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Cruising Drivers’ Response to Changes in Parking Prices in a Serious Game
Abstract
Scarcity of on-street parking in cities centers is a known factor motivating drivers to drive slowly (“to cruise”) while searching for an available parking place and is associated with negative externalities e.g., congestion, accidents, fuel waste and air pollution. Finding the correct prices is suggested to bring cruising to a sustainable level. However, current research methods based on surveys and simulations fail to provide a full understanding of drivers’ cruising preference and their behavioral response to price changes. We used the PARKGAME serious game, which provides a real-world abstraction of the dynamic cruising experience. Eighty-three players participated in an experiment under two pricing scenarios. Pricing was spatially designed as “price rings”, decreasing when receding from the desired destination point. Based on the data, we analyzed search time, parking distance, parking location choice and spatial searching patterns. We show that such a pricing policy may substantially reduce the cruising problem, motivating drivers to park earlier—further away from the destination or in the lot, especially when occupancy levels are extremely high. We further discuss the policy implications of these findings.KeywordsSerious gamesCruisingDriver behaviorParking search
I introduce parameterised response zero intelligence (PRZI), a new form of zero intelligence (ZI) trader intended for use in simulation studies of the dynamics of continuous double auction markets. Like Gode and Sunder’s classic ZIC trader, PRZI generates quote prices from a random distribution over some specified domain of discretely valued allowable quote prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0,+1.0] that determines the strategy for that trader. When s=0s=0, a PRZI trader behaves identically to the ZIC strategy, with a uniform PMF; but when s≈±1 the PRZI trader’s PMF becomes maximally skewed to one extreme or the other of the price range, thereby making it more or less “urgent” in the prices that it generates, biasing the quote price distribution towards or away from the trader’s limit price. To explore the co-evolutionary dynamics of populations of PRZI traders that dynamically adapt their strategies, I show initial results from long-term market experiments in which each trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate alternative s-values and choose the most profitable at any given time. In these experiments the profitability of any particular s-value may be non-stationary because the profitability of one trader’s strategy at any one time can depend on the mix of strategies being played by the other traders at that time, which are each themselves continuously adapting. Results from these market experiments demonstrate that the population of traders’ strategies can exhibit rich dynamics, with periods of stability lasting over hundreds of thousands of trader interactions interspersed by occasional periods of change. Python source code for PRZI traders, and for the stochastic hill-climber, have been made publicly available on GitHub.
This article describes and justifies a reporting standard to improve data use documentation in Agent-Based Modelling. Following the development of reporting standards for models themselves, empirical modelling has now developed to the point where these standards need to take equally effective account of data use (which previously has tended to be an afterthought to model description). It is particularly important that a standard should allow the reporting of the different uses to which data may be put (specification, calibration and validation), but also that it should be compatible with the integration of different kinds of data (for example, survey, ethnographic and experimental) sometimes known as mixed methods research. The article motivates the need for standards generally, and positions the distinctive contribution of the RAT-RS reporting standard. It describes how the standard was developed to ensure its usability, presents and explains it, and describes possibilities for future development.
Energy communities are key elements for local energy transitions, collectively generating, distributing and consuming energy, using renewable energy technologies. Thermal energy communities, as one type of energy community, are focused on thermal energy applications, such as heating, cooling, bathing, showering and providing hot tap water. As thermal energy applications and systems receive increasing academic and policy attention, there is a need to better understand the formation processes they undergo. In this study, various technical and institutional conditions are explored that influence thermal energy community formation processes by using an agent-based modelling approach. The results show that technology selection is not the most crucial and determining factor for the success of thermal energy communities, yet the surrounding institutional conditions are. Key factors that influence these formation processes pertain to providing training, so that the thermal energy community leaders become more skilled, and allocating subsidies based on the projects’ degree of environmental friendliness. For all stakeholders, finding the balance between all of the decision-making criteria is key to success. The results are useful for practitioners - and especially for policy makers - to develop more impactful policies and strategies to support the expansion of local thermal energy communities.
The environmental footprint of housing is greatly influenced by the size of a dwelling. Housing size is the result of households’ dwelling selections; accordingly, it is critical to consider residential preferences and choices to inform efforts towards housing sustainability. This study aimed to understand tenants’ preferences for and choices of housing size as one amongst several dwelling characteristics and identify obstacles and opportunities for reducing size in the light of promoting sustainable housing. We employed logistic regression models to analyse a survey with 878 Swiss tenants, and our results identify preference for large dwellings as a major obstacle for reducing dwelling size among affluent tenants. Conversely, tenants with lower income might be forced to move to a smaller dwelling due to financial constraints or attribute higher importance to the financial benefit of lower rents. However, financial disincentives along with substantial non-monetary costs of moving, such as the disruption of local bonds and the difficulty of finding a satisfactory dwelling, can outweigh the benefits of moving to a smaller dwelling. To overcome such obstacles, we suggest offering incentives and other facilitating measures for downsizing moves as well as ensuring an adequate supply of smaller dwellings capable of providing high living quality. We highlight the potential of studying housing functions to conceptualize dwellings fulfilling these requirements.
