Competition and cooperation in a community of autonomous agents

Esteban, Pablo Gómez; Liu, Si; Insua, David Rı́os; González‐Ortega, Jorge

doi:10.1007/s10514-019-09867-y

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…Since its introduction, it has been used to model a variety of problems such as network routing for insurgency , international piracy , counter-terrorism , autonomous social agents , urban security resource allocation , adversarial classification , computational advancements (Ekin et al, 2019), reinforcement learning (Gallego et al, 2019), counter-terrorist online surveillance , cyber-security (Ríos , autonomous agents (Esteban et al, 2020), insider threat and military command and control decision making (Caballero et al, 2021).…”

Section: Adversarial Risk Analysismentioning

confidence: 99%

Adversarial risk analysis for first‐price sealed‐bid auctions

Ejaz

Joshi

Joe

2021

Aus NZ J of Statistics

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Summary Adversarial risk analysis (ARA) is an upcoming methodology that is considered to have advantages over the traditional decision‐theoretic and game‐theoretic approaches. ARA solutions for first‐price sealed‐bid (FPSB) auctions have been found but only under strong assumptions which make the model somewhat unrealistic. In this paper, we use ARA methodology to model FPSB auctions using more realistic assumptions. We define a new utility function that considers bidders’ wealth, we assume a reserve price and find solutions not only for risk‐neutral but also for risk‐averse as well as risk‐seeking bidders. We model the problem using ARA for non‐strategic play and level‐k thinking solution concepts.

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Section: Adversarial Risk Analysismentioning

confidence: 99%

Adversarial risk analysis for first‐price sealed‐bid auctions

Ejaz

Joshi

Joe

2021

Aus NZ J of Statistics

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“…A major tenet in game theory is that the actions proposed for the agents should be non-dominated. It is easy to show that ARA actions are non-dominated under fairly general conditions (Esteban, Liu, Rios Insua, & Ortega, 2020).…”

Section: Ara and Game Theory: Further Relationsmentioning

confidence: 99%

“…Proposition 2 (Esteban et al, 2020). Let U A (d, a) be the (random) utility that A receives, according to D, when he implements a and she plays d. Suppose that there are two actions a and a 0 for the attacker such that…”

Section: Ara and Game Theory: Further Relationsmentioning

confidence: 99%

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Adversarial risk analysis: An overview

Banks

Gallego

Naveiro

et al. 2020

WIREs Computational Stats

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Adversarial risk analysis (ARA) is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It is a decision-theoretic alternative to game theory. ARA enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities, and the type of strategic calculations that the opponent is using to make his decision. Within that framework, the analyst then solves the problem from the perspective of the opponent. This calculation produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This review covers conceptual, modeling, computational, and applied issues in ARA as well as interesting open research issues. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and TheoryApplications of Computational Statistics > Defense and National Securityauctions, Bayes Nash equilibrium, decision theory, game theory, level-k thinking | INTRODUCTIONAdversarial risk analysis (ARA) guides decision-making when there are intelligent opponents who reason strategically about each other in the context of uncertain outcomes. It is a decision-theoretic alternative to classical game theory that uses Bayesian subjective distributions to model the goals, resources, beliefs, and reasoning of the opponent. Within this framework, the analyst solves the problem from the perspective of her opponent while placing subjective probability distributions on all unknown quantities. This structure provides a distribution over the actions of the opponent that enables her to maximize her expected utility, accounting for the uncertainty she has about the opponent. ARA applications include convoy routing through an insurgent city with improvised explosive devices (Banks, Petralia, & Wang, 2011), managing Somali piracy (Sevillano, Insua, & Rios, 2012), dealing with crime in a public transportation system (Banks, Aliaga, & Insua, 2015), Emile Borel's game La Relance (Banks et al., 2011), and cybersecurity (Rios Insua et al., 2019). It is relevant whenever one party is trying to model the decision-making process of one or more other parties, in order to achieve an outcome sought by the first party. The mathematics behind ARA can be quite complicated, but the essential idea is very natural. When asking the boss for a raise, one has a mental model for what the boss values (e.g., performance, flattery, punctual paperwork) and his likely response to various pitches. If the model is correct, one has a good chance of obtaining a raise; if not, then success is unlikely.

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Section: Sequential Defend-attack Gamesmentioning

confidence: 99%

Adversarial Risk Analysis (Overview)

Banks¹,

Gallego²,

Naveiro³

et al. 2020

Preprint

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Adversarial risk analysis (ARA) is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities and the type of strategic calculation that the opponent is using.Within that framework, the analyst then solves the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This overview covers conceptual, modeling, computational and applied issues in ARA.

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Competition and cooperation in a community of autonomous agents

Cited by 6 publications

References 32 publications

Adversarial risk analysis for first‐price sealed‐bid auctions

Adversarial risk analysis for first‐price sealed‐bid auctions

Adversarial risk analysis: An overview

Adversarial Risk Analysis (Overview)

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