The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of operators from the minimum to the maximum. In decision analysis, it unifies the classical methods for decision making under uncertainty in the same formulation including the Laplace and the Hurwicz criteria. Thus, it is possible to establish the attitudinal character of the decision maker according to a degree of optimism or pessimism. This presentation gives a general overview of the OWA operator and some of its main generalizations including the induced OWA, the probabilistic OWA and the OWA distance. It is seen that some of the generalizations of the OWA operator includes the weighted average and some other key statistical concepts as particular cases of a more general framework. Therefore, the applicability of the OWA operator is very broad because all the previous studies that use one of these techniques can be revised with a more general formulation that considers more complex environments. But at the same time, it can be reduced to the classical approach if the available information is very simple. A general overview of decision making applications with the OWA operator is also presented.
José M. Merigó is a Full Professor at the School of Systems, Management and Leadership at the Faculty of Engineering and Information Technology at the University of Technology Sydney and a Part-Time Full Professor at the Department of Management Control and Information Systems at the School of Economics and Business at the University of Chile. Previously, he was Full Professor at the University of Chile, a Senior Research Fellow at the Manchester Business School, University of Manchester (UK) and an Assistant Professor at the Department of Business Administration at the University of Barcelona (Spain). He holds a Master and a PhD degree in Business Administration from the University of Barcelona. He also holds a Bachelor degree of Science and of Social Sciences in Economics and a Master in European Business Administration and Business Law from Lund University (Sweden). He has published more than 400 articles in journals, books and conference proceedings, including journals such as Information Sciences, European Journal of Operational Research, IEEE Transactions on Fuzzy Systems, Expert Systems with Applications, International Journal of Intelligent Systems, Scientometrics, Journal of Business Research and Knowledge-Based Systems. He has also published many books including several ones with Springer and with World Scientific. He is on the editorial board of many journals including Computers & Industrial Engineering, Applied Soft Computing, Technological and Economic Development of Economy, Journal of Intelligent & Fuzzy Systems, International Journal of Fuzzy Systems, Kybernetes, Computer Science and Information Systems, Applied Intelligence, IEEE Latin America Transactions and Economic Computation and Economic Cybernetics Studies and Research. He has also been a guest editor for several international journals, member of the scientific committee of several conferences and reviewer in a wide range of international journals. Since 2015, Thomson & Reuters (Clarivate Analytics) has distinguished him as a Highly Cited Researcher in Computer Science. He is currently interested in Decision Making, Aggregation Operators, Computational Intelligence, Bibliometrics and Applications in Business, Economics and Engineering.
Activity-based models appeared as an answer to the limitations of the traditional trip-based and tour-based four-stage models. The fundamental assumption of activity-based models is that travel demand is originated from people performing their daily activities. This is why they include a consistent representation of time, of the persons and households, time-dependent routing, and microsimulation of travel demand and traffic. In spite of their potential to simulate traffic demand management policies, their practical application is still limited. One of the main reasons is that these models require a huge amount of very detailed input data hard to get with surveys. However, the pervasive use of mobile devices has brought a valuable new source of data. The work presented here has a twofold objective: first, to demonstrate the capability of mobile phone records to feed activity-based transport models, and, second, to assert the advantages of using activity-based models to estimate the effects of traffic demand management policies. Activity diaries for the metropolitan area of Barcelona are reconstructed from mobile phone records. This information is then employed as input for building a transport MATSim model of the city. The model calibration and validation process proves the quality of the activity diaries obtained. The possible impacts of a cordon toll policy applied to two different areas of the city and at different times of the day are then studied. Our results show the way in which the modal share is modified in each of the considered scenarios. The possibility of evaluating the effects of the policy at both aggregated and traveler level, together with the ability of the model to capture policy impacts beyond the cordon toll area confirm the advantages of activity-based models for the evaluation of traffic demand management policies.
Jose Ramasco is a staff scientist of the Spanish National Research Council (CSIC) and he is working at the Instituto de Física Interdisciplinar y Sistemas Complejos IFISC, a joint research center between the Universitat de les Illes Balears UIB and CSIC located in Palma. His main research areas are complex systems, including networks and their applications to socio-technical systems. In particular, his work in the last years have been focused on mobility, transport networks and urban systems. Jose is author to over 80 articles published in international journals, including a recent review on the use of computation and data from communication and information technologies to characterize mobility.
Multi-Agent Systems (MAS) are systems composed of two or more computational autonomous entities (agents) which interact between them. MAS can model a huge number of realistic scenarios, such as groups of people or colonies of robots performing a set of tasks. One of the main decision that each agent must make is to select the next place to visit or the next task to perform. In many cases this decision is made following probabilistic Markov chain, where the next task to carry out depends only on its current task and on a probabilistic transition function. Such probabilistic approaches presents several handicaps, for instance, a asymptotic converge to the system's stable state, problems when more than two tasks are considered, ans so on. Due to those inconveniences, we will introduce a new possibilistic theoretical formalism for multi-agent systems. The task allocation is implemented considering transition possibilities instead of transition probabilities and possibilistic Markov chains (also known as fuzzy Markov chains) instead of the classical probabilistic ones. The theoretical and empirical results demonstrate, among other advantages, that the number of steps needed to get a stable state with fuzzy Markov chains is reduced more than 10 times and the system's behaviour prediction is improved compared with their probabilistic counterpart. Moreover, most of the possibility transition function are a specific kind of mathematical functions called indistinguishability operators. This kind of operators provides an excellent theoretical foundation to study the properties of a great number of MAS.
José Guerrero received his degree and PhD (2011) in Computer Science from the University of the Balearic Islands (UIB) where he is currently a lecturer and post-doctoral researcher at the Department of Mathematics and Computer Science. His research interest includes multi-agent task allocation mechanisms with auction and swarm coordination mechanisms using fuzzy and possibilistic approaches.