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This study was aimed to estimate  off-farm labor supply model.  Data were collected randomly from 267 wheat producers in Salah El-Din province for the year 2020, 67.4% of them are produced using pivot sprinklers for irrigation and with three tenure categories (60,80,12) dunums. Furthermore. The KS coefficient was used to analyze the producers' risk-taking behavior after estimating the production function and determining the area variable that has the most influence on the production process. If it increased by 1%, production would increase by 0.802%. The farmer's decision to adopt the technology was based on economic, social, and institutional factors. It turns out that 40% of farmers make their decision based on financing. When analyzing the decisions of farmers under risk, it was found that 35% of them make the decision in relation to the price, which is the main incentive for production. In order to understand the relationship between the risk, the return, and the size of the farm, it was found that when the area is increased, the return increases, and the revenue of a dunum at the tenure size of 10 dunams amounted to 481,600 dinars and at a higher KS level, what is known as risk-haters, the revenue was the highest possible and the risk also increased with it, and the farmers became among the large holdings they prefer. In any case, some farmers believe that when you want to get a higher return, it is important to keep in mind that there is a greater risk. Risk is affected by a number of factors, including economic, divided into price and productivity, social ones, and others related to the farmer himself in terms of efficiency, management, skill, and experience. . The research recommended a review of tenure laws and the development of risk management strategies by providing adequate funding that ensures ,Providing factories within rural areas that contribute to absorbing surplus production, creating price stability, as well as creating a labor market that reduces poverty in the rural area.

The problem of revealed stochastic preference is whether probability distributions of observed choices in a population for various choice situations are consistent with a hypothesis of maximization of preference preorders by members of the population. This is a population analog of the classical revealed preference problem in economic consumer theory. This paper synthesizes the solutions to this problem that have been obtained by Marcel K. Richter and the author, and by J. C. Falmagne, in the case of finite sets of alternatives, and utilizes unpublished research of Richter and the author to give results for the non-finite choice sets encountered in economic consumer theory.

The Industrial Internet of Things (IIoT) envisions enhanced surveillance and control for industrial applications through diverse IoT devices. However, the increasing heterogeneity of deployed end devices poses challenges to current practices, hampering overall performance as device numbers escalate. To tackle this issue, we introduce an innovative distributed power control algorithm leveraging the wireless channel's nature to approximate the centralized maximum-weight scheduling algorithm. Employing ubiquitous multi-protocol mobile devices as intermediaries, we propose a concurrent dual-hop/multi-hop backhauling strategy, improving interoperability and facilitating data relay, translation, and forwarding from end IoT devices. Our focus is directed towards addressing large-scale network stability and queue management challenges. We formulate a long-term time-averaged optimization problem, incorporating considerations of end-to-end rate control, routing, link scheduling, and resource allocation to guarantee essential network-wide throughput. Furthermore, we present a real-time decomposition-based approximation algorithm that ensures adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with the highest energy efficiency. Comprehensive numerical results verify significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. This work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications.

What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A \"utilized\" signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework.

We provide an account of recent work that formulates and addresses problems that arise when employing wireless networks to serve clients that generate real-time flows. From a queueing systems perspective, these problems can be described as single-server problems where there are several customer classes. Customers balk when their delay exceeds a threshold. There are a range of issues that are of interest. One of the first such issues is to determine what throughput rate vectors are feasible, and to determine the server’s schedule. Another is to maximize a utility function of the departure rates of the customer classes. Real-time flows have a delay bound for each of their packets. It is particularly challenging to provide delay guarantees for real-time flows in wireless networks since wireless transmissions are unreliable. We propose a model that jointly considers the delay bounds of packets, the unreliable wireless channels, and the throughput requirements of clients. We then determine the necessary and sufficient condition for feasibility of the client requirements. The analysis and condition are interesting since this problem gives rise to some new features concerning unavoidable idle times in a system. We further derive an efficient, nearly linear time algorithm for admission control, which precisely determines whether it is feasible to fulfill the requirements of all clients in the system. We also propose two on-line scheduling policies and prove that they can fulfill the requirements of all clients whenever that is feasible. We next turn to the scenario where the throughput requirements of clients are elastic, but with hard delay bounds. We formulate this as a utility maximization problem, where client utilities are based on their throughputs. We decompose this problem into two subproblems, and show that this decomposition can be naturally implemented as a bidding game among all clients and the access point, which plays the role of a centralized scheduler. In the bidding game, the strategy of each client is to carry out a simple selfish optimization. We show that the strategy of the access point can be implemented by a simple on-line scheduling policy. A surprising result is that the channel reliabilities need not be known a priori.

