Filippo Neri
ABSTRACT
We will discuss the notions of explainability and interpretability when using agent based modeling to approximate market indexes. As working context we will use the L-FABS system [22, 28] where agent based modeling, whose parameters are learned by simulated annealing, is used to explain and predict financial time series like: SP500, DJIA, GLD, SLV, etc. We will assume the following definitions for interpretability: being able to make sense of system output, and explainability: understanding how that output was generated as in [18]. Novelty of the paper: the discussion of explainability and interpretability in agent based modelling as implemented in L-FABS. An empirical case study will be discussed. Please note that the goal of this paper is not to describe how L-FABS works.
- B. Alexandrova-Kabadjova, A. Krause, and E. Tsang. 2007. An agent-based model of interactions in the payment card market. In Intelligent Data Engineering and Automated Learning-IDEAL 2007. Springer, 1063–1072.Google Scholar
- W. B. Arthur, J. H. Holland, B. LeBaron, R. Palmer, and P. Taylorm. 1997. Asset Pricing Under Endogenous Expectation in an Artificial Stock Market. In The Economy as an Evolving Complex System II. Santa Fe Institute Studies in the Sciences of Complexity Lecture Notes, 15–44.Google Scholar
- R. Axelrod. 2006. Agent-based Modeling as a Bridge between Disciplines. In Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, Kenneth L. Judd and Leigh Tesfatsion (Eds.). North-Holland, Amsterdam, The Nederlands, 1562–1583.Google Scholar
- Charlotte Bruun. 2003. The economy as an agent-based whole simulating schumpeterian dynamics. Industry and Innovation 10 (2003).Google Scholar
- M. Camilleri and F. Neri. 2014. Parameter optimization in decision tree learning by using simple genetic algorithms. WSEAS Transactions on Computers 13 (2014), 582–591.Google Scholar
- M. Camilleri, F. Neri, and M. Papoutsidakis. 2014. An algorithmic approach to parameter selection in machine learning using meta-optimization techniques. WSEAS Transactions on Systems 13, 1 (2014), 203–212.Google Scholar
- S. Chen. 2002. Agent-Based Artificial Markets in the AI-ECON Research Center: A Retrospect from 1995 to the Present. Systems, Control and Information 46, 9 (2002), 23–30.Google Scholar
- M. A. H. Dempster, Tom W. Payne, Yazann Romahi, and G. W. P. Thompson. 2001. Computational Learning Techniques for Intraday FX Trading Using Popular Technical Indicators. IEEE Transactions on Neural Networks 12, 4 (2001), 744–754.Google ScholarDigital Library
- Changyu Deng, Xunbi Ji, Colton Rainey, Jianyu Zhang, and Wei Lu. 2020. Integrating Machine Learning with Human Knowledge. iScience 23, 11 (2020), 101656.Google Scholar
- Derek Doran, Sarah Schulz, and Tarek R. Besold. 2017. What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. arxiv:1710.00794 [cs.AI]Google Scholar
- Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. arXiv: Machine Learning (2017).Google Scholar
- Filip Karlo Došilović, Mario Brčić, and Nikica Hlupić. 2018. Explainable artificial intelligence: A survey. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 0210–0215. https://doi.org/10.23919/MIPRO.2018.8400040Google ScholarCross Ref
- Joshua M. Epstein and Robert Axtell. 1996. Growing artificial societies: social science from the bottom up. The Brookings Institution, Washington, DC, USA.Google Scholar
- Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, and Ankur Taly. 2019. Explainable AI in Industry. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD ’19). Association for Computing Machinery, New York, NY, USA, 3203–3204. https://doi.org/10.1145/3292500.3332281Google ScholarDigital Library
- R. Gall. 2018. Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI. https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.htmlGoogle Scholar
- I. García-Margariño, I. Plaza, and F. Neri. 2019. ABS-MindBurnout: An agent-based simulator of the effects of mindfulness-based interventions on job burnout. Journal of Computational Science 36 (2019).Google Scholar
- Manas Gaur, Keyur Faldu, and Amit Sheth. 2021. Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?IEEE Internet Computing 25, 1 (2021), 51–59. https://doi.org/10.1109/MIC.2020.3031769Google ScholarCross Ref
- Lydia P. Gleaves, Reva Schwartz, and David A. Broniatowski. 2020. The Role of Individual User Differences in Interpretable and Explainable Machine Learning Systems. arxiv:2009.06675 [cs.CY]Google Scholar
