Enrico Harm Gerding

In modern urban transportation networks, multiple self-interested travel providers (public transit, micromobility providers, ride-sharing platforms and toll roads) compete for heterogenous transportation users that wish to balance time and cost. Traditional congestion models assume fixed, exogenous costs, while dynamic-pricing frameworks typically focus on a single operator, overlooking the rich strategic interplay among decentralised transportation providers. This paper introduces the Multi-Market Routing Problem (MMRP), a game-theoretic model in which each provider utilises adaptive pricing to maximise profit and heterogeneous transportation users aim to minimise their travel time and cost. We present the MMRP as an extension of traditional congestion games, and extend it to consider online instances for adaptive pricing under dynamic and stochastic congestion. We demonstrate the computational complexity for game-theoretic and exact solutions to the MMRP, reflecting the computational complexity of coordinating routing in dynamic and uncertain settings. To address this, we propose the use of independent Proximal Policy Optimisation as a decentralised and effective solution to the online MMRP, demonstrating reduced travel times and more equitable and fair outcomes for transportation users, and increased profitability for transportation providers. The MMRP framework and learning algorithms offer a principled foundation for competitive, multimodal routing in modern urban transportation networks.

Sustainability is the ability to maintain and preserve natural and manmade systems for the benefit of current and future generations. The three pillars of sustainability are social, economic, and environmental. These pillars are interdependent and interconnected, meaning that progress in one area can have positive or negative impacts on the others. This calls for smart methods to balance such benefits and find solutions that are optimal with respect to all the three pillars of sustainability. By using AI methods, in particular, genetic algorithms for multiobjective optimisation, we can better understand and manage complex systems in order to achieve sustainability. In the context of sustainability-oriented ridesharing, genetic algorithms can be used to optimise route finding in order to lower the cost of transportation and reduce emissions. This work contributes to this domain by using AI, specifically genetic algorithms for multiobjective optimisation, to improve the efficiency and sustainability of transportation systems. By using this approach, we can make progress towards achieving the goals of the three pillars of sustainability.

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

Being able to infer ground truth from the responses of multiple imperfect advisors is a problem of crucial importance in many decision-making applications, such as lending, trading, investment, and crowd-sourcing. In practice, however, gathering answers from a set of advisors has a cost. Therefore, finding an advisor selection strategy that retrieves a reliable answer and maximizes the overall utility is a challenging problem. To address this problem, we propose a novel strategy for optimally selecting a set of advisers in a sequential binary decision-making setting, where multiple decisions need to be made over time. Crucially, we assume no access to ground truth and no prior knowledge about the reliability of advisers. Specifically, our approach considers how to simultaneously (1) select advisors by balancing the advisors' costs and the value of making correct decisions, (2) learn the trustworthiness of advisers dynamically without prior information by asking multiple advisers, and (3) make optimal decisions without access to the ground truth, improving this over time. We evaluate our algorithm through several numerical experiments. The results show that our approach outperforms two other methods that combine state-of-the-art models.

Purpose: The Covid-19 pandemic affected customers' shopping and returns behaviours and significantly aggravated the problem of high product returns rates and returns fraud. Measures for public health and safety resulted in retailers modifying their returns processes. This study describes the changes observed and experienced by retailers as well as consumers and concludes what lessons retailers can learn from the experience. Design/methodology/approach: 18 semi-structured interviews were conducted with multichannel retailers and retail experts. This was complemented with a consumer survey focusing on returns behaviour (497 valid responses). Findings: Our project outputs include a list of recommendations for retailers to mitigate the effects of the pandemic on returns and related fraud and what they can learn from the experience with the goal of reducing returns rates and fraudulent returns in non-pandemic situations. Originality: The effects of the observed returns process changes on returns management were unclear; very little was known about what retailers experienced in terms of product returns during the pandemic. We shed light on this and identify opportunities for improvement in multichannel returns management.