Computational social science

The authors find, through experimental data and computational modeling, that altruistic acts stem from a motive cocktail of up to seven social and economic motives, whose strengths explain distinct behavior patterns across individuals and situations.

This paper introduces an algorithm to uncover laws of skill acquisition from naturally occurring data. By combining deep learning and symbolic regression, it accurately identifies cognitive states and extracts algebraic equations.

Cooperation is not merely a dyadic phenomenon, it also includes multi-way social interactions. A mathematical framework is developed to study how the structure of higher-order interactions influences cooperative behavior.

Using registry data from Denmark, Lehmann et al. create individual-level trajectories of events related to health, education, occupation, income and address, and also apply transformer models to build rich embeddings of life-events and to predict outcomes ranging from time of death to personality.

Zhi Liu et al. develop a method to measure disparities in reporting delays in urban crowdsourcing systems, uncovering socioeconomic disparities and providing actionable insights for interventions that enhance the efficiency and equity of city services.

The reasoning capabilities of OpenAI’s generative pre-trained transformer family were tested using semantic illusions and cognitive reflection tests that are typically used in human studies. While early models were prone to human-like cognitive errors, ChatGPT decisively outperformed humans, avoiding the cognitive traps embedded in the tasks.

A graph-based artificial intelligence model for urban planning outperforms human-designed plans in objective metrics, offering an efficient and adaptable collaborative workflow for future sustainable cities.

Real-world social networks are often ephemeral and subject to exogenous restructuring. Q. Su et al. show that dynamic networks can foster cooperative behavior.

The study presents a mobility centrality index to delineate urban dynamics in quasi-real time with mobile-phone data. The results indicate that urban structures were becoming more monocentric during the COVID-19 lockdown periods in major cities in Spain.

Mental conflict has been regarded as subjective instead of quantitative. This study developed a data-driven method to decode temporal dynamics of conflict between reward and curiosity, which can elucidate mechanisms of irrational decision-making.

A neural network-based language model of supra-word meaning, that is, the combined meaning of words in a sentence, is proposed. Analysis of functional magnetic resonance imaging and magnetoencephalography data helps identify the regions of the brain responsible for understanding this meaning.

A strategy for cooperation in repeated games, called cumulative reciprocity, is proposed. This strategy is robust with respect to errors, enforces fair outcomes, and evolves in environments that are usually hostile to cooperation.

A dynamic network model using data from ten European countries indicates that differences in micro-level social interactions can explain a substantial part of variations in the success of national pandemic policies.

This study suggests that a lack of co-location hinders the formation of ‘weak ties’—which are crucial for information spread—in communication networks on the basis of an analysis of an email network of more than 2,800 university researchers.

Smart pandemic mitigation strategies are proposed to strategically close higher-risk economic sectors, while allowing dozens of other economic sectors to continue. This would enable schools to remain open and keep hospitalizations within capacity.

The study shows that a memory-aware and socially coupled human movement model can reproduce urban growth patterns at the macro level, providing a bottom-up approach to understand urban growth and to reveal its connection to human mobility behavior.

An analysis of GPS pedestrian traces shows that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases and that (2) chosen paths are statistically different when origin and destination are swapped. Ultimately, this can explain the observed human attitude in selecting different paths upon return trips.

Combining human mobility data and nonlinear mathematical analysis techniques, this study offers insights into the interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic.

While large-scale GPS location datasets have been instrumental to applications in epidemiology, there are still several challenges with these data that should be considered and addressed to make data-driven epidemiology more reliable.

Although digital twins first originated as models of physical systems, they are rapidly being applied to social systems, such as cities. This Perspective discusses the development and use of digital twins for urban planning.

While the adherence to fairness constraints has become common practice in the design of algorithms across many contexts, a more holistic approach should be taken to avoid inflicting additional burdens on individuals in all groups, including those in marginalized communities.

The field of human mobility has evolved drastically in the past 20 years. In this Perspective, the authors discuss three key areas in human mobility, framed as minds, societies and algorithms, where they expect to see substantial improvements in the future.

While digital twins have been recently used to represent cities and their physical structures, integrating complexity science into the digital twin approach will be key to deliver more explicable and trustworthy models and results.

While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.

The widespread availability of digital traces capturing individuals’ daily mobility has the potential to enrich the understanding of the relationship between mobility, gender and socioeconomic factors. In fact, it has led to a heightened interest in deriving policy insights from these data. However, it is also essential to put the focus on methodological aspects to address the data gaps and biases.

Human mobility research intersects with various disciplines, with profound implications for urban planning, transportation engineering, public health, disaster management, and economic analysis. Here, we discuss the urgent need for open and standardized datasets in the field, including current challenges and lessons from other computational science domains, and propose collaborative efforts to enhance the validity and reproducibility of human mobility research.

Software is much more than just code. It is time to confront the complexity of licenses, uses, governance, infrastructure and other facets of software in science. Their influence is ubiquitous yet overlooked.

Nature Computational Science wants to publish your computational social science research.

Wildfires have increased in frequency and intensity due to climate change and have had severe impacts on the built environment worldwide. Moving forward, models should take inspiration from epidemic network modeling to predict damage to individual buildings and understand the impact of different mitigations on the community vulnerability in a network setting.

Urban digital twins hold immense promise as live computational models of cities, synthesizing diverse knowledge, streaming data, and supporting decisions towards more inclusive planning and policy. The size, heterogeneity, and open-ended character of cities, however, pose many difficult questions, at the frontiers of what is currently possible in computational science. Overcoming these challenges provides pathways for fundamental progress in the field and a proving ground for its economic value and social relevance.

Dr Diyi Yang, Assistant Professor of computer science at Stanford University, talks to Nature Computational Science about understanding human communication in a social context, building natural language processing systems that are human-centered, and the challenges that female researchers face in the field.

As we approach the half-way point in the implementation of the Sustainable Development Goals, we discuss how computational science could help in reaching some of these goals by 2030.

Progress towards universal access to safe drinking water and nutritious food has been moving forward at a slower than desired rate. Computational tools can help accelerate progress towards these goals, but solutions need to be open source, and designed, developed and implemented in a participatory manner.

Rapid urban expansion presents a major challenge to delivering the United Nations Sustainable Development Goals. Urban populations are forecast to increase by 2.2 billion by 2050, and business as usual will condemn many of these new citizens to lives dominated by disaster risk. This need not be the case. Computational science can help urban planners and decision-makers to turn this threat into a time-limited opportunity to reduce disaster risk for hundreds of millions of people.

Social media and other internet platforms are making it even harder for researchers to investigate their effects on society. One way forward is user-sourced data collection of data to be shared among many researchers, using robust ethics tools to protect the interests of research participants and society.

Data science studies have provided evidence that regulating gun ownership can save lives. However, US lawmakers still fail to follow the science.

Dr Srijan Kumar, assistant professor at Georgia Institute of Technology and a Forbes 30 Under 30 honoree in science, discusses with Nature Computational Science how he uses machine learning and data science to identify and mitigate malicious activities on online platforms, including misinformation and anti-Asian hate speech.