Agent based modeling

The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a highe...

We study the feasibility of using Belief, Desire and Intention agents for modeling the phenomena of urban growth and segregation. Uncertainty, typical of real world situations is modeled using possibility theory. We have also implemented a simple visualization tool whose aim is to track the changes in the model. Some preliminary experiments suggest that such an approach might allow a decision-maker to dynamically track the changes in the model. Besides, it is also possible to interact with the different steps of the simulation via the model. Our proposal makes it possible to simulate the interactions between cognitive agents in an economical environment while taking the spatial context into account.

Since the s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures a ecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution a ecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.

Agent-based, spatially-explicit models that incorporate movement rules are used across ecological disciplines for a variety of applications. However, appropriate movement rules may be difficult to implement due to the complexity of an individual's response to both proximate and ultimate cues, as well as the difficulty in directly assessing how organisms choose to move across their environment. Environmental cues may be complex and dynamic, and therefore, movement responses may require tradeoffs between preferred levels of different environmental variables (e.g., temperature, light level, and prey availability). Here, we present an approach to determine appropriate movement rules by setting them as heritable traits in an eco-genetic modeling framework and allowing movement rules to evolve during the model rather than setting them a priori . We modeled yellow perch, Perca flavescens , movement in a simulated environment and allowed perch to move in response to high-resolution vertical gradients in temperature, dissolved oxygen, light, predators, and prey. Evolving movement rules ultimately increased fish growth and survival over generations in our model, indicating that evolving movement rules led to improved individual performance. We found that emergent movement rules were consistent across trials, with evolved movement rules incorporating different weights of these environmental factors and the most rapid selection on temperature preference. This case study presents a flexible method using ecogenetic modeling to determine appropriate movement rules that can be applied to diverse scenarios in spatially-explicit ecological modeling.

This article is concerned with a relatively straightforward research question: how do attitudes toward the formerly Thomson Reuter's Impact Factor (IF) affect the reliability of the review process? Consistent with the literature (e.g., Moed & Van Leeuwen 1996;, the angle with which we tackle the issue is the assumption that IF has no relation with the quality of either a journal nor the articles published in it. Given this assumption, we study cognitive and psychological biases (e.g., Gigerenzer & Selten 2001; Kahneman 2003) related to the perception that quality is represented by IF, held by some reviewers. In addition to that we also attempt to understand the influence that reviewers receive from professional academic communities with stronger or weaker conviction on IF as a quality measure. Consistently with a recent article on the perception of scientific value and IF (Secchi & Cowley 2018), we develop an agent-based simulation model to reason on some of the critical aspects of peer review. In the following pages, we introduce a theoretical framework that is rooted in cognitive biases and offer a distributed cognition perspective. We then introduce our simulation model and analyse a selection of results before offering some concluding remarks.

A CROSS-DISCIPLINARY APPROACH TO THE MARITIME SECURITY RISK OF PIRACY AND LESSONS LEARNED FROM AGENT-BASED MODELING Joanne Marie Fish Old Dominion University, 2017 Director: Dr. Jesse T. Richman This dissertation takes a cross-disciplinary approach to understanding pirate activity. Maritime piracy presents a dynamic ever-evolving problem. In today’s globalized world, contemporary maritime piracy presents a transnational threat. It is a complex socio-economic and political problem which the modern world considers to be criminal activity. Like all complex problems it must be deconstructed to fully comprehend it. All criminal activity, maritime piracy included, has certain elements of supply and demand. For the activity to occur there must be a certain level, or supply, of targets. At the same time, we can posit that there must be a lack of other opportunities for the pirates, who calculate that the risk of engaging in piracy is worthwhile. This risk calculation is a function of the po...

Agent-Based Modelling is gaining wider acceptance as a paradigm for social research. However, it still present limitations in the management of the process to generate the simulations from the initial conceptual models. Thus, it is difficult to reuse the knowledge from available models and adapt it to different hypotheses. This paper proposes the use of metamodels in order to define explicitly the core concepts of a problem and to differentiate the aspects involved in the process. A case study for continuous double auctions shows how to define the related metamodel and use it to address alternative situations in these auctions. The case study drives the discussion on the advantages and limitations that metamodelling can bring to social simulation.

Agent-based simulation is being recognized as a useful tool for the study of social systems. It is based on the idea that agents can be used as a good abstraction of members of a society, and by simulating their interactions, observe the emergent behavior. Usually, agent-based models for social simulation are rather simple, but there are more and more works that try to apply this technique for rather complex systems, both in the typology of the agents and their relationships, and in scalability. For this reason, software engineering techniques are required that would facilitate the development of agent-based simulation systems. In this sense, it can be useful to consider agentoriented methodologies, as they cope with these requirements. This work explores the application of a model-driven methodology for the development of Multi-Agent Systems, INGENIAS. There are several advantages of this approach. One is the possibility to define specific modeling languages that are conceptually close to the domain-expert, in this case for simulation of social systems. This will facilitate the communication of multidisciplinary teams and, by generating code from models, the social scientist is alleviated from the concerns of programming for a simulation platform. This is illustrated with a case study of urban dynamics.

Abstract. Although there are many methodologies for the development of multi-agent systems, these are scarcely applied for agent-based simulation. The different activities for the creation of an agent based model and its simulation for the study of a social system, which include abstraction, design, approximation and coding, are usually performed in a monolithic way. Besides, when several roles take part in this process, communication problems may arise between people with different backgrounds and perspectives. This ...