In previous chapters we have described the results and analysis of social simulations for crisis situations based on the experiences of the ASSOCC framework. Whereas we managed to build an implementation in a very short time, based on many years of fundamental research, such an implementation is inherently limited due to the many tasks and challenges that are to be dealt within high time pressure (e.g. keeping up with the many emerging public concerns and specific stakeholder issues with relatively unstable theoretical background while dealing with scaling up technical challenges). This chapter proposes and discusses what can be prepared for future crises, from fundamental conceptual building blocks, to decision components, and technical implementations. Building upon the experience of what we missed and had to create or deal with(out) during the crisis, this chapter is dedicated to point how, as a community, we can be ready to adaptively respond to future crises.
Statistical models attempting to predict who will disaffiliate from religions have typically accounted for less than 15% of the variation in religious affiliations, suggesting that we have only a partial understanding of this vital social process. Using agent-based simulations in three “artificial societies” (one predominantly religious; one predominantly secular; and one in between), we demonstrate that worldview pluralism within one’s neighborhood and family social networks can be a significant predictor of religious (dis)affiliation but in pluralistic societies worldview diversity is less important and, instead, people move toward worldview neutrality. Our results suggest that there may be two phases in religious disaffiliation: (1) the early adopters initially disaffiliate regardless of social support, and subsequently (2) disaffiliation spreads as support for it within local social networks widens and it appears more acceptable. An important next step is for sociologists to confirm or correct the theoretical findings of this model using real-world social-network data, which will require overcoming the measurement difficulties involved in estimating each individual’s degree of local network pluralism.
This article proposes (and demonstrates the effectiveness of) a new strategy for assessing the results of epidemic models which we designate reproduction. The strategy is to build an independent model that uses (as far as possible) only the published information about the model to be assessed. In the example presented here, the independent model also follows a different modelling approach (agent-based modelling) to the model being assessed (the London School of Hygiene and Tropical Medicine compartmental model which has been influential in COVID lockdown policy). The argument runs that if the policy prescriptions of the two models match then this independently supports them (and reduces the chance that they are artefacts of assumptions, modelling approach or programming bugs). If, on the other hand, they do not match then either the model being assessed is not provided with sufficient information to be relied on or (perhaps) there is something wrong with it. In addition to justifying the approach, describing the two models and demonstrating the success of the approach, the article also discusses additional benefits of the reproduction strategy independent of whether match between policy prescriptions is actually achieved.
Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.
Protocol registration
The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1.
Climate change and rapid urbanization exacerbate flood risks worldwide. The recognition of the crucial role that human actors play in altering risks and resilience of flood-prone cities triggers a paradigm shift in climate risks assessments and drives the proliferation of computational models that include societal dynamics. Yet, replacing a representative rational actor dominant in climate policy models with a variety of behaviorally-rich agents that interact, learn, and adapt is not straightforward. Focusing on the costliest climate-exacerbated hazard, flooding, we review computational agent-based models that include behavioral change and societal dynamics. We distinguish between two streams of literature: one stemming from economics & behavioral sciences and another from hydrology. Our findings show that most studies focus on households while representing decisions of other agents (government, insurance, urban developers) simplistically and entirely overlooking firms' choices in the face of risks. The two communities vary in the extent they ground agents' rules in social theories and behavioral data when modeling boundedly-rational decisions. While both aspire to trace feedbacks that agents collectively instigate, they employ different learning and interactions when computing societal dynamics in the face of climate risks. Dynamics of hazard, exposure, and vulnerability components of flood risks driven by incremental adaptation of agents are well represented. We highlight that applying a complex adaptive system perspective to trace the evolution of resilience can lead to a better understanding of transformational adaptation. The methodological advances in computational models with heterogeneous behaviorally-rich adaptive agents are relevant for adaptation to different climate-driven hazards beyond flooding.
Intracerebral haemorrhage (ICH) accounts for half of the disability-adjusted life years lost due to stroke worldwide. Care pathways for acute stroke result in the rapid identification of ICH, but its acute management can prove challenging because no individual treatment has been shown definitively to improve its outcome. Nonetheless, acute stroke unit care improves outcome after ICH, patients benefit from interventions to prevent complications, acute blood pressure lowering appears safe and might have a modest benefit, and implementing a bundle of high-quality acute care is associated with a greater chance of survival. In this article, we address the important questions that neurologists face in the diagnosis and acute management of ICH, and focus on the supporting evidence and practical delivery for the main acute interventions.
This article begins with a brief outline of recent advances in the application of computer modeling to sustainability research, identifying important gaps in coverage and associated limits in methodological capability, particularly in regard to taking account of the tangled human factors that are often impediments to a sustainable future. It then describes some of the ways in which a new transdisciplinary approach within “human simulation” can contribute to the further development of sustainability modeling, more effectively addressing such human factors through its emphasis on stakeholder, policy professional, and subject matter expert participation, and its focus on constructing more realistic cognitive architectures and artificial societies. Finally, the article offers philosophical reflections on some of the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation, considered in light of the importance of human factors, including values and worldviews, in the modeling process. Based on this philosophical analysis, we encourage more explicit conversations about the value of naturalism and secularism in finding and facilitating effective and ethical strategies for sustainable development.
