10,159 research outputs found
Topology and Memory Effect on Convention Emergence
Abstract—Social conventions are useful self-sustaining proto-cols for groups to coordinate behavior without a centralized entity enforcing coordination. We perform an in-depth study of different network structures, to compare and evaluate the effects of different network topologies on the success and rate of emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory or history of past activities on the reward received by interacting agents have not been adequately investigated. We propose a reward metric that takes into consideration the past action choices of the interacting agents. The research question to be answered is what effect does the history based reward function and the learning approach have on convergence time to conventions in different topologies. We experimentally investigate the effects of history size, agent population size and neighborhood size or the emergence of social conventions. I
The Emergence of Norms via Contextual Agreements in Open Societies
This paper explores the emergence of norms in agents' societies when agents
play multiple -even incompatible- roles in their social contexts
simultaneously, and have limited interaction ranges. Specifically, this article
proposes two reinforcement learning methods for agents to compute agreements on
strategies for using common resources to perform joint tasks. The computation
of norms by considering agents' playing multiple roles in their social contexts
has not been studied before. To make the problem even more realistic for open
societies, we do not assume that agents share knowledge on their common
resources. So, they have to compute semantic agreements towards performing
their joint actions. %The paper reports on an empirical study of whether and
how efficiently societies of agents converge to norms, exploring the proposed
social learning processes w.r.t. different society sizes, and the ways agents
are connected. The results reported are very encouraging, regarding the speed
of the learning process as well as the convergence rate, even in quite complex
settings
Non-equilibrium phase transition in negotiation dynamics
We introduce a model of negotiation dynamics whose aim is that of mimicking
the mechanisms leading to opinion and convention formation in a population of
individuals. The negotiation process, as opposed to ``herding-like'' or
``bounded confidence'' driven processes, is based on a microscopic dynamics
where memory and feedback play a central role. Our model displays a
non-equilibrium phase transition from an absorbing state in which all agents
reach a consensus to an active stationary state characterized either by
polarization or fragmentation in clusters of agents with different opinions. We
show the exystence of at least two different universality classes, one for the
case with two possible opinions and one for the case with an unlimited number
of opinions. The phase transition is studied analytically and numerically for
various topologies of the agents' interaction network. In both cases the
universality classes do not seem to depend on the specific interaction
topology, the only relevant feature being the total number of different
opinions ever present in the system.Comment: 4 pages, 4 figure
Destabilising conventions using temporary interventions
Conventions are an important concept in multi-agent systems as they allow increased coordination amongst agents and hence a more efficient system. Encouraging and directing convention emergence has been the focus of much research, particularly through the use of fixed strategy agents. In this paper we apply temporary interventions using fixed strategy agents to destabilise an established convention by (i) replacing it with another convention of our choosing, and (ii) allowing it to destabilise in such a way that no other convention explicitly replaces it. We show that these interventions are effective and investigate the minimum level of intervention needed
A Unified Framework for Multi-Agent Agreement
Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems, no unifying framework exists for comparing MAPs that vary in, e.g., strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement Framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power, we apply it to two well-known MAPs: convention evolution and language convergence. We demonstrate the insights DOA provides toward improving known approaches to these problems. Using a careful comparative analysis of a range of MAPs and solution approaches via the DOA framework, we identify a single critical differentiating factor: how accurately an agent can discern other agent.s states. To demonstrate how variance in this factor influences solution tractability and complexity we show its effect on the convergence time and quality of Particle Swarm Optimization approach to a generalized MAP
The Contagion Effects of Repeated Activation in Social Networks
Demonstrations, protests, riots, and shifts in public opinion respond to the
coordinating potential of communication networks. Digital technologies have
turned interpersonal networks into massive, pervasive structures that
constantly pulsate with information. Here, we propose a model that aims to
analyze the contagion dynamics that emerge in networks when repeated activation
is allowed, that is, when actors can engage recurrently in a collective effort.
We analyze how the structure of communication networks impacts on the ability
to coordinate actors, and we identify the conditions under which large-scale
coordination is more likely to emerge.Comment: Submitted for publicatio
A Spatial Agent-Based Model of N-Person Prisoner's Dilemma Cooperation in a Socio-Geographic Community
The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems
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