A new framework for resolving conflicts over transboundary rivers using bankruptcy methods

A novel bankruptcy approach is proposed for resolving transboundary river conflicts in which the total water demand or claim of the riparian parties is more than the available water. Bankruptcy solution methods can allocate the available water to the conflicting parties with respect to their claims. Four commonly used bankruptcy methods in the economic literature are used here to develop new river bankruptcy solution methods for allocating water to the riparian parties of river systems. Given the non-uniform spatial and temporal distribution of water across river basins, the proposed solution methods are formulated as non-linear network flow optimization models to allocate water with respect to time sensitivity of water deliveries at different locations in a river network during the planning horizon. Once allocation optimization solutions are developed, their acceptability and stability must be evaluated. Thus, a new bankruptcy allocation stability index (BASI) is developed for evaluating the acceptability of river bankruptcy solutions. To show how the proposed river bankruptcy framework can be helpful in practice, the suggested methods are applied to a real-world transboundary river system with eight riparians under various hydrologic regimes. Stability analysis based on the proposed stability evaluation method suggests that the acceptability of allocation rules is sensitive to hydrologic conditions and demand values. This finding has an important policy implication suggesting that fixed allocation rules and treaties may not be reliable for securing cooperation over transboundary water resources as they are vulnerable to changing socioeconomic and climatic conditions as well as hydrologic non-stationarity

Interpretable machine learning for genomics

High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines

Load Balancing for Dynamic Clients, Game Theory Approach

We address the problem of load balancing which is considered as a technique to spread work between two or more computers in order to get optimal response time and resource utilization between servers by using Nash equilibrium which is the central concern of game theory. We use a normal form table to express the payoff for every client, every client has estimate time for execution and every server has speed, from these facts, we innovate a dynamic payoff matrix to evaluate a Nash equilibrium point and then determine which server can serve an appropriate client to achieve best load balancing which is called "server matching" (Each client is matched to exactly one server, but a server can be matched to multiple clients or none.). We use Netlogo simulation to implement this matching, besides a useful game theory oolset called GAMBIT (Gambit toolset homepage, 2005) to solve the payoff matrix and compute Nash equilibrium point. This paper was done between the years 2007 – 2009, and to know what was done in this paper we can argue that we contribute in accomplishing the load balancing between servers, using new technique depends on game theory perspective, for that reason we can answer the question why this thesis was done, because it is very vital in achieving this goal. We use in our thesis a simulation methodology to prove the results that we have obtained from the simulation program which is a Netlogo V4.0.2, and we compare the results with traditionally techniques in load balancing. Finally, the results show that we improve the performance for the whole system by 4% in achieving load balancing and overweight the possibilities of using this technique in real system around the world

Who Gets What, When, How? Power, Organization, Markets, Money and the Allocation of Resources

Money is a mystery and financial institutions are often regarded as guardians and promoters of the mystery. These sketches are designed to help an individual interested in, but not technically trained in economics, understand markets, money, credit and the evolution of a mass market system embedded in the rich context of its political environment and society. The efficient functioning of a dynamic economy requires the presence of money and financial institutions. The great variety of financial institutions in any advanced economy requires that a synthetic approach is used to understand what the whole looks like. Verbal description provides an overarching view of the mixture of history, law, philosophy, social mores, and political structure that supplies the context for the functioning of the economy. This has been vividly illustrated by Adam Smith, his teacher the Reverend Francis Hutcheson and his close friend David Hume. There are two different but highly allied themes in this single slim volume. Chapters 1, 2, and 3 supply the rich context of history, society, polity and law in which every economy is embedded. Chapters 12 and 13 sketch what might lie ahead given the current state of the world. These chapters require no symbols or technical depth. In contrast Chapters 4 to 11 offers a reasonably nontechnical exposition of some of the considerable development in formal economic theory pertaining to money and financial institutions as economics struggles towards emerging as a science, balancing quantitative measures with qualifications that help to explain what the numbers mean

Contributions to Game Theory and Management. Vol. III. Collected papers presented on the Third International Conference Game Theory and Management.

The collection contains papers accepted for the Third International Conference Game Theory and Management (June 24-26, 2009, St. Petersburg University, St. Petersburg, Russia). The presented papers belong to the field of game theory and its applications to management. The volume may be recommended for researches and post-graduate students of management, economic and applied mathematics departments.

Modelling human fairness in cooperative games : a goal programming approach

The issues of rationality in human behavior and fairness in cooperation have gained interest in various economic studies. In many prescriptive models of games, rationality of human decision makers implicitly assumes exchange-ability. This means that real people are assumed to adopt the beliefs of a player as expressed in the game when placed in the shoes of that particular player. However, it is a well debated topic in the literature that this modeling assumption is not in accordance to what behavioral economists have observed in some games played with real human subjects. Even when assuming the role of the same player in the game, different people think differently about the fairness of a particular outcome. People also view fairness as an essential ingredient of their decision making processes in games on cooperation. The aim of this research is to develop a new modeling approach to decision making in games on cooperation in which fairness is an important consideration. The satisficing and egilitarian philosophies on which weighted and Chebyshev Goal Programming (GP) rely, seem to offer an adequate and natural way for modeling human decision processes in at least the single-shot games of coordination that are investigated in this work. The solutions returned by the proposed GP approach aim to strike the right balance on several dimensions of con icting goals that are set by players themselves and that arise in the mental models these players have of other relevant players.EThOS - Electronic Theses Online ServiceGBUnited Kingdo