Jesus Rios

Adversarial Risk Analysis for Counterterrorism Modeling

Recent large-scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game-theoretic flavor although there are also several interesting decision-analytic-based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision-making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models, and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game-theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversarys actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems.

A framework for participatory budget elaboration support

Participatory budgets are becoming increasingly popular in many municipalities all around the world. The underlying idea is to allow citizens to participate in the allocation of a municipal budget. Little decision support methodology is used in this type of activities, frequently based on physical meetings and some kind of voting mechanism. We describe models and methods to support the elaboration of a participatory budget. We model this problem as one of resource allocation, in which citizens attempt to maximize group satisfaction in view of multiple criteria, subject to, possibly, other constraints. Based on a negotiation approach, we propose a general methodology to deal with such a problem.

Participatory budget formation through the web

We describe a web-based system to support groups in elaborating participatory budgets. Rather than using physical meetings with voting mechanisms, we promote virtual meetings with explicit preference elicitation, guided negotiations and, only if consensus is not reached, voting.

Quixote: supporting group decisions through the web

We describe a web-based architecture to support participation in group decision making. Emphasis is placed on security aspects related with our architecture, designed to enhance trust on the system.

Adversarial Risk Analysis for Urban Security Resource Allocation.

Adversarial risk analysis (ARA) provides a framework to deal with risks originating from intentional actions of adversaries. We show how ARA may be used to allocate security resources in the protection of urban spaces. We take into account the spatial structure and consider both proactive and reactive measures, in that we aim at both trying to reduce criminality as well as recovering as best as possible from it, should it happen. We deal with the problem by deploying an ARA model over each spatial unit, coordinating the models through resource constraints, value aggregation, and proximity. We illustrate our approach with an example that uncovers several relevant policy issues.

Concept Uncertainty in Adversarial Statistical Decision Theory

We focus on concept uncertainty which adds a new layer to the traditional risk analysis distinction between aleatory and epistemic uncertainties, when adversaries are present. The idea is illustrated with a problem in adversarial point estimation framed as a specific case of adversarial statistical decision theory.

A framework for strategic financial risk management

Strategic risks represent the largest challenge for corporate risk management, often due to lack of data or incompatibility with existing financial modeling frameworks. Indeed, industry surveys found that from 2002 to 2012 strategic risks accounted for over 80% of the cases of significant shareholder value loss among Top 1000 companies. To meet the challenge, we propose a practical framework that includes (i) a collaborative environment to build (in an intuitive yet structured fashion) a graph representing the relationships between mitigation actions, risks, and affected drivers of a companys financial model, (ii) a set of rules to transform this graph into a probabilistic model, allowing for the automatic generation of questionnaires to elicit risk information from domain experts, (iii) an aggregation technique to combine opinions of multiple experts, (iv) simulation to quantify the effects of the risks and mitigation actions on key financial outcomes, and to compute, for example, the probability of achieving a given profit target conditional on a portfolio of mitigation actions, (v) an evaluation of mitigation actions in terms of their risk reduction, and (vi) a cost-benefit analysis to help decision makers determine a mitigation investment strategy.