arXiv subjects

Chris T. Bauch

Publications and source records attributed to Chris T. Bauch.

17 recordsLinked to original sources

arXiv2026

Impacts of social and impact heterogeneity on social-climate outcomes

Regional heterogeneity in social characteristics, temperature change, and vulnerability to climate impacts is likely to influence the magnitude of anthropogenic climate change, but has not been considered in coupled social-climate models, which seek to represent interactions between social and climate dynamics. Here, we examine how the projected mean global temperature anomaly and population support for mitigation respond to heterogeneity in these factors across five regions of the world, using a coupled social-climate model. We find that heterogeneity in climate impacts increases the temperature anomaly by 0.2$^\circ$C, while social heterogeneity increases it by an additional 0.1$^\circ$C. The projected temperature anomaly also increases with higher variability in climate impacts across regions, even for the same average global climate impact. Finally, we identify a social-climate tipping point, where low vulnerability to impacts under existing social conditions in one region can tip the system into an alternative slow-mitigation, high-temperature state. Our results show that heterogeneity in climate impacts leads to higher global mean temperatures and efforts to reduce global disparities could improve both social and climate outcomes.

physics.soc-ph↗

arXiv2026

A systematic review of COVID-19 epidemic models with endogenous human behaviour. What's next?

Human behaviour and epidemic dynamics are intertwined, yet accounting for this feedback remains one of the key challenges of epidemiological modelling. The COVID-19 pandemic was an opportunity to overcome the traditional limitations of the field, raising expectations that data-informed endogenous approaches to behaviour modelling would advance substantially. To quantify the progresses made, we conducted a systematic review of SARS-CoV-2 transmission models endogenously including human behaviour in response to epidemic dynamics. The COVID-19 pandemic saw great strides in terms of the expanded use of empirical data in epi-behavioural modelling. However, it also showed shortcomings with respect to limited use of behavioural empirical data, lack of innovation in model structure, and limited engagement with other disciplines and decision-makers. Overall, our results suggest that identifying priorities in model design and behavioural data, building an adequate data collection infrastructure, leveraging on AI advancements, and fostering interdisciplinarity are strategies of utmost importance for pandemic preparedness.

physics.soc-ph↗

arXiv2025

Space, time and altruism in pandemics and the climate emergency

Climate change is a global emergency, as was the COVID-19 pandemic. Why was our collective response to COVID-19 so much stronger than our response to the climate emergency, to date? We hypothesize that the answer has to do with the scale of the systems, and not just spatial and temporal scales but also the `altruistic scale' that measures whether an action must rely upon altruistic motives for it to be adopted. We treat COVID-19 and climate change as common pool resource problems that exemplify coupled human-environment systems. We introduce a framework that captures regimes of containment, mitigation, and failure to control. As parameters governing these three scales are varied, it is possible to shift from a COVID-like system to a climate-like system. The framework replicates both inaction in the case of climate change mitigation, as well as the faster response that we exhibited to COVID-19. Our cross-system comparison also suggests actionable ways that cooperation can be improved in large-scale common pool resources problems, like climate change. More broadly, we argue that considering scale and incorporating human-natural system feedbacks are not just interesting special cases within non-cooperative game theory, but rather should be the starting point for the study of altruism and human cooperation.

physics.soc-ph↗

arXiv2025

When Simple is Enough, Binary Models Capture Social Complexity in Coupled Human-Environment Systems

Models of coupled human-environment systems often face a tradeoff between realism and tractability. Spectrum opinion models, where social preferences vary continuously, offer descriptive richness but are computationally demanding and parameter-heavy. Binary formulations, in contrast, are analytically simpler but raise concerns about whether they can capture key socio-ecological feedbacks. Here we systematically compare binary and spectrum social models across four benchmark settings: (i) replicator dynamics coupled to a climate-carbon system, (ii) FJ opinion dynamics coupled to the climate-carbon system, (iii) replicator dynamics coupled to a forest-grassland ecological system, and (iv) FJ opinion dynamics coupled to a forest-grassland ecological system. We employ the relative integrated absolute error (RIAE) to quantify deviations between binary (N=2) and spectrum (N=100) formulations of social opinion dynamics in feedback with ecological subsystems. Across systematic parameter sweeps of learning rates, reluctance, conformity, susceptibility, runaway amplitudes, and ecological turnover, the binary formulation typically tracks its spectrum counterpart to within 15 percent for most parameter combinations. Deviations beyond this arise mainly under very high social susceptibility or near-vanishing ecological turnover, where additional opinion modes and nonlinear feedbacks matter. We therefore present the binary formulation as a practical surrogate, not a universal replacement. As a rule of thumb, it is adequate when susceptibility is moderate, ecological turnover appreciable, and runaway amplitudes not extreme; in high-susceptibility or low-turnover regimes, especially near critical transitions, the full-spectrum model is preferable. This framing guides readers on when a binary reduction is sufficient versus when full-spectrum detail is warranted.

