedler, 1973\u003c/a\u003e). It is higher if there are more node-independent paths (\u003citalic\u003eλ\u003c/italic\u003e\u003csub\u003e2\u003c/sub\u003e = 2/3 wheel; \u003citalic\u003eλ\u003c/italic\u003e\u003csub\u003e2\u003c/sub\u003e = 1/3 bow-tie), consistent with K-connectivity, hence it varies across sub-graphs, just like K-connectivity. Furthermore, it is lower for longer average distances, which neither K-connectivity nor k-core is responsive to. Information transmission deteriorates with distance, certainly off-line, and it’s therefore important to have a measure that takes distance into account. Algebraic connectivity distinguishes nexuses, and equals \u003citalic\u003eλ\u003c/italic\u003e\u003csub\u003e2\u003c/sub\u003e = 2/3 for the network with nexuses in \u003ca ref-type=\"fig\" href=\"#j_connections-2019.020_fig_002\"\u003eFigure 2\u003c/a\u003e and \u003citalic\u003eλ\u003c/italic\u003e\u003csub\u003e2\u003c/sub\u003e = 1/2 for the other one. Last but not least, it predicts how quickly consensus is achieved (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_016\"\u003eOlfati-Saber and Murray, 2004\u003c/a\u003e), consistent with experimental outcomes (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_013\"\u003eJudd et al., 2010\u003c/a\u003e). Consensus is important in groups to decide which public goods to realize and how.\u003c/p\u003e\n\u003cp\u003eIn our experiment, the positive effect of the multiple paths in the wheel might have been nullified by the positive effect of the additional nexus in each sub-network of the bow-tie. If for social cohesion, algebraic connectivity is a significantly better measure than K-connectivity remains a question for future research. Whereas in all likelihood, social cohesion, measured in one way or another, is important for cooperation, we note that the effect is nonmonotonic. High cohesion overburdens group members with social pressure, decreases innovation, and may strengthen rather than dampen false information (\u003ca ref-type=\"bibr\" href=\"#j_connections-2019.020_ref_002\"\u003eBurt, 2008\u003c/a\u003e). At too low cohesion, in contrast, groups fall apart. The sweet spot must be somewhere in between the extremes, which poses a challenge for future studies to discover.\u003c/p\u003e\n\u003c/sec\u003e\n\u003c/div\u003e","keywords":[{"title":"Keywords","language":null,"keywords":["Public goods","Cooperation","Social cohesion","K-connectivity"]}],"recentIssues":{"10.21307/connections-2021.022":"\u003carticle-title\u003eThe impact of contact tracing on the spread of COVID-19: an egocentric agent-based model\u003c/article-title\u003e","10.21307/connections-2021.023":"\u003carticle-title\u003eChaos from order: a network analysis of in-fighting before and after El Chapo’s arrest\u003c/article-title\u003e","10.21307/connections-2021.021":"\u003carticle-title\u003eA brokerage-based measure of organizational diversity and exploratory analysis of regulatory violations among Fortune 100 corporations\u003c/article-title\u003e","10.21307/connections-2019.020":"\u003carticle-title\u003eSocial Cohesion and Cooperation for Public 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