Mechanopathology of biofilm-like Mycobacterium tuberculosis cords.

Mycobacterium tuberculosis (Mtb) cultured axenically without detergent forms biofilm-like cords, a clinical identifier of virulence. In lung-on-chip (LoC) and mouse models, cords in alveolar cells contribute to suppression of innate immune signaling via nuclear compression. Thereafter, extracellular cords cause contact-dependent phagocyte death but grow intercellularly between epithelial cells. The absence of these mechanopathological mechanisms explains the greater proportion of alveolar lesions with increased immune infiltration and dissemination defects in cording-deficient Mtb infections. Compression of Mtb lipid monolayers induces a phase transition that enables mechanical energy storage. Agent-based simulations demonstrate that the increased energy storage capacity is sufficient for the formation of cords that maintain structural integrity despite mechanical perturbation. Bacteria in cords remain translationally active despite antibiotic exposure and regrow rapidly upon cessation of treatment. This study provides a conceptual framework for the biophysics and function in tuberculosis infection and therapy of cord architectures independent of mechanisms ascribed to single bacteria.

Simulating institutional heterogeneity in sustainability science.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

Explaining the evolution of gossip.

Gossip, the exchange of personal information about absent third parties, is ubiquitous in human societies. However, the evolution of gossip remains a puzzle. The current article proposes an evolutionary cycle of gossip and uses an agent-based evolutionary game-theoretic model to assess it. We argue that the evolution of gossip is the joint consequence of its reputation dissemination and selfishness deterrence functions. Specifically, the dissemination of information about individuals' reputations leads more individuals to condition their behavior on others' reputations. This induces individuals to behave more cooperatively toward gossipers in order to improve their reputations. As a result, gossiping has an evolutionary advantage that leads to its proliferation. The evolution of gossip further facilitates these two functions of gossip and sustains the evolutionary cycle.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Uncertainty in boundedly rational household adaptation to environmental shocks.

Despite the growing calls to integrate realistic human behavior in sustainability science models, the representative rational agent prevails. This is especially problematic for climate change adaptation that relies on actions at various scales: from governments to individuals. Empirical evidence on individual adaptation to climate-induced hazards reveals diverse behavioral and social factors affecting economic considerations. Yet, implications of replacing the rational optimizer by realistic human behavior in nature-society systems models are poorly understood. Using an innovative evolutionary economic agent-based model we explore different framings regarding household adaptation behavior to floods, leveraging on behavioral data from a household survey in Miami, USA. We find that a representative rational agent significantly overestimates household adaptation diffusion and underestimates damages compared to boundedly rational behavior revealed from our survey. This "adaptation deficit" exhibited by a population of empirically informed agents is explained primarily by diverse "soft" adaptation constraints-awareness, social influences-rather than heterogeneity in financial constraints. Besides initial inequality disproportionally impacting low/medium adaptive capacity households post-flood, our findings suggest that even under a nearly complete adaptation diffusion, adaptation benefits are uneven, with late or less-efficient actions locking households to a path of higher damages, further exacerbating inequalities. Our exploratory modeling reveals that behavioral assumptions shape the uncertainty of physical factors, like exposure and objective effectiveness of flood-proofing measures, traditionally considered crucial in risk assessments. This unique combination of methods facilitates the assessment of cumulative and distributional effects of boundedly rational behavior essential for designing tailored climate adaptation policies, and for equitable sustainability transitions in general.

Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission.

Nonrandom foraging can cause animals to aggregate in resource dense areas, increasing host density, contact rates and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the agent-based model and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behaviour and resource distributions alter contact rate functions and pathogen transmission.

Understanding the Role of Naive Learners in Cultural Change.

AbstractA change to a population's social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover-or the repeated replacement of individuals by naive individuals. Experimental data have led to the hypothesis that naive learners can drive cultural evolution by better assessing the relative value of behaviors, although this hypothesis has been expressed only verbally. We conducted a formal exploration of this hypothesis using a generative model that concurrently simulated its two key ingredients: social transmission and reinforcement learning. We simulated competition between high- and low-reward behaviors while varying turnover magnitude and tempo. Variation in turnover influenced changes in the distributions of cultural behaviors, irrespective of initial knowledge-state conditions. We found optimal turnover regimes that amplified the production of higher reward behaviors through two key mechanisms: repertoire composition and enhanced valuation by agents that knew both behaviors. These effects depended on network and learning parameters. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability. By moving from experimental data to theory, we illuminate an underdiscussed generative process that can lead to changes in cultural behavior, arising from an interaction between social dynamics and learning.

