12,245 research outputs found
Foraging as an evidence accumulation process
A canonical foraging task is the patch-leaving problem, in which a forager
must decide to leave a current resource in search for another. Theoretical work
has derived optimal strategies for when to leave a patch, and experiments have
tested for conditions where animals do or do not follow an optimal strategy.
Nevertheless, models of patch-leaving decisions do not consider the imperfect
and noisy sampling process through which an animal gathers information, and how
this process is constrained by neurobiological mechanisms. In this theoretical
study, we formulate an evidence accumulation model of patch-leaving decisions
where the animal averages over noisy measurements to estimate the state of the
current patch and the overall environment. Evidence accumulation models belong
to the class of drift diffusion processes and have been used to model decision
making in different contexts. We solve the model for conditions where foraging
decisions are optimal and equivalent to the marginal value theorem, and perform
simulations to analyze deviations from optimal when these conditions are not
met. By adjusting the drift rate and decision threshold, the model can
represent different strategies, for example an increment-decrement or counting
strategy. These strategies yield identical decisions in the limiting case but
differ in how patch residence times adapt when the foraging environment is
uncertain. To account for sub-optimal decisions, we introduce an
energy-dependent utility function that predicts longer than optimal patch
residence times when food is plentiful. Our model provides a quantitative
connection between ecological models of foraging behavior and evidence
accumulation models of decision making. Moreover, it provides a theoretical
framework for potential experiments which seek to identify neural circuits
underlying patch leaving decisions
A reliability-based approach for influence maximization using the evidence theory
The influence maximization is the problem of finding a set of social network
users, called influencers, that can trigger a large cascade of propagation.
Influencers are very beneficial to make a marketing campaign goes viral through
social networks for example. In this paper, we propose an influence measure
that combines many influence indicators. Besides, we consider the reliability
of each influence indicator and we present a distance-based process that allows
to estimate the reliability of each indicator. The proposed measure is defined
under the framework of the theory of belief functions. Furthermore, the
reliability-based influence measure is used with an influence maximization
model to select a set of users that are able to maximize the influence in the
network. Finally, we present a set of experiments on a dataset collected from
Twitter. These experiments show the performance of the proposed solution in
detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc
Automated Bilateral Bargaining about Multiple Attributes in a One to ÂMany Setting
Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller agent negotiates over multiple interdependent attributes with many buyer agents in a bilateral fashion. In this setting, "fairness", which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller agent, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining is bilateral
The case of muddled units in temporal discounting
While parameters are crucial components of cognitive models, relatively little importance has been given to their units. We show that this has lead to some parameters to be contaminated, introducing an artifactual correlation between them. We also show that this has led to the illegal comparison of parameters with different units of measurement – this may invalidate parameter comparisons across participants, conditions, groups, or studies. We demonstrate that this problem affects two related models: Stevens' power law and Rachlin's delay discounting model. We show that it may even affect models which superficially avoid the incompatible units problem, such as hyperbolic discounting. We present simulation results to demonstrate the extent of the issues caused by the muddled units problem. We offer solutions in order to avoid the problem in the future or to aid in re-interpreting existing datasets
Analysis of a Reputation System for Mobile Ad-Hoc Networks with Liars
The application of decentralized reputation systems is a promising approach
to ensure cooperation and fairness, as well as to address random failures and
malicious attacks in Mobile Ad-Hoc Networks. However, they are potentially
vulnerable to liars. With our work, we provide a first step to analyzing
robustness of a reputation system based on a deviation test. Using a mean-field
approach to our stochastic process model, we show that liars have no impact
unless their number exceeds a certain threshold (phase transition). We give
precise formulae for the critical values and thus provide guidelines for an
optimal choice of parameters.Comment: 17 pages, 6 figure
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