Version 1
: Received: 22 October 2024 / Approved: 24 October 2024 / Online: 24 October 2024 (11:48:45 CEST)
How to cite:
Sankaran, G.; Palomino, M. A.; Knahl, M.; Siestrup, G. Towards a System Dynamics Framework for Human-ML Decisions: A Case Study on New York Citi Bike. Preprints2024, 2024101907. https://doi.org/10.20944/preprints202410.1907.v1
Sankaran, G.; Palomino, M. A.; Knahl, M.; Siestrup, G. Towards a System Dynamics Framework for Human-ML Decisions: A Case Study on New York Citi Bike. Preprints 2024, 2024101907. https://doi.org/10.20944/preprints202410.1907.v1
Sankaran, G.; Palomino, M. A.; Knahl, M.; Siestrup, G. Towards a System Dynamics Framework for Human-ML Decisions: A Case Study on New York Citi Bike. Preprints2024, 2024101907. https://doi.org/10.20944/preprints202410.1907.v1
APA Style
Sankaran, G., Palomino, M. A., Knahl, M., & Siestrup, G. (2024). Towards a System Dynamics Framework for Human-ML Decisions: A Case Study on New York Citi Bike. Preprints. https://doi.org/10.20944/preprints202410.1907.v1
Chicago/Turabian Style
Sankaran, G., Martin Knahl and Guido Siestrup. 2024 "Towards a System Dynamics Framework for Human-ML Decisions: A Case Study on New York Citi Bike" Preprints. https://doi.org/10.20944/preprints202410.1907.v1
Abstract
The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework's viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York's Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why these dynamics are necessary for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.
Keywords
Machine learning; system dynamics; simulation modelling; algorithmic decision-making; supply chain planning; NY Citi Bike
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