Occupancy analyses represent a crucial topic for building performance. At present, this is even true because of the pandemic emergency due to SARS-CoV-2 and the need to support the functional analysis of building spaces in relation to social distancing rules. Moreover, the need to assess the suitability of spaces in high occupancy buildings as the educational ones, for which occupancy evaluations result pivotal to ensure the safety of the end-users in their daily activities, is a priority. The proposed paper investigates the steps that are needed to secure a safe reopening of an educational building. A case study has been selected as a test site to analyse the reopening steps as required by Italian protocols and regulations. This analysis supported the school director of a 2-to-10 year old school and its team in the decision-making process that led to the safe school reopening. Available plants and elevations of the building were collected and a fast digital survey was carried out using the mobile laser scanner technology (iMMS-Indoor Mobile Mapping System) in order to acquire three-dimensional geometries and digital photographic documentation of the spaces. A crowd simulation software (i.e. Oasys MassMotion) was implemented to analyse end-users flows; the social distance parameter was set in its proximity modelling tools in order to check the compliance of spaces and circulation paths to the social distancing protocols. Contextually to the analysis of users flows, a plan of hourly air changes to maintain a high quality of the environments has been defined.
The smart city trend has generated considerable interest in using digital technology to transform urban planning and governance, and in the UK the government funded Connected Places Catapult has been given the remit of stimulating innovation in cities. One of its focuses is urban planning and technology (#PlanTech) which has garnered attention from the Royal Town Planning Institute, a vast number of the UK local authorities, academia and technology companies. #PlanTech aims to revolutionise the urban planning industry across public, private and not for profit sectors in an era where fiscal austerity has catalysed a drive for using advanced technologies to improve the efficiency of operations and decision making. Technological innovation is being promoted to enable local authorities to deliver services with significantly reduced financial resources while simultaneously creating a modernised and more efficient public sector. Within this context, this article uses a detailed ethnographic study of planning functions in Coventry City Council, UK, to analyse how they have adapted so far in response to both austerity and the drive for digital innovation. The article concludes by examining how #PlanTech and digital social innovation may help deliver the broader smart city strategy.
In this work we develop an agent-based model where hysteresis in major macroeconomic variables (e.g., gross domestic product, productivity, unemployment) emerges out of the decentralized interactions of heterogeneous firms and workers. Building upon the “Schumpeter meeting Keynes” family of models (cf. in particular Dosi et al. (2016b, 2017c)), we specify an endogenous process of accumulation of workers’ skills and a state-dependent process of firms entry. Indeed, hysteresis is ubiquitous. However, this is not due to market imperfections, but rather to the very functioning of decentralized economies characterized by coordination externalities and dynamic increasing returns. So, contrary to the insider–outsider hypothesis (Blanchard and Summers, 1986), the model does not support the findings that rigid industrial relations may foster hysteretic behavior in aggregate unemployment. On the contrary, this contribution provides evidence that during severe downturns, and thus declining aggregate demand, phenomena like decreasing investment and innovation rates, skills deterioration, and declining entry dynamics are better candidates to explain long-run unemployment spells and reduced output growth. In that, more rigid labor markets may well dampen hysteretic dynamics by sustaining aggregate demand, thus making the economy more resilient.
Local energy initiatives (LEIs) are communities of households who self-organize to meet their energy demand with locally produced green energy. They facilitate citizen participation by developing context-specific solutions, which calls for leadership and complex social dynamics. We present an agent-based simulation model to explore the formation of community energy initiatives from the bottom-up, accounting for social networks and evolution of opinions facilitating or hindering LEIs. Our novel model relies on well-established social theories and uses empirical data on community energy systems in the Netherlands and individual citizens’ preferences. Specifically, our computational model captures behavioural drivers and social value orientations, and relates individuals behavioural traits to aggregated stylized facts about energy initiatives at the community level. The results indicate that when communities lack participants with cooperative orientation, altruistic citizens with prosocial social value orientations become essential for the creation of LEIs, revealing different pathways to achieve public good benefits. Our analysis systematically demonstrate that leaders can be a bottleneck in the LEIs’ formation and that an increase in initiators is conducive to the creation of LEIs. Therefore, policies aiming at increasing the number of community initiatives should target small groups and individuals with the leadership potential, who could lead projects, and explore synergies with wider community benefits.
The development of smart city systems connected to the Internet of Things (IoT) has been the goal of several multi-agent system research works. However, few works show how to deploy and make the connection among the employed systems. This paper proposes an approach towards the integration of a MAS using JaCaMo framework along with the Urban Simulation Tool (SUMO), and some IoT applications. The integration presented here is applied in a Smart Parking scenario with real features, where is shown the integration and the connection throughout all layers, from agent level to artifacts, including real environment and simulation, as well as IoT applications. In future works, we intend to establish a methodology which shows how to properly integrate these different applications no matter the applied scenario nor the used tools.
