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The effects of repression on dissent are debated widely. We contribute to the debate by developing an agent-based model grounded in ethnographic interviews with dissidents. Building on new psychology research, the model integrates emotions as a dynamic context of dissent. The model moreover differentiates between four repression types: violence, street blockages, curfews and Facebook cuts. The simulations identify short-term dampening effects of each repression type, with a maximum effect related to non-violent forms of repression. The simulations also show long-term spurring effects, which are most strongly associated with state violence. In addition, the simulations identify nonlinear short-term spurring effects of state violence on early stage dissent. Such effects are not observed for the remaining repressive measures. Contrasting with arguments that violence deters dissent, this suggests that violence may fuel dissent, while non-violent repression might suppress it. [ABSTRACT FROM AUTHOR]

Background: Trees are a critical part of urban infrastructure. Cities worldwide are pledging afforestation objectives due to net-zero targets; however, their realisation requires a comprehensive framework that combines science, policy, and practice. Methods: The paper presents the Green Urban Scenarios (GUS) framework for designing and monitoring green infrastructures. GUS considers weather, maintenance, tree species, diseases, and spatial distributions of trees to forecast their impacts. The framework uses agent-based modelling (ABM) and simulation paradigm to integrate green infrastructure into a city’s ecological, spatial, economic, and social context. ABM enables the creation of digital twins for urban ecosystems at any level of granularity, including individual trees, to accurately predict their future trajectories. Digital representation of trees is created using a combination of datasets such as earth observations from space, street view images, field surveys, and qualitative descriptions of typologies within existing and future projects. Machine learning and statistical models calibrate biomass growth patterns and carbon release schemes. Results: The paper examines various green area typologies, simulating several hypothetical scenarios based on Glasgow’s urban forests. It exhibits the emergence of heterogeneity features of the forests due to interactions among trees. The growth trajectory of trees has a non-linear transition phase toward stable growth in its maturity. Reduced maintenance deteriorates the health of trees leading to lower survival rate and increased CO2 emissions, while the stormwater alleviation capacity may differ among species. Conclusions: The paper demonstrates how GUS can facilitate policies and maintenance of urban forests with environmental, social, and economic benefits. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape. [ABSTRACT FROM AUTHOR]

The water–tourism nexus requires better knowledge, management and governance to address environmental and societal challenges. This review takes stock of the approaches used to address this nexus in the Mediterranean from 2000 to 2020. Bibliometric and exploratory content analysis targeted tourism impacts on water supply, determinants of water consumption, and water-saving mechanisms and technologies. A fundamental insight is that the literature remains rather water centric and technical, paying little attention to behavioural change and stakeholder action. Promising avenues to reinforce sustainable water use include transdisciplinary approaches and integrated tools such as hydrosocial cycle analysis, concept mapping and agent-based modelling. [ABSTRACT FROM AUTHOR]

This editorial paper for the special section on “Modelling Values in Socio/Technical/Ecological Systems” introduces interdisciplinary perspectives on values and reflects on growing appeals for modelling values. In public and academic discourses, values typically relate to matters of importance (e.g., beliefs, priorities) and principles about what is considered to be good (e.g., moral values) and are often seen as shaping individual and collective behaviour. As shown by eight contributions to this special section, it is relevant for social simulation modelling to dive deeper into embedding values in models in order to explore behavioural change on different levels and across contexts. Our goal with this special section is to stimulate interest in developing various approaches that study and operationalise values in agent-based models to investigate the complex problems raised in social, socio-technical and socio-ecological systems. We conclude with a call for future research to be explicit in their modelling assumptions, thus fostering a vigorous foundation for scientific discourse. [ABSTRACT FROM AUTHOR]

