Decision Analysis – Strategic [Urban] Ecologies, Engineering, and Decision-Making

Herb Simon on Complexity and Planning

seedMay 13, 2014Leave a comment

Herb Simon's book, The Sciences of the Artificial, has instantly become one of the more indispensable books on my shelf. Even though I spent five years across the quad from a building with his name on it, I never really learned what he did or why his work was so important. So it is with a bit of embarrassment that I admit this book was an unexpected pleasure.

I stumbled across Simon's book as an accident. One of my students recommended we read Ethiraj and Levinthal's "Modularity and Innovation in Complex Systems" to inform our discussion about information sharing in support of infrastructure system emergency preparedness. One of their references to "The Architecture of Complexity" seemed interesting, and I wanted to learn more about system architecture so I could understand what one of my newest colleagues, David Broniatowski, was saying when he discussed the role of architecture in system flexibility and controllability. So I set out in search of "Architecture of Complexity," and the librarian instead pointed me to The Sciences of the Artificial. What a blessing!

I truly want you to read the book, so I won't say too much. For me, my most cherished insight from Simon was the following:

A man [An ant], viewed as a behaving system, is quite simple. The apparent complexity of his behavior over time is largely a reflection of the complexity of the environment in which he [it] finds himself [itself].

To me, the simplicity and elegance of this hypothesis characterizes the entire book. Although we may disagree on the specific mechanisms, or on the plausibility of this hypothesis, its influence on the practice of engineering and policy design cannot be doubted. I also see the practical results of exploration of this hypothesis everywhere I look in research and technical literature. This hypothesis and many other insights (e.g., satisficing, hierarchical organization of complex systems, valuing the search vs. valuing the outcome, etc.) immediately resonated with my experiences and pulled me all the way through the book.

Because I was trained as a civil engineer, it has taken a decade after my undergraduate to encounter Simon's work. I believe I can say that it has been worth the wait.

New SEED paper on sustainability definition accepted to JMCDA

seedOctober 21, 2013Leave a comment

Today we received word that a SEED paper co authored by Dr. Francis and Cassandra Reyes-Jones has been accepted for publication in the Journal of Multi Criteria Decision Analysis [Francis, R.A. and Reyes-Jones, C. (2014). “Decision-Analytic Approach for Water Sustainability Definition: A Higher Education Case Study."]. In collaboration with the GW Office of Sustainability, we used a decision analytic process to demonstrate an approach to articulating a sustainability "definition" using an objective-value hierarchy and elicited value functions. The author version will be posted soon for your interest!

new RESS paper! on entropy-weighted metric for resilience analysis

seedJuly 15, 2013Leave a comment

I have been away from writing on the blog, even my personal opinions on current research topics (OK, that's what almost all of this writing is) due to travel, deadlines, and other obligations. I do want to take an opportunity to announce that a new paper from the SEED research group co-authored by Dr. Francis and Behailu Bekera has just been accepted for publication in the journal Reliability Engineering and System Safety. I am very excited about this, because I enjoy reading articles from this journal, and have found this research community engaging and interesting in person, as well as on paper. I'll write a more "reflective" entry about this sometime later, but if you'd like to take a look at the paper, please find it here. We will be presenting an earlier version of this work as a thought piece at ESREL 2013. More on the conference paper closer to the date of the conference in October.

Robust Decision Making for Confronting Deep Uncertainties in Risk Analysis

seedNovember 12, 2012Leave a comment

In this post, Behailu Bekera, a 2nd-year PhD student in the SEED group discusses the role of robust decision making under deep uncertainties. This post was inspired by a reading of Louis Anthony Cox's "Confronting Deep Uncertainties in Risk Analysis" in a recent issue of Risk Analysis.

There is no one good model that can be used to assess deep uncertainties. Hence our decisions about complex systems or decision contexts are typically made based on insufficient knowledge about the situation. Deep uncertainties are characterized by multiplicity of future events and an unknown future. So, being able to precisely anticipate undesired events in the future and conducting the necessary preparations would be an example of a decision context with deep uncertainty. In this article, Tony offers recognition to ideas from robust optimization, adaptive control and machine learning that seem promising for dealing with deep uncertainties in risk analysis.

Using multiple models and relevant data to improve decisions, average forecasting, resampling data that allows robust statistical inferences despite model uncertainty, adaptive sampling and modeling, and Bayesian model averaging for statistical are some of the tools that can assist in robust risk analysis involving deep uncertainties.

The robust risk analysis techniques shift the focus of risk analysis from addressing passive aspects of it, such as identifying likelihood events (disruptions) and their associated [estimated] consequences to more action-oriented questions. Active questions such as how we should act now to effectively mitigate the occurrence or consequences of events with highly undesirable effects in the future.

Robust decision making, for instance, is used by developing countries to identify potential large-scale infrastructure development projects and investigate possible vulnerabilities that require profound attention of all stakeholders. Additionally, adaptive risk management may be used to maintain reliable network structure to ensure service continuity despite failures. This sort of techniques can be considerably important in the areas of critical infrastructure protection and resilience.

Through these emerging methods, Dr. Cox, makes important suggestions for making robust decisions in the face of extreme uncertainty in spite of our incomplete or inadequate knowledge. This will be an important paper for those looking to advance the science of robust decision-making and risk analysis.

SEED Group Collaborators present “Preferences related to urban sustainability under risk, uncertainty, and dynamics” at SRA 2011

seedDecember 11, 2011Leave a comment

This past week the Annual Meeting of the Society for Risk Analysis took place in Charleston, SC. Dr. Francis and collaborators George Gray, John Carruthers, and Robert Lee presented a paper titled "Preferences related to urban sustainability under risk, uncertainty, and dynamics." The abstract is included here:

Numerous older cities in the US are experiencing a state of decline, due to shrinking populations, economic hardship, and many other factors. Large areas of these cities are comprised of contaminated and vacant land. We explore the decision context around land redevelopment approaches focused upon reducing risk, improving quality of life, and fostering sustainability. Characterizing the preferences and objectives of diverse stakeholders in a multi-attribute framework may improve decisions and planning. However, traditional decision analytic approaches tend to be ‘static’, and do not capture the temporal and spatial dynamics of this problem. We propose a framework that integrates stated and revealed preferences in a dynamic modeling environment designed to capture key attributes of urban sustainability identified by stakeholders. The utility of this model will be demonstrated through an observational experiment. Key attributes and preferences will be elicited from a population of stakeholders in a Web environment. After eliciting these preferences, the participants will then engage in a dynamic modeling exercise in which they are able to interactively explore land use decisions considering the complexities of urban dynamics; the numerous tradeoffs, risks, and uncertainties; the resource constraints; and so on. We call this model DMASE (for Dynamic/Multi-Attribute/Spatially-Explicit). Preferences over the key attributes will then be elicited again. We hypothesize that the key attributes and preferences will change appreciably based upon interaction with the DMASE model. Additionally, the model can be modified in an iterative fashion to capture the decision context and preferences of the participants in a more meaningful way. This work will lead to a decision support tool that will allow stakeholders and decision-makers in declining cities to make more informed decisions about changes in the complex urban environment.