Traditionally, disaster management information systems have been designed to facilitate communication and coordination along stable hierarchical lines and roles. However, to support coordination in disaster response, disaster management information systems need to cater for the emerging roles, responsibilities and information needs of the actors, often referred to as self-organization. To address this challenge, this paper proposes a framework for disaster management information systems that embraces an actor-centered perspective to explicitly support coordination and self-organization. The framework is designed and validated to (i) analyze the current practice of disaster information management, including the way changes occur through self-organization, and (ii) study how to design disaster management information systems that support coordination and self-organization within the current practice. A case study in Jakarta is used to modify and validate the framework, and to illustrate its potential to capture self-organization in practice. The analysis showed that analyzing the actors’ activities through the framework can provide insights on the way self-organization occurs. Moreover, networking, preparedness and centralization were found to be key elements in the design of disaster management information systems with an actor-centered perspective.
This paper explores how the European Commission promotes the concept of Sustainable Urban Mobility Planning (SUMP) among European cities. Despite the strong uptake of the SUMP concept, mobility-related problems persist in European municipalities. Linking theoretical approaches to understand the diffusion of policies with empirical findings from working with cities in the SUMP context, this article explores channels of policy diffusion and investigates shortcomings related to the respective approaches. Studies on the diffusion, the transfer and the convergence of policies identify formal hierarchy, coercion, competition, learning and networking, and the diffusion of international norms as channels for policy transfer. The findings which are presented in this paper are twofold: First, the paper finds evidence that the Commission takes different roles and uses all mechanisms in parallel, albeit with different intensity. It concludes that the approaches to explain policy diffusion are not competing or mutually exclusive but are applied by the same actor to address different aspects of a policy field, or to reach out to different actors. Second, the article provides first evidence of factors that limit the mechanisms' abilities to directly influence urban mobility systems and mobility behaviour.
Drivers cruising for scarce on-street parking in city centers create negative externalities, including congestion and pollution. We apply a serious game – PARKGAME – to understand and model drivers’ two intertwined instantaneous parking choices: when to quit cruising and where to cruise. Forty-nine participants took part in a lab experiment in which they had to arrive on time to a fictional appointment or face monetary penalties, and had to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Participants played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and a nearby parking lot of unlimited capacity. Players’ parking choices were analyzed with accelerated-failure time (AFT) and multinomial logistic regression models. Results show that drivers are mostly myopic and risk-averse, and quit their on-street parking search long before the optimal moment. Spatially, drivers are attracted by the lot-destination axis, and their turn choices at junctions comply with a second-order biased random walk. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.
A model is “any representation or concept that helps us to understand the world whenever common sense or direct observations are inadequate.” Common sense and direct observation often prove inadequate to the complexities of the twenty-first-century cities. Thus, models abound in urban life and governance. However, a model is not only a tool for control but a way of defining a situation. Framing the city so as to render it susceptible to interpretation and intervention is an exercise not merely with scientific or technological value but with rhetorical power. The tradition of comprehensive urban models, beginning with the advent of computers and culminating in the self-analyzing “smart city,” I argue, sidelines this rhetorical power in favor of a tone of scientific authority that, while justifiable in technical domains, does not legitimately scale to the level of a political community. Making good on the civic potential of Big Data thus requires recontextualizing properly scientific enterprises within an adequate political philosophy of the city, allowing for the construction of cultural urban models that set human freedom at the core of its inner workings.
The hidden-action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed (Holmström in Bell J Econ 10(1):74, 1979). However, the hidden-action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included in the hidden-action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and about feasible actions for the agent. We follow an approach to transfer closed-form mathematical models into agent-based computational models and show that the extent of information about feasible options to carry out a task only has an impact on performance if decision makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, on the contrary, appears to be crucial in almost all situations.
The abandonment of supernatural religious beliefs and rituals seems to occur quite easily in some contexts, but post-supernaturalist cultures require a specific set of conditions that are difficult to produce and sustain on a large scale and thus are historically rare. Despite the worldwide resurgence of supernaturalist religion, some subcultures reliably produce people who deny the existence of supernatural entities. This social phenomenon has evoked competing explanations, many of which enjoy empirical support. We synthesize six of the most influential social-science explanations, demonstrating that they provide complementary perspectives on a complex causal architecture. We incorporate this theoretical synthesis into a computer simulation, identifying conditions under which the predominant attitude toward supernaturalism in a population shifts from acceptance to rejection (and vice versa). The model suggests that the conditions for producing widespread rejection of supernatural worldviews are highly specific and difficult to produce and sustain. When those conditions combine, which is historically rare, a stable social equilibrium emerges within which post-supernaturalist worldviews are widespread; however, this equilibrium is easier to disrupt than equilibria whose cohesion is stabilized by supernatural religion due to persistent cognitive tendencies toward supernaturalism in evolved human minds.
Data capture and analysis are transforming entire industries, enabling novel solutions developed from a numeric evaluation of real-world phenomena. This generally relies on gathering data on physical conditions and users to create accurate, predictive models and provide customized solutions. Increasingly, data-driven approaches are also becoming a part of architectural design, with the goal of creating user-centric and sustainable buildings. However, while simulation software can accurately model deterministic physical effects, it is still difficult to incorporate stochastic effects related to human factors. This paper analyses one aspect of occupant behavior-window operation-to give designers an intuition of the impact of occupant behavior and associated modelling approaches on building performance. To this end, behavioral patterns observed in a previous field study were incorporated into a dynamic energy simulation and compared to a deterministically modelled baseline. While the stochastic models appear to better capture the dynamic and probabilistic nature of occupants' actions, the present study highlights the extent to which the assumption with regard to occupant behavior can influence the simulation-assisted performance based design process. The paper also makes suggestions as to how to interpret such simulation results in a way that quantifies the intrinsic uncertainty in stochastic models. We argue that increased data capture and analysis of building inhabitants could lead to a better understanding of their behavior, thereby affecting the decision-making process in favor of a more sustainable and responsive architecture.
