Disaster Information, Resilience, for Emergency and Crisis Technologies

Permanent URI for this collectionhttps://hdl.handle.net/10125/112460

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Now showing 1 - 9 of 9

Reactions for Geopolitical Shocks on Online Labour Platform: Empirical Evidence Under Ukraine-Russia War
(2026-01-06) Lin, Qingyuan; Lu, Angela
As digital labor platforms play an increasingly central role in the global workforce, their governance decisions—especially in response to geopolitical crises—have profound implications for market outcomes and worker livelihoods. Despite their growing importance, little is known about how platforms strategically respond to external shocks and how these decisions reshape opportunity structures for different types of freelancers. This study exploits a natural experiment arising from a platform governance decision to remove all Russian freelancers in May 2022, following the onset of the Russia–Ukraine war. Drawing on a granular panel dataset of 428,401 daily work records linked to 80,141 freelancer profiles, we apply a regression discontinuity in time (RDiT) design to estimate the causal effects of this exogenous labor supply shock. We find that the intervention significantly increased average earnings for the remaining workforce, primarily through higher income per task rather than an increase in task volume. By situating the analysis within a platform governance perspective, this study contributes to research on labor market shocks, platform strategy, and algorithmic reallocation. Our findings show that platform responses to geopolitical risk can reinforce structural inequalities through selective opportunity redistribution, highlighting the strategic role of platform governance in shaping post-shock labor market dynamics.
Structural Complexities and Dependencies in Layered Security: Baking in Deterrence through Target Shifting
(2026-01-06) John, Richard; Kapadia, Kevin; Unson, Ian; Zhuang, Jun
This study examined how defense complexity and dependency, expected value (EV), rewards, and individual characteristics influence target selection for multi-layered security systems. Participants (N = 366) selected targets with one, two, or three security layers across 12 scenarios where EV was held constant within scenarios. We discovered (a) when the structure of security layers was independent, participants were more likely to choose single-layer targets, highlighting the role of choice architecture in shaping adversarial preferences; (b) rewards (incentivized vs. non-incentivized) did not significantly affect target selection; (c) target choice followed a linear and quadratic relationship with EV, with multi-layered targets most preferred at moderate EVs, and (d) individual traits, such as meanness and financial risk-taking, predicted preference towards single-layer targets and boldness towards multiple security layers. These findings demonstrate that structural complexity and decision framing can meaningfully influence attacker behavior and provide practical insights for designing more effective, deterrence-based security strategies.
Digital Frontlines: Social Media Engendered Polarization During Geopolitical Crises
(2026-01-06) Jain, Gaurav; Kamble, Aakash; Narbariya, Sonali; Gupta , Samrat
The international landscape has become increasingly volatile, marked by a growing number of military conflicts and geopolitical confrontations. Social media platforms play a crucial role in managing and shaping such episodes of crisis because content shared through these platforms drive user engagement. Despite the growing interest in investigating the relationship between social media and polarization, existing research presents contradictory findings across different socio-cultural and empirical contexts. This inconsistency highlights the need for investigating whether user engagement on geopolitical war or conflict related social media content contributes to increasing or decreasing polarization. In this paper, we examine the user generated content on the Russia-Ukraine war and the Israel-Hamas carnage. Employing network-based opinion dynamics we explore how exposure to video content in social media evolves. The results reveal that polarization steadily grows with the maturity of the discussions as the consensus emerges after numerous rounds of interaction among users. The study has implications on crisis governance and digital literacy efforts that aim at reducing platform driven fracture during geopolitical conflicts.
AI for Aid: Using ReliefNET-GNN to Enhance Transportation Resilience in Disaster Response
(2026-01-06) Aguirre, Daniel; Cardozo, Nicolas; Herrera, Andrea
Transport networks are essential for disaster response, yet identifying critical links within them remains a computationally intensive task, especially at scale. Traditional vulnerability assessment requires exhaustive simulations, limiting their practical use in time-sensitive crisis situations. We present ReliefNET-GNN, a graph neural network model that efficiently predicts the criticality of transport links relevant to humanitarian aid delivery. Trained on synthetic and real-world networks labeled with established vulnerability metrics, the model enables rapid inference without costly scenario evaluations.Preliminary results show that ReliefNET-GNN achieves comparable accuracy to traditional methods while drastically reducing computational time, making it suitable for real-time governmental decision-making during emergencies. This research contributes to the development of scalable, AI-driven tools that enhance disaster resilience and crisis management capabilities, particularly for public sector actors.
