Interdependent network restoration games with incomplete information and bounded rationality.

Communities face the challenge of finding restoration strategies in the aftermath of disasters. In particular, independent and self-interested utility managers devise such strategies for infrastructure through a heuristic decentralized process. This paper takes a game-theoretic approach to model the decentralized and strategic restoration decision making with application to interdependent infrastructure. Particularly, we model the decision process using simultaneous games to investigate decision makers' conflicting preferences. We employ Bayesian games to incorporate the realistic assumptions of poor interagent communication, resulting in incomplete information. Also, we account for behavioral biases such as bounded rationality, cooperative behavior or lack thereof, and equality-driven resource allocations. We test our models using ideal, synthetic interdependent networks, and the realistic infrastructure of Shelby County, TN. Results show that cooperation leads to the best-performing decisions even if others are not cooperative. The necessity of cooperation is even higher when there is a dominant player whose service is vital to other players. Our sensitivity results highlight the significant influence of resource availability and allocation on the performance of restoration plans. Our approach enhances the practicality of decision models for community resilience, and unravels novel policy strategies such as cooperation incentives.

Interdependent Network Recovery Games.

Recovery of interdependent infrastructure networks in the presence of catastrophic failure is crucial to the economy and welfare of society. Recently, centralized methods have been developed to address optimal resource allocation in postdisaster recovery scenarios of interdependent infrastructure systems that minimize total cost. In real-world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making recovery decisions, resulting in suboptimal decentralized processes. With the goal of minimizing recovery cost, a best-case decentralized model allows controllers to develop a full recovery plan and negotiate until all parties are satisfied (an equilibrium is reached). Such a model is computationally intensive for planning and negotiating, and time is a crucial resource in postdisaster recovery scenarios. Furthermore, in this work, we prove this best-case decentralized negotiation process could continue indefinitely under certain conditions. Accounting for network controllers' urgency in repairing their system, we propose an ad hoc sequential game-theoretic model of interdependent infrastructure network recovery represented as a discrete time noncooperative game between network controllers that is guaranteed to converge to an equilibrium. We further reduce the computation time needed to find a solution by applying a best-response heuristic and prove bounds on ε-Nash equilibrium, where ε depends on problem inputs. We compare best-case and ad hoc models on an empirical interdependent infrastructure network in the presence of simulated earthquakes to demonstrate the extent of the tradeoff between optimality and computational efficiency. Our method provides a foundation for modeling sociotechnical systems in a way that mirrors restoration processes in practice.

Competitive percolation strategies for network recovery.

Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability. We aim to narrow the gap between the two approaches. Analyzing realistic recovery strategies, we identify over- and undersupply penalties of commodities as primary contributions to reconstruction cost, and we demonstrate traditional network science methods, which maximize the largest connected component, are cost inefficient. We propose a novel competitive percolation recovery model accounting for node demand and supply, and network structure. Our model well approximates realistic recovery strategies, suppressing growth of the largest connected component through a process analogous to explosive percolation. Using synthetic power grids, we investigate the effect of network characteristics on recovery process efficiency. We learn that high structural redundancy enables reduced total cost and faster recovery, however, requires more information at each recovery step. We also confirm that decentralized supply in networks generally benefits recovery efforts.

Exotic states in a simple network of nanoelectromechanical oscillators.

Synchronization of oscillators, a phenomenon found in a wide variety of natural and engineered systems, is typically understood through a reduction to a first-order phase model with simplified dynamics. Here, by exploiting the precision and flexibility of nanoelectromechanical systems, we examined the dynamics of a ring of quasi-sinusoidal oscillators at and beyond first order. Beyond first order, we found exotic states of synchronization with highly complex dynamics, including weak chimeras, decoupled states, traveling waves, and inhomogeneous synchronized states. Through theory and experiment, we show that these exotic states rely on complex interactions emerging out of networks with simple linear nearest-neighbor coupling. This work provides insight into the dynamical richness of complex systems with weak nonlinearities and local interactions.

Advanced Materials, Technologies, and Complex Systems Analyses: Emerging Opportunities to Enhance Urban Water Security.

Innovation in urban water systems is required to address the increasing demand for clean water due to population growth and aggravated water stress caused by water pollution, aging infrastructure, and climate change. Advances in materials science, modular water treatment technologies, and complex systems analyses, coupled with the drive to minimize the energy and environmental footprints of cities, provide new opportunities to ensure a resilient and safe water supply. We present a vision for enhancing efficiency and resiliency of urban water systems and discuss approaches and research needs for overcoming associated implementation challenges.

A new mutually reinforcing network node and link ranking algorithm.

This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.

Time-dependent resilience assessment and improvement of urban infrastructure systems.

This paper introduces an approach to assess and improve the time-dependent resilience of urban infrastructure systems, where resilience is defined as the systems' ability to resist various possible hazards, absorb the initial damage from hazards, and recover to normal operation one or multiple times during a time period T. For different values of T and its position relative to current time, there are three forms of resilience: previous resilience, current potential resilience, and future potential resilience. This paper mainly discusses the third form that takes into account the systems' future evolving processes. Taking the power transmission grid in Harris County, Texas, USA as an example, the time-dependent features of resilience and the effectiveness of some resilience-inspired strategies, including enhancement of situational awareness, management of consumer demand, and integration of distributed generators, are all simulated and discussed. Results show a nonlinear nature of resilience as a function of T, which may exhibit a transition from an increasing function to a decreasing function at either a threshold of post-blackout improvement rate, a threshold of load profile with consumer demand management, or a threshold number of integrated distributed generators. These results are further confirmed by studying a typical benchmark system such as the IEEE RTS-96. Such common trends indicate that some resilience strategies may enhance infrastructure system resilience in the short term, but if not managed well, they may compromise practical utility system resilience in the long run.

Who evacuates when hurricanes approach? The role of risk, information, and location.

OBJECTIVE: This article offers an expanded perspective on evacuation decision making during severe weather. In particular, this work focuses on uncovering determinants of individual evacuation decisions. METHODS: We draw on a survey conducted in 2005 of residents in the eight-county Houston metropolitan area after Hurricane Rita made landfall on September 24, 2005. RESULTS: We find that evacuation decisions are influenced by a heterogeneous set of parameters, including perceived risk from wind, influence of media and neighbors, and awareness of evacuation zone, that are often at variance with one of the primary measures of risk used by public officials to order or recommend an evacuation (i.e., storm surge). We further find that perceived risk and its influence on evacuation behavior is a local phenomenon more readily communicated by and among individuals who share the same geography, as is the case with residents living inside and outside official risk areas. CONCLUSIONS: Who evacuates and why is partially dependent on where one lives because perceptions of risk are not uniformly shared across the area threatened by an approaching hurricane and the same sources and content of information do not have the same effect on evacuation behavior. Hence, efforts to persuade residential populations about risk and when, where, and how to evacuate or shelter in place should originate in the neighborhood rather than emanating from blanket statements from the media or public officials. Our findings also raise important policy questions (included in the discussion section) that require further study and consideration by those responsible with organizing and implementing evacuation plans.