While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2. An agent-based model of SARS-CoV-2 transmission shows that testing, contact tracing and household quarantine could keep new COVID-19 waves under control while allowing the reopening of the economy with minimal social-distancing interventions.
This research studied how stakeholders organize themselves in order to collectively manage the presence or absence of genetically modified organisms (GMOs) at the level of agricultural regions. Using case studies, we show that cooperatives have been able to bundle, coordinate, or influence all stakeholders. We show that these new methods of territorial governance require cooperative relations among cooperatives in direct competition with each other on the market, and a type of "collectivism" on a larger scale. The scale is no longer that of mutualism among farmers but of mutualism among competing cooperatives. This new agricultural collectivism permits cooperatives to reinforce their bargaining power and act as a nexus of relationships between the different stakeholders in the supply chain. This competing cooperatives’ unions enabled a multistakeholders’ dialogues and a self-organizations of the agricultural production areas.
This article demonstrates how a technique called Agent-Based Modelling can address a significant challenge for effective interdisciplinarity. Different disciplines and research methods make divergent assertions about what a satisfactory explanation requires. However, without a unified framework analysing the implications of these differences systematically, debate cannot transcend mere competing assertion. Using a sequence of examples, I demonstrate that Agent-Based Modelling provides such a unified framework by showing how pairs of models may display quantitatively distinct behaviours when differing only in (for example) including a social network. The ability to quantify differences arising from divergent assumptions about what models should include makes them subject to empirical evaluation (rather than mere contention). Although the article uses social network examples for accessible presentation, the approach of building paired models is quite general and can, therefore, illuminate other significant social science controversies (like the role of rationality and the importance of ethnographic detail).
Objectives
The Coronavirus Disease 2019 (COVID-19) outbreak simultaneously occurred at a church and a long-term care facility in Daegu. This study aimed to investigate the epidemiological characteristics of COVID-19 and factors related to severe outcomes.
Methods
We enrolled all inpatients diagnosed with COVID-19 between February 21 and April 2, 2020 in Daegu Dongsan Hospital, South Korea. We analyzed their clinical and demographic data, laboratory parameters, radiological findings, symptoms, and treatment outcomes.
Results
Of 694 patients, severe cases accounted for 19.7% (137 patients). No severe case was observed among patients aged ≤19 years. Hypertension was the most common co-morbidity (27%), and cough was the most common symptom (59%). Besides, asymptomatic patients accounted for 14.4% of cases. Lymphopenia, lactate dehydrogenase, C-reactive protein, and albumin were associated with severe outcomes. The first outbreak was mostly associated with younger age groups, and asymptomatic patients mostly showed mild progression. In the second outbreak involving a long-term care facility, both the number of severe patients and the mortality rate were high.
Conclusions
The overall mortality in Daegu was low, which might have resulted from large scale mass screening to detect patients and starting appropriate treatment, including hospitalization for severe cases, and quarantine for asymptomatic patients.
In order to reduce the spread of COVID-19, most countries around the world have decided to temporarily close educational institutions. However, learning has not stopped but is now fully taking place online as schools and universities provide remote schooling. Using existing literature and evidence from recent international data (Eurostat, PISA, ICILS, PIRLS, TALIS), this report attempts to gain a better understanding of how the COVID-19 crisis may affect students’ learning. It looks at the different direct and indirect ways through which the virus and the measures adopted to contain it may impact children’s achievement. ‘Conservative’ estimates for a few selected EU countries consistently indicate that, on average, students will suffer a learning loss. It is also suggested that COVID-19 will not affect students equally, will influence negatively both cognitive and non-cognitive skills acquisition, and may have important long-term consequences in addition to the short-term ones.
In Understanding Religion through Artificial Intelligence, Justin E. Lane looks at the reasons why humans feel they are part of a religious group, despite often being removed from other group members by vast distances or multiple generations. To achieve this, Lane offers a new perspective that integrates religious studies with psychology, anthropology, and data science, as well as with research at the forefront of Artificial Intelligence (AI).
After providing a critical analysis of approaches to religion and social cohesion, Lane proposes a new model for religious studies, which he calls the “Information Identity System.” This model focuses on the idea of conceptual ties: links between an individual’s self-concept and the ancient beliefs of their religious group. Lane explores this idea through real-world examples, ranging from the rise in global Pentecostalism, to religious extremism and self-radicalization, to the effect of 9/11 on sermons. Lane uses this lens to show how we can understand religion and culture today, and how we can better contextualize the changes we see in the social world around us
Two widely heralded yet contested approaches to economics have emerged in recent years. One follows an older, rather neglected approach which emphasizes evolutionary theory in terms of individuals and institutions. The other emphasizes economies as complex adaptive systems. Important concepts from evolutionary theory include the distinction between proximate and ultimate causation, multilevel selection, cultural change as an evolutionary process, and human psychology as a product of gene–culture coevolution. Relevant concepts from complexity theory include self-organization, fractals, chaos, sensitive dependence, basins of attraction, and path dependence. This book explores these two bodies of theory and their potential impact on economics. Central themes include the challenges that emerge through integration, evolutionary behavioral economics, and the evolution of institutions. Practical applications are provided and avenues for future research highlighted.
Many studies of wealth inequality make the ergodic hypothesis that rescaled wealth converges rapidly to a stationary distribution. Under this assumption, changes in distribution are expressed as changes in model parameters, reflecting shocks in economic conditions, with rapid equilibration thereafter. Here we test the ergodic hypothesis in an established model of wealth in a growing and reallocating economy. We fit model parameters to historical data from the United States. In recent decades, we find negative reallocation, from poorer to richer, for which no stationary distribution exists. When we find positive reallocation, convergence to the stationary distribution is slow. Our analysis does not support using the ergodic hypothesis in this model for these data. It suggests that inequality evolves because the distribution is inherently unstable on relevant timescales, regardless of shocks. Studies of other models and data, in which the ergodic hypothesis is made, would benefit from similar tests.
The use of computation in archaeology is a kind of magic, a way of heightening the archaeological imagination. Agent-based modelling allows archaeologists to test the ‘just-so’ stories they tell about the past. It requires a formalization of the story so that it can be represented as a simulation; researchers are then able to explore the unintended consequences or emergent outcomes of stories about the past. Agent-based models are one end of a spectrum that, at the opposite side, ends with video games. This volume explores this spectrum in the context of Roman archaeology, addressing the strengths, weaknesses, and opportunities of a formalized approach to computation and archaeogaming.
Are teens more religious when (consistent with “religious monopoly” arguments) they live in an area where many people share their parents’ religious identity? Or are teens more religious when (as religious economies models suggest) they live in areas where their parent's religious identity has a smaller population share (or “market share”)? We examine these questions using Wave 1 of the National Study of Youth and Religion combined with county‐level variables from the 2000 Religious Congregations and Membership Study and the 2000 U.S. Census. Parental religiosity has a huge effect on teen's religiosity, so much so that when parents are very religious, the religious context of the surrounding county does not matter. However, when parents are not very religious, results are consistent with the religious monopolies model but not the religious economies model. The percent of the county in the parents’ religious tradition tends to boost teen's religiosity.
While there is a number of frameworks and protocols in Agent-Based Modeling (ABM) that support the documentation of different aspects of a simulation study, it is surprising to find only a small number dealing with the handling of data. Here we present the results of discussions we had on the topic at the Lorentz Center workshop on Integrating Qualitative and Quantitative Evidence using Social Simulation (8-12 April 2019, Leiden, the Netherlands). We believe that important distinctions to be considered in the context of data use documentation are the differences of data use in relation to modeling approaches (theory driven etc.) and data documentation needs at the different stages in the modeling process (conceptualization, specification, calibration, and validation). What we hope to achieve by presenting this paper at this conference, with the help of the community, is to move forward the development of a generally acceptable protocol for documenting data use in the ABM process.
As social context becomes a more central concept for social simulation, different approaches to context have been developed. We discuss four of these with the help of an overall Contextual Action Framework for Computational Agents (CAFCA). More in particular, we describe how the consumat model, social norms, collective reasoning, and social practices can be related to each other using CAFCA. Following this we show how these approaches than can co-exist in the analysis and simulation of social phenomena rather than compete or be seen as mutually exclusive.
Most human societies are characterized by the presence of groups which cooperate through joint actions but also compete for resources and power. The processes of within- and between- group cooperation and competition have shaped human history over the last several millennia. To deepen our understanding of the underlying social dynamics, we model a society subdivided into groups with constant sizes and dynamically changing powers. Both individuals within groups and groups themselves participate in collective actions. The groups are also engaged in political contests over power which determines how resources are distributed. Using analytical approximations and agent-based simulations, we show that the model exhibits rich behavior characterized by multiple stable equilibria and, under some conditions, non-equilibrium dynamics. The strength of democratic institutions plays a key role: increasing it promotes cooperation, reduces variation in power, and mitigates inequality among groups. We show that increasing potential benefits of between-group cooperation promotes it only in societies with strong democratic institutions. We show that small groups are successful in competition if the jointly-produced goods are rivalrous and the potential benefit of cooperation is small. Otherwise large groups dominate. Overall our model contributes towards a better understanding of the causes of variation between societies in terms of the economic and political inequality within them.
