14,389 research outputs found
The Effect of Biased Communications On Both Trusting and Suspicious Voters
In recent studies of political decision-making, apparently anomalous behavior
has been observed on the part of voters, in which negative information about a
candidate strengthens, rather than weakens, a prior positive opinion about the
candidate. This behavior appears to run counter to rational models of decision
making, and it is sometimes interpreted as evidence of non-rational "motivated
reasoning". We consider scenarios in which this effect arises in a model of
rational decision making which includes the possibility of deceptive
information. In particular, we will consider a model in which there are two
classes of voters, which we will call trusting voters and suspicious voters,
and two types of information sources, which we will call unbiased sources and
biased sources. In our model, new data about a candidate can be efficiently
incorporated by a trusting voter, and anomalous updates are impossible;
however, anomalous updates can be made by suspicious voters, if the information
source mistakenly plans for an audience of trusting voters, and if the partisan
goals of the information source are known by the suspicious voter to be
"opposite" to his own. Our model is based on a formalism introduced by the
artificial intelligence community called "multi-agent influence diagrams",
which generalize Bayesian networks to settings involving multiple agents with
distinct goals
Adversarial Risk Análysis for Counterterrorism Modelling
Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game theoretic flavor although there are also several interesting decision analytic based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversary’s actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems
A Continuation Method for Nash Equilibria in Structured Games
Structured game representations have recently attracted interest as models
for multi-agent artificial intelligence scenarios, with rational behavior most
commonly characterized by Nash equilibria. This paper presents efficient, exact
algorithms for computing Nash equilibria in structured game representations,
including both graphical games and multi-agent influence diagrams (MAIDs). The
algorithms are derived from a continuation method for normal-form and
extensive-form games due to Govindan and Wilson; they follow a trajectory
through a space of perturbed games and their equilibria, exploiting game
structure through fast computation of the Jacobian of the payoff function. They
are theoretically guaranteed to find at least one equilibrium of the game, and
may find more. Our approach provides the first efficient algorithm for
computing exact equilibria in graphical games with arbitrary topology, and the
first algorithm to exploit fine-grained structural properties of MAIDs.
Experimental results are presented demonstrating the effectiveness of the
algorithms and comparing them to predecessors. The running time of the
graphical game algorithm is similar to, and often better than, the running time
of previous approximate algorithms. The algorithm for MAIDs can effectively
solve games that are much larger than those solvable by previous methods
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