State Space Exploration of Spatially Organized Populations of Agents

Abstract-In this paper, we aim at modeling and analyzing the behavior of a spatial population of agents through an exploration of their state space. Agents are localized on a dynamic graph and they have internal states. They interact with an environment. The evolution of the agents and of the environment is specified by a set of rules. The framework is carefully designed to enable the construction of a global state space that can be automatically build and analyzed. The formalism, called IRNs for integrated regulatory networks, may be seen as an extension of logical regulatory networks (à la Thomas) developed in systems biology with spatial information and generalized to use arbitrary data values and update functions of this values. This thus allows to model systems with multiple agents that may be located on a varying spatial structure, may store and update local information, may depend on varying global information and may communicate in their neighborhood. A model of such a system can be defined as an IRN, and then analyzed using model-checking to asses its properties. This paper sketches the modeling framework and its semantics. We show how IRN may be used for the modeling of a population of simple agents, the automatic analysis of various reachability properties and the use of symmetries to reduce the size of the state space

Biology of Applied Digital Ecosystems

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).Comment: 9 pages, 4 figure, conferenc

Spatio-Temporal Patterns for a Generalized Innovation Diffusion Model

We construct a model of innovation diffusion that incorporates a spatial component into a classical imitation-innovation dynamics first introduced by F. Bass. Relevant for situations where the imitation process explicitly depends on the spatial proximity between agents, the resulting nonlinear field dynamics is exactly solvable. As expected for nonlinear collective dynamics, the imitation mechanism generates spatio-temporal patterns, possessing here the remarkable feature that they can be explicitly and analytically discussed. The simplicity of the model, its intimate connection with the original Bass' modeling framework and the exact transient solutions offer a rather unique theoretical stylized framework to describe how innovation jointly develops in space and time.Comment: 20 pages, 4 figure

Urban comix: Subcultures, infrastructures and “the right to the city” in Delhi

This article argues that comics production in India should be configured as a collaborative artistic endeavour that visualizes Delhi’s segregationist infrastructure, claiming a right to the city through the representation and facilitation of more socially inclusive urban spaces. Through a discussion of the work of three of the Pao Collective’s founding members – Orijit Sen, Sarnath Banerjee and Vishwajyoti Ghosh – it argues that the group, as for other comics collectives in cities across the world, should be understood as a networked urban social movement. Their graphic narratives and comics art counter the proliferating segregation and uneven development of neo-liberal Delhi by depicting and diagnosing urban violence. Meanwhile, their collaborative production processes and socialized consumption practices, and the radical comix traditions on which these movements draw (and which are sometimes occluded by the label “Indian Graphic Novel”) create socially networked and politically active spaces that resist the divisions marking Delhi’s contemporary urban fabric

Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields

Research into several aspects of robot-enabled reconnaissance of random fields is reported. The work has two major components: the underlying theory of information acquisition in the exploration of unknown fields and the results of experiments on how humans use sensor-equipped robots to perform a simulated reconnaissance exercise. The theoretical framework reported herein extends work on robotic exploration that has been reported by ourselves and others. Several new figures of merit for evaluating exploration strategies are proposed and compared. Using concepts from differential topology and information theory, we develop the theoretical foundation of search strategies aimed at rapid discovery of topological features (locations of critical points and critical level sets) of a priori unknown differentiable random fields. The theory enables study of efficient reconnaissance strategies in which the tradeoff between speed and accuracy can be understood. The proposed approach to rapid discovery of topological features has led in a natural way to to the creation of parsimonious reconnaissance routines that do not rely on any prior knowledge of the environment. The design of topology-guided search protocols uses a mathematical framework that quantifies the relationship between what is discovered and what remains to be discovered. The quantification rests on an information theory inspired model whose properties allow us to treat search as a problem in optimal information acquisition. A central theme in this approach is that "conservative" and "aggressive" search strategies can be precisely defined, and search decisions regarding "exploration" vs. "exploitation" choices are informed by the rate at which the information metric is changing.Comment: 34 pages, 20 figure

Minority Games, Local Interactions, and Endogenous Networks

In this paper we study a local version of the Minority Game where agents are placed on the nodes of a directed graph. Agents care about beingin the minority of the group of agents they are currently linked to and employ myopic best-reply rules to choose their next-period state. We show that, in this benchmark case, the smaller the size of local networks, the larger long-run population-average payoffs. We then explore the collective behavior of the system when agents can: (i) assign weights to each link they hold and modify them over time in response to payoff signals; (ii) delete badly-performing links (i.e. opponents) and replace them with randomly chosen ones. Simulations suggest that, when agents are allowed to weight links but cannot delete/replace them, the system self-organizes into networked clusters which attain very high payoff values. These clustered configurations are not stable and can be easily disrupted, generating huge subsequent payoff drops. If however agents can (and are sufficiently willing to) discard badly performing connections, the system quickly converges to stable states where all agents get the highest payoff, independently of the size of the networks initially in placeMinority Games, Local Interactions, Non-Directed Graphs, Endogenous Networks, Adaptive Systems.

Equation-free modeling of evolving diseases: Coarse-grained computations with individual-based models

We demonstrate how direct simulation of stochastic, individual-based models can be combined with continuum numerical analysis techniques to study the dynamics of evolving diseases. % Sidestepping the necessity of obtaining explicit population-level models, the approach analyzes the (unavailable in closed form) `coarse' macroscopic equations, estimating the necessary quantities through appropriately initialized, short `bursts' of individual-based dynamic simulation. % We illustrate this approach by analyzing a stochastic and discrete model for the evolution of disease agents caused by point mutations within individual hosts. % Building up from classical SIR and SIRS models, our example uses a one-dimensional lattice for variant space, and assumes a finite number of individuals. % Macroscopic computational tasks enabled through this approach include stationary state computation, coarse projective integration, parametric continuation and stability analysis.Comment: 16 pages, 8 figure