Computation Institute

Motivation: In recent years, single-cell biology has focused on the relationship between the stochastic nature of molecular interactions and variability of cellular behavior. To describe this relationship, it is necessary to develop new computational approaches at the single-cell level. Results: We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS "is a third way of doing science," in addition to traditional deductive and inductive reasoning (Axelrod 1997b). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model of shopper behavior using spreadsheets.

Electricity markets are complex adaptive systems that operate under a wide range of rules that span a variety of time scales. These rules are imposed both from above by society and below by physics. Many electricity markets are undergoing or are about to undergo a transition from centrally regulated systems to decentralized markets. Furthermore, several electricity markets have recently undergone this transition with extremely unsatisfactory results, most notably in California. These high stakes transitions require the introduction of largely untested regulatory structures. Suitable laboratories that can be used to test regulatory structures before they are applied to real systems are needed. Agent-based models can provide such electronic laboratories or "e-laboratories." To better understand the requirements of an electricity market e-laboratory, a live electricity market simulation was created. This experience helped to shape the development of the Electricity Market Complex Adaptive Systems (EMCAS) model. To explore EMCAS' potential as an e-laboratory, several variations of the live simulation were created. These variations probed the possible effects of changing power plant outages and price setting rules on electricity market prices.

The present paper takes the position that social action inherently involves meaning and, thus, cannot be adequately modeled without representing the interpretive process among agents. The development of interpretive models is challenging, however, and quickly raises issues of computational tractability. A strategy is developed based on three assumptions (agent focus, continuity reduction and orientation fields) and three mechanisms (prototype inference, orientation accounting, and situational definition). When the three mechanisms are incorporated into an action selection mechanism, they provide a model of the interpretive process of social interpretation with a higher level of verisimilitude than many other approaches.

This paper interleaves two themes: 1) the prospective role of agent simulation in developing a high-consensus, rapid-discovery social science, and 2) the use of situation models to illustrate how the interaction of social theory and agent architectures might contribute toward such a development. The first theme suggests that computational techniques have the potential to contribute to the advancement of broadly shared social science goals. The second theme uses a relatively specialized epistemological focus, the social situation as a focus of attention and action, to illustrate how agent architectures based upon that focus might contribute to scientific progress. While a situationist epistemology is invoked primarily to convey the important role of ontological assumptions and architectural frameworks in social scientific modeling, a secondary goal of the discussion is the advancement of situational models of social interaction.

As the U.S. experience in Vietnam and Iraq attests, occupation by a foreign power can generate counterproductive conflict dynamics. Winning hearts and minds is the key to the long-term success of occupations, and both occupation forces and resistance movements employ a mix of coercive and non-coercive strategies (punishments and rewards) to shape support within the occupied public. The resulting cross-cutting pressures and the problem of preference falsification can lead to fragile social orders vulnerable to tipping, complicating the short-term evaluation and long-term success of occupation/resistance strategies. This paper presents a preliminary implementation of the Occupation Dynamics (OD) model which uses social agent simulation to explore the complex interaction of occupation and resistance strategies on the making of social order.

The emerging cloud computing model has recently gained a lot of interest both from commercial companies and from the research community. XtreemOS is a distributed operating system for large-scale wide-area dynamic infrastructures spanning multiple administrative domains. XtreemOS, which is based on the Linux operating system, has been designed as a Grid operating system providing native support for virtual organizations. In this paper, we discuss the positioning of XtreemOS technologies with regard to cloud computing. More specifically, we investigate a scenario where XtreemOS could help users take full advantage of clouds in a global environment including their own resources and cloud resources. We also discuss how the XtreemOS system could be used by cloud service providers to manage their underlying infrastructure. This study shows that the XtreemOS distributed operating system is a highly relevant technology in the new era of cloud computing where future clouds seamlessly span multiple bare hardware providers and where customers extend their IT infrastructure by provisioning resources from different cloud service providers.

Live virtual machine migration is a powerful feature of virtualization technologies. It enables efficient load balancing, reduces energy consumption through dynamic consolidation, and makes infrastructure maintenance transparent to users. While live migration is available across wide area networks with state of the art systems, it remains expensive to use because of the large amounts of data to transfer, especially when migrating virtual clusters rather than single virtual machine instances. As evidenced by previous research, virtual machines running identical or similar operating systems have significant portions of their memory and storage containing identical data. We propose Shrinker, a live virtual machine migration system leveraging this common data to improve live virtual cluster migration between data centers interconnected by wide area networks. Shrinker detects memory pages and disk blocks duplicated in a virtual cluster to avoid sending multiple times the same content over WAN links. Virtual machine data is retrieved in the destination site with distributed content-based addressing. We implemented a prototype of Shrinker in the KVM hypervisor and present a performance evaluation in a distributed environment. Experiments show that it reduces both total data transferred and total migration time.

A wide variety of software packages has been developed specially to support multiple criteria decision making, more commonly called Multi Criteria Decision Support System (MCDSS). Such a software uses a user friendly human-computer interface in order to assist a decision maker at various stages of structuring and solving decision problems within an interactive process. The RECIFE software presented in this paper is a MCDSS dedicated to the railway capacity evaluation problem at the station or junction level. It has been developed to help experts in railway transportation. The decision support process is organised around two criteria: the first criterion aims to maximize the number of train scheduled, and the second criterion maximizes the stability of the timetables. Functionalities of RECIFE are illustrated using a real traffic of trains crossing the Pierrefitte-Gonesse junction, located on the North of Paris.

I examine the role of Occupy Wall Street in shifting presidential and congressional discourse on economic fairness and inequality. Using data from 4646 presidential speeches and 1256 congressional records from 2009 to 2015, I test different mechanisms, including repression, media coverage, public opinion, and presidential agenda-setting by applying a novel combination of web scraping, natural language processing, and time series models. I suggest that movement success can be measured in its ability to shape discursive opportunity structures, and I argue that the role of the president should be at the forefront of social movements research. Ultimately, I demonstrate (1) that the repression of Occupy protesters not only predicts media coverage but also increases discursive opportunities through President Obama and Congress, (2) that media coverage of Occupy predicts presidential discourse, (3) that the president's rhetorical shift increases congressional response, and (4) that this change persists after the movement faltered.