For many applications of agent-based models (ABMs), an agent’s age influences important decisions (e.g. their contribution to/withdrawal from pension funds, their level of risk aversion in decision-making, etc.) and outcomes in their life cycle (e.g. their susceptibility to disease). These considerations make it crucial to accurately capture the age distribution of the population being considered. Often, empirical survival probabilities cannot be used in ABMs to generate the observed age structure. This may be due to discrepancies between samples (e.g. when empirical survival probabilities are calculated across the whole population, but only a subpopulation is being modelled) or models (between the survival model underpinning the ABM and the statistical model used to produce empirical survival probabilities). In these cases, imputing empirical survival probabilities will not generate the observed age structure of the population, and assumptions such as exogenous agent inflows are required (but not necessarily empirically valid). In this paper, we propose a method that allows for the preservation of agent age-structure without the exogenous influx of agents, even when only a subset of the population is being modelled. We demonstrate the flexibility and accuracy of our methodology by performing simulations of several real-world age distributions. This method is a useful tool for those developing ABMs across a broad range of applications. [ABSTRACT FROM AUTHOR]

Value and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. These are usually based on certain theoretical and conceptual assumptions. Their strengths and weaknesses are often difficult to determine and compare because they are based on limited empirical evidence. We propose agent-based modeling to build simulation models that can theoretically help us explore accounts of value change. We can investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we build a model based on the pragmatist account of value change proposed by Van De Poel & Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) societies differ in openness and resistance to change, 3) moral revolutions, and 4) lock-in. This makes this account promising, although more research is needed to see how well it can explain other relevant phenomena and compare its strengths and weaknesses to other accounts. On a more methodological level, our contribution suggests that simulation models might be useful to theoretically explore accounts of value change and make further progress in this area [ABSTRACT FROM AUTHOR]

Epiabm is an open-source software package for epidemiological agent-based modelling, re-implementing the well-known CovidSim model from the MRC Centre for Global Infectious Disease Analysis at Imperial College London. It has been developed as part of the first-year training programme in the EPSRC SABS:R3 Centre for Doctoral Training at the University of Oxford. The model builds an age-stratified, spatially heterogeneous population and offers a modular approach to configure and run epidemic scenarios, allowing for a broad scope of investigative and comparative studies. Two simulation backends are provided: a pedagogical Python backend (with full functionality) and a high performance C++ backend for use with larger population simulations. Both are highly modular, and include a comprehensive testing suite (including 100% coverage on unit tests alongside a range of functional and integration tests), as well as complete online documentation for ease of understanding and extensibility. Epiabm is publicly available through GitHub at github.com/SABS-R3-Epidemiology/epiabm. [ABSTRACT FROM AUTHOR]

Normative reasoning within the mainstream economic framework has been largely shaped by utilitarian ethics. The growing popularity of effective altruism indicates that the utilitarian spirit has also permeated the sphere of social sentiment, evaluating our pro-social behaviour and charitable giving in terms of efficiency. The present study assesses the appropriateness of judging social outcomes through the prism of allocative efficiency by questioning to what extent the society of effective altruists is robust, sustainable, and resilient. Using computer simulations based on the dictator game, we demonstrate that a society of welfare-maximising effective altruists can achieve an optimal outcome alongside equality under extremely restrictive assumptions, such as the uniformity of giving strategies (i.e. interacting with other effective altruists exclusively) and initial equality of wealth distribution. Yet, in the world of unequal opportunities, utilitarian giving tends to increase wealth disparity. In addition, in polymorphic societies, effective altruists underperform compared to deontological (or unconditional) altruists. Consequently, we demonstrate that striving for allocative efficiency might undermine the equality and resilience objectives and question whether the former should remain the dominant economic normative principle. [ABSTRACT FROM AUTHOR]

Technology is a complex adaptive system that is both shaped by, and shapes, institutional arrangements. This critical insight, developed in depth by W. Brian Arthur – the father of complexity economics – is relevant to researchers interested in institutions. Arthur provides a method for capturing the underlying dynamics. He offers conceptual tools centred on the concept of increasing returns to make sense of some crucial challenges. He also suggests technical tools, including agent-based modelling, to tackle ill-defined economic, legal, and institutional problems. This article explores his body of work and derives some institutional insights from it. [ABSTRACT FROM AUTHOR]