physics.soc-ph↗

arXiv2025

Implications of regional variations in climate change vulnerability and mitigation behaviour for social-climate dynamics

How regional heterogeneity in social and cultural processes drive--and respond to--climate dynamics is little studied. Here we present a coupled social-climate model stratified across five world regions and parameterized with geophysical, economic and social survey data. We find that support for mitigation evolves in a highly variable fashion across regions, according to socio-economics, climate vulnerability, and feedback from changing temperatures. Social learning and social norms can amplify existing sentiment about mitigation, leading to better or worse global warming outcomes depending on the region. Moreover, mitigation in one region, as mediated by temperature dynamics, can influence other regions to act, or just sit back, thus driving cross-regional heterogeneity in mitigation opinions. The peak temperature anomaly varies by several degrees Celsius depending on how these interactions unfold. Our model exemplifies a framework for studying how global geophysical processes interact with population-scale concerns to determine future sustainability outcomes.

physics.soc-ph↗

arXiv2025

Echoes Before Collapse: Deep Learning Detection of Flickering in Complex Systems

Deep learning offers powerful tools for anticipating tipping points in complex systems, yet its potential for detecting flickering (noise-driven switching between coexisting stable states) remains unexplored. Flickering is a hallmark of reduced resilience in climate systems, ecosystems, financial markets, and other systems. It can precede critical regime shifts that are highly impactful but difficult to predict. Here we show that convolutional long short-term memory (CNN LSTM) models, trained on synthetic time series generated from simple polynomial functions with additive noise, can accurately identify flickering patterns. Despite being trained on simplified dynamics, our models generalize to diverse stochastic systems and reliably detect flickering in empirical datasets, including dormouse body temperature records and palaeoclimate proxies from the African Humid Period. These findings demonstrate that deep learning can extract early warning signals from noisy, nonlinear time series, providing a flexible framework for identifying instability across a wide range of dynamical systems.

cs.LG↗

arXiv2025

Neural models for prediction of spatially patterned phase transitions: methods and challenges

Dryland vegetation ecosystems are known to be susceptible to critical transitions between alternative stable states when subjected to external forcing. Such transitions are often discussed through the framework of bifurcation theory, but the spatial patterning of vegetation, which is characteristic of drylands, leads to dynamics that are much more complex and diverse than local bifurcations. Recent methodological developments in Early Warning Signal (EWS) detection have shown promise in identifying dynamical signatures of oncoming critical transitions, with particularly strong predictive capabilities being demonstrated by deep neural networks. However, a machine learning model trained on synthetic examples is only useful if it can effectively transfer to a test case of practical interest. These models' capacity to generalize in this manner has been demonstrated for bifurcation transitions, but it is not as well characterized for high-dimensional phase transitions. This paper explores the successes and shortcomings of neural EWS detection for spatially patterned phase transitions, and shows how these models can be used to gain insight into where and how EWS-relevant information is encoded in spatiotemporal dynamics. A few paradigmatic test systems are used to illustrate how the capabilities of such models can be probed in a number of ways, with particular attention to the performances of a number of proposed statistical indicators for EWS and to the supplementary task of distinguishing between abrupt and continuous transitions. Results reveal that model performance often changes dramatically when training and test data sources are interchanged, which offers new insight into the criteria for model generalization.

physics.comp-ph↗

arXiv2025

Social dynamics can delay or prevent climate tipping points by speeding the adoption of climate change mitigation

Social behaviour models are increasingly integrated into climate change studies, and the significance of climate tipping points for `runaway' climate change is well recognised. However, there has been insufficient focus on tipping points in social-climate dynamics. We developed a coupled social-climate model consisting of an Earth system model and a social behaviour model, both with tipping elements. The social model explores opinion formation by analysing social learning rates, the net cost of mitigation, and the strength of social norms. Our results indicate that the net cost of mitigation and social norms have minimal impact on tipping points when social norms are weak. As social norms strengthen, the climate tipping point can trigger a tipping element in the social model. However, faster social learning can delay or prevent the climate tipping point: sufficiently fast social learning means growing climate change mitigation can outpace the oncoming climate tipping point, despite social-climate feedback. By comparing high- and low-risk scenarios, we demonstrated high-risk scenarios increase the likelihood of tipping points. We also illustrate the role of a critical temperature anomaly in triggering tipping points. In conclusion, understanding social behaviour dynamics is vital for predicting climate tipping points and mitigating their impacts.

