Seyed M. Mussavi Rizi

Axelrod's Metanorm Games on Networks

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrods analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

How corruption blunts counternarcotic policies in Afghanistan: a multiagent investigation

We report the results of multiagent modeling experiments on interactions between the drug industry and corruption in Afghanistan. The model formalizes assumptions on the motivations of players in the Afghan drug industry, quantifies the tradeoffs among various choices players face and enables inspection of the time, space and level of supply chain in which one can expect positive and negative impacts of counternarcotic policies. If reducing opium exports is one measure of effectiveness for NATO operations in Afghanistan, grasping the links between corruption and the drug industry should provide a better picture of the second-order interactions between corruption and investment in improving the governance quality, in deploying security forces tasked with eradication and interdiction and in programs to enhance rural livelihoods.

Verification through calibration: an approach and a case study of a model of conflict in Syria

In this paper we introduce a workflow for multiagent modeling that relies on piecemeal calibration to verify the model, and discuss how modelers can organize this workflow to accelerate model building, improve the quality and technical soundness of the final model and be able to attribute dynamics of model outputs to causal mechanisms represented in the model. To this end, we apply the proposed workflow step by step to the development process of a multiagent model of the civil war in Syria, and visualize model validity and dynamics across individual development sprints.

Fusing remote sensing with sparse demographic data for synthetic population generation: an algorithm and application to rural Afghanistan

We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

Optimal Blends of History and Intelligence for Robust Antiterrorism Policy

Antiterrorism analysis requires that security agencies blend evidence on historical patterns of terrorist behavior with incomplete intelligence on terrorist adversaries to predict possible terrorist operations and devise appropriate countermeasures. We model interactions between reactive, adaptive and intelligent adversaries embedded in minimally sufficient organizational settings to study the optimal analytic mixture, expressed as historical memory reach-back and the number of anticipatory scenarios, that should be used to design antiterrorism policy. We show that history is a valuable source of information when the terrorist organization evolves and acquires new capabilities at such a rapid pace that makes optimal strategies advocated by game-theoretic reasoning unlikely to succeed.

Social Simulations for Border Security

EADS North America and George Mason University have partnered to build analytical tools for border security that incorporate social, cultural, behavioral and organizational aspects of interactions among border security forces, smugglers and the population and represent integrated technology architectures made up of fixed and mobile sensor and surveillance networks. These tools provide critical capabilities that influence border security operations, planning, analysis and training. We present the results of the first sprint of our effort, demonstrating the feasibility of social simulation for the security of the Southwestern U.S. border. First, we recount how we used open-source data on border security forces and smuggling organizations, replicating for 2009 the landscape of gateway organizations and cartels in Sonora along with the border security architecture for the Tuscon sector of Customs and Border Patrol (CBP). We then describe the architecture of the model that connects a disaggregated view of these organizations to a high-fidelity representation of the physical environment and sensor networks. Finally, we conclude with a short discussion of model dynamics, validity and generalizability of our approach.

Using Social Simulation for Aid and Development Analytics during Armed Conflict: Experiences from Uruzgan

A participatory social simulation framework is presented to support context analysis in Uruzgan provinces Baluchi Valley region, Afghanistan under the banner of context sensitivity. Working in fragile states and areas affected by armed conflict is challenging for locals, development workers and researchers alike. Meaningful interaction with locals for data collection purposes is difficult and techniques applied should balance the desire for broad and in-depth data elicitation with sound risk management. Rarely can standard solutions deliver the results needed for making informed decisions in such circumstance. The reader is shown protocols for data collection and analytics that are tailored to doing context analysis and decision support for development cooperation in areas affected by armed conflict. Select results are presented to demonstrate the usefulness of the framework. The article concludes with a comparison of the proposed framework with development cooperation requirements.

Using participatory elicitation to identify population needs and power structures in conflict environments

A methodological approach is reported to produce a context analysis in South Afghanistan under the banner of Do No Harm (DNH). The difficult work environment for locals, development workers and researchers alike is briefly described; and the problem that is supposed to be solved is derived from it, namely how to elicit the needs and requirements of the population. Step by step the reader is guided through the approach proposed and a selection of results is presented that (arguably) demonstrate the usefulness of our ideas for optimal (DNH) project portfolio design.