In the rapidly evolving global marketplace, the logistics sector faces a multitude of challenges that demand implementation of more resilient solutions to respond to any future disturbance. Synchromodal transport, which is viewed as an extension of multimodal transport, is known as a key answer to this issue, as it provides more flexible and sustainable freight transport and also focuses on collaboration between different logistics players. We consider synchromodal transport as a collection of agents that not only have their own characteristics and behaviors, but also interact with each other, which impacts the entire system. In this paper, we study the system using an Agent-Based Modeling approach. The network represents the combination of long-haul and drayage transport, where pre-haulage and end-haulage are done only by truck, and the rest can be done by trucks, trains, or barges. A numerical experiment is conducted to evaluate cost savings and emissions reduction under different logistics service providers' relation and re-routing scenarios. Our findings show that synchromodal scenarios are more economically and environmentally efficient, and that they lead to higher flexibility and reliability compared to business-as-usual scenarios. Additionally, our model verifies that the cost saving is considerable when logistics service providers collaborate with each other. The results of sensitivity analyses show consistent overall trends when comparing the different scenarios. Therefore, the conclusions drawn from the original experiment appear to be applicable, not only for that specific instance, but have broader relevance and applicability. [ABSTRACT FROM AUTHOR]

Background Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an imperfect radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time. Results In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC. Conclusions We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

Abstract According to the EU’s organic regulation, the use of organic seed is generally binding in organic farming. Because of an organic seed shortage, derogations to use nonorganic seed can be obtained. By 2036, the EU plans to phase out these derogations and achieve 100% organic seed use. Previous attempts at achieving this, though, have failed. Ensuring organic seed supply is of particular EU-wide importance to meet EU policy goals, such as the farm-to-fork strategy. To assess the impact of measures to smooth this transition, we developed the VAL-MAS model (VALue chain Multi-Agent System). VAL-MAS is a multiagent model based on a heterogeneous agent population and mathematical programming that can provide insights into the performance of different seed system interventions. We selected organic fresh market carrots in Germany for their importance in the national and European organic sector as an example case. Our model suggests that the end of the derogation system poses a challenge to the seed value chain in terms of seed supply and farm incomes. The most effective mitigation solution is an investment in improved pest control during seed multiplication, accompanied by a stepwise phasing out of derogations for the use of nonorganic seed.

Background Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children’s battleship game, where each battleship is an agent. Methods The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an imperfect radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time. Results In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC. Conclusions We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

How to use appropriate policy measures to intervene industrial symbiosis is valuable in theory but still lacks exploration. This paper discusses the effect of environmental taxation on industrial symbiosis networks. Firstly, the formation mechanism of industrial symbiotic network is analysed with the idea of agent-based modelling. Then, a simulation model was built to simulate the emergence process of industrial symbiosis networks. On this basis, the influence of environmental taxation on the structure and performance of the industrial symbiosis networks is explored. The results show that when the intensity of environmental tax is low, the industrial symbiotic network has the structural characteristics of random network. With the increase of environmental tax intensity, the cyclic ordering of network structure is gradually enhanced. The collection of environmental tax will not only reduce pollution, but also reduce the economic output of the network and reduce enterprise income to a greater extent. Finally, some relevant suggestions for the government to formulate environmental tax policy are provided based on the results.

The sterile insect technique (SIT) is an environmentally friendly pest control strategy that consists of inundative releases of mass-reared sterilized males over defined areas, where they mate with wild females, resulting in no offspring and a declining pest population. The technique has effectively managed many crop pests and vector-borne diseases worldwide. A new approach, called boosted SIT, has been proposed to gain efficiency. It combines SIT with the contamination of wild females by sterile males previously coated with biocides. The present study investigated to what extent life history traits of the target pest and biocides can make the boosted SIT more efficient than the classical SIT. We built a generic agent-based model (SIT++) that simulates the population dynamics of insect pests. We then explored parameters related to the mating system, spermatic competition, and fecundity, taking examples from the biology of three well-known Dipteran pest species (Bactrocera dorsalis, Ceratitis capitata, and Glossina palpalis gambiensis). We found that for boosted SIT to be more beneficial than SIT, horizontal transmission of the biocide to the same generation and to the progeny must be very high. Female fecundity was the other key parameter behind the success of boosted SIT, which was more efficient with insect pests having low reproduction rates. In particular, vertical transmission and late killing time were critical parameters. We also observed that a high level of virulence can help, but only when the boosted SIT is already advantageous; otherwise, it becomes detrimental. The boosted SIT might be advantageous depending on the life history traits of the target pest and transmission routes. For a more extensive exploration, the model can easily be tailored to pests with very different life history traits.

Sustainable development within a sharing economy requires close cooperation between participating organisations, while a more complicated management problem involves how to improve the cooperation performance between such organisations. This paper researches multi-organisational cooperation under a shared economy model to analyse the impact of the organisation's revenue distribution plan, the organisational members' characteristic abilities and other factors on the cooperation performance among organisations and core enterprises' profit performance. It aims to provide a theoretical basis for core enterprises to rationally formulate revenue distribution strategy and the promotion of sustainable development of the shared economy. The research results reveal that under the shared economy model, the core enterprise's revenue-distribution method based on fixed revenue and revenue-sharing can improve cooperation performance among organisational members to some extent. Moreover, the barriers and obstacles in organisations' cooperation process are small, which will positively increase system profits, core enterprise profit and the proportion of core enterprise profits, but does not necessarily improve system profit margins. The core enterprise should control the cooperation costs between organisations through different incentive strategies to achieve better system profits and system profit margins. [ABSTRACT FROM AUTHOR]

Hypermedia Multi-Agent System (MAS) Simulation is a novel approach to building agent-based simulations in which the environment is modelled as a set of linked hypermedia resources that are implemented using microservices. This paper discusses the implementation of an prototypical simulation system based on this concept and the lessons learnt in the process. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical model is developed on the basis of systems theory, which structures the livelihood system of low-income households in a European country characterized by a semi-peripheral economy. Based on the proposed model, the complex system of network connections and formal and informal financial transactions, which households use in their daily lives to cover their expenses, becomes graspable. The proposed theoretical model is analyzed through simulations based on agent-based modelling (ABM) centred on empirical network data. Through the simulations, the author explores the mechanisms of the market and asks what formal and informal credit transactions determine its operation, how these factors shape the local social structure and how resilient the market is to crises. The results show that this dynamic, complex risk-sharing system has an inherent logic and it can mitigate the small liquidity shocks but it is not resistant to bigger financial shocks or overconsumptions. [ABSTRACT FROM AUTHOR]

Successful climate change adaptation depends on the spread and maintenance of adaptive behaviours. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority–majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority–majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout populations better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs. This article is part of the theme issue 'Climate change adaptation needs a science of culture'. [ABSTRACT FROM AUTHOR]

Service systems for children and families have been shaped by standard approaches to knowledge‐building, which reflect a reductionist approach and assume linearity and/or that individuals and experiences are normally distributed. Yet, these approaches may be inadequate for clients most at‐risk, especially those who would be analytic 'outliers'. A complexity lens focuses on the whole system and seeks to identify patterns, including the dynamic interactions between components of the system. Social work scholars have begun to apply complexity theory to social work research efforts, demonstrating the conceptual potential of incorporating this theoretical approach into social work theories and models such as the person‐in‐environment framework and the ecosystems perspective. Yet, frameworks informed by complexity theory may require ontological and epistemological shifts in thinking and new methodological approaches in order to fully embody a complexity approach. Complexity theory offers the opportunity to consider social work clients who are most at‐risk, as it is better suited for power law distributions. We can, therefore, reconceptualize the most 'at‐risk' clients as being in a state of transition, which is also the space of most creativity and possibility. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects. Methods: Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions. Results: We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission. Conclusions: Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density. [ABSTRACT FROM AUTHOR]

Receptor dimerisation and higher order oligomerisation regulates signalling by a wide variety of transmembrane receptors. We discuss how agent-based modelling (ABM) combined with advanced microscopy and structural studies can provide new insights into the regulation of clustering, including spatial considerations, revealing novel targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

In the bounded confidence model (BC-model) (Hegselmann & Krause 2002), period by period, all agents average over all opinions that are no further away from their actual opinion than a given distance ?, i.e., their 'bound of confidence'. With the benefit of hindsight, it is clear that we completely overlooked a crucial feature of our model back in 2002. That is for increasing values of ?, our analysis suggested smooth transitions in model behaviour. However, the transitions are in fact wild, chaotic and non-monotonic-as described by Jan Lorenz (2006). The most dramatic example of these effects is a consensus that breaks down for larger values of ϵ. The core of this article is a fundamentally new approach to the analysis of the BC-model. This new approach makes the non-monotonicities unmissable. To understand this approach, we start with the question: how many different BC processes can we initiate with any given start distribution? The answer to this question is almost certainly for all possible start distributions and certainly in all cases analysed here, it is always a finite number of ϵ-values that make a difference for the processes we start. Moreover, there is an algorithm that finds, for any start distribution, the complete list of ?,-values that make a difference. Using this list, we can then go directly through all the possible BC-processes given the start distribution. We can therefore check them for non-monotonicity of any kind, and will be able to find them all. This good news comes however with bad news. That is the algorithm that inevitably and without exception finds all the ?-values that matter requires exact arithmetics, without any rounding and without even the slightest rounding error. As a consequence, we have to abandon the usual floating-point arithmetic used in today's computers and programming languages. What we need to use instead is absolutely exact fractional arithmetic with integers of arbitrary length. This numerical approach is feasible on all modern computers. The new analytical approach and results are likely to have implications for many applications of the BC-model. [ABSTRACT FROM AUTHOR]

Natural languages vary in their quantity expressions, but the variation seems to be constrained by general properties, so-called universals. Their explanations have been sought among constraints of human cognition, communication, complexity, and pragmatics. In this article, we apply a state-of-the-art language coordination model to the semantic domain of quantities to examine whether two quantity universals—monotonicity and convexity—arise as a result of coordination. Assuming precise number perception by the agents, we evolve communicatively usable quantity terminologies in two separate conditions: a numeric-based condition in which agents communicate about a number of objects and a quotient-based condition in which agents communicate about the proportions. We find out that both universals take off in all conditions but only convexity almost entirely dominates the emergent languages. Additionally, we examine whether the perceptual constraints of the agents can contribute to the further development of universals. We compare the degrees of convexity and monotonicity of languages evolving in populations of agents with precise and approximate number sense. The results suggest that approximate number sense significantly reinforces monotonicity and leads to further enhancement of convexity. Last but not least, we show that the properties of the evolved quantifiers match certain invariance properties from generalized quantifier theory. [ABSTRACT FROM AUTHOR]

The integration of early warning and evacuation systems (EWES) with estimations for mitigating the loss of life in flood risk assessments marks an advancement towards developing robust emergency action plans for dam breaks. Through the simulation of diverse EWES scenarios, the impact of these systems, coupled with community preparedness, on minimising the potential for loss of life could be calculated. This study was conducted in the theoretical context of a dam break located upstream from a densely populated urban region in Brazil. Hydrodynamic and agent-based models were utilised to estimate potential loss of life across various scenarios and simulations. The Monte Carlo approach, in combination with the LifeSim model, was applied to assess how factors such as warning issuance timing, evacuation strategies and community responses impact the model's outcomes. Sensitivity analysis was performed considering the overall exposed area and specific areas at risk for different spatiotemporal EWES strategies. The results of simulations highlighted the EWES' great potential for risk mitigation and displayed optimal times for warning issuance. The warning diffusion and the protective action initiation parameters proved crucial for improving EWES. The spatiotemporal sectorisation of the alert and evacuation was also an effective strategy to optimise EWES. This methodology should allow for further similar tests and incite EWES improvements based on consistent loss of life alleviation simulations. [ABSTRACT FROM AUTHOR]