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Islam H. El-adaway

Bio: Islam H. El-adaway is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Project management & Contract management. The author has an hindex of 16, co-authored 140 publications receiving 902 citations. Previous affiliations of Islam H. El-adaway include Arizona State University & American University in Cairo.


Papers
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Journal ArticleDOI
TL;DR: No enough research studies have been conducted to provide guidelines for responding to the coronavirus disease 2019 (COVID-19), and the lack of measurable data means there is no consensus on how to respond to the disease.
Abstract: Due to the novelty of coronavirus disease 2019 (COVID-19) and the lack of measurable data, no enough research studies have been conducted to provide guidelines for responding to the coronav...

84 citations

Journal ArticleDOI
TL;DR: The winner's curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project as discussed by the authors, and the winner will most likely earn negative or at least below normal profits.
Abstract: In the construction industry, competitive bidding has long been used as a method for contractor selection. Because the true cost of construction is not known until the completion of the project, adverse selection is a major concern. Adverse selection is when the winner of the contract has underestimated the project’s true cost. Thus, the winning contractor will most likely earn negative or at least below normal profits. The winner’s curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project. In the multistage bidding environment, where subcontractors are hired by a general contractor, the winner’s curse may be compounded. In general, contractors suffer from the winner’s curse for a variety of reasons including inaccurate estimates of project cost; new contractors entering the construction market; minimizing losses in case of recession of the construction industry; strong competition within the construction market; differential oppor...

51 citations

Journal ArticleDOI
TL;DR: The authors created a multiagent system for construction dispute resolution (MAS-COR) that automates the developed formal logic algorithm that is based on adversarial precedent law and develops theoretical foundation and implements technologies for generation of legal arguments based on precedent construction disputes.
Abstract: This paper develops theoretical foundation and implements technologies for generation of legal arguments based on precedent construction disputes. First, the authors simulated the process of legal discourse in construction disputes using a formal logic algorithm that is based on adversarial precedent law. In this regard: (1) facts associated with construction change order cases were factorized into binary, dimensional, and abstract factors; (2) relevance of the developed factors was associated with the disputing parties; (3) logical predicates and rules were generated based on the said factors; (4) factors were logically analyzed into distinct classifications; and (5) an 11 stage logical induction algorithm was used to show similarities, differences, strengths, and weaknesses between current and precedent construction disputes. Second, the authors created a multiagent system for construction dispute resolution (MAS-COR) that automates the developed algorithm. In this connection: (1) an agent-based role model was developed to represent the developed algorithms; (2) an agent-based role model was built to represent the developed algorithm; and (3) system implementation was carried using object-oriented programming on NetBean's integrated development environment. Using 30 previously arbitrated construction disputes, testing and validation steps were rigorously applied to assess the developed formal logic algorithm as well as the associated created agents and their integration into the MAS-COR system through syntactical debugging using theorem proving, model checking, and system testing. The results of this validation process illustrated that the system was capable of deriving significant legal arguments that help save time and effort of construction claim and dispute professionals while preparing the defense for their respective positions.

51 citations

Journal ArticleDOI
TL;DR: With a compound annual growth rate of 5.69%, the modular construction market is forecasted to increase to a market value of $154.8 million by the end of 2023 as mentioned in this paper.
Abstract: With a compound annual growth rate of 5.69%, the modular construction market is forecasted to increase to a market value of $154.8 million by the end of 2023. Traditional stick-built constr...

47 citations

Journal ArticleDOI
TL;DR: In this paper, cost and schedule overruns are always regarded as being of paramount importance in the project controls, regardless of the different project characteristics in the construction industry, and therefore, they are considered to be a major concern.
Abstract: Regardless of the different project characteristics in the construction industry, cost and schedule overruns are always regarded as being of paramount importance in the project controls are...

45 citations


Cited by
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Book
01 Jan 2001
TL;DR: This chapter discusses Decision-Theoretic Foundations, Game Theory, Rationality, and Intelligence, and the Decision-Analytic Approach to Games, which aims to clarify the role of rationality in decision-making.
Abstract: Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

3,569 citations

01 Jan 1993

2,271 citations