- A. O. I. Hoffmann, S. A. Delre, J. H. von Eije, and W. Jager. 2006. Artificial Multi-Agent Stock Markets: Simple Strategies, Complex Outcomes. In Advances in Artificial Economics, volume 584 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, 167–176.Google Scholar
- Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy 23, 1 (2021). https://doi.org/10.3390/e23010018Google ScholarCross Ref
- F. Neri. 2011. Learning and predicting financial time series by combining natural computation and agent simulation. Lecture Notes in Computer Science 6625 (2011), 111–119.Google ScholarCross Ref
- F. Neri. 2012. Agent-based modeling under partial and full knowledge learning settings to simulate financial markets. AI Communications 25, 4 (2012), 295–304.Google ScholarDigital Library
- F. Neri. 2012. A comparative study of a financial agent based simulator across learning scenarios. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7103 (2012), 86–97.Google Scholar
- F. Neri. 2012. Learning predictive models for financial time series by using agent based simulations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7190 (2012), 202–221.Google Scholar
- F. Neri. 2014. Open research issues on computational techniques for financial applications. WSEAS Transactions on Systems 13, 1 (2014), 390–391.Google Scholar
- F. Neri. 2018. Case study on modeling the silver and nasdaq financial time series with simulated annealing. Advances in Intelligent Systems and Computing 746 (2018), 755–763.Google ScholarCross Ref
- F. Neri. 2019. Combining Machine Learning and Agent Based Modeling for Gold Price Prediction. Artificial life and Evolutionary Computation Communications in Computer and Information Science 900 (2019), 91–100.Google Scholar
- Filippo Neri. 2021. How to Identify Investor’s types in real financial markets by means of agent based simulation. In 6th International Conference on Machine Learning Technologies ICMLT 2021. ACM, 144–149.Google ScholarDigital Library
- F. Neri. 2022. Coevolution and learning symbolic concepts: statistical validation: Empirical statistical validation of co-evolutive machine learning systems. In 7th International Conference on Machine Learning Technologies ICMLT 2022, F. Neri (Ed.). ACM, 244–248.Google ScholarDigital Library
- F. Neri. 2023. Explainability and Interpretability in Decision Trees and Agent based Modelling when Approximating Financial Time series. A matter of balance with performance.. In 2023 8th International Conference on Computational Intelligence and Applications ICCIA 2023, F. Neri (Ed.). Springer press, in press.Google Scholar
- F. Neri. 2023. Mapping Learning Algorithms on Data, a promising novel methodology to compare learning algorithms. In 2023 International Conference on Computer and Communication Engineering CCCE 2023, F. Neri (Ed.). Springer press, in press.Google ScholarCross Ref
- Filippo Neri. U.P.. Unpublished result: Domain Specific Concept Drift Detectors for Predicting Financial Time Series. Submitted, available at https://arxiv.org/abs/2103.14079. arxiv.org (U.P.).Google Scholar
- Filippo Neri. U.P.. Unpublished result: Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison. Available at https://arxiv.org/abs/2107.06981. arxiv.org (U.P.).Google Scholar
- F. Neri and I. Margariño. 2020. Simulating and modeling the DAX index and the USO Etf financial time series by using a simple agent-based learning architecture. Expert Systems 37, 4 (2020).Google Scholar
- S. G. Rodríguez, D. Quintana, I. M. Galván, and Pedro Isasi. 2011. Portfolio Optimization Using SPEA2 with Resampling. In in Intelligent data engineering and automated learning - IDEAL 2011: 12th International Conference, LNCS 6936. Springer, 127–134.Google Scholar
- Sonia Schulenburg, Peter Ross, and South Bridge. 2000. An Adaptive Agent Based Economic Model. In Learning Classifier Systems: From Foundations to Applications, volume 1813 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 265–284.Google ScholarCross Ref
- Jaspreet Singh and Avishek Anand. 2019. EXS: Explainable Search Using Local Model Agnostic Interpretability. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM ’19). Association for Computing Machinery, New York, NY, USA, 770–773. https://doi.org/10.1145/3289600.3290620Google ScholarDigital Library
- Leigh Tesfatsion. 2002. Agent-Based Computational Economics: Growing Economies From the Bottom Up. Artif. Life 8, 1 (2002), 55–82. https://doi.org/10.1162/106454602753694765Google ScholarDigital Library
Index Terms
- Explainability and Interpretability in Agent based Modelling to Approximate Market Indexes
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