Groupme: city data and multi-preference activity allocation

Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

The Resource-Constrained Project Scheduling Problem with Work-Content Constraints

For executing the activities of a project, one or several resources are required, which are in general scarce. Many resource-allocation methods assume that the usage of these resources by an activity is constant during execution; in practice, however, the project manager may vary resource usage by individual activities over time within prescribed bounds. This variation gives rise to the project scheduling problem which consists in allocating the scarce resources to the project activities over time such that the project duration is minimized, the total number of resource units allocated equals the prescribed work content of each activity, and precedence and various work-content-related constraints are met.

Counting, scoring and classifying hunger to allocate resources targeted to solve the problem

A proper allocation of resources targeted to solve hunger is essential to optimize the efficacy of actions and maximize results. This requires an adequate measurement and formulation of the problem as, paraphrasing Einstein, the formulation of a problem is essential to reach a solution. Different measurement methods have been designed to count, score, classify and compare hunger at local level and to allow comparisons between different places. However, the alternative methods produce significantly reach different results. These discrepancies make decisions on the targeting of resource allocations difficult. To assist decision makers, a new method taking into account the dimension of hunger and the coping capacities of countries, is proposed enabling to establish both geographical and sectoral priorities for the allocation of resources.

Counting, scoring and classifying hunger to allocate resources targeted to solve the problem

A proper allocation of resources targeted to solve hunger is essential to optimize the efficacy of actions and maximize results. This requires an adequate measurement and formulation of the problem as, paraphrasing Einstein, the formulation of a problem is essential to reach a solution. Different measurement methods have been designed to count, score, classify and compare hunger at local level and to allow comparisons between different places. However, the alternative methods reach significantly different results. These discrepancies make decisions on the targeting of resource allocations difficult. To assist decision makers, a new method taking into account the dimension of hunger and the coping capacities of countries is proposed enabling to establish both geographical and sectoral priorities for the allocation of resources

Extended theory about the allocation of time: Description and application to the increase in the retirement age policies

This paper revises mainstream economic models which include time use in an explicit and endogenous manner, suggesting a extended theory which escape from the main problem existing in the literature. In order to do it, we start by presenting in section 2 the mainstream time use models in economics, showing their main features. Once this is done, we introduce the reader in the main problems this kind of well established models imply, within section 3, being the most highlighted the problem of joint production. Subsequently, we propose an extended theory which solves the problem of joint production; this is extensively described in section 4. Last, but not least, we apply this model to offer a time use analysis of the effect of a policy which increases the retirement age in a life-cycle perspective for a representative individual.

Measuring competitiveness in Europe: resource allocation, granularity and trade. Bruegel Blueprint Series Volume XVIV, January 2016

One of the most important lessons learned during the 2008-09 financial crisis was that the informational toolbox on which policymakers base their decisions about competitiveness became outdated in terms of both data sources and data analysis. The toolbox is particularly outdated when it comes to tapping the potential of micro data for the analysis of competitiveness – a serious problem given that it is firms, rather than countries that compete on global markets.

Allocation of road and street costs.

pt. 1. An equitable solution to the problem. Sect. 1-5 by R. G. Hennes [et al.]--pt. 2. Classification, traffic volumes and annual costs of county roads and city streets in the state of Washington, by G. A. Riedesel.--pt. 3. Bases for weight-distance taxation in the state of Washington. v. 1. Automobiles & taxicabs. v. 2. Trucks & buses.--pt. 4. The benefits of rural roads to rural property, by Wm. L. Garrison.--pt. 5. The effect of freeway access uponsuburban real property values, by R. O. Wheeler.

Resource allocation in sparse graphs

Resource allocation in sparsely connected networks, a representative problem of systems with real variables, is studied using the replica and Bethe approximation methods. An efficient distributed algorithm is devised on the basis of insights gained from the analysis and is examined using numerical simulations,showing excellent performance and full agreement with the theoretical results. The physical properties of the resource allocation model are discussed.

Evolution of competing strategies in a threshold model for task allocation

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task allocation in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. The problem is constrained so that agents are penalised for switching mail types. When an agent process a mail batch of different type to the previous one, it must undergo a change-over, with repeated change-overs rendering the agent inactive. The efficiency (average amount of mail retrieved), and the flexibility (ability of the agents to react to changes in the environment) are investigated both in static and dynamic environments and with respect to sudden changes. New rules for mail selection and specialisation are proposed and are shown to exhibit improved efficiency and flexibility compared to existing ones. We employ a evolutionary algorithm which allows the various rules to evolve and compete. Apart from obtaining optimised parameters for the various rules for any environment, we also observe extinction and speciation.

