Models, Methods and Network Topology: Experimental Design for the Study of Interference

How should a network experiment be designed to achieve high statistical power? Ex- perimental treatments on networks may spread. Randomizing assignment of treatment to nodes enhances learning about the counterfactual causal effects of a social network experiment and also requires new methodology (ex. Aronow and Samii 2017a; Bow- ers et al. 2013; Toulis and Kao 2013). In this paper we show that the way in which a treatment propagates across a social network affects the statistical power of an ex- perimental design. As such, prior information regarding treatment propagation should be incorporated into the experimental design. Our findings justify reconsideration of standard practice in circumstances where units are presumed to be independent even in simple experiments: information about treatment effects is not maximized when we assign half the units to treatment and half to control. We also present an exam- ple in which statistical power depends on the extent to which the network degree of nodes is correlated with treatment assignment probability. We recommend that re- searchers think carefully about the underlying treatment propagation model motivat- ing their study in designing an experiment on a network

Myopic or Farsighted? An Experiment on Network Formation

Pairwise stability (Jackson and Wolinsky, 1996) is the standard stability concept in network formation. It assumes myopic behavior of the agents in the sense that they do not forecast how others might react to their actions. Assuming that agents are farsighted, related stability concepts have been proposed. We design a simple network formation experiment to test these theories. Our results provide support for farsighted stability and strongly reject the idea of myopic behavior.Network Formation, Experiment, Myopic and Farsighted Stability

Experimenting with ecosystem interaction networks in search of threshold potentials in real-world marine ecosystems

Thresholds profoundly affect our understanding and management of ecosystem dynamics, but we have yet to develop practical techniques to assess the risk that thresholds will be crossed. Combining ecological knowledge of critical system interdependencies with a large-scale experiment, we tested for breaks in the ecosystem interaction network to identify threshold potential in real-world ecosystem dynamics. Our experiment with the bivalves Macomona liliana and Austrovenus stutchburyi on marine sandflats in New Zealand demonstrated that reductions in incident sunlight changed the interaction network between sediment biogeochemical fluxes, productivity, and macrofauna. By demonstrating loss of positive feedbacks and changes in the architecture of the network, we provide mechanistic evidence that stressors lead to break points in dynamics, which theory predicts predispose a system to a critical transition

Analyzing covert social network foundation behind terrorism disaster

This paper addresses a method to analyze the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. The method aims at integrating the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory, and computational data processing. The social network responsible for the 9/11 attack in 2001 is used to execute simulation experiment to evaluate the performance of the method.Comment: 17pages, 10 figures, submitted to Int. J. Services Science

Viewing a Graph in a Virtual Reality Display is Three Times as Good as a 2D Diagram

An experiment is reported which tests whether network information is more effectively displayed in a three dimensional space than in a two dimensional space. The experimental task is to trace a path in a network and the experiment is carried out in 2D, in a 3D stereo view, in a 2D view with head coupled perspective, and in a 3D stereo view with head coupled perspective; this last condition creates a localized virtual reality display. The results show that the motion parallax obtained from the head coupling of perspective is more important than stereopsis in revealing structural information. Overall the results show that three times as much information can be perceived in the head coupled stereo view as in the 2D view

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support