arXiv subjects

Ching Jin

Publications and source records attributed to Ching Jin.

5 recordsLinked to original sources

arXiv2026

Human adaptive variability stabilises collective traffic dynamics

Automated systems are often designed on the assumption that replacing human behavioural variability with precise, uniform algorithmic control improves collective performance. In automotive traffic, this principle underlies commercial adaptive cruise control (ACC). Using two large-scale human-driving experiments comprising 2.95 million car-following observations, a 25-vehicle platoon experiment and a controlled 11-driver protocol, cross-validated with 0.77 million observations from the NGSIM dataset and data from 22 production ACC systems, together with empirically calibrated ACC simulations, we show the opposite: rigid algorithmic uniformity creates systemic fragility. Commercial rule-based controllers amplify small local perturbations into severe stop-and-go waves, increasing fuel consumption and carbon emissions by approximately 2.7- to 5.0-fold across scenarios. Human-driven platoons, by contrast, progressively dissipate disturbances and maintain smoother flow. We identify the behavioural mechanism behind this advantage: human car-following does not follow a fixed proportional spacing rule. Drivers continuously reshape their time-headway distributions across speed regimes, exhibiting a non-monotonic shift from efficiency-oriented to risk-sensitive regulation. This speed-dependent variability generates nonlinear damping that suppresses the synchronisation and propagation of local errors. Our findings challenge the view that human variability is merely suboptimal noise to be eliminated. More broadly, they suggest that robust large-scale interactive AI systems should embed adaptive, human-inspired behavioural flexibility rather than rely on rigid uniformity.

physics.soc-ph↗

arXiv2024

Quantifying the Dynamics of Innovation Abandonment Across Scientific, Technological, Commercial, and Pharmacological Domains

Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle.

physics.soc-ph↗

arXiv2024

The Innovative Distinctiveness of Prizewinners and their Networks

Science prizes purportedly reward innovation and explorations of new phenomena. Yet, in practice prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, knowledge of how prizewinning relates to innovation is nascent even as prizes proliferate widely. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched non-prizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic's historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched non-prizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners' papers become more innovative than their matched peers, a difference that widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to non-prizewinners, prizewinners' collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The implications of the findings for the efficacy of reward systems and innovation in science are discussed.

cs.DL↗

arXiv2020

Scientific Prizes and the Extraordinary Growth of Scientific Topics

Fast growing scientific topics have famously been key harbingers of the new frontiers of science, yet, large-scale analyses of their genesis and impact are rare. We investigate one possible factor connected with a topic's extraordinary growth: scientific prizes. Our longitudinal analysis of nearly all recognized prizes worldwide and over 11,000 scientific topics from 19 disciplines indicates that topics associated with a scientific prize experience extraordinary growth in productivity, impact, and new entrants. Relative to matched non-prizewinning topics, prizewinning topics produce 40% more papers and 33% more citations, retain 55% more scientists, and gain 37% and 47% more new entrants and star scientists, respectively, in the first five-to-ten years after the prize. Funding do not account for a prizewinning topic's growth. Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment.

cs.DL↗

arXiv2017

Emergence of Scaling in Complex Substitutive Systems

Diffusion processes are central to human interactions. One common prediction of the current modeling frameworks is that initial spreading dynamics follow exponential growth. Here, we find that, ranging from mobile handsets to automobiles, from smart-phone apps to scientific fields, early growth patterns follow a power law with non-integer exponents. We test the hypothesis that mechanisms specific to substitution dynamics may play a role, by analyzing a unique data tracing 3.6M individuals substituting for different mobile handsets. We uncover three generic ingredients governing substitutions, allowing us to develop a minimal substitution model, which not only explains the power-law growth, but also collapses diverse growth trajectories of individual constituents into a single curve. These results offer a mechanistic understanding of power-law early growth patterns emerging from various domains and demonstrate that substitution dynamics are governed by robust self-organizing principles that go beyond the particulars of individual systems.

physics.soc-ph↗