An Eulerian Approach to the Analysis of Krause's Consensus Models

Abstract. In this paper we analyze a class of multi-agent consensus dynamical systems inspired by Krause’s original model. As in Krause’s, the basic assumption is the so-called bounded confidence: two agents can influence each other only when their state values are below a given distance threshold R. We study the system under an Eulerian point of view considering (possibly continuous) probability distributions of agents and we present original convergence results. The limit distribution is always necessarily a convex combination of delta functions at least R far apart from each other: in other terms these models are locally aggregating. The Eulerian perspective provides the natural framework for designing a numerical algorithm, by which we obtain several simulations in 1 and 2 dimensions

Control of Networked Multiagent Systems with Uncertain Graph Topologies

Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure

Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.Comment:

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data

We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending publication.) Last conference version: submitted March 30, 2012 to UAI 2012. First conference version: entitled, Learning Influence Games, initially submitted on June 1, 2010 to NIPS 201