A cooperative appproach to queue allocation of indivisible objects

We consider the allocation of a finite number of indivisible objects to the same number of agents according to an exogenously given queue. We assume that the agents collaborate in order to achieve an efficient outcome for society. We allow for side-payments and provide a method for obtaining stable outcomes.

Resource-monotonicity for house allocation problems

We study a simple model of assigning indivisible objects (e.g., houses, jobs, offices, etc.) to agents. Each agent receives at most one object and monetary compensations are not possible. We completely describe all rules satisfying efficiency and resource-monotonicity. The characterized rules assign the objects in a sequence of steps such that at each step there is either a dictator or two agents "trade" objects from their hierarchically specified "endowments."

Personal redistribution and the regional allocation of public investment

How should an equity-motivated policy-marker allocate public capital (infrastructure) across regions. Should it aim at reducing interregional differences in per capita output, or at maximizing total output? Such a normative question is examined in a model where the policy-marker is exclusively concerned about personal inequality and has access to two policy instruments. (i) a personal tax-transfer system (taxation is distortionary), and (ii) the regional allocation of public investment. I show that the case for public investment as a significant instrument for interpersonal redistribution is rather weak. In the most favorable case, when the tax code is constrained to be uniform across regions, it is optimal to distort the allocation of public investment in favor of the poor regions, but only to a limited extent. The reason is that poor individuals are relatively more sensitive to public trans fers, which are maximized by allocating public investment efficiently. If! the tax code can vary across regions then the optimal policy may involve an allocation of public investment distorted in favor of the rich regions.

The learning process as a cooperative task among teachers and students

This research project gathers several ideas and guidelines on professional improvement as a teacher. This study includes two empirical studies. The first one focuses mainly on the teacher's figure. It is meant to be a study of the several resources that the teacher uses in order to construct the student's knowledge in an English classroom context. The second empirical study focuses on the students. It is a study on how students learn cooperatively by analyzing their oral productions when working in small groups

Teachers' reactions on students' responses: essential for an adequate task monitoring

This research project analyzes the reactions the teacher has on students' responses. Different techniques as discourse markers, types of questions and repair sequences are taken into account, but the author puts a special emphasis on non-verbal communication. To be aware of all these ways of reacting in a class interaction is essential for an adequate task monitoring

Bandwidth allocation based on real time calculations using the convolution approach

This paper focuses on one of the methods for bandwidth allocation in an ATM network: the convolution approach. The convolution approach permits an accurate study of the system load in statistical terms by accumulated calculations, since probabilistic results of the bandwidth allocation can be obtained. Nevertheless, the convolution approach has a high cost in terms of calculation and storage requirements. This aspect makes real-time calculations difficult, so many authors do not consider this approach. With the aim of reducing the cost we propose to use the multinomial distribution function: the enhanced convolution approach (ECA). This permits direct computation of the associated probabilities of the instantaneous bandwidth requirements and makes a simple deconvolution process possible. The ECA is used in connection acceptance control, and some results are presented

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Intelligent task-level grasp mapping for robot control

In the future, robots will enter our everyday lives to help us with various tasks.For a complete integration and cooperation with humans, these robots needto be able to acquire new skills. Sensor capabilities for navigation in real humanenvironments and intelligent interaction with humans are some of the keychallenges.Learning by demonstration systems focus on the problem of human robotinteraction, and let the human teach the robot by demonstrating the task usinghis own hands. In this thesis, we present a solution to a subproblem within thelearning by demonstration field, namely human-robot grasp mapping. Robotgrasping of objects in a home or office environment is challenging problem.Programming by demonstration systems, can give important skills for aidingthe robot in the grasping task.The thesis presents two techniques for human-robot grasp mapping, directrobot imitation from human demonstrator and intelligent grasp imitation. Inintelligent grasp mapping, the robot takes the size and shape of the object intoconsideration, while for direct mapping, only the pose of the human hand isavailable.These are evaluated in a simulated environment on several robot platforms.The results show that knowing the object shape and size for a grasping taskimproves the robot precision and performance

Optimum power allocation for parallel Gaussian channels with arbitrary input distributions

The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signalling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error proves key to solving the power allocation problem.

