Dynamical complexity in the perception-based network formation model

Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erd\H{o}s-R\'enyi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is discussed.Comment: 8 pages, 7 figure

Endogenous network formation in patent contests and its role as a barrier to entry

In a setting of R&D co-opetition we study, by using an all-pay auction approach, how collaboration affects strategic decisions during a patent contest, and how the latter influences the possible collaboration network structures the firms can hope to form. The all pay auction approach allows us to 1) endogenize both network formation and R&D intensities and 2) take heterogeneous and private valuations for patents into account. We find that, different from previous literature, the complete network is not always the only pairwise stable network, even and especially if the benefits from cooperating are important. Interestingly, the other possible stable networks all have the realistic property that some firms decide not to participate in the contest. Thus, weak cooperation through network formation can serve as a barrier to entry on the market for innovation. We further show that there need not be any network that survives a well known refinement of pairwise stability, strong stability, which imposes networks to be immune to coalitional deviations.patent game, networks, R&D cooperation, all-pay auction

Small World Networks with Segregation Patterns and Brokers

Many social networks have the following properties: (i) a short average distance between any two individuals; (ii) a high clustering coefficient; (iii) segregation patterns; the presence of (iv) brokers and (v) hubs. (i) and (ii) define a small world network. This paper develops a strategic network formation model where agents have heterogeneous knowledge of the network: cognizant agents know the whole network, while ignorant ones are less knowledgeable. For a broad range of parameters, all pairwise Nash (PN) networks have properties (i)-(iv). There are some PN networks with one hub. Cognizant agents have higher betweenness centrality: they are the brokers who connect different parts of the network. Ignorant agents cause the emergence of segregation patterns. The results are robust to varying the number of cognizant agents and to increasing the knowledge level of ignorant ones. An application shows the relevance of the results to assessing the welfare impact of an increase in network knowledge due to, e.g., improved access to social networking tools.Network, Cognitive Network, Small World, Broker, Segregation

Efficient Network Structures with Separable Heterogeneous Connection Costs

We introduce a heterogeneous connection model for network formation to capture the effect of cost heterogeneity on the structure of efficient networks. In the proposed model, connection costs are assumed to be separable, which means the total connection cost for each agent is uniquely proportional to its degree. For these sets of networks, we provide the analytical solution for the efficient network and discuss stability impli- cations. We show that the efficient network exhibits a core-periphery structure, and for a given density, we find a lower bound for clustering coefficient of the efficient network.Comment: 9 page

Metric clusters in evolutionary games on scale-free networks

The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here, we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always favor--but can even hinder--cooperation in social dilemmas. Our findings provide a new perspective to understand the emergence of cooperation in evolutionary games in realistic structured populations