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

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

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

A chromosome conformation capture ordered sequence of the barley genome

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Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

I mötet mellan fälten -Sociala medier som en arena för Place branding

Denna uppsats syftar till att bidra till en sammankoppling utav de vetenskapliga fälten Place branding och sociala medier, eftersom vi inte har funnit någon litteratur som tillgodoser denna länk. Detta leder oss in på frågan: Varför är detta område viktigt att studera? Fler och fler företag väljer idag att marknadsföra sig via sociala medier på grund av dess enkelhet, snabbhet och kostnadseffektivitet. Genom att veta hur man ska använda sig av medierna på mest effektivt sätt kan man därmed ha mycket att vinna på detta. Vår studie kan med detta bidra till att stärka destinationers arbete med denna marknadsföringskanal

I mötet mellan fälten- Sociala medier som en arena för Place branding

Denna uppsats syftar till att bidra i sammankopplingen utav de vetenskapliga fälten Place branding och sociala medier, eftersom vi inte har funnit någon litteratur som tillgodoser denna länk. Detta leder oss in på frågan: Varför är detta område viktigt att studera? Fler och fler företag väljer idag att marknadsföra sig via sociala medier på grund av dess enkelhet, snabbhet och kostnadseffektivitet. Genom att veta hur man ska använda sig av medierna på det mest effektiva sättet kan man därmed ha mycket att vinna på detta. Vår studie kan därmed bidra till städers arbete med denna marknadsföringskanal

Exploring User Heterogeneity in Human Delegation Behavior towards AI

As artificial intelligence (AI) can increasingly be used to support decision-making in various areas, enhancing the understanding of human-AI collaboration is more important than ever. We study delegation between humans and AI as one form of collaboration. Specifically, we investigate whether there exist distinct patterns of human delegation behavior towards AI. In a laboratory experiment, subjects performed an image classification task with 100 images to be classified. For the last 50 images, the treatment group had the option to delegate images to an AI. By performing a cluster analysis on this treatment, we find four types of delegation behavior towards AI that differ in their overall performance, delegation rate, and their accuracy of self-assessment. Our results motivate further research on delegation of humans to AI and act as a starting point to research on human collaboration with AI on an individual level

Epidemiology of Plasmids in <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> with Acquired Extended Spectrum Beta-Lactamase Genes Isolated from Chronic Wounds in Ghana

Little information is available on the local epidemiology of mobile genetic elements such as plasmids harboring acquired beta-lactamase genes in Western African Ghana. In the present study, we screened for plasmids in three Escherichia coli and four Klebsiella pneumoniae isolates expressing extended spectrum beta-lactamases (ESBL) mediated by the blaCTX-M-15 gene from chronically infected wounds of Ghanaian patients. Bacterial isolates were subjected to combined short-read and long-read sequencing to obtain the sequences of their respective plasmids. In the blaCTX-M-15-gene-carrying plasmids of the four ESBL-positive K. pneumoniae isolates, IncFIB/IncFII (n = 3) and FIA (n = 1) sequences were detected, while in the blaCTX-M-15-gene-carrying plasmids of the three ESBL-positive E. coli isolates, IncFIA/IncFIB (n = 2) and IncFIB (n = 1) sequences were found. The three IncFIB/IncFII sequence-containing plasmids were almost identical to a K. pneumoniae plasmid reported from France. They belonged to the clonal lineages ST17, ST36 and ST39 of K. pneumoniae, suggesting transversal spread of this obviously evolutionary successful plasmid in Ghana. Other resistance gene-encoding plasmids observed in the assessed Enterobacterales harbored IncFIA/IncR and IncFII sequences. International spread was confirmed by the high genetic similarity to resistance-mediating plasmids published from Asia, Australia, Europe and Northern America, including a blaCTX-M-15-gene-carrying plasmid isolated from a wild bird in Germany. In conclusion, the study contributed to the scarcely available information on the epidemiology of third-generation cephalosporine resistance-mediating plasmids in Ghana. Furthermore, the global spread of resistance-mediating plasmids provided hints on the evolutionary success of individual resistance-harboring plasmids by transversal spread among K. pneumoniae lineages in Ghana