32,004 research outputs found
An Adaptive Entanglement Distillation Scheme Using Quantum Low Density Parity Check Codes
Quantum low density parity check (QLDPC) codes are useful primitives for
quantum information processing because they can be encoded and decoded
efficiently. Besides, the error correcting capability of a few QLDPC codes
exceeds the quantum Gilbert-Varshamov bound. Here, we report a numerical
performance analysis of an adaptive entanglement distillation scheme using
QLDPC codes. In particular, we find that the expected yield of our adaptive
distillation scheme to combat depolarization errors exceed that of Leung and
Shor whenever the error probability is less than about 0.07 or greater than
about 0.28. This finding illustrates the effectiveness of using QLDPC codes in
entanglement distillation.Comment: 12 pages, 6 figure
Minority Game With Peer Pressure
To study the interplay between global market choice and local peer pressure,
we construct a minority-game-like econophysical model. In this so-called
networked minority game model, every selfish player uses both the historical
minority choice of the population and the historical choice of one's neighbors
in an unbiased manner to make decision. Results of numerical simulation show
that the level of cooperation in the networked minority game differs remarkably
from the original minority game as well as the prediction of the
crowd-anticrowd theory. We argue that the deviation from the crowd-anticrowd
theory is due to the negligence of the effect of a four point correlation
function in the effective Hamiltonian of the system.Comment: 10 pages, 3 figures in revtex 4.
Expert system development for probabilistic load simulation
A knowledge based system LDEXPT using the intelligent data base paradigm was developed for the Composite Load Spectra (CLS) project to simulate the probabilistic loads of a space propulsion system. The knowledge base approach provides a systematic framework of organizing the load information and facilitates the coupling of the numerical processing and symbolic (information) processing. It provides an incremental development environment for building generic probabilistic load models and book keeping the associated load information. A large volume of load data is stored in the data base and can be retrieved and updated by a built-in data base management system. The data base system standardizes the data storage and retrieval procedures. It helps maintain data integrity and avoid data redundancy. The intelligent data base paradigm provides ways to build expert system rules for shallow and deep reasoning and thus provides expert knowledge to help users to obtain the required probabilistic load spectra
Probabilistic load simulation: Code development status
The objective of the Composite Load Spectra (CLS) project is to develop generic load models to simulate the composite load spectra that are included in space propulsion system components. The probabilistic loads thus generated are part of the probabilistic design analysis (PDA) of a space propulsion system that also includes probabilistic structural analyses, reliability, and risk evaluations. Probabilistic load simulation for space propulsion systems demands sophisticated probabilistic methodology and requires large amounts of load information and engineering data. The CLS approach is to implement a knowledge based system coupled with a probabilistic load simulation module. The knowledge base manages and furnishes load information and expertise and sets up the simulation runs. The load simulation module performs the numerical computation to generate the probabilistic loads with load information supplied from the CLS knowledge base
Modeling the Psychology of Consumer and Firm Behavior with Behavioral Economics
Marketing is an applied science that tries to explain and influence how firms and
consumers actually behave in markets. Marketing models are usually applications of
economic theories. These theories are general and produce precise predictions, but they
rely on strong assumptions of rationality of consumers and firms. Theories based on
rationality limits could prove similarly general and precise, while grounding theories in
psychological plausibility and explaining facts which are puzzles for the standard
approach.
Behavioral economics explores the implications of limits of rationality. The goal is to
make economic theories more plausible while maintaining formal power and accurate
prediction of field data. This review focuses selectively on six types of models used in
behavioral economics that can be applied to marketing.
Three of the models generalize consumer preference to allow (1) sensitivity to reference
points (and loss-aversion); (2) social preferences toward outcomes of others; and (3)
preference for instant gratification (quasi-hyperbolic discounting). The three models are
applied to industrial channel bargaining, salesforce compensation, and pricing of virtuous
goods such as gym memberships. The other three models generalize the concept of gametheoretic
equilibrium, allowing decision makers to make mistakes (quantal response
equilibrium), encounter limits on the depth of strategic thinking (cognitive hierarchy),
and equilibrate by learning from feedback (self-tuning EWA). These are applied to
marketing strategy problems involving differentiated products, competitive entry into
large and small markets, and low-price guarantees.
The main goal of this selected review is to encourage marketing researchers of all kinds
to apply these tools to marketing. Understanding the models and applying them is a
technical challenge for marketing modelers, which also requires thoughtful input from
psychologists studying details of consumer behavior. As a result, models like these could
create a common language for modelers who prize formality and psychologists who prize
realism
Individual Differences in EWA Learning with Partial Payoff Information
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible
foregone payoffs from unchosen strategies are known, and estimate parameters separately for each
player to study heterogeneity. We assume players estimate unknown foregone payoffs from a strategy,
by substituting the last payoff actually received from that strategy, by clairvoyantly guessing the actual
foregone payoff, or by averaging the set of possible foregone payoffs conditional on the actual
outcomes. All three assumptions improve predictive accuracy of EWA. Individual parameter estimates
suggest that players cluster into two separate subgroups (which differ from traditional reinforcement
and belief learning)
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