Optimum power allocation for multiuser OFDM with arbitrary signal constellations

This paper formulates power allocation policies that maximize the region of mutual informationsachievable in multiuser downlink OFDM channels. Arbitrary partitioning ofthe available tones among users and arbitrary modulation formats, possibly different forevery user, are considered. Two distinct policies are derived, respectively for slow fadingchannels tracked instantaneously by the transmitter and for fast fading channels knownonly statistically thereby. With instantaneous channel tracking, the solution adopts theform of a multiuser mercury/waterfilling procedure that generalizes the single-user mercury/waterfilling introduced in [1, 2]. With only statistical channel information, in contrast,the mercury/waterfilling interpretation is lost. For both policies, a number of limitingregimes are explored and illustrative examples are provided.

Using qualia information to identify lexical semantic classes in an unsupervised clustering task

Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.

Parental investments in children: how bargaining and educational homogamy affect time allocation

This study examines parental time investment in their children, distinguishing between developmental and non-developmental care. Our analyses centre on three influential determinants: educational background, marital homogamy, and spouses' relative bargaining power. We find that the emphasis on quality care time is correlated with parents' education, and that marital homogamy reduces couple specialization, but only among the highly educated. In line with earlier research, we identify gendered parental behaviour. The presence of boys is an important condition for fathers' time dedication, but primarly among lower educated fathers. To the extent that parental stimulation is decisive for child outcomes, our findings suggest the persistence of important inequalities. This emerges through our special attention to behavioural differences across the educational distribution among households.

On Certain Greedoid Polyhedra, Partially Indexable Scheduling Problems, and Extended Restless Bandit Allocation Indices

We present a polyhedral framework for establishing general structural properties on optimal solutions of stochastic scheduling problems, where multiple job classes vie for service resources: the existence of an optimal priority policy in a given family, characterized by a greedoid (whose feasible class subsets may receive higher priority), where optimal priorities are determined by class-ranking indices, under restricted linear performance objectives (partial indexability). This framework extends that of Bertsimas and Niño-Mora (1996), which explained the optimality of priority-index policies under all linear objectives (general indexability). We show that, if performance measures satisfy partial conservation laws (with respect to the greedoid), which extend previous generalized conservation laws, then the problem admits a strong LP relaxation over a so-called extended greedoid polytope, which has strong structural and algorithmic properties. We present an adaptive-greedy algorithm (which extends Klimov's) taking as input the linear objective coefficients, which (1) determines whether the optimal LP solution is achievable by a policy in the given family; and (2) if so, computes a set of class-ranking indices that characterize optimal priority policies in the family. In the special case of project scheduling, we show that, under additional conditions, the optimal indices can be computed separately for each project (index decomposition). We further apply the framework to the important restless bandit model (two-action Markov decision chains), obtaining new index policies, that extend Whittle's (1988), and simple sufficient conditions for their validity. These results highlight the power of polyhedral methods (the so-called achievable region approach) in dynamic and stochastic optimization.

Money, Fame and the Allocation of Talent: Brain Drain, the Matthew Effect and the Institution of Science

The earning structure in science is known to be flat relative to the one in the private sector, which could cause a brain drain toward the private sector. In this paper, we assume that agents value both money and fame and study the role of the institution of science in the allocation of talent between the science sector and the private sector. Following works on the Sociology of Science, we model the institution of science as a mechanism distributing fame (i.e. peer recognition). We show that since the intrinsic performance is less noisy signal of talent in the science sector than in the private sector, a good institution of science can mitigate the brain drain. We also find that providing extra monetary incentives through the market might undermine the incentives provided by the institution and thereby worsen the brain drain. Finally, we study the optimal balance between monetary and non-monetary incentives in science.

Financial Repression in a Natural Experiment: Loan Allocation and the Change in the Usury Laws in 1714

If financial deepening aids economic growth, then financial repression should be harmful. We use a natural experiment – the change in the English usury laws in 1714 – to analyze the effects of interest rate restrictions. We use a sample of individual loan transactions to demonstrate how the reduction of the legal maximum rate of interest affected the supply and demand for credit. Average loan size and minimum loan size increased strongly, and access to credit worsened for those with little ‘social capital.’ While we have no direct evidence that loans were misallocated, the discontinuity in loan receipts makes this highly likely. We conclude that financial repression can undermine the positive effects of financial deepening.