Dirk Helbing

Evangelos Pournaras, Mark Yao, Dirk Helbing

Supply-demand systems in Smart City sectors such as energy, transportation, telecommunication, are subject of unprecedented technological transformations by the Internet of Things. Usually, supply-demand systems involve actors that produce and consume resources, e.g. energy, and they are regulated such that supply meets demand, or demand meets available supply. Mismatches of supply and demand may increase operational costs, can cause catastrophic damage in infrastructure, for instance power blackouts, and may even lead to social unrest and security threats. Long-term, operationally offline and top-down regulatory decision-making by governmental officers, policy makers or system operators may turn out to be ineffective for matching supply-demand under new dynamics and opportunities that Internet of Things technologies bring to supply-demand systems, for instance, interactive cyber-physical systems and software agents running locally in physical assets to monitor and apply automated control actions in real-time. e.g. power flow redistributions by smart transformers to improve the Smart Grid reliability. Existing work on online regulatory mechanisms of matching supply-demand either focuses on game-theoretic solutions with assumptions that cannot be easily met in real-world systems or assume centralized management entities and local access to global information. This paper contributes a generic decentralized self-regulatory framework, which, in contrast to related work, is shaped around standardized control system concepts and Internet of Things technologies for an easier adoption and applicability. The framework involves a decentralized combinatorial optimization mechanism that matches supply-demand under different regulatory scenarios.

Joshua C. Yang, Damian Dailisan, Marcin Korecki et al. · eth-zurich

This paper investigates the voting behaviors of Large Language Models (LLMs), specifically GPT-4 and LLaMA-2, their biases, and how they align with human voting patterns. Our methodology involved using a dataset from a human voting experiment to establish a baseline for human preferences and conducting a corresponding experiment with LLM agents. We observed that the choice of voting methods and the presentation order influenced LLM voting outcomes. We found that varying the persona can reduce some of these biases and enhance alignment with human choices. While the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process. We also identified a trade-off between preference diversity and alignment accuracy in LLMs, influenced by different temperature settings. Our findings indicate that LLMs may lead to less diverse collective outcomes and biased assumptions when used in voting scenarios, emphasizing the need for cautious integration of LLMs into democratic processes.

Carina I Hausladen, Javier Argota Sánchez-Vaquerizo, Michael Siebenmann et al.

Participatory urban planning is central to sustainable city-making, yet the technically demanding nature of such interventions often limits meaningful involvement by diverse publics. We introduce a scalable digital participation platform that embeds sustainability projects within a navigable digital twin. Citizens experience a guided virtual walkthrough with audio narration employing the method of loci and spatial anchoring to support mnemonic encoding and recall. This immersive interface is augmented by two purpose-built LLM assistants: one delivers source-grounded factual clarifications, while the other facilitates reflective discussion. We evaluated this system in a randomized controlled online experiment (N = 195) against conventional industry practices (static visualizations and text-based consultations). Results show that spatially anchored immersive presentation significantly improved information recall, which substantially shifted participants' attention from individual inconveniences to collective, community-oriented sustainability benefits. Consequently, participants provided significantly more constructive, solution-focused feedback to the (simulated) municipality. These findings establish a practical tool for cities and policymakers to foster inclusive, democratic participation in sustainability transitions.

Arthur Capozzi, Dirk Helbing

We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data. Public registries are often formally accessible, yet difficult to use in practice because they combine structured records with large volumes of unstructured legal text. This limits conventional keyword and vector-only retrieval, especially for multi-hop, temporal, and entity-centric investigations. Our approach builds a Neo4j knowledge graph through a three-phase pipeline: (i) deterministic ingestion of strong nodes from verified structured fields, (ii) LLM-based extraction of weak nodes from unstructured notices, and (iii) deterministic identity resolution and deduplication. On top of this graph, we introduce an analytical modular agent that integrates zero-shot intent routing, a bounded reflection loop, secure tool-mediated graph access, and state-aware response synthesis. A human-in-the-loop dashboard exposes evidence and execution traces to support transparency and auditability. We evaluate the framework on the Swiss Official Gazette of Commerce, a multilingual corpus of more than seven million publications over seven years. We further contribute a multi-tier evaluation protocol covering entity-resolution precision, tool-routing behavior, answer quality, and multi-turn conversational performance. Across automated, human-curated, and conversational benchmarks, the proposed agentic GraphRAG system consistently outperforms a standard agentic vector-RAG baseline, with strong gains in correctness, answer relevance, information recall, turn success rate, and context carryover accuracy. The architecture is modular, reproducible, and transferable to other commercial gazettes and public-sector registry systems.

