Optimal Design, Robustness, and Risk Aversion

Newman, M. E. J.; Girvan, Michelle; Farmer, J. Doyne

doi:10.1103/physrevlett.89.028301

Cited by 64 publications

(47 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, systemic failures occur due to fragility in complex systems [11][12][13][14]. Such complex systems are challenging because they are highly interconnected among a large number of subsystems and components.…”

Section: Introductionmentioning

confidence: 99%

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Venkatasubramanian

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

View full text Add to dashboard Cite

The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong to the same class. Given the size, scope, and complexity of these modern engineered systems and their interactions, it is becoming increasingly difficult for people to anticipate, diagnose and control serious abnormal events in a timely manner. Practitioners in the process industries view this as the next major challenge in control systems research and application. There are two different, but related, components of the overall systemic risks management problem. One deals with the problem of process safety during real-time operations. The other deals with safety issues during the design and/or modifications of the plant or the processes. In this paper, I will present an overview of the challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for a variety of applications in the chemical, petrochemical, and pharmaceutical industries for inherently safer design, operator training, abnormal events management, and optimal process operations.

show abstract

Section: Introductionmentioning

confidence: 99%

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Venkatasubramanian

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

View full text Add to dashboard Cite

show abstract

“…Robustness has been defined in the past in very different ways, and mostly informal [1,5,9,10,16,11]. In [11], it is defined as the "system's strength in constitution and resistibility to any sort of external or internal changes, perturbations, fluctuations, and noise".…”

Section: Introductionmentioning

confidence: 99%

Robustness in SOC Design

Waldschmidt

2006

9th EUROMICRO Conference on Digital System Design (DSD'06)

View full text Add to dashboard Cite

Embedded systems, ubiquitous computing and networked architectures are getting more and more important within our society. System parts are often completely implemented as integrated circuits (SoC = System on chip). Consequently, their complexity and heterogeneity have grown dramatically in the recent past. Moreover, embedded systems are used in environments where parameters are subject to continuous changes. Hence, they have to respond to environmental requirements and changes of their own system parameters in a robust manner. To gain this robustness and to cope with the design methodology, formal measures and metrics are of great importance.Such measures need to be combined with the still increasing requirement for computing performance. The implementation of robust features requires adap-tivity by reconfiguration and parallelism. We will call the corresponding sys-tems adaptive computing systems (ACS). The ACS class offers the opportunity to adapt the whole architecture or parts of the architecture to the changing needs of applications or changing environments.The paper addresses some of these aspects and presents some ideas for mod-elling and designing adaptive computing systems (ACS). Especially measures, metrics and taxonomies for reliability, adaptivity and robustness are analysed and discussed. Robust behaviour of electronic systems will contribute to significantly higher trust of the society in modern technology. Therefore it is of very high eco-nomical relevance for industry and commerce.

show abstract

“…Among the important features of SOC are (a) self-similarity and homogeneity of the landscape, (b) fractal structure of cascades, (c) a small power-law exponent (i.e., heavier tails), and (d) low density and low yield (e.g., in the context of the forest fire model, described below). HOT [26,27,28,85], in contrast, models complex systems that emerge as a result of optimization in the face of persistent threats. While SOC is motivated by largely mechanical processes, the motivation for HOT comes from evolutionary processes and deliberately engineered systems, such as the electric power grid.…”

Section: Introductionmentioning

confidence: 99%

Strategic analysis of complex security scenarios.

Vorobeychik¹

2012

View full text Add to dashboard Cite

Many advanced technical tools are available to prevent attacks on national infrastructure. Nevertheless, while traditional analyses of security problems have succeeded in producing good technical solutions, they have often ignored the human factor integral to these problems. Human attackers (who may be individuals or state-level attackers) expend substantial effort to breach security because they have the incentive for doing so. People involved in implementing security follow individual incentives, which need not align with global security concerns; consequently, desired security solutions are often implemented poorly, or not at all. This complex interplay between individual incentives and global (organizational and/or national) goals can be modeled and analyzed using game theoretic techniques. By analyzing not only what is possible, but also what is motivated, a holistic approach to security problems can be developed, informing policy and providing tools to policy makers.We study game theoretic models that unify several current incentive-based approaches to security, and develop simulation-based and mathematical optimization methods for analyzing such models that exploit the high-performance computing capabilities at Sandia. Our first model studies security in interdependent settings, offering a scalable local search heuristic to approximate optimal security decisions in general, and a linear programming approach, coupled with simulations of consequences, to optimally compute security in an important special case. Our second class of models addresses security patrolling problems when an adversary gets to observe the patrol location. We present a general framework, based on stochastic games, for computing optimal security policies in such settings, and present more scalable tools that apply in the important special cases. Our third contribution is a model of security that involves many defenders, but only models nonadaptive attackers (or natural disasters, inadvertent errors, etc). In this model, we demonstrate that the security decisions of many players result in global security configuration that is not very far from optimal, and is much more resilient to environment changes that an optimal solution. This positive effect dissipates, however, when the number of decision makers becomes too large.3

show abstract

Optimal Design, Robustness, and Risk Aversion

Cited by 64 publications

References 7 publications

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Robustness in SOC Design

Strategic analysis of complex security scenarios.

Contact Info

Product

Resources

About