scholarly journals Allocation Mechanisms without Reduction

2021 ◽  
Vol 3 (4) ◽  
pp. 455-470
Author(s):  
David Dillenberger ◽  
Uzi Segal
Keyword(s):  
Probability Distribution ◽  
Allocation Problem ◽  
The Other ◽  
Allocation Mechanism ◽  
Recursive Preferences ◽  
House Allocation ◽  
Simple Variant ◽  
Standard Models ◽  
Serial Dictatorship ◽  
Top Cycle

We study a simple variant of the house allocation problem (one-sided matching). We demonstrate that agents with recursive preferences may systematically prefer one allocation mechanism to the other, even among mechanisms that are considered to be the same in standard models, in the sense that they induce the same probability distribution over successful matchings. Using this, we propose a new priority groups mechanism and provide conditions under which it is preferred to two popular mechanisms, random top cycle and random serial dictatorship. (JEL C78, D44, D82)

Water Scarcity and Conflict Between Upstream and Downstream Riparian Countries

2021 ◽  
Vol 07 (03) ◽  
pp. 2150012
Author(s):  
Sahar Farid Yousef
Keyword(s):  
Water Availability ◽  
Water Scarcity ◽  
Water Allocation ◽  
Allocation Problem ◽  
The Other ◽  
Backward Induction ◽  
Transboundary River ◽  
Militarized Conflicts ◽  
The Future ◽  
Water Scarce

More than one-quarter of the world’s population lives in water-scarce areas, while most countries share at least one transboundary river. If water scarcity is this prevalent, should we expect riparian countries to fight over the water allocation of shared rivers? To answer this question, I develop a modified one-shot three-stage river-sharing game where countries can resort to force to solve their water allocation problem. Using backward induction, I solve for the probability of the downstream country initiating conflict against the upstream country and the likelihood of the latter responding with force to the former’s hostile actions. I test the model empirically using a set of all upstream–downstream riparian dyads with available data from AQUASTAT and the Correlates of War Project for the years 1960–2010. The main contribution of this paper is that it demonstrates how upstream and downstream riparian countries differ in their decision to use force against the other country when experiencing water scarcity. I find that water scarcity increases the likelihood of the downstream country initiating the conflict, but it has no effect on the upstream country’s likelihood of responding with force. If history is a predictor of the future, then the results imply that as more riparian countries become water-scarce, militarized conflicts between upstream and downstream countries are likely to increase, especially if there is heterogeneity in water availability between the riparian dyad.

scholarly journals The Evolutionary Status of the Secondaries of Cataclysmic Binaries

1983 ◽  
Vol 72 ◽  
pp. 257-262
Author(s):  
H. Ritter
Keyword(s):  
Probability Distribution ◽  
Main Sequence ◽  
The Other ◽  
Initial Mass ◽  
Evolutionary Status ◽  
Mass Ratio ◽  
Main Sequence Stars ◽  
Cataclysmic Binaries ◽  
Low Mass ◽  
Orbital Periods

ABSTRACTIt is shown that the secondary components of cataclysmic binaries with orbital periods of less than ~10 hours are indistinguishable from ordinary low-mass main-sequence stars and that, therefore, they are essentially unevolved. On the other hand, it is shown that, depending on the mass ratio of the progenitor system, the secondary of a cataclysmic binary could be significantly evolved. The fact that nevertheless most of the observed secondaries are essentially unevolved can be accounted for by assuming that the probability distribution for the initial mass ratio is not strongly peaked towards unity mass ratio.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

New Characteristic Relations in Type-II and III Intermittency

1997 ◽  
Vol 07 (04) ◽  
pp. 831-836 ◽  
Author(s):  
M. O. Kim ◽  
Hoyun Lee ◽  
Chil-Min Kim ◽  
Hyun-Soo Pang ◽  
Eok-Kyun Lee ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Lower Bound ◽  
Probability Distribution ◽  
Critical Exponent ◽  
Simulation Study ◽  
Critical Exponents ◽  
Control Parameter ◽  
The Other ◽  
Square Root ◽  
Type Ii

We obtained new characteristic relations in Type-II and III intermittencies according to the reinjection probability distribution. When the reinjection probability distribution is fixed at the lower bound of reinjection, the critical exponents are -1, as is well known. However when the reinjection probability distribution is uniform, the critical exponent is -1/2, and when it is of form [Formula: see text], -3/4. On the other hand, if the square root of Δ, which represents the lower bound of reinjection, is much smaller than the control parameter ∊, i.e. ∊ ≫ Δ1/2, critical exponent is always -1, independent of the reinjection probability distribution. Those critical exponents are confirmed by numerical simulation study.

scholarly journals Properties of quantum stochastic walks from the asymptotic scaling exponent

2018 ◽  
Vol 18 (3&4) ◽  
pp. 181-197
Author(s):  
Krzysztof Domino ◽  
Adam Glos ◽  
Mateusz Ostaszewski ◽  
Lukasz Pawela ◽  
Przemyslaw Sadowski
Keyword(s):  
Probability Distribution ◽  
Random Walks ◽  
Quantum Walks ◽  
The Other ◽  
Scaling Exponent ◽  
Continuous Change ◽  
Ballistic Regime ◽  
Second Moment ◽  
The Mean ◽  
Asymptotic Scaling

This work focuses on the study of quantum stochastic walks, which are a generalization of coherent, \ie unitary quantum walks. Our main goal is to present a measure of a coherence of the walk. To this end, we utilize the asymptotic scaling exponent of the second moment of the walk \ie of the mean squared distance covered by a walk. As the quantum stochastic walk model encompasses both classical random walks and quantum walks, we are interested how the continuous change from one regime to the other influences the asymptotic scaling exponent. Moreover this model allows for behavior which is not found in any of the previously mentioned model -- a model with global dissipation. We derive the probability distribution for the walker, and determine the asymptotic scaling exponent analytically, showing that ballistic regime of the walk is maintained even at large dissipation strength.

scholarly journals Dirichlet Polynomials and Entropy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1085
Author(s):  
David I. Spivak ◽  
Timothy Hosgood
Keyword(s):  
Probability Distribution ◽  
Shannon Entropy ◽  
Empirical Distribution ◽  
Geometric Mean ◽  
The Other ◽  
Cross Entropy ◽  
Dirichlet Polynomial ◽  
Area Formula ◽  
Additive Structure ◽  
Dirichlet Polynomials

A Dirichlet polynomial d in one variable y is a function of the form d(y)=anny+⋯+a22y+a11y+a00y for some n,a0,…,an∈N. We will show how to think of a Dirichlet polynomial as a set-theoretic bundle, and thus as an empirical distribution. We can then consider the Shannon entropy H(d) of the corresponding probability distribution, and we define its length (or, classically, its perplexity) by L(d)=2H(d). On the other hand, we will define a rig homomorphism h:Dir→Rect from the rig of Dirichlet polynomials to the so-called rectangle rig, whose underlying set is R⩾0×R⩾0 and whose additive structure involves the weighted geometric mean; we write h(d)=(A(d),W(d)), and call the two components area and width (respectively). The main result of this paper is the following: the rectangle-area formula A(d)=L(d)W(d) holds for any Dirichlet polynomial d. In other words, the entropy of an empirical distribution can be calculated entirely in terms of the homomorphism h applied to its corresponding Dirichlet polynomial. We also show that similar results hold for the cross entropy.

scholarly journals Ensemble probability distribution for novelty detection

2018 ◽  
Vol 189 ◽  
pp. 03008
Author(s):  
Xiaoshuang Qiao ◽  
Hui Wang ◽  
Gongde Guo ◽  
Yuanyuan Liu
Keyword(s):  
Probability Distribution ◽  
Real World ◽  
Mixed Models ◽  
Novelty Detection ◽  
The Other ◽  
Support Vector ◽  
Ensemble Approach ◽  
Overall Performance ◽  
Real World Datasets ◽  
Domain Description

This paper explores a new ensemble approach called Ensemble Probability Distribution Novelty Detection (EPDND) for novelty detection. The proposed ensemble approach provides a metric to characterize different classes. Experimental results on 4 real-world datasets show that EPDND exhibits competitive overall performance to the other two common novelty detection approaches - Support Vector Domain Description and Gaussian Mixed Models in terms of accuracy, recall and F1 scores in many cases.

Matching with Stochastic Arrival

2019 ◽  
Vol 109 ◽  
pp. 209-212 ◽  
Author(s):  
Neil Thakral
Keyword(s):  
Public Housing ◽  
Allocation Problem ◽  
Dynamic Allocation ◽  
Allocation Mechanism ◽  
Ex Post ◽  
Different Types ◽  
Over Time ◽  
Housing Units

This paper examines efficiency and fairness properties in a dynamic allocation problem. The model applies to situations in which objects of different types arrive stochastically over time and must be assigned to agents in a queue, such as the allocation of public housing units. The main result demonstrates the impossibility of designing an allocation mechanism in an environment with stochastic arrival that can guarantee resulting assignments that are efficient or fair ex post.

Randomness

2018 ◽  
Author(s):  
William A. Dembski
Keyword(s):  
Computer Simulation ◽  
Probability Distribution ◽  
Equilibrium State ◽  
Random Sample ◽  
Random Number Generator ◽  
The Other ◽  
Number Generator ◽  
Fluid Forces ◽  
Sequence Of Events ◽  
To Come

The fundamental intuition underlying randomness is the absence of order or pattern. To cash out this intuition philosophers and scientists employ five approaches to randomness. (1) Randomness as the output of a chance process. Thus an event is random if it is the output of a chance process. Moreover, a sequence of events constitutes a random sample if all events in the sequence derive from a single chance process and no event in the sequence is influenced by the others. (2) Randomness as mimicking chance. Statisticians frequently wish to obtain a random sample (in the sense of (1)) according to some specified probability distribution. Unfortunately, a chance process corresponding to this probability distribution may be hard to come by. In this case a statistician may employ a computer simulation to mimic the desired chance process (for example, a random number generator). Randomness qua mimicking chance is also known as pseudo-randomness. (3) Randomness via mixing. Consider the following situation: particles are concentrated in some corner of a fluid; forces act on the fluid so that eventually the particles become thoroughly mixed throughout the fluid, reaching an equilibrium state. Here randomness is identified with the equilibrium state reached via mixing. (4) Randomness as a measure of computational complexity. Computers are ideally suited for generating bit strings. The length of the shortest program that generates a given bit string, as well as the minimum time it takes for a program to generate the string, both assign measures of complexity to the strings. The higher the complexity, the more random the string. (5) Randomness as pattern-breaking. Given a specified collection of patterns, an object is random if it breaks all the patterns in the collection. If, on the other hand, it fits at least one of the patterns in the collection, then it fails to be random.

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