Towards autonomous decision-making: A probabilistic model for learning multi-user preferences

Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf

Human versus automated agents: how user preferences affect future mobility systems

Along with rapid advancements in digital, and physical technologies, shared autonomous electric vehicles are forecasted to gradually complement and replace traditional human-based mobility systems. Information systems play a key role in such a deep socio-technical system to pave the path toward a more sustainable future. This study investigates a hybrid ride-hailing platform of automated and human-driven vehicles. Our focus lies on the demand side where we evaluate the influence of user behaviors on economic and environmental system performance. For this, we employ a data-driven agent-based simulation modeling heterogeneous vehicle and user agents calibrated by rental data of a leading vehicle-sharing company. Our findings declare that diverse customer responses to the introduction of shared autonomous electric vehicles yield significantly different fleet performance and ecological costs. We also observe that the status quo customer communication design of ride-hailing platforms need adjustments to maximize the potentials of future hybrid shared mobility systems

Sustainability vs. Price: Analysis of Electric Multi-Modal Vehicle Sharing Systems under Substitution Effects

To pave the path for sustainable mobility, Information Systems are a promising tool to encourage users to adopt more sustainable mobility behavior. In this study, we investigate how potential demand management interventions affect the economic and environmental metrics of a multi-modal vehicle sharing operator. To this end, we narrow our focus on two important user characteristics, namely the users\u27 flexibility and willingness to pay an additional premium for more environmentally sustainable vehicles. Our study employs a combined discrete-event and multi-agent simulation approach, which we calibrate with real-world rental data of leading free-floating vehicle sharing platforms. The results show that it is economically and ecologically disadvantageous for both the society and the fleet operator to simply increase users\u27 mode choice flexibility. However, we clearly observe that this picture flips once users are willing to pay a surcharge to rent more environmentally sustainable vehicles

Electric Vehicle Storage Management in Operating Reserve Auctions

Carsharing operators, which rent out electric vehicles for minutes or hours, lose money on idle vehicles. We develop a model that allows carsharing operators to offer the storage of these vehicles on operating reserve markets (market for quickly rampable back-up power sources that replace for instance failing power plants). We consider it a dispatch and pricing problem with the tradeoff between the payoffs of offering vehicles for rental and selling their storage. This is a problem of stochastic nature taking into account that people can rent electric vehicles at any time. To evaluate our model we tracked the location and status of 350 electric vehicles from the carsharing company Car2Go and simulated the dispatch in the Dutch market. This market needs to be redesigned for optimal use of storage. We make recommendations for the market redesign and show that carsharing operators can make substantial additional profits in operating reserve markets

Envisioning and Enabling Sustainable Smart Markets

__Abstract__ Many of the world’s most urgent problems such as climate change, population growth, poverty, malnutrition and environmental degradation not only demand solutions but also require us to find more sustainable ways of living. Market mechanisms can be effective in solving large-scale resource allocation problems of this kind, but only if the market design reflects the social costs. The growth and spread of advanced information and communication technologies mean that new smart markets offer a way to achieve this and will become central to many areas of economic activity. However, the volumes of data and speed of transactions involved place a burden on human decisionmaking capabilities, and information systems can have a central role to play in helping to devise solutions – in particular, in developing intelligent software agents to provide decision support. This address looks at the challenges and opportunities involved for information systems researchers, and sets out an agenda for sustainable smart markets research, centered on collaborative approaches. It focuses on three overlapping areas: market and learning agent design; market evaluation using autonomous learning agents; and real-time decision support. Examples are included of current work on sustainable smart markets for electricity (smart grid) and for flowers (Dutch Flower Auctions)

The Effect of AI Advice on Human Confidence in Decision-Making

As artificial intelligence advances, it can increasingly be applied in collaborative decision-making contexts with humans. However, questions on the design of different collaborative environments remain open. In the context of AI-assisted human decision-making processes, we analyze the influence of AI advice on human confidence in the final decision. In a laboratory experiment, 458 subjects performed an image classification task. We compare their confidence over three treatments: i) a baseline case where subjects do not receive any AI advice; ii) where subjects receive AI advice; and iii) in addition to AI advice subjects also see the certainty of AI for its choice. Our results suggest that while AI advice can increase human overconfidence, this effect can be mitigated by augmenting the AI advice with its certainty. Our result not only contributes to the growing literature of human-AI collaboration, but also bears important practical implications for the design of collaborative systems

Calibrating Users’ Mental Models for Delegation to AI

Artificial intelligence (AI) has the potential to dramatically change the way decisions are made and organizations are managed. As of today, AI is mostly applied as a collaboration partner for humans, amongst others through delegation of tasks. However, it remains to be explored how AI should be optimally designed to enable effective human-AI collaboration through delegation. We analyze influences on human delegation behavior towards AI by studying whether increasing users\u27 knowledge of AI\u27s error boundaries leads to improved delegation behavior and trust in AI. Specifically, we analyze the effect of showing AI\u27s certainty score and outcome feedback alone and in combination using a 2x2 between-subject experiment with 560 subjects. We find that providing both pieces of information can have a positive effect on collaborative performance, delegation behavior, and users\u27 trust in AI. Our findings contribute to the design of AI for collaborative settings and motivate research on factors promoting delegation to AI