Steep Hierarchies without Skew? Modeling How Ecology and Decision-Making Shape Despotism of Relationships.

AbstractAnimals can form dominance relationships that vary from highly unequal, or despotic, to egalitarian, and this variation likely impacts the fitness of individuals. How and why these differences in relationships and fitness exist among groups, populations, and species has been the subject of much debate. Here, we investigated the influence of two major factors: (1) spatial resource distribution and (2) the presence or absence of winner-loser effects. To determine the effects of these factors, we built an agent-based model that represented 10 agents directly competing over food resources on a simple landscape. By varying the food distribution and using either asymmetry of strength or experience, we contrasted four scenarios from which we recorded attack decisions, fight outcomes, and individual energy intake to calculate dominance hierarchy steepness and energetic skew. Surprisingly, resource distribution and winner-loser effects did not have the predicted effects on hierarchy steepness. However, skew in energy intake arose when resources were distributed heterogeneously, despite hierarchy steepness frequently being higher in the homogeneous resource scenarios. Thus, this study confirms some decades-old predictions about feeding competition but also casts doubt on the ability to infer energetic consequences from observations of agonistic interactions.

Antibody-dependent enhancement and neutralization against CVB4 investigated in vitro and in silico through an agent-based model.

The infection with coxsackievirus B4 (CVB4) can be enhanced in vitro by antibodies directed against the viral capsid protein VP4. In peripheral blood mononuclear cells, antibody-dependent enhancement (ADE) of CVB4 infection leads to the production of interferon alpha (IFN-α). To investigate ADE of CVB4-induced production of IFN-α, an agent-based model was constructed with enhancing and neutralizing antibodies. The model recapitulates viral neutralization and ADE in silico. The enhancing and neutralizing activities of serum samples were evaluated in vitro to confront the model predictions with experimental results. Increasing the incubation time of CVB4 with serum samples improves virus neutralization in silico as well as in vitro. It also results in ADE at lower antibody numbers in silico, which is confirmed in vitro with IFN-α production at lower serum concentrations. Furthermore, incubation of CVB4 with serum at a low temperature does not induce IFN-α production in vitro. Thus, taken together our results suggest that enhancing antibodies bind cryptic epitopes, more accessible with longer incubation time and at higher temperature due to changes in capsid conformation, consistent with previous results indicating that enhancing antibodies are anti-VP4 antibodies.

Uncovering physical activity trade-offs in transportation policy: A spatial agent-based model of Bogotá, Colombia.

BACKGROUND: Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity. METHODS: The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios. RESULTS: Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups. CONCLUSIONS: Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

Eradicating an invasive mammal requires local elimination and reduced reinvasion from an urban source population.

Invasive mammal eradications are increasingly attempted across large, complex landscapes. Sequentially controlled management zones can be at risk of reinvasion from adjacent uncontrolled areas, and managers must weigh the relative benefits of ensuring complete elimination from a zone or minimizing reinvasion risk. This is complicated in urban areas, where habitat heterogeneity and a lack of baseline ecological knowledge increase uncertainty. We applied a spatial agent-based model to predict the reinvasion of a well-studied species, the brushtail possum (Trichosurus vulpecula), across an urban area onto a peninsula that is the site of an elimination campaign in Aotearoa New Zealand. We represented fine-scale urban habitat heterogeneity in a land cover layer and tested management scenarios that varied four factors: the density of possums remaining following an elimination attempt, the maintenance trap density on the peninsula, and effort expended toward preventing reinvasion by means of a high-density trap buffer at the peninsula isthmus or control of the source population adjacent to the peninsula. We found that achieving complete elimination on the peninsula was crucial to avoid rapid repopulation. The urban isthmus was predicted to act as a landscape barrier and restrict immigration onto the peninsula, but reliance on this barrier alone would fail to prevent repopulation. In combination, complete elimination, buffer zone, and source population control could reduce the probability of possum repopulation to near zero. Our findings support urban landscape barriers as one tool for sequential invasive mammal elimination but reaffirm that novel methods to expose residual individuals to control will be necessary to secure elimination in management zones. Work to characterize the urban ecology of many invasive mammals is still needed.

