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

FLAIM: A Multi-level Anonymization Framework for Computer and Network Logs

FLAIM (Framework for Log Anonymization and Information Management) addresses two important needs not well addressed by current log anonymizers. First, it is extremely modular and not tied to the specific log being anonymized. Second, it supports multi-level anonymization, allowing system administrators to make fine-grained trade-offs between information loss and privacy/security concerns. In this paper, we examine anonymization solutions to date and note the above limitations in each. We further describe how FLAIM addresses these problems, and we describe FLAIM's architecture and features in detail.Comment: 16 pages, 4 figures, in submission to USENIX Lis

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Centers, Peripheries, and Popularity: The Emergence of Norms in Simulated Networks of Linguistic Influence

We simulate the dynamics of diffusion and establishment of norms, variants adopted by the majority of agents, in a large social influence network with scale-free small-world properties. Diffusion is modeled as the probabilistic uptake of one of several competing variants by agents of unequal social standing. We find that novel variants diffuse following an S-curve and stabilize as norms when three conditions are simultaneously satisfied: the network comprises both extremely highly connected agents (centers) and very isolated members (peripheries), and agents pay proportionally more attention to better connected, more “popular”, neighbors. These findings shed light on little known dynamic properties of centers and peripheries in large influence networks. They show that centers, structural equivalents of highly influential leaders in empirical studies of social networks, are propagators of linguistic influence, while certain peripheral individuals, or loners, can act either as repositories of old forms or initiators of new variants depending on the current state of the rest of the population

Wargames as Data: Addressing the Wargamer's Trilemma

Policymakers often want the very best data with which to make decisions--particularly when concerned with questions of national and international security. But what happens when this data is not available? In those instances, analysts have come to rely on synthetic data-generating processes--turning to modeling and simulation tools and survey experiments among other methods. In the cyber domain, where empirical data at the strategic level are limited, this is no different--cyber wargames are quickly becoming a principal method for both exploring and analyzing the security challenges posed by state and non-state actors in cyberspace. In this chapter, we examine the design decisions associated with this method.Comment: 3 figure

Analysis of a consumer survey on plug-in hybrid electric vehicles

Plug-in Hybrid Electric Vehicles (PHEVs) show potential to reduce greenhouse gas (GHG) emissions, increase fuel efficiency, and offer driving ranges that are not limited by battery capacity. However, these benefits will not be realized if consumers do not adopt this new technology. Several agent-based models have been developed to model potential market penetration of PHEVs, but gaps in the available data limit the usefulness of these models. To address this, we administered a survey to 1000 stated US residents, using Amazon Mechanical Turk, to better understand factors influencing the potential for PHEV market penetration. Our analysis of the survey results reveals quantitative patterns and correlations that extend the existing literature. For example, respondents who felt most strongly about reducing US transportation energy consumption and cutting greenhouse gas emissions had, respectively, 71 and 44 times greater odds of saying they would consider purchasing a compact PHEV than those who felt least strongly about these issues. However, even the most inclined to consider a compact PHEV were not generally willing to pay more than a few thousand US dollars extra for the sticker price. Consistent with prior research, we found that financial and battery-related concerns remain major obstacles to widespread PHEV market penetration. We discuss how our results help to inform agent-based models of PHEV market penetration, governmental policies, and manufacturer pricing and marketing strategies to promote consumer adoption of PHEVs. © 2014 The Authors

Diffusion among cognitively complex agents : final report.

Abstract Not Provide

Agreement, Information and Time in Multiagent Systems

This dissertation studies multiagent agreement problems -- problems in which a population of agents must agree on some quantity or behavior in a distributed manner. Agreement problems are central in many areas, from the study of magnetism (Ising model), to understanding the diffusion of innovations (such as the diffusion of hybrid corn planting in Illinois), to modeling linguistic change. The thesis of this dissertation is that the ability for agents to optimally allocate resources towards 1) gaining information from which to infer the agreeing population's global agreement state (``information gathering'') and 2) effectively using that information to make convergence decisions that move towards agreement (``information use''), are the fundamental factors that explain the performance of a distributed agreement-seeking collective, and that variations on these processes capture all prevalent styles of agreement problems. In this dissertation we develop a taxonomic framework that organizes a wide range of agreement problems according to constraints on information gathering and information use. We explore two specific instances of agreement problems in more depth; the first modulates information gathering by constraining the ability of agents to communicate; the second modulates information use by constraining the ability of agents to change states. An understanding of these two components will allow the application of insights from fields such as statistical physics, distributed algorithms, and multiagent systems to bear on language -- and in turn carry insights from linguistic agreement to these fields. Note, however, that the purpose of this dissertation is not to model natural phenomena, but rather to explore, through abstract models, some of the fundamental processes that underlie natural phenomena. Our first contribution is to develop the \emph{Distributed Optimal Agreement} framework -- a taxonomic framework through which we can formally identify potential constraints on the two processes of information gathering and use. Our second contribution is to develop an understanding of the \emph{Fundamental Agreement Tradeoff}, which is a relation between the effort an agent expends to gather information, the accuracy of the information gathered, and the amount of time it takes for a population to reach agreement. We develop the \gssm\ process as a way to explore the fundamental agreement tradeoff by modulating the amount of effort an agent can expend, which in turn affects the accuracy of information gathered. We show, surprisingly, that a population can reach agreement quickly even with a minimal expenditure of effort. This result has impact for any setting in which communication is a resource intensive procedure (e.g., energy constrained sensor networks). We provide extensive numerical simulations of the \gssm\ process in a variety of settings. In addition, we we analytically show that the \gssm\ process reaches agreement under a mean-field assumption. Our third contribution is to study agreement in complex spaces with boundedly rational agents where there are significant restrictions on communication. We develop the \emph{Distributed Constraint Agreement} problem (which itself is a type of agreement problem that can be captured by the DOA framework) in order to explore the impact of bounded rationality and communication on agreement in complex spaces. As an example scenario we abstractly model the linguistic phenomenon of the Great English Vowel Shift (GEVS) -- a shift in the pronunciation of certain vowels that took place between 1450 and 1750. We define a simple algorithm and through extensive simulation show that a vowel shift could have occurred if a new population of linguistic users, with slightly different pronunciations, entered the linguistic community. These results lend support to the ``migration'' hypothesis for the GEVS -- that due to casualties from the Black Death the linguistic composition of upper class England changed to incorporate individuals with different pronunciations. Together, these three contributions move us closer to forming a general theory of agreement