scholarly journals Bridging Level-K to Nash Equilibrium

2020 ◽  
pp. 1-44
Author(s):  
Dan Levin ◽  
Luyao Zhang
Keyword(s):  
Experimental Data ◽  
Nash Equilibrium ◽  
Lower Number ◽  
Heterogeneous Beliefs ◽  
Simple Version

We introduce NLK, a model that connects the Nash equilibrium (NE) and Level-K. It allows a player in a game to believe that her opponent may be either less or as sophisticated as, she is, a view supported in psychology. We apply NLK to data from five published papers on static, dynamic, and auction games. NLK provides different predictions than those of the NE and Level-K; moreover, a simple version of NLK explains the experimental data better in many cases, with the same or lower number of parameters. We discuss extensions to games with more than two players and heterogeneous beliefs.

scholarly journals POTENTIAL MAXIMIZATION AND COALITION GOVERNMENT FORMATION

2005 ◽  
Vol 07 (04) ◽  
pp. 407-429 ◽  
Author(s):  
ROD GARRATT ◽  
JAMES E. PARCO ◽  
CHENG-ZHONG QIN ◽  
AMNON RAPOPORT
Keyword(s):  
Experimental Data ◽  
Nash Equilibrium ◽  
Winning Coalition ◽  
Coalition Government ◽  
Government Formation ◽  
Data Support ◽  
Dominated Strategies ◽  
The Government ◽  
Minimal Winning Coalitions ◽  
Unique Potential

A model of coalition government formation is presented in which inefficient, non-minimal winning coalitions may form in Nash equilibrium. Predictions for five games are presented and tested experimentally. The experimental data support potential maximization as a refinement of Nash equilibrium. In particular, the data support the prediction that non-minimal winning coalitions occur when the distance between policy positions of the parties is small relative to the value of forming the government. These conditions hold in games 1, 3, 4 and 5, where subjects played their unique potential-maximizing strategies 91, 52, 82 and 84 percent of the time, respectively. In the remaining game (Game 2) experimental data support the prediction of a minimal winning coalition. Players A and B played their unique potential-maximizing strategies 84 and 86 percent of the time, respectively, and the predicted minimal-winning government formed 92 percent of the time (all strategy choices for player C conform with potential maximization in Game 2). In Games 1, 2, 4 and 5 over 98 percent of the observed Nash equilibrium outcomes were those predicted by potential maximization. Other solution concepts including iterated elimination of weakly dominated strategies and strong/coalition-proof Nash equilibrium are also tested.

scholarly journals Modeling Energy Depletion in Rat Livers Using Nash Equilibrium Metabolic Pathway Analysis

2021 ◽  
Author(s):  
Angelo Lucia ◽  
Emily Ferrarese ◽  
Korkut Uygun
Keyword(s):  
Experimental Data ◽  
Nash Equilibrium ◽  
Pathway Analysis ◽  
Cold Storage ◽  
Metabolic Pathway ◽  
Energy Charge ◽  
Model Parameters ◽  
Metabolic Pathway Analysis ◽  
Energy Depletion ◽  
University Of Wisconsin

Abstract The current gold standard of Static Cold Storage (SCS), which is static cold storage on ice (about +4°C) in a specialized media such as the University of Wisconsin solution (UW), limits storage to few hours for vascular and metabolically active tissues such as the liver and the heart. The liver is arguably the pinnacle of metabolism in human body and therefore metabolic pathway analysis immediately becomes very relevant. In this article, a Nash Equilibrium (NE) approach, which is a first principles approach, is used to model and simulate the static cold storage of a proposed model of liver cells. Simulations of energy depletion in the liver in static cold storage measured by ATP content and energy charge are presented along with comparisons to experimental data. In addition, conversion of Nash Equilibrium iterations to time are described along with an uncertainty analysis for the parameters in the model. Results in this work show that the Nash Equilibrium approach provides a good match to experimental data for energy depletion and that the uncertainty in model parameters is very small with percent variances less than 0.1%.

scholarly journals Mutual Cooperation and Tolerance to Defection in the Context of Socialization: The Theoretical Model and Experimental Evidence

2021 ◽  
Author(s):  
T. S. Kozitsina ◽  
I. V. Kozitsin ◽  
I. S. Menshikov
Keyword(s):  
Experimental Data ◽  
Nash Equilibrium ◽  
Theoretical Model ◽  
Experimental Evidence ◽  
Quantal Response ◽  
Mutual Cooperation ◽  
Theoretical Concepts ◽  
Before And After ◽  
Low Levels ◽  
Markov Strategies

Abstract The study of the nature of human cooperation still contains gaps needing investigation. Previous findings reveal that socialization effectively promotes cooperation in the well-known Prisoner’s dilemma (PD) game. However, theoretical concepts fail to describe high levels of cooperation (probability higher than 50%) that were observed empirically. In this paper, we derive a symmetrical quantal response equilibrium (QRE) in PD in Markov strategies and test it against experimental data. Our results indicate that for low levels of rationality, QRE manages to describe high cooperation. In contrast, for high rationality QRE converges to the Nash equilibrium and describes low-cooperation behavior of participants. In the area of middle rationality, QRE matches the curve that represents the set of Nash equilibrium in Markov strategies. Further, we find that QRE serves as a dividing line between behavior before and after socialization, according to the experimental data. Finally, we successfully highlight the theoretically-predicted intersection of the set of Nash equilibrium for PD in Markov strategies and the QRE curve.

