Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities

Weaving a fabric of socially aware agents

The expansion of web-enabled social interaction has shed light on social aspects of intelligence that have not been typically studied within the AI paradigm so far. In this context, our aim is to understand what constitutes intelligent social behaviour and to build computational systems that support it. We argue that social intelligence involves socially aware, autonomous individuals that agree on how to accomplish a common endeavour, and then enact such agreements. In particular, we provide a framework with the essential elements for such agreements to be achieved and executed by individuals that meet in an open environment. Such framework sets the foundations to build a computational infrastructure that enables socially aware autonomy.This work has been supported by the projects EVE(TIN2009-14702-C02-01) and AT (CSD2007-0022)Peer Reviewe

Automating decision making to help establish norm-based regulations

Norms have been extensively proposed as coordination mechanisms for both agent and human societies. Nevertheless, choosing the norms to regulate a society is by no means straightforward. The reasons are twofold. First, the norms to choose from may not be independent (i.e, they can be related to each other). Second, different preference criteria may be applied when choosing the norms to enact. This paper advances the state of the art by modeling a series of decision-making problems that regulation authorities confront when choosing the policies to establish. In order to do so, we first identify three different norm relationships -namely, generalisation, exclusivity, and substitutability- and we then consider norm representation power, cost, and associated moral values as alternative preference criteria. Thereafter, we show that the decision-making problems faced by policy makers can be encoded as linear programs, and hence solved with the aid of state-of-the-art solvers

Online Automated Synthesis of Compact Normative Systems

Peer reviewedPostprin

Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

On the use of multiple criteria distance indexes to find robust cash management policies

[EN] Cash management decision-making can be handled from a multiobjective perspective by optimizing not only cost but also risk. Nevertheless, choosing the best policies under a changing context is by no means straightforward. To this end, we rely on compromise programming to incorporate robustness as an additional goal to cost and risk within a multiobjective framework. As a result, we propose to calculate robustness as a multiple criteria distance index that is able to identify the best compromise policies in terms of cost and risk. Such policies are also robust to cash flow regime changes. We show its utility by transforming the Miller and Orr s cash management model into its robust counterpart using real data from an industrial company.Ministerio de Economia y Competitividad [grant number Collectiveware TIN2015-66863-C2-1-R], [grant number 2014 SGR 118]. Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2019). On the use of multiple criteria distance indexes to find robust cash management policies. INFOR Information Systems and Operational Research. 57(3):345-360. https://doi.org/10.1080/03155986.2017.1282291S34536057

An analytic derivation of the efficient frontier in biobjective cash management and its implications for policies

[EN] Cash managers who optimize returns and risk rely on biobjective optimization models to select the best policies according to their risk preferences. In the related portfolio selection problem, Merton (J Financ Quant Anal 7(4):1851¿1872, 1972) provided the first analytical derivation of the efficient frontier with all non-dominated return and risk combinations. This first proposal was later extended to account for three or more criteria by other authors. However, the cash management literature needs an analytical derivation of the efficient frontier to help cash managers evaluate the implications of selecting policies and risk measures. In this paper, we provide three analytic derivations of the efficient frontier determining a closed-form solution for the expected returns and risk relationship using three different risk measures. We study its main properties and its theoretical implications for policies. Using the variance of returns as a risk measure imposes limitations due to invertibility reasons.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Salas-Molina, F.; Pla Santamaría, D.; Rodriguez-Aguilar, JA. (2023). An analytic derivation of the efficient frontier in biobjective cash management and its implications for policies. Annals of Operations Research (Online). 328(2):1523-1536. https://doi.org/10.1007/s10479-023-05433-z152315363282Baumol, W. J. (1952). The transactions demand for cash: An inventory theoretic approach. The Quarterly Journal of Economics, 66(4), 545–556.Constantinides, G. M., & Richard, S. F. (1978). Existence of optimal simple policies for discounted-cost inventory and cash management in continuous time. Operations Research, 26(4), 620–636.da Costa Moraes, M. B., Nagano, M. S., Sobreiro, V. A., et al. (2015). Stochastic cash flow management models: A literature review since the 1980s. In P. Guarnieri (Ed.), Decision Models in Engineering and Management (pp. 11–28). Berlin: Springer.Markowitz, H. (1952). The Portfolio selection. Journal of Finance, 7(1), 77–91.Merton, R. C. (1972). An analytic derivation of the efficient portfolio frontier. Journal of Financial and Quantitative Analysis, 7(4), 1851–1872.Miller, M. H., & Orr, D. (1966). A model of the demand for money by firms. The Quarterly Journal of Economics, 80(3), 413–435.Qi, Y. (2020). Parametrically computing efficient frontiers of portfolio selection and reporting and utilizing the piecewise-segment structure. Journal of the Operational Research Society, 71(10), 1675–1690.Qi, Y. (2022). Classifying the minimum-variance surface of multiple-objective portfolio selection for capital asset pricing models. Annals of Operations Research, 311(2), 1203–1227.Qi, Y., & Li, X. (2020). On imposing ESG constraints of portfolio selection for sustainable investment and comparing the efficient frontiers in the weight space. SAGE Open, 10(4), 1–17.Qi, Y., & Steuer, R. E. (2020). On the analytical derivation of efficient sets in quad-and-higher criterion portfolio selection. Annals of Operations Research, 293(2), 521–538.Qi, Y., Steuer, R. E., & Wimmer, M. (2017). An analytical derivation of the efficient surface in portfolio selection with three criteria. Annals of Operations Research, 251(1–2), 161–177.Salas-Molina, F. (2019). Selecting the best risk measure in multiobjective cash management. International Transactions in Operational Research, 26(3), 929–945.Salas-Molina, F. (2020). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159–1176.Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2018). Empowering cash managers through compromise programming. In H. Masri, B. Perez-Gladish, & C. Zopounidis (Eds.), Financial decision aid using multiple criteria (pp. 149–173). Berlin: Springer.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2018). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1), 515–529.Salas-Molina, F., Rodriguez-Aguilar, J. A., & Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research, 261(1), 275–288.Salas-Molina, F., Rodriguez-Aguilar, J. A., Pla-Santamaria, D., & García-Bernabeu, A. (2021). On the formal foundations of cash management systems. Operational Research, 21(2), 1081–1095.Salas-Molina, F., Rodríguez-Aguilar, J. A., & Guillen, M. (2023). A multidimensional review of the cash management problem. Financial Innovation, 9(67), 1–35.Savage, L. J. (1951). The theory of statistical decision. Journal of the American Statistical Association, 46(253), 55–67.Schroeder, P., & Kacem, I. (2019). Optimal cash management with uncertain, interrelated and bounded demands. Computers & Industrial Engineering, 133, 195–206.Schroeder, P., & Kacem, I. (2020). Competitive difference analysis of the cash management problem with uncertain demands. European Journal of Operational Research, 283(3), 1183–1192

