Directed generosity and network formation: network dimension matters

We explore network effects on generosity for different network dimensions. To this end we elicit multiple network dimensions (friendship, social support, economic exchange, etc.) in a rural village in the Southern hemisphere and measure generosity with a sequence of dictator games conducted in the field. We find that networks of different dimensions differ substantially in density, clustering, and centrality. When relating generosity to networks we observe that social distance only matters for friendship ties but that structural network variables are important in all network dimensions. Importantly, these effects are not invariant across different network dimensions. We also find that individual characteristics are unrelated with generosity per se but that they have strong explanatory power for network formation

Reciprocity-driven Sparse Network Formation

A resource exchange network is considered, where exchanges among nodes are based on reciprocity. Peers receive from the network an amount of resources commensurate with their contribution. We assume the network is fully connected, and impose sparsity constraints on peer interactions. Finding the sparsest exchanges that achieve a desired level of reciprocity is in general NP-hard. To capture near-optimal allocations, we introduce variants of the Eisenberg-Gale convex program with sparsity penalties. We derive decentralized algorithms, whereby peers approximately compute the sparsest allocations, by reweighted l1 minimization. The algorithms implement new proportional-response dynamics, with nonlinear pricing. The trade-off between sparsity and reciprocity and the properties of graphs induced by sparse exchanges are examined.Comment: 19 page

Inequality and Network Formation Games

This paper addresses the matter of inequality in network formation games. We employ a quantity that we are calling the Nash Inequality Ratio (NIR), defined as the maximal ratio between the highest and lowest costs incurred to individual agents in a Nash equilibrium strategy, to characterize the extent to which inequality is possible in equilibrium. We give tight upper bounds on the NIR for the network formation games of Fabrikant et al. (PODC '03) and Ehsani et al. (SPAA '11). With respect to the relationship between equality and social efficiency, we show that, contrary to common expectations, efficiency does not necessarily come at the expense of increased inequality.Comment: 27 pages. 4 figures. Accepted to Internet Mathematics (2014

Strategies in social network formation

We run a computerised experiment of network formation where all connections are beneficial and only direct links are costly. Players simultaneously submit link proposals; a connection is made only when both players involved agree. We use both simulated and experimentally generated data to test the determinants of individual behaviour in network formation. We find that approximately 40% of the network formation strategies adopted by the experimental subjects can be accounted for as best responses. We test whether subjects follow alternative patterns of behaviour and in particular if they: propose links to those from whom they have received link proposals in the previous round; propose links to those who have the largest number of direct connections. We find that together with best response behaviour, these strategies explain approximately 75% of the observed choices. We estimate individual propensities to adopt each of these strategies, controlling for group effects. Finally we estimate a mixture model to highlight the proportion of each type of decision maker in the population

Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control

Stochastic network formation and homophily

This is a chapter of the forthcoming Oxford Handbook on the Economics of Networks