Microdiffraction Patterns of Small Metallic Particles II; Theoretical Analysis

1982 ◽  
Vol 40 ◽  
pp. 668-669
Author(s):  
A. Gómez ◽  
P. Schabes-Retchkiman ◽  
M. José-Yacamán ◽  
T. Ocaña
Keyword(s):  
Experimental Data ◽  
Electron Diffraction ◽  
Theoretical Analysis ◽  
Size Range ◽  
Dynamical Theory ◽  
Metallic Particles ◽  
Splitting Effect

The splitting effect that is observed in microdiffraction pat-terns of small metallic particles in the size range 50-500 Å can be understood using the dynamical theory of electron diffraction for the case of a crystal containing a finite wedge. For the experimental data we refer to part I of this work in these proceedings.

Evidence for ordered Fe3Ni

1987 ◽  
Vol 45 ◽  
pp. 216-217
Author(s):  
K.B. Reuter ◽  
D.B. Williams ◽  
J.I. Goldstein
Keyword(s):  
Experimental Data ◽  
Martensitic Transformation ◽  
Electron Diffraction ◽  
Room Temperature ◽  
Direct Evidence ◽  
Transformation Product ◽  
Iron Meteorites ◽  
X Ray ◽  
Ni Content ◽  
Temperature Transformation

In the Fe-Ni system, although ordered FeNi and ordered Ni3Fe are experimentally well established, direct evidence for ordered Fe3Ni is unconvincing. Little experimental data for Fe3Ni exists because diffusion is sluggish at temperatures below 400°C and because alloys containing less than 29 wt% Ni undergo a martensitic transformation at room temperature. Fe-Ni phases in iron meteorites were examined in this study because iron meteorites have cooled at slow rates of about 10°C/106 years, allowing phase transformations below 400°C to occur. One low temperature transformation product, called clear taenite 2 (CT2), was of particular interest because it contains less than 30 wtZ Ni and is not martensitic. Because CT2 is only a few microns in size, the structure and Ni content were determined through electron diffraction and x-ray microanalysis. A Philips EM400T operated at 120 kV, equipped with a Tracor Northern 2000 multichannel analyzer, was used.

EELS white line intensities calculated for the 3d and 4d metals

1989 ◽  
Vol 47 ◽  
pp. 388-389
Author(s):  
C. C. Ahn ◽  
D. H. Pearson ◽  
P. Rez ◽  
B. Fultz
Keyword(s):  
Experimental Data ◽  
Line Intensity ◽  
Transition Probabilities ◽  
Initial Increase ◽  
White Line ◽  
Hartree Fock ◽  
Line Intensities ◽  
Integrated Intensity ◽  
X Ray Absorption ◽  
The Continuum

Previous experimental measurements of the total white line intensities from L2,3 energy loss spectra of 3d transition metals reported a linear dependence of the white line intensity on 3d occupancy. These results are inconsistent, however, with behavior inferred from relativistic one electron Dirac-Fock calculations, which show an initial increase followed by a decrease of total white line intensity across the 3d series. This inconsistency with experimental data is especially puzzling in light of work by Thole, et al., which successfully calculates x-ray absorption spectra of the lanthanide M4,5 white lines by employing a less rigorous Hartree-Fock calculation with relativistic corrections based on the work of Cowan. When restricted to transitions allowed by dipole selection rules, the calculated spectra of the lanthanide M4,5 white lines show a decreasing intensity as a function of Z that was consistent with the available experimental data.Here we report the results of Dirac-Fock calculations of the L2,3 white lines of the 3d and 4d elements, and compare the results to the experimental work of Pearson et al. In a previous study, similar calculations helped to account for the non-statistical behavior of L3/L2 ratios of the 3d metals. We assumed that all metals had a single 4s electron. Because these calculations provide absolute transition probabilities, to compare the calculated white line intensities to the experimental data, we normalized the calculated intensities to the intensity of the continuum above the L3 edges. The continuum intensity was obtained by Hartree-Slater calculations, and the normalization factor for the white line intensities was the integrated intensity in an energy window of fixed width and position above the L3 edge of each element.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Use of Posterior Probabilities to Evaluate Methods of Discriminant Analysis

1981 ◽  
Vol 20 (04) ◽  
pp. 207-212 ◽  
Author(s):  
J. Hermans ◽  
B. van Zomeren ◽  
J. W. Raatgever ◽  
P. J. Sterk ◽  
J. D. F. Habbema
Keyword(s):  
Experimental Data ◽  
Discriminant Analysis ◽  
Posterior Probabilities

By means of a case study the choice between several methods of discriminant analysis is presented. Experimental data of a two-groups problem with one or two variables is analysed. The different methods are compared according to posterior probabilities which can be computed for each subject and which are the basis of discriminant analysis. These posterior probabilities are analysed graphically as well as numerically.

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

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