Characterizing compromise solutions for investors with uncertain risk preferences

[EN] The optimum portfolio selection for an investor with particular preferences was proven to lie on the normalized efficient frontier between two bounds defined by the Ballestero (1998) bounding theorem. A deeper understanding is possible if the decision-maker is provided with visual and quantitative techniques. Here, we derive useful insights as a way to support investor's decision-making through: (i) a new theorem to assess balance of solutions; (ii) a procedure and a new plot to deal with discrete efficient frontiers and uncertain risk preferences; and (iii) two quality metrics useful to predict long-run performance of investors.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2019). Characterizing compromise solutions for investors with uncertain risk preferences. Operational Research. 19(3):661-677. https://doi.org/10.1007/s12351-017-0309-6S661677193Amiri M, Ekhtiari M, Yazdani M (2011) Nadir compromise programming: a model for optimization of multi-objective portfolio problem. Expert Syst Appl 38(6):7222–7226Ballestero E (1998) Approximating the optimum portfolio for an investor with particular preferences. J Oper Res Soc 49:998–1000Ballestero E (2007) Compromise programming: a utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. Eur J Oper Res 182(3):1369–1382Ballestero E, Pla-Santamaria D (2004) Selecting portfolios for mutual funds. Omega 32(5):385–394Ballestero E, Pla-Santamaria D, Garcia-Bernabeu A, Hilario A (2015) Portfolio selection by compromise programming. In: Ballestero E, Pérez-Gladish B, Garcia-Bernabeu A (eds) Socially responsible investment. A multi-criteria decision making approach, vol 219. Springer, Switzerland, pp 177–196Ballestero E, Romero C (1996) Portfolio selection: a compromise programming solution. J Oper Res Soc 47(11):1377–1386Ballestero E, Romero C (1998) Multiple criteria decision making and its applications to economic problems. Kluwer Academic Publishers, BerlinBilbao-Terol A, Pérez-Gladish B, Arenas-Parra M, Rodríguez-Uría MV (2006) Fuzzy compromise programming for portfolio selection. Appl Math Comput 173(1):251–264Bravo M, Ballestero E, Pla-Santamaria D (2012) Evaluating fund performance by compromise programming with linear-quadratic composite metric: an actual case on the caixabank in spain. J Multi-Criteria Decis Anal 19(5–6):247–255Ehrgott M, Klamroth K, Schwehm C (2004) An MCDM approach to portfolio optimization. Eur J Oper Res 155(3):752–770Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874Hernández-Orallo J, Flach P, Ferri C (2013) ROC curves in cost space. Mach Learn 93(1):71–91Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91Pla-Santamaria D, Bravo M (2013) Portfolio optimization based on downside risk: a mean-semivariance efficient frontier from dow jones blue chips. Ann Oper Res 205(1):189–201Ringuest JL (1992) Multiobjective optimization: behavioral and computational considerations. Springer Science & Business Media, BerlinSteuer RE, Qi Y, Hirschberger M (2007) Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Ann Oper Res 152(1):297–317Xidonas P, Mavrotas G, Krintas T, Psarras J, Zopounidis C (2012) Multicriteria portfolio management. Springer, BerlinYu P-L (1973) A class of solutions for group decision problems. Manag Sci 19(8):936–946Yu P-L (1985) Multiple criteria decision making: concepts, techniques and extensions. Plenum Press, BerlinZeleny M (1982) Multiple criteria decision making. McGraw-Hill, New Yor

Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent’s probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper, we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2×105 possible allocations in 40 seconds).

Instilling moral value alignment by means of multi-objective reinforcement learning

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent's individual and ethical objectives. The second step consists in designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour