Evolutionary multiobjective optimization for automatic agent-based model calibration: A comparative study

This work was supported by the Spanish Agencia Estatal de Investigacion, the Andalusian Government, the University of Granada, and European Regional Development Funds (ERDF) under Grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475), and AIMAR (A-TIC-284-UGR18). Manuel Chica was also supported by the Ramon y Cajal program (RYC-2016-19800).The authors would like to thank the ``Centro de Servicios de Informática y Redes de Comunicaciones'' (CSIRC), University of Granada, for providing the computing resources (Alhambra supercomputer).Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.Spanish Agencia Estatal de InvestigacionAndalusian GovernmentUniversity of GranadaEuropean Commission PGC2018-101216-B-I00 P18-TP-4475 A-TIC-284-UGR18Spanish Government RYC-2016-1980

Celebration of Learning 2015: Full Program

Full program of the 2015 Celebration of Learning at Augustana College

An adaptive agent-based model of homing pigeons : a genetic algorithm approach

Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In todays data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.(VLID)219568

Income inequality, consumption, credit and credit risk in a data-driven agent-based model

The issue of income inequality occupies a prominent position in the research agenda of academic and policy circles alike, especially after the crisis of 2008, due to its potential causal link with the development of credit bubbles and therefore the emergence of financial crises. This paper examines the long-run effect of income inequality on consumption, consumer credit and non-performing loans through the means of a data-driven agent-based model. The data-driven nature of the model enhances its ability to match historical series and thus makes it suitable for policy simulations tailored for specific economies. The analysis indicates that higher income inequality has a detrimental impact on consumption and is associated with lower volumes of consumer credit. However, the ratio of non-performing loans as a share of total loans seems to be independent of income inequality

Automatic determination of information system subsystems development order

Prilikom razvoja informacijskog sustava potrebno je odrediti slijed razvoja podsustava informacijskog sustava. Ovaj problem trenutno nije formalno riješen. Stoga predlažemo rješenje koje će kao kriterij, određivanja slijeda razvoja podsustava informacijskog sustava, imati sumu težina povratnih lukova u slijedu podsustava informacijskog sustava. Nadalje, dokazali smo kako je ovaj problem NP-potpun, NP-težak, i APX-težak. Isto tako, kako bismo riješili ovaj problem osmislili smo: algoritam temeljen na metodi Grananja i ograničenja, Monte Carlo randomizirani algoritam, i heuristički algoritam. Za sva tri algoritma smo procijenili složenost. Sva tri algoritma su implementirana i empirijski testirana. Na kraju smo pokazali na koji način se u praksi, po potrebi, mogu uvrštavati dodatna ograničenja, i gdje se još osmišljeni algoritmi potencijalno mogu koristiti.When we are developing Information System we must determine development order of its subsystems. Currently, this problem is not formally solved. Therefore, we have proposed a solution which takes sum of weights of feedback arcs as a criteria for determining development order of Information System subsystems. Furthermore, we have proved that the problem of Information System Subsystems Development Order is NP-complete, NP-hard, and APX-hard. Also, in order to solve this problem we have created: Branch and Bound algorithm, Monte Carlo randomized algorithm, and heuristic algorithm. Complexity has been calculated for all three algorithms. All three algorithms have been implemented and empirically analysed. Lastly, we have showed how one can apply additional constraints upon the problem of Information System Subsystems Development Order, and where can one potentially use developed algorithms outside of Information System Subsystems Development Order problem

Automatic determination of information system subsystems development order

Prilikom razvoja informacijskog sustava potrebno je odrediti slijed razvoja podsustava informacijskog sustava. Ovaj problem trenutno nije formalno riješen. Stoga predlažemo rješenje koje će kao kriterij, određivanja slijeda razvoja podsustava informacijskog sustava, imati sumu težina povratnih lukova u slijedu podsustava informacijskog sustava. Nadalje, dokazali smo kako je ovaj problem NP-potpun, NP-težak, i APX-težak. Isto tako, kako bismo riješili ovaj problem osmislili smo: algoritam temeljen na metodi Grananja i ograničenja, Monte Carlo randomizirani algoritam, i heuristički algoritam. Za sva tri algoritma smo procijenili složenost. Sva tri algoritma su implementirana i empirijski testirana. Na kraju smo pokazali na koji način se u praksi, po potrebi, mogu uvrštavati dodatna ograničenja, i gdje se još osmišljeni algoritmi potencijalno mogu koristiti.When we are developing Information System we must determine development order of its subsystems. Currently, this problem is not formally solved. Therefore, we have proposed a solution which takes sum of weights of feedback arcs as a criteria for determining development order of Information System subsystems. Furthermore, we have proved that the problem of Information System Subsystems Development Order is NP-complete, NP-hard, and APX-hard. Also, in order to solve this problem we have created: Branch and Bound algorithm, Monte Carlo randomized algorithm, and heuristic algorithm. Complexity has been calculated for all three algorithms. All three algorithms have been implemented and empirically analysed. Lastly, we have showed how one can apply additional constraints upon the problem of Information System Subsystems Development Order, and where can one potentially use developed algorithms outside of Information System Subsystems Development Order problem