We propose a framework to investigate consumers’ brand choice and purchase incidence decisions across multiple categories, where both decisions are modeled as an outcome of a consumer’s basket utility maximization. We build the model from first principles by theoretically explicating a general model of basket utility maximization and then examining the reasonable restrictions that can be placed to make the solution tractable without sacrificing its flexibility. Comparing with prior models, we show why prior multicategory purchase incidence models overemphasize the role of the cross effects of a market mix of brands in other categories on the purchase incidence decision of a given category. Additionally, we show that prior single-category models are a special case of the proposed model when further restrictions are placed on the basket utility structure. We estimate the model on household basket data for the laundry family of categories. We show (i) why prior single-category and multicategory models would systematically bias the estimates of the own- and cross-price/promotional purchase incidence elasticities; and (ii) how the market mix of each brand in each category affects the purchases across all categories, which can help retailers make promotional decisions across a portfolio of products.

We consider the robust exponential utility maximization problem in discrete time: An investor maximizes the worst case expected exponential utility with respect to a family of nondominated probabilistic models of her endowment by dynamically investing in a financial market, and statically in available options. We show that, for any measurable random endowment (regardless of whether the problem is finite or not) an optimal strategy exists, a dual representation in terms of (calibrated) martingale measures holds true, and that the problem satisfies the dynamic programming principle (in case of no options). Further, it is shown that the value of the utility maximization problem converges to the robust superhedging price as the risk aversion parameter gets large, and examples of nondominated probabilistic models are discussed.

For a stochastic factor model we maximize the long-term growth rate of robust expected power utility with parameter λ Î (0,1). Using duality methods the problem is reformulated as an infinite time horizon, risk-sensitive control problem. Our results characterize the optimal growth rate, an optimal long-term trading strategy and an asymptotic worst-case model in terms of an ergodic Bellman equation. With these results we propose a duality approach to a \"robust large deviations\" criterion for optimal long-term investment.

A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information maximizes utility while minimizing privacy, while publishing random noise accomplishes the opposite. Privacy can be rigorously quantified using the framework of differential privacy, which requires that a mechanism's output distribution is nearly the same whether a given database row is included. The goal of this paper is to formulate and provide strong and general utility guarantees, subject to differential privacy. We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a symmetric and monotone loss function). Our main result is the following: for each fixed count query and differential privacy level, there is a geometric mechanism$M^*$ ---a discrete variant of the simple and well-studied mechanism that adds random noise from a Laplace distribution---that is simultaneously expected loss-minimizing for every possible user, subject to the differential privacy constraint. This is an extremely strong utility guarantee: every potential user$u$ , no matter what its side information and preferences, derives as much utility from$M^*$as from interacting with a differentially private mechanism$M_u$that is optimally tailored to$u$ . More precisely, for every user$u$there is an optimal mechanism$M_u$for it that factors into a user-independent part (the geometric mechanism$M^*$ ) and a user-specific postprocessing step that depends only on the output of the geometric mechanism and not on the underlying database. The first part of our proof of this result characterizes the optimal differentially private mechanism for a user as a certain basic feasible solution to a linear program with a user-specific objective function and user-independent constraints that encode differential privacy. The second part shows that all of the relevant vertices of the feasible region (ranging over all possible users) are derivable from the geometric mechanism via suitable remappings of its range. [PUBLICATION ABSTRACT]

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with the highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility functions. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency.