Armando Geller

Networks in Agent-Based Social Simulation

Computational social science and in particular agent-based social simulation continue to gain momentum in the academic community. Social network analysis enjoys even more popularity. They both have much in common. In agent-based models, individual interactions are simulated to generate social patterns of all kinds, including relationships that can then be analyzed by social network analysis. This chapter describes and discusses the role of agent-based modeling in the generative-analytical part of this symbiosis. More precisely, we look at what concepts are used, how they are used (implemented), and what kind of validation procedures can be applied.

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.

The use of complexity-based models in international relations: a technical overview and discussion of prospects and challenges

Complexity has become an übernotion for a contemporary perception of many social phenomena. This is mirrored by the existence and advent of an array of methods and theories that describe complex social phenomena, including in International Relations (IR). However, many scholars fall short of explaining what it is they call ‘complexity’, how the notion of complexity can help to better understand IR and which kinds of methodologies support such analyses. This article provides an overview and critical discussion of the latter. The notion of complexity and its underlying concepts are briefly clarified and methodological aspects and challenges are discussed. It is explained which social phenomena within the realm of IR are most promisingly studied under a complexity paradigm. Three modelling examples are given for illustration that allow for the identification of methodological and epistemological limits and the anticipation of potential prospects of a complexity-based modelling approach to IR.

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.

Modeling Power and Authority: An Emergentist View from Afghanistan

The aim of this chapter is to provide a critical overview of state-of-the-art models that deal with power and authority and to present an alternative research design. The chapter is motivated by the fact that research on power and authority is confined by a general lack of statistical data. However, the literal complexity of structures and mechanisms of power and authority requires a formalized and dynamic approach of analysis if more than a narrative understanding of the object of investigation is sought. It is demonstrated that evidence-driven and agent-based social simulation (EDABSS) can contend with the inclusion of qualitative data and the effects of social complexity at the same time. A model on Afghan power structures exemplifying this approach is introduced and discussed in detail from the data collection process and the creation of a higher order intuitive model to the derivation of the agent rules and the model’s computational implementation. EDABSS not only deals in a very direct way with social reality but also produces complex artificial representations of this reality. Explicit sociocultural and epistemological couching of an EDABSS model is therefore essential and treated as well.

Building Empirical Multiagent Models from First Principles When Fieldwork Is Difficult or Impossible

This Chapter informs the reader about how to create and parameterize empirical multiagent models from first principles when fieldwork is difficult or impossible to conduct and data is primarily of qualitative nature. Empirical multiagent models have become ever more popular over the last decade. While informing models using statistical and geospatial data can orient itself on more established techniques and standards, methodological challenges persist in regards to using qualitative data for informing and parameterizing models. Protocols such as ODD are welcome and helpful devices—and hence used in this Chapter—but qualitative data comes with its own peculiarities. The most important of which is, for modeling purposes, that qualitative data tends to inform the logic of agent behavior. The emphasis I thus put on qualitative data to make model design decisions based on evidence and first principles will be reflected by soft adaptations of the ODD protocol. Arguably this may amount to a deeper insight the Chapter is providing: Whereas the usage of such frameworks as ODD increases model reliability, validity is built using qualitative empirical data for informing and parameterizing the agent and model behavior.

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.