The social and behavioral sciences have a long-standing interest in the factors that foster selfish (or individualistic) versus altruistic (or cooperative) behavior. Since the 1960s, evolutionary biologists have also devoted considerable attention to this issue. In the last 25 years, mathematical models (reviewed in Wilson and Sober 1994) have shown that, under particular demographic conditions, natural selection can favor traits that benefit group members as a whole, even when the bearers of those traits experience reduced reproductive success relative to other members of their group. This process, often referred to as "trait group selection" (D. S. Wilson 1975) can occur when the population consists of numerous, relatively small "trait groups," defined as collections of individuals who influence one another's fitness as a result of the trait in question. For example, consider a cooperative trait such as alarm calling, which benefits only individuals near the alarm caller. A trait group would include all individuals whose fitness depends on whether or not a given individual gives an alarm call. If the cooperative trait confers sufficiently large reproductive benefits on the average group member, it can spread. This is because trait groups that happen to include a large proportion of cooperators will send out many more offspring into the population as a whole than will groups containing few, or no cooperators. Thus, even though noncooperators out reproduce cooperators within trait groups (because they experience the benefits of the presence of cooperators without incurring the costs), this advantage can be offset by differences in rates of reproduction between trait groups. Numerous models of group selection (Wilson and Sober 1994) show that whether cooperative traits can spread depends on the relative magnitude of fitness effects at these two levels of selection (within and between trait groups). In addition, there is a growing body of empirical evidence for the operation of group selection in nature (e.g., Colwell 1981; Breden and Wade 1989; Bourke and Pranks 1995; Stevens et al. 1995; Seeley 1996; Miralles et al. 1997; Brookfield 1998) and under experimental conditions (reviewed in Goodnight and Stevens 1997).
Background and techniques for formalizing deductive argumentation in a logic-based framework for artificial intelligence.
Logic-based formalizations of argumentation, which assume a set of formulae and then lay out arguments and counterarguments that can be obtained from these formulae, have been refined in recent years in an attempt to capture more closely real-world practical argumentation. In Elements of Argumentation, Philippe Besnard and Anthony Hunter introduce techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation. Besnard and Hunter discuss how arguments can be constructed, how key intrinsic and extrinsic factors can be identified, and how these analyses can be harnessed for formalizing argumentation for use in real-world problem analysis and decision making.
The book focuses on a monological approach to argumentation, in which there is a set of possibly conflicting pieces of information (each represented by a formula) that has been collated by an agent or a pool of agents. The role of argumentation is to construct a collection of arguments and counterarguments pertaining to some particular claim of interest to be used for analysis or presentation.
Elements of Argumentation is the first book to elucidate and formalize key elements of deductive argumentation. It will be a valuable reference for researchers in computer science and artificial intelligence and of interest to scholars in such fields as logic, philosophy, linguistics, and cognitive science.
Introduction
During the COVID-19 pandemic, excess mortality has been reported, while hospitalisations for acute cardiovascular events reduced. Brazil is the second country with more deaths due to COVID-19. We aimed to evaluate excess cardiovascular mortality during COVID-19 pandemic in 6 Brazilian capital cities.
Methods
Using the Civil Registry public database, we evaluated total and cardiovascular excess deaths, further stratified in specified cardiovascular deaths (acute coronary syndromes and stroke) and unspecified cardiovascular deaths in the 6 Brazilian cities with greater number of COVID-19 deaths (São Paulo, Rio de Janeiro, Fortaleza, Recife, Belém, Manaus). We compared observed with expected deaths from epidemiological weeks 12–22 of 2020. We also compared the number of hospital and home deaths during the period.
Results
There were 65 449 deaths and 17 877 COVID-19 deaths in the studied period and cities for 2020. Cardiovascular mortality increased in most cities, with greater magnitude in the Northern capitals. However, while there was a reduction in specified cardiovascular deaths in the most cities, the Northern capitals showed an increase of these events. For unspecified cardiovascular deaths, there was a marked increase in all cities, which strongly correlated to the rise in home deaths (r=0.86, p=0.01).
Conclusion
Excess cardiovascular mortality was greater in the less developed cities, possibly associated with healthcare collapse. Specified cardiovascular deaths decreased in the most developed cities, in parallel with an increase in unspecified cardiovascular and home deaths, presumably as a result of misdiagnosis. Conversely, specified cardiovascular deaths increased in cities with a healthcare collapse.
A city comprises an ecological environment, a living and architectural space, the product of a history of human interactions that determines its morphology and destiny. Cities are complex systems that encompass elements of diverse types, such as natural objects, technical artifacts, human actors and social entities, including the rules or laws governing their behavior. Despite cities complexity, conventional urban policy models have focused on expanding and building places geared toward satisfying economic activities and markets. In this paper we propose an agent based model (ABM) for urban development planning based on the relationship between city inhabitants and the satisfaction of their basic needs with their physical environment. Our design recognizes human complexity within the urban contexts and establishes a new method for planning city development with the help of a tool geared toward simulating participation. This simulation platform makes it possible to consider the effects of human behavior as a determinant of the success or failure of urban interventions from the point of view of planning. The central elements of the simulation model are the relationship of each individual to the physical environment of the city and the satisfaction of their basic needs. This simulation platform can be used as a starting point on a collective and prospective vision of the city, grounded in the approach and experience of participatory modeling with multiple stakeholders.