The effect of load on agent-based algorithms for distributed task allocation

Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Distributed sequential task allocation in foraging swarms

When designing a practical swarm robotics system, self-organized task allocation is key to make best use of resources. Current research in this area focuses on task allocation which is either distributed (tasks must be performed at different locations) or sequential (tasks are complex and must be split into simpler sub-tasks and processed in order). In practice, however, swarms will need to deal with tasks which are both distributed and sequential. In this paper, a classic foraging problem is extended to incorporate both distributed and sequential tasks. The problem is analysed theoretically, absolute limits on performance are derived, and a set of conditions for a successful algorithm are established. It is shown empirically that an algorithm which meets these conditions, by causing emergent cooperation between robots can achieve consistently high performance under a wide range of settings without the need for communication. © 2013 IEEE.

Evolutionary market agents for resource allocation in decentralised systems

We introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. Our agents competitively co-evolve in the live market, driving it towards the Bertrand equilibrium, the non-cooperative Nash equilibrium, at which all sellers charge their reserve price and share the market equally. We demonstrate that this outcome results in even load-balancing between the service providers. Our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe. Unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. This makes our problem a non-trivial and more realistic one.

Message passing for distributed optimisation of power allocation with renewable resources

The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global approximate and distributed optimisation method for power allocation that accommodates uncertainties and volatility is suggested and analysed. It is based on a probabilistic method known as message passing [1], which has deep links to statistical physics methodology. This principled method of optimisation is based on local calculations and inherently accommodates uncertainties; it is of modest computational complexity and provides good approximate solutions.We consider uncertainty and fluctuations drawn from a Gaussian distribution and incorporate them into the message-passing algorithm. We see the effect that increasing uncertainty has on the transmission cost and how the placement of volatile nodes within a grid, such as renewable generators or consumers, effects it.

Tőkeallokációs módszerek és tulajdonságaik a gyakorlatban (Methods of capital allocation and their characteristics in practice)

A pénzügyekben mind elméletileg, mind az alkalmazások szempontjából fontos kérdés a tőkeallokáció. Hogyan osszuk szét egy adott portfólió kockázatát annak alportfóliói között? Miként tartalékoljunk tőkét a fennálló kockázatok fedezetére, és a tartalékokat hogyan rendeljük az üzleti egységekhez? A tőkeallokáció vizsgálatára axiomatikus megközelítést alkalmazunk, tehát alapvető tulajdonságok megkövetelésével dolgozunk. Cikkünk kiindulópontja Csóka-Pintér [2010] azon eredménye, hogy a koherens kockázati mértékek axiómái, valamint a tőkeallokációra vonatkozó méltányossági, ösztönzési és stabilitási követelmények nincsenek összhangban egymással. Ebben a cikkben analitikus és szimulációs eszközökkel vizsgáljuk ezeket a követelményeket. A gyakorlati alkalmazások során használt, illetve az elméleti szempontból érdekes tőkeallokációs módszereket is elemezzük. A cikk fő következtetése, hogy a Csóka-Pintér [2010] által felvetett probléma gyakorlati szempontból is releváns, tehát az nemcsak az elméleti vizsgálatok során merül fel, hanem igen sokszor előforduló és gyakorlati probléma. A cikk további eredménye, hogy a vizsgált tőkeallokációs módszerek jellemzésével segítséget nyújt az alkalmazóknak a különböző módszerek közötti választáshoz. / === / Risk capital allocation in finance is important theoretically and also in practical applications. How can the risk of a portfolio be shared among its sub-portfolios? How should the capital reserves be set to cover risks, and how should the reserves be assigned to the business units? The study uses an axiomatic approach to analyse risk capital allocation, by working with requiring basic properties. The starting point is a 2010 study by Csoka and Pinter (2010), who showed that the axioms of coherent measures of risk are not compatible with some fairness, incentive compatibility and stability requirements of risk allocation. This paper discusses these requirements using analytical and simulation tools. It analyses methods used in practical applications that have theoretically interesting properties. The main conclusion is that the problems identified in Csoka and Pinter (2010) remain relevant in practical applications, so that it is not just a theoretical issue, it is a common practical problem. A further contribution is made because analysis of risk allocation methods helps practitioners choose among the different methods available.

Properties of risk capital allocation methods: Core Compatibility, Equal Treatment Property and Strong Monotonicity

In finance risk capital allocation raises important questions both from theoretical and practical points of view. How to share risk of a portfolio among its subportfolios? How to reserve capital in order to hedge existing risk and how to assign this to different business units? We use an axiomatic approach to examine risk capital allocation, that is we call for fundamental properties of the methods. Our starting point is Csóka and Pintér (2011) who show by generalizing Young (1985)'s axiomatization of the Shapley value that the requirements of Core Compatibility, Equal Treatment Property and Strong Monotonicity are irreconcilable given that risk is quantified by a coherent measure of risk. In this paper we look at these requirements using analytic and simulations tools. We examine allocation methods used in practice and also ones which are theoretically interesting. Our main result is that the problem raised by Csóka and Pintér (2011) is indeed relevant in practical applications, that is it is not only a theoretical problem. We also believe that through the characterizations of the examined methods our paper can serve as a useful guide for practitioners.