Data-Driven Behavioral Agent Modeling for Effective Resident’S Evacuation Prediction on Flood Disasters
(2026-01-06) Matsuki, Akira; Ikeda, Keisuke; Senzaki, Kenta; Tani, Masahiro; Hatayama, Michinori
We propose a novel methodology for modeling residents’ evacuation behavior during flood disasters, based on empirical data. Existing evacuation behavior models have not sufficiently evaluated prediction accuracy nor extracted characteristics of residents. Recently, people flow data during disasters has become increasingly available. In this study, we developed a data-driven agent model utilizing resident behavioral data under disaster events. As a result, our models demonstrated a marked improvement in predictive accuracy compared to conventional empirically-based models, especially by incorporating behavioral characteristics, like evacuation to higher ground. Specifically, we constructed both a “data-fitting typed agent model,” which reproduces individual residents’ evacuation decisions regarding occurrence, timing, and destination, as well as a “statistical decision-making agent model,” which incorporates psychological factors and regional characteristics. We quantitatively evaluated the predictive accuracy and compared distinctive features of both models. Experimental results confirmed that the proposed approach reduced prediction error by up to 14.8%.
The Roles and Implications of Black Swan Events in Tackling Deep Uncertainty in Relief Distribution: A Collective Case Study
(2026-01-06) Rahman, Mohammad Tafiqur; Sein, Maung
Relief distribution (RD) after a natural disaster is conducted under conditions of deep uncertainty (DU). The dynamic context is characterized by limited and often conflicting, ever-changing information flows, as well as interrupted material flows, even when response operations are well-prepared. RD decision making (RDDM) is thus challenging. In this paper, we draw upon concepts of Black Swan Events (BSE) to understand how DU challenges (DUC) can be mitigated. By revealing known and unknown consequences of BSEs during operations, decision-makers can identify and evaluate critical points in relief distribution management steps. We analyzed the relief efforts during the “2007 Sidr Cyclone” case in Bangladesh to identify and understand potential DUCs and related BSEs. Then we verified the concepts through two cases, the “2015 Gorkha Earthquake” in Nepal and the “2018 Sulawesi Earthquake” in Indonesia. Based on our findings, we propose a BSE-informed DUC-tackling framework for more effective RDDM in practice.
From Alerts to Action: Designing Crisis Apps that Enable Self-Help in Storm-Flood Events
(2026-01-06) Milutzki, Enrico; Borchers, Marten; Hummel, Jan; Magdych, Valeria; Bittner, Eva; Semmann, Martin
This paper explores the design of mobile emergency applications to enhance citizen self-help during storm flood events, addressing critical gaps in current warning systems. Through a comprehensive literature review and the application of the design science research paradigm, we identify 13 socio-technical issues, such as low user motivation, mistrust, information overload, and fragmented community support. We formulated twelve meta-requirements and derived eight actionable design principles. Based on these findings, we developed a prototype and evaluated it through qualitative walk-throughs with ten citizens. Our findings indicate high usability, trust, and perceived helpfulness, validating the layered, credibility-centered, and community-aware approach. The findings suggest that effective storm-flood apps should incorporate straightforward navigation, action-linked alerts, visual hazard communication, step-by-step guidance, and neighborhood networks. This work extends existing crisis communication guidelines, offering practical insights for designing citizen-empowering emergency tools that align with the Sustainable Development Goals for resilient urban communities.
Pumped Up Kicks: The Impact of Social Contagion and Informational Cues on Evacuation Behavior and Exposure to Threat in a Simulated School Crisis
(2026-01-06) Kapadia, Kevin; Franczak, Alyssa; Yongsatianchot, Nutchanon; Marsella, Stacy; John, Richard
As school shootings increase in frequency, understanding behavior in response to active shooter threats is essential for emergency disaster preparedness. This study utilized a 3D Unity simulation to examine how social and informational cues influence evacuation and exposure to threat. A total of 842 participants were assigned to one of 27 conditions in a 3 (NPC behavior: run, hide, mixed) × 3 (proximal information: run, hide, none) × 3 (public address: run, hide, none) design. Participants were more likely to evacuate when cues encouraged running, particularly when proximal information was present. However, congruent run cues increased exposure to threat, likely due to impulsive crowd-following. Individual factors such as age, positive affect, and experience with first-person shooter games predicted outcomes, suggesting variability in how people process and respond to high-stress situations. These findings highlight the need for emergency protocols that integrate clear communication and account for differences in response and awareness.
Introduction to the Minitrack on Disaster Information, Resilience, for Emergency and Crisis Technologies
(2026-01-06) Radianti, Jaziar; Negre, Elsa; Gjøsæter, Terje

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