10,199 research outputs found
Persuasion, Political Warfare, and Deterrence: Behavioral and Behaviorally Robust Models
This dissertation examines game theory models in the context of persuasion and competition wherein decision-makers are not completely rational by considering two complementary threads of research. The first thread of research pertains to offensive and preemptively defensive behavioral models. Research in this thread makes three notable contributions. First, an offensive modeling framework is created to identify how an entity optimally influences a populace to take a desired course of action. Second, a defensive modeling framework is defined wherein a regulating entity takes action to bound the behavior of multiple adversaries simultaneously attempting to persuade a group of decision-makers. Third, an offensive influence modeling framework under conditions of ambiguity is developed in accordance with historical information limitations, and we demonstrate how it can be used to select a robust course of action on a specific, data-driven use case. The second thread of research pertains to behavioral and behaviorally robust approaches to deterrence. Research in this thread makes two notable contributions. First, we demonstrate the alternative insights behavioral game theory generates for the analysis of classic deterrence games, and explicate the rich analysis generated from its combined use with standard equilibrium models. Second, we define behaviorally robust models for an agent to use in a normal form game under varying forms of uncertainty in order to inform deterrence policy decisions
On Proportionate and Truthful International Alliance Contributions: An Analysis of Incentive Compatible Cost Sharing Mechanisms to Burden Sharing
Burden sharing within an international alliance is a contentious topic, especially in the current geopolitical environment, that in practice is generally imposed by a central authority\u27s perception of its members\u27 abilities to contribute. Instead, we propose a cost sharing mechanism such that burden shares are allocated to nations based on their honest declarations of the alliance\u27s worth. Specifically, we develop a set of multiobjective nonlinear optimization problem formulations that respectively impose Bayesian Incentive Compatible (BIC), Strategyproof (SP), and Group Strategyproof (GSP) mechanisms based on probabilistic inspection efforts and deception penalties that are budget balanced and in the core. Any feasible solution to these problems corresponds to a single stage Bayesian stochastic game wherein a collectively honest declaration is a Bayes-Nash equilibrium, a Nash Equilibrium in dominant strategies, or a collusion resistant Nash equilibrium, respectively, but the optimal solution considers the alliance\u27s central authority preferences. Each formulation is shown to be a nonconvex optimization problem. The solution quality and computational effort required for three heuristic algorithms as well as the BARON global solver are analyzed to determine the superlative solution methodology for each problem. The Pareto fronts associated with each multiobjective optimization problem are examined to determine the tradeoff between inspection frequency and penalty severity required to obtain truthfulness under stronger assumptions. Memory limitations are examined to ascertain the size of alliances for which the proposed methodology can be utilized. Finally, a full block design experiment considering the clustering of available alliance valuations and the member nations\u27 probability distributions therein is executed on an intermediate-sized alliance motivated by the South American alliance UNASUR
Effective Labor Regulation and Microeconomic Flexibility
Microeconomic flexibility, by facilitating the process of creative-destruction, is at the core of economic growth in modern market economies. The main reason for why this process is not infinitely fast, is the presence of adjustment costs, some of them technological, other institutional. Chief among the latter is labor market regulation. While few economists would object to such a view, its empirical support is rather weak. In this paper we revisit this hypothesis and find strong evidence for it. We use a new sectoral panel for 60 countries and a methodology suitable for such a panel. We find that job security regulation clearly hampers the creative-destruction process, especially in countries where regulations are likely to be enforced. Moving from the 20th to the 80th percentile in job security, in countries with strong rule of law, cuts the annual speed of adjustment to shocks by a third while shaving off about one percent from annual productivity growth. The same movement has negligible effects in countries with weak rule of law.
The Infrared Database of Extragalactic Observables from Spitzer I: the redshift catalog
This is the first of a series of papers on the Infrared Database of
Extragalactic Observables from Spitzer (IDEOS). In this work we describe the
identification of optical counterparts of the infrared sources detected in
Spitzer Infrared Spectrograph (IRS) observations, and the acquisition and
validation of redshifts. The IDEOS sample includes all the spectra from the
Cornell Atlas of Spitzer/IRS Sources (CASSIS) of galaxies beyond the Local
Group. Optical counterparts were identified from correlation of the extraction
coordinates with the NASA Extragalactic Database (NED). To confirm the optical
association and validate NED redshifts, we measure redshifts with unprecedented
accuracy on the IRS spectra ({\sigma}(dz/(1+z))=0.0011) by using an improved
version of the maximum combined pseudo-likelihood method (MCPL). We perform a
multi-stage verification of redshifts that considers alternate NED redshifts,
the MCPL redshift, and visual inspection of the IRS spectrum. The statistics is
as follows: the IDEOS sample contains 3361 galaxies at redshift 0<z<6.42 (mean:
0.48, median: 0.14). We confirm the default NED redshift for 2429 sources and
identify 124 with incorrect NED redshifts. We obtain IRS-based redshifts for
568 IDEOS sources without optical spectroscopic redshifts, including 228 with
no previous redshift measurements. We provide the entire IDEOS redshift catalog
in machine-readable formats. The catalog condenses our compilation and
verification effort, and includes our final evaluation on the most likely
redshift for each source, its origin, and reliability estimates.Comment: 11 pages, 6 figures, 1 table. Accepted for publication in MNRAS. Full
redshift table in machine-readable format available at
http://ideos.astro.cornell.edu/redshifts.htm
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested
on unseen contrasts and organs. We then propose to generalise the network by
training with large publicly-available natural image datasets with synthesised
phase information to achieve high cross-domain reconstruction performance which
is competitive with domain-specific training. To explain its generalisation
mechanism, we have also analysed patch sets for different training datasets.Comment: Accepted for ISBI2019 as a 1-page abstrac
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