arXiv subjects

Simone Daniotti

Publications and source records attributed to Simone Daniotti.

7 recordsLinked to original sources

arXiv2026

The Changing Global Division of Labor in Software: Emergence and Diffusion of New Programming Skills across IT Hubs

With the rise of new industries, often new jobs emerge. Evolutionary Economic Geography and in particular Industry Life Cycle perspectives predict that these activities first emerge in a limited number of cities to then diffuse to other locations as job descriptions become more standardized. Here, we focus on a particularly important new industry: software development, an activity that is economically important, quickly changing, and has a pronounced spatial concentration in a small number of global IT hubs. We use an online database of over 60 million questions and answers about problems in software development that yields a longitudinal dataset of 237 software skills. By geo-locating 3 million posting users at regular intervals, we link these skills to cities worldwide. We find that, in spite of its digital nature, the software industry exhibits similar spatial regularities as previously observed in more traditional sectors. First, cities diversify into skills that are related to their existing ones. Second, new skills first emerge in cities with large and diversified software sectors, and later diffuse -- mostly unhindered by geographical distance -- to smaller cities specialized in closely related skills. We find suggestive but limited support for a windows of locational opportunity account: although even brand-new skills still emerge first in cities with strong prior specialization in related skills, concentrations of related activities impact less the emergence of new skills than the diffusion of existing ones.

cs.SI↗

arXiv2025

Who is using AI to code? Global diffusion and impact of generative AI

Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in over 30 million GitHub commits by 170,000 developers, tracking how fast -- and where -- these tools take hold. Today, AI writes an estimated 29% of Python functions in the US, a modest and shrinking lead over other countries. We estimate that quarterly output, measured in online code contributions, has increased by 3.6% because of this. Our evidence suggests that programmers using AI may also more readily expand into new domains of software development. However, experienced programmers capture nearly all of these productivity and exploration gains, widening rather than closing the skill gap.

cs.CY↗

arXiv2025

Using digital traces to analyze software work: skills, careers and programming languages

Recent waves of technological transformation are reshaping work in uncertain and hard-to-predict ways. However, jobs at the forefront of the digitizing economy offer an early glimpse of these changes and leave rich activity traces. We exploit such traces in tens of millions of Question and Answer posts on Stack Overflow for the creation of a fine-grained taxonomy of software skills to analyze human capital in the global software industry. Constructing a software skill space that maps relations among these skills reveals that real-world software jobs demand highly coherent skill sets and that programmers learn through a process of related diversification. The latter process often leads to the acquisition of lower-value skills. However, when programmers use Python they preferentially target higher-value skills, offering a potential explanation for Python's successful rise as a dominant general purpose language.

econ.GN↗

arXiv2025

The Coherence of US cities

Diversified economies are critical for cities to sustain their growth and development, but they are also costly because diversification often requires expanding a city's capability base. We analyze how cities manage this trade-off by measuring the coherence of the economic activities they support, defined as the technological distance between randomly sampled productive units in a city. We use this framework to study how the US urban system developed over almost two centuries, from 1850 to today. To do so, we rely on historical census data, covering over 600M individual records to describe the economic activities of cities between 1850 and 1940, and 8 million patent records as well as detailed occupational and industrial profiles of cities for more recent decades. Despite massive shifts in the economic geography of the U.S. over this 170-year period, average coherence in its urban system remains unchanged. Moreover, across different time periods, datasets and relatedness measures, coherence falls with city size at the exact same rate, pointing to constraints to diversification that are governed by a city's size in universal ways.

physics.soc-ph↗

arXiv2023

Systemic risk approach to mitigate delay cascading in railway networks

In public railway systems, minor disruptions can trigger cascading events that lead to delays in the entire system. Typically, delays originate and propagate because the equipment is blocking ways, operational units are unavailable, or at the wrong place at the needed time. The specific understanding of the origins and processes involved in delay-spreading is still a challenge, even though large-scale simulations of national railway systems are becoming available on a highly detailed scale. Without this understanding, efficient management of delay propagation, a growing concern in some Western countries, will remain impossible. Here, we present a systemic risk-based approach to manage daily delay cascading on national scales. We compute the {\em systemic impact} of every train as the maximum of all delays it could possibly cause due to its interactions with other trains, infrastructure, and operational units. To compute it, we design an effective impact network where nodes are train services and links represent interactions that could cause delays. Our results are not only consistent with highly detailed and computationally intensive agent-based railway simulations but also allow us to pinpoint and identify the causes of delay cascades in detail. The systemic approach reveals structural weaknesses in railway systems whenever shared resources are involved. We use the systemic impact to optimally allocate additional shared resources to the system to reduce delays with minimal costs and effort. The method offers a practical and intuitive solution for delay management by optimizing the effective impact network through the introduction of new cheap local train services.

physics.soc-ph↗

arXiv2022

Maximum Entropy Approach for the Prediction of Urban Mobility Patterns

The science of cities is a relatively new and interdisciplinary topic. It borrows techniques from agent-based modeling, stochastic processes, and partial differential equations. However, how the cities rise and fall, how they evolve, and the mechanisms responsible for these phenomena are still open questions. Scientists have only recently started to develop forecasting tools, despite their importance in urban planning, transportation planning, and epidemic spreading modeling. Here, we build a fully interpretable statistical model that, incorporating only the minimum number of constraints, can predict different phenomena arising in the city. Using data on the movements of car-sharing vehicles in different Italian cities, we infer a model using the Maximum Entropy (MaxEnt) principle. With it, we describe the activity in different city zones and apply it to activity forecasting and anomaly detection (e.g., strikes, and bad weather conditions). We compare our method with different models explicitly made for forecasting: SARIMA models and Deep Learning Models. We find that MaxEnt models are highly predictive, outperforming SARIMAs and having similar results as a Neural Network. These results show how relevant statistical inference can be in building a robust and general model describing urban systems phenomena. This article identifies the significant observables for processes happening in the city, with the perspective of a deeper understanding of the fundamental forces driving its dynamics.

physics.soc-ph↗

arXiv2018

Qubit systems subject to unbalanced random telegraph noise: quantum correlations, non-Markovianity and teleportation

We address the dynamics of quantum correlations in a two-qubit system subject to unbalanced random telegraph noise (RTN) and discuss in details the similarities and the differences with the balanced case. We also evaluate quantum non-Markovianity of the dynamical map. Finally, we discuss the effects of unbalanced RTN on teleportation, showing that noise imbalance mitigates decoherence and preserves teleportation fidelity.

quant-ph↗