In complex systems theory, two meanings of a complex adaptive system (CAS) need to be distinguished. The first, CAS1, refers to a complex system that is adaptive as a system; the second, CAS2, refers to a complex system of agents which follow adaptive strategies. Examples of CAS1 include the brain, the immune system, and social insect colonies. Examples of CAS2 include multispecies ecosystems and the biosphere. This chapter uses multilevel selection theory to clarify the relationships between CAS1 and CAS2. The general rule is that for a complex system to qualify as CAS1, selection must occur at the level of the complex system (e.g., individual-level selection for brains and the immune system, colony-level selection for social insect colonies). Selection below the level of the system tends to undermine system-level functional organization. This general rule applies to human social systems as well as biological systems and has profound consequences for economics and public policy.
In this work we develop an agent-based model that offers an alternative to standard, computable general equilibrium integrated assessment models (IAMs). The Dystopian Schumpeter meeting Keynes (DSK) model is composed of heterogeneous firms belonging to capital-good, consumption-good and energy sectors. Production and energy generation lead to greenhouse gas emissions, which affect temperature dynamics. Climate damages are modelled at the individual level as stochastic shocks hitting workers' labour productivity, energy efficiency, capital stock and inventories of firms. In that, aggregate damages emerge from the aggregation of losses suffered by heterogeneous, interacting and boundedly rational agents. The model is run focusing on a business-as-usual carbon-intensive scenario consistent with a Representative Concentration Pathway 8.5. We find that the DSK model is able to account for a wide ensemble of micro- and macro-empirical regularities concerning both economic and climate dynamics. Simulation experiments show a substantial lack of isomorphism between the effects of micro- and macro-level shocks, as it is typical in complex system models. In particular, different types of shocks have heterogeneous impact on output growth, unemployment rate, and the likelihood of economic crises, pointing to the importance of the different economic channel affected by the shock. Overall, we report much larger climate damages than those projected by standard IAMs under comparable scenarios, suggesting possible shifts in the growth dynamics, from a self-sustained pattern to stagnation and high volatility, and the need of urgent policy interventions.
Cruising for parking has long been perceived as a major source of congestion and emissions in urban areas, but recent empirical work suggests that parking may not be as onerous as folklore suggests, and that the amount of vehicle travel attributable to cruising is minimal. In this paper, we reconcile these perspectives through a dynamic programming model of parking search, and empirical insights from a large-scale GPS dataset in San Francisco and the California Household Travel Survey. We first draw a conceptual distinction between parking search, the time between the driver’s decision to park and when a parking space is taken; and cruising, defined as excess vehicle travel due to parking search. In places with little or no through traffic, up to half of traffic can be searching for parking, but cruising can be zero. We then operationalize this distinction through a dynamic programming model. The model predicts that when parking is perceived to be scarce, drivers are more willing to take a convenient available space, even if it is some distance from their destination. Counter-intuitively, scarce parking can even suppress vehicle travel as perceived parking scarcity leads drivers to stop short of their destinations and accept a longer walk. Empirical data from California indicate that neighborhood density (a proxy for parking availability) has little impact on cruising for parking, but increases walk distances from parking locations to final destinations. We conclude that cruising for parking is self-regulating, and that in certain circumstances parking scarcity can even reduce vehicle travel.
It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g., left vs right) and become increasingly polarized. We provide an agent-based model that reproduces alignment and polarization as emergent properties of opinion dynamics in a multi-dimensional space of continuous opinions. The mechanisms for the change of agents’ opinions in this multi-dimensional space are derived from cognitive dissonance theory and structural balance theory. We test assumptions from proximity voting and from directional voting regarding their ability to reproduce the expected emerging properties. We further study how the emotional involvement of agents, i.e., their individual resistance to change opinions, impacts the dynamics. We identify two regimes for the global and the individual alignment of opinions. If the affective involvement is high and shows a large variance across agents, this fosters the emergence of a dominant ideological dimension. Agents align their opinions along this dimension in opposite directions, i.e., create a state of polarization.
目的： 新型冠状病毒肺炎在武汉暴发流行以来，已在全国范围内蔓延。对截至2020年2月11日中国内地报告所有病例的流行病学特征进行描述和分析。 方法： 选取截至2020年2月11日中国内地传染病报告信息系统中上报所有新型冠状病毒肺炎病例。分析包括：①患者特征；②病死率；③年龄分布和性别比例；④疾病传播的时空特点；⑤所有病例、湖北省以外病例和医务人员病例的流行病学曲线。 结果： 中国内地共报告72 314例病例，其中确诊病例44 672例（61.8%），疑似病例16 186例（22.4%），临床诊断病例10 567例（14.6%），无症状感染者889例（1.2%）。在确诊病例中，大多数年龄在30～79岁（86.6%），湖北省（74.7%），轻/中症病例为主（80.9%）。确诊病例中，死亡1 023例，粗病死率为2.3%。个案调查结果提示，疫情在2019年12月从湖北向外传播，截至2020年2月11日，全国31个省的1 386个县区受到了影响。流行曲线显示在1月23－26日达到峰值，并且观察到发病数下降趋势。截至2月11日，共有1 716名医务工作者感染，其中5人死亡，粗病死率为0.3%。 结论： 新型冠状病毒肺炎传播流行迅速，从首次报告病例日后30 d蔓延至31个省（区/市），疫情在1月24－26日达到首个流行峰，2月1日出现单日发病异常高值，而后逐渐下降。随着人们返回工作岗位，需积极应对可能出现的疫情反弹。.