Evangelos Pournaras, Srijoni Majumdar, Thomas Wellings et al.

Voting methods are instrumental design element of democracies. Citizens use them to express and aggregate their preferences to reach a collective decision. However, voting outcomes can be as sensitive to voting rules as they are to people's voting choices. Despite the significance and inter-disciplinary scientific progress on voting methods, several democracies keep relying on outdated voting methods that do not fit modern, pluralistic societies well, while lacking social innovation. Here, we demonstrate how one can upgrade real-world democracies, namely by using alternative preferential voting methods such as cumulative voting and the method of equal shares designed for a proportional representation of voters' preferences. By rigorously assessing a new participatory budgeting approach applied in the city of Aarau, Switzerland, we unravel the striking voting outcomes of fair voting methods: more winning projects with the same budget and broader geographic and preference representation of citizens by the elected projects, in particular for voters who used to be under-represented, while promoting novel project ideas. We provide profound causal evidence showing that citizens prefer proportional voting methods, which possess strong legitimacy without the need of very technical specialized explanations. We also reveal strong underlying democratic values exhibited by citizens who support fair voting methods such as altruism and compromise. These findings come with a global momentum to unleash a new and long-awaited participation blueprint of how to upgrade democracies.

Dirk Helbing, Matjaž Perc

In a non-sustainable, "over-populated" world, what might the use of nanotechnology-based targeted, autonomous weapons mean for the future of humanity? In order to gain some insights, we make a simplified game-theoretical thought experiment. We consider a population where agents play the public goods game, and where in parallel an epidemic unfolds. Agents that are infected defectors are killed with a certain probability and replaced by susceptible cooperators. We show that such "nanowars", even if aiming to promote good behavior and planetary health, fail not only to promote cooperation, but they also significantly increase the probability of repetitive epidemic waves. In fact, newborn cooperators turn out to be easy targets for defectors in their neighborhood. Therefore, counterintuitively, the discussed intervention may even have the opposite effect as desired, promoting defection. We also find a critical threshold for the death rate of infected defectors, beyond which resurgent epidemic waves become a certainty. In conclusion, we urgently call for international regulation of nanotechnology and autonomous weapons.

Mark C. Ballandies, Marcus M. Dapp, Benjamin A. Degenhart et al.

Cryptoeconomic systems derive their power but can not be controlled by the underlying software systems and the rules they enshrine. This adds a level of complexity to the software design process. At the same time, such systems, when designed with human values in mind, offer new approaches to tackle sustainability challenges, that are plagued by commons dilemmas and negative external effects caused by a one-dimensional monetary system. This paper proposes a design science research methodology with value-sensitive design methods to derive design principles for a value-sensitive socio-ecological cryptoeconomic system that incentivizes actions toward sustainability via multi-dimensional token incentives. These design principles are implemented in a software that is validated in user studies that demonstrate its relevance, usability and impact. Our findings provide new insights on designing cryptoeconomic systems. Moreover, the identified design principles for a value-sensitive socio-ecological financial system indicate opportunities for new research directions and business innovations.

Thomas Asikis, Johannes Klinglmayr, Dirk Helbing et al.

In a so-called overpopulated world, sustainable consumption is of existential importance.However, the expanding spectrum of product choices and their production complexity challenge consumers to make informed and value-sensitive decisions. Recent approaches based on (personalized) psychological manipulation are often intransparent, potentially privacy-invasive and inconsistent with (informational) self-determination. In contrast, responsible consumption based on informed choices currently requires reasoning to an extent that tends to overwhelm human cognitive capacity. As a result, a collective shift towards sustainable consumption remains a grand challenge. Here we demonstrate a novel personal shopping assistant implemented as a smart phone app that supports a value-sensitive design and leverages sustainability awareness, using experts' knowledge and "wisdom of the crowd" for transparent product information and explainable product ratings. Real-world field experiments in two supermarkets confirm higher sustainability awareness and a bottom-up behavioral shift towards more sustainable consumption. These results encourage novel business models for retailers and producers, ethically aligned with consumer preferences and with higher sustainability.

Johannes Beck, Roberta Huang, David Lindner et al.

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.

Carlos Gershenson, Dirk Helbing

The slower is faster (SIF) effect occurs when a system performs worse as its components try to do better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples, we generalize common features of the SIF effect and suggest possible future lines of research.