math.DS↗

arXiv2024

Early Warning Signals for Bifurcations Embedded in High Dimensions

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about the properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.

physics.comp-ph↗

arXiv2024

Predicting discrete-time bifurcations with deep learning

Many natural and man-made systems are prone to critical transitions -- abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal (EWS) for critical transitions by learning generic features of bifurcations (dynamical instabilities) from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an EWS for the five local discrete-time bifurcations of codimension-1. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier outperforms commonly used EWS under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.

q-bio.QM↗

arXiv2022

Universal Early Warning Signals of Phase Transitions in Climate Systems

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical data sets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on 2D Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially-resolved Earth systems.

physics.ao-ph↗

arXiv2021

Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts--between individuals or between population centres--are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

cs.SI↗

arXiv2020

A well-timed switch from local to global agreements accelerates climate change mitigation

Recent attempts at cooperating on climate change mitigation highlight the limited efficacy of large-scale agreements, when commitment to mitigation is costly and initially rare. Bottom-up approaches using region-specific mitigation agreements promise greater success, at the cost of slowing global adoption. Here, we show that a well-timed switch from regional to global negotiations dramatically accelerates climate mitigation compared to using only local, only global, or both agreement types simultaneously. This highlights the scale-specific roles of mitigation incentives: local incentives capitalize on regional differences (e.g., where recent disasters incentivize mitigation) by committing early-adopting regions, after which global agreements draw in late-adopting regions. We conclude that global agreements are key to overcoming the expenses of mitigation and economic rivalry among regions but should be attempted once regional agreements are common. Gradually up-scaling efforts could likewise accelerate mitigation at smaller scales, for instance when costly ecosystem restoration initially faces limited public and legislative support.

nlin.AO↗

arXiv2019

Cooperation in a generalized age-structured spatial game

The emergence and prevalence of cooperative behavior within a group of selfish individuals remains a puzzle for \text{evolutionary game theory} precisely because it conflicts directly with the central idea of natural selection. Accordingly, in recent years, the search for an understanding of how cooperation can be stimulated, even when it conflicts with individual interest, has intensified. We investigate the emergence of cooperation in an age-structured evolutionary spatial game. In it, players age with time and the payoff that they receive after each round \text{depends on} their age. \text{We find that t}he outcome of the game is strongly influenced by the type of distribution used to modify the payoffs according to the age of each player. The results show that, under certain circumstances, cooperators may not only survive but dominate the population.

physics.soc-ph↗

arXiv2018

Truncation selection and diffusion on lattices

Evolutionary games on graphs have been extensively studied. A variety of graph structures, graph dynamics, and behaviours of replicators have been explored. These models have primarily been studied in the framework of facilitation of cooperation, and much previous research has shed light on this field of study. However, there has been little attention devoted to truncation selection as most models employ proportional selection (such as in the replicator equation) or `imitate the best.' Here we systematically explore truncation selection on periodic square lattices, where replicators below a fitness threshold are culled and the reproduction probabilities are equal for all survivors. We employ two variations of this method: independent truncation, where the threshold is fixed; and dependent truncation, which is a generalization of `imitate the best.' Further, we explore the effects of diffusion in our networks in the following orders of operation: contest-diffusion-offspring (CDO), and diffusion-contest-offspring (DCO). CDO and DCO frequently facilitate and diminish cooperation, respectively. For independent truncation, we find three regimes determined by the fitness threshold: cooperation decreases as we raise the threshold; polymorphisms and extinction can occur; and the entire population goes extinct. Further, we show how an intermediate sucker's payoff maximizes cooperation in the DCO independent truncation model. We find that dependent truncation affects games differently; lower levels reduce cooperation for the Hawk Dove game and increase it for the Stag Hunt, and higher levels produce the opposite effects. We compare these truncation methods to proportional selection, and show that they can facilitate cooperation. We conclude that truncation selection can impact the prevalence of cooperation in complex ways, and therefore merit further study.

q-bio.PE↗

arXiv2016

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

physics.soc-ph↗

arXiv2006

Epidemic branching processes with and without vaccination

Here we treat the transmission of disease through a population as a standard Galton-Watson branching process, modified to take the presence of vaccination into account. Vaccination reduces the number of secondary infections produced per infected individual. We show that introducing vaccination in a population therefore reduces the expected time to extinction of the infection. We also prove results relating the distribution of number of secondery infections with and without vaccinations.

math.PR↗