Evaluating Losses from Water Scarcity and Benefits of Water Conservation Measures to Intercity Supply Chains in China.

The severe water scarcity in China poses significant economic risks to its agriculture, energy, and manufacturing sectors, which can have a cascading effect through the supply chains. Current research has assessed water scarcity losses for global countries and Chinese provinces by using the water scarcity risk (WSR) method. However, this method involves subjective functions and parameter settings, and it fails to capture the adaptive behaviors of economies to water scarcity, compromising the reliability of quantified water scarcity loss. There is a pressing need for a new method to assess losses related to water scarcity. Here, we develop an agent-based complex network model to estimate the inter-regional and intersectoral impacts of water scarcity on both cities and basins. Subsequently, we evaluate the supply chain-wide economic benefits of four different water conservation measures as stipulated by the 14th Five-Year Plan for the Construction of a Water-Saving Society. These measures include increasing the utilization rate of recycled water in water-scarce cities, reducing the national water consumption per industrial value-added, and implementing agricultural and residential water conservation measures. Results show that direct losses constitute only 9% of the total losses from water scarcity. Approximately 37% of the losses can be attributed to interregional impacts. Among the water-scarce cities, Qingdao, Lanzhou, Jinan, and Zhengzhou pose a significant threat to China's supply chains. Agricultural water conservation yields the highest amount of water savings and economic benefits, while residential water conservation provides the highest economic benefit per unit of water saved. The results provide insights into managing water scarcity, promoting cross-regional cooperation, and mitigating economic impacts.

Impact of interventions to reduce nosocomial transmission of SARS-CoV-2 in English NHS Trusts: a computational modelling study.

BACKGROUND: Prior to September 2021, 55,000-90,000 hospital inpatients in England were identified as having a potentially nosocomial SARS-CoV-2 infection. This includes cases that were likely missed due to pauci- or asymptomatic infection. Further, high numbers of healthcare workers (HCWs) are thought to have been infected, and there is evidence that some of these cases may also have been nosocomially linked, with both HCW to HCW and patient to HCW transmission being reported. From the start of the SARS-CoV-2 pandemic interventions in hospitals such as testing patients on admission and universal mask wearing were introduced to stop spread within and between patient and HCW populations, the effectiveness of which are largely unknown. MATERIALS/METHODS: Using an individual-based model of within-hospital transmission, we estimated the contribution of individual interventions (together and in combination) to the effectiveness of the overall package of interventions implemented in English hospitals during the COVID-19 pandemic. A panel of experts in infection prevention and control informed intervention choice and helped ensure the model reflected implementation in practice. Model parameters and associated uncertainty were derived using national and local data, literature review and formal elicitation of expert opinion. We simulated scenarios to explore how many nosocomial infections might have been seen in patients and HCWs if interventions had not been implemented. We simulated the time period from March-2020 to July-2022 encompassing different strains and multiple doses of vaccination. RESULTS: Modelling results suggest that in a scenario without inpatient testing, infection prevention and control measures, and reductions in occupancy and visitors, the number of patients developing a nosocomial SARS-CoV-2 infection could have been twice as high over the course of the pandemic, and over 600,000 HCWs could have been infected in the first wave alone. Isolation of symptomatic HCWs and universal masking by HCWs were the most effective interventions for preventing infections in both patient and HCW populations. Model findings suggest that collectively the interventions introduced over the SARS-CoV-2 pandemic in England averted 400,000 (240,000 - 500,000) infections in inpatients and 410,000 (370,000 - 450,000) HCW infections. CONCLUSIONS: Interventions to reduce the spread of nosocomial infections have varying impact, but the package of interventions implemented in England significantly reduced nosocomial transmission to both patients and HCWs over the SARS-CoV-2 pandemic.

Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation.

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

Flow-mediated olfactory communication in honeybee swarms.

Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

HyPedSim: A Multi-Level Crowd-Simulation Framework-Methodology, Calibration, and Validation.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based model to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data are extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimises the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimising computational performance compared to other models.

Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency.

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation management. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifications for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolutions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or underestimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs.

Introducing wetland offset markets under development-restoration conflicts: The role of public offset credit supply.

Wetland offset markets (WOMs) are increasingly applied worldwide as powerful tools for mitigating conflicts between wetland development and restoration. Reducing benefit uncertainty is key to promoting private restoration and introducing WOMs, which necessitates sufficient and stable price signals. Given that governments are important suppliers in WOMs, this article aims to explore the role of public offset credit (OC) supply in delivering and adjusting price signals during WOM formation and evolution. A general spatial agent-based wetland offset market model is built to simulate landowners' behavior, price dynamics, and WOM evolution under different public OC supply schemes. The results show that the spontaneous formation of WOMs is a time-consuming process. Price signals of public OCs reduce price fluctuations at the early stage of WOMs. This price stabilizing effect can cause a long-term reduction in benefit uncertainty perceived by landowners. Therefore, public OCs can facilitate WOM formation either through the supply side with high supply prices or through the demand side with low supply prices. During the entire WOM evolution process, due to landowners' readaptation, cheap public OCs can cause significant market fluctuations following the ceasing of cheap public supplies. The impacts of public OC on wetland development and restoration might change over time, and the suitability of public OC supplies under different long-term wetland management preferences was analyzed. These findings can further the understanding of the process of introducing a new market mechanism, such as WOMs, and the role of the government as a supplier. The research results provide insights for WOM practices, public restoration and OC supply scheme design, and wetland development-restoration conflict coordination.

Exploring Winegrowers' Behaviours and Ecological Impacts Under Climate Change and Policy Scenarios-Examples from Three European Winegrowing Regions.

Viticulture is an example of a socio-ecological system that poses serious challenges for sustainable soil management and pesticide use, with various interactions between winegrowers' decision-making and ecological consequences. This study introduces an agent-based model (ABM) on winegrowers' decision on inter-row management and pesticide use. The ABM builds upon an empirical study of winegrowers' decision-making in European viticultural landscapes and has been built for three case study regions: Leithaberg (Austria), Palatinate (Germany) and Târnave (Romania). The ABM allows for analysing potential effects of policy instruments including mandatory vegetation cover in the inter-rows, the reduction of fungicide use and ban of insecticides against Lobesia botrana. The effects of policies differ between the case study regions, indicating how important the local context is for effective policies. For example, policies aiming at higher inter-row vegetation cover had the strongest effects on vegetation cover, landscape aesthetics and soil loss in Târnave since many vineyards are currently intensively tilled and there exist no policies supporting inter-row vegetation cover in Romania.

A Dynamic Network Model of Societal Complexity and Resilience Inspired by Tainter's Theory of Collapse.

In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies. Examples include the COVID-19 pandemic, which brought unprecedented health challenges and economic disruptions, and the emergence of geopolitical tensions and conflicts that have further strained international relations and economic stability. While empirical evidence on the dynamics and drivers of past societal collapse is mounting, a process-based understanding of these dynamics is still in its infancy. Here, we aim to identify and illustrate the underlying drivers of such societal instability or even collapse. The inspiration for this work is Joseph Tainter's theory of the "collapse of complex societies", which postulates that the complexity of societies increases as they solve problems, leading to diminishing returns on complexity investments and ultimately to collapse. In this work, we abstract this theory into a low-dimensional and stylized model of two classes of networked agents, hereafter referred to as "laborers" and "administrators". We numerically model the dynamics of societal complexity, measured as the fraction of "administrators", which was assumed to affect the productivity of connected energy-producing "laborers". We show that collapse becomes increasingly likely as the complexity of the model society continuously increases in response to external stresses that emulate Tainter's abstract notion of problems that societies must solve. We also provide an analytical approximation of the system's dominant dynamics, which matches well with the numerical experiments, and use it to study the influence on network link density, social mobility and productivity. Our work advances the understanding of social-ecological collapse and illustrates its potentially direct link to an ever-increasing societal complexity in response to external shocks or stresses via a self-reinforcing feedback.