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Counterdrug interdiction efforts designed to seize or disrupt cocaine shipments between South American source zones and US markets remain a core US “supply side” drug policy and national security strategy. However, despite a long history of US-led interdiction efforts in the Western Hemisphere, cocaine movements to the United States through Central America, or “narco-trafficking,” continue to rise. Here, we developed a spatially explicit agent-based model (ABM), called “NarcoLogic,” of narco-trafficker operational decision making in response to interdiction forces to investigate the root causes of interdiction ineffectiveness across space and time. The central premise tested was that spatial proliferation and resiliency of narco-trafficking are not a consequence of ineffective interdiction, but rather part and natural consequence of interdiction itself. Model development relied on multiple theoretical perspectives, empirical studies, media reports, and the authors’ own years of fiel...

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Managing wild populations in rapidly changing, human-dominated landscapes requires models that accommodate complex interactions among climate, land use, disease, and evolution. Agent-based models (ABMs) are well suited to this task but are often difficult to parameterize, calibrate, and interpret at management-relevant scales. Objectives: We discuss how artificial-intelligence (AI) techniques, including machine-learning regression, data-mining diagnostics, geospatial informatics, and large-language-model code aides, can streamline ABM parameter estimation and scenario testing, enhance extraction of decision-support metrics, and broaden the accessibility of ABMs for conservation planning. Methods: We considered examples of AI use in ecology and evolution, including where AI was paired with ABMs, highlighting use cases such as calibration, rule discovery, data fusion, and code generation. Results: We show how supervised machine learning can supplement parameterization by learning relationships between empirical observations and model outputs. Data-mining methods may also be useful to identify parameters that drive most output variance. In addition, deep-learning remote-sensing products in ABMs allows landscape dynamics to be represented at ecologically relevant resolutions. Despite this, key obstacles, such as limited long-term ecological data, high computational demand, and the need for explainable-AI safeguards against biased predictions remain. Conclusions: Expanding the use of AI in ABMs will require interdisciplinary collaborations that pair ecologists with computer and geo-information scientists and explicit workflows for auditing AI decisions. However, leveraging AI enhanced ABMs will improve predictive modeling of species responses to environmental change, optimize conservation strategies, and develop more effective data-driven management.

Agent-based modeling and simulation (ABMS) is an approach to modeling systems comprised of individual, autonomous, interacting "agents." There is much interest in many application problem domains in developing agent-based models. Agent-based modeling offers ways to model individual behaviors and how behaviors affect others in ways that have not been available before. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling by describing the basic ideas of ABMS, discussing some applications, and addressing methods for developing agent-based models.

This paper describes the Weather model, a procedural generation model that creates realistic daily weather data. The Weather model generates synthetic weather time series (solar radiation, temperature, precipitation) using algorithms based on sinusoidal and double logistic functions, incorporating stochastic variation to mimic unpredictable weather patterns. It aims to provide realistic yet flexible weather inputs for exploring diverse climate scenarios. The model is implemented in NetLogo and R, offering a computationally efficient method to generate extensive weather data for socioecological simulations. We further discuss the caveats and advantages of the procedural approach over data-driven or mechanistic methods for simulating past climates in complex systems. The Weather model was designed as a component of the Indus Village model, which is a larger simulation framework studying the Indus Civilisation, but it can be used independently.

In a simulation project, a good conceptual model representation is critical for communicating conceptual models between stakeholders. A conceptual model describes the problem domain and model specifications. The description of the problem domain includes the objectives, inputs, outputs, content, assumptions and simplifications made in the model. The model specifications are used to specify the model's behavior. This article focuses on the representation of the model content (structure, boundary and level of detail) component of an agent-based simulation (ABS) model. For this, we propose the use of Business Process Model and Notation (BPMN) from the Object Management Group. A Web-based visual modeling tool has been developed using JavaScript to demonstrate how BPMN can be used to represent an ABS conceptual model and how the tool translates the conceptual model into code ready for execution using Repast HPC. Conceptual modeling is the process of abstracting a model from a real or proposed system into a conceptual model (Robinson 2010). There is a point in a simulation project where the conceptual modeling process occurs inside an individual modeler's mind. This 'thinking' process includes reflection on how to structure the problem and how the simulation model should be designed to help decision-makers solve the problem to hand, subject to certain constraints. At some point in the simulation project, the conceptual model needs to be communicated to other stakeholders (such as clients, simulation modelers and domain experts). Nance (1994) refers to explicit representation of the conceptual model as the communicative model. The importance of the quality of communication between stakeholders to make a simulation project (Robinson and Pidd 1998) successful makes the role of (explicit) conceptual model representation important. Onggo ( ) classifies methods for conceptual model representation into three categories: textual, pictorial and multi-faceted. It is clear that the definition of a conceptual model must affect its representation. Unfortunately, there is no single accepted definition of what a conceptual model is. Robinson (2010) discusses various conceptual model definitions and concludes that complete consensus is difficult to achieve. This is due partly to the wide range of applications of computer simulation; each application domain may have different characteristics (such as project scale and team size) and requirements (such as the criticality of model reusability). This article uses the conceptual model definition proposed in Robinson (2010), i.e., a "conceptual model is a non-software specific description of the computer simulation model (that will be, is or has been developed), describing the objectives, inputs, outputs, content, assumptions and simplifications of the model." Based on the separation between domain-oriented components and design-oriented components in a conceptual model, as proposed in , we extend Robinson's definition to include design-oriented components. These provide a representation of the problem domain which is consistent with

This chapter aims to contribute to the debate on the role of modelbased approaches, such as agent-based modelling, in the future of demography. First we call attention to the developments of the discipline since the 17 th century, and we describe its four successive paradigms related to the period, cohort, eventhistory and multilevel perspectives. We argue that these paradigms are complementary and that demography, since its beginnings, has subscribed to the classical scientific research programme launched by the promoters of modern science. Next, we examine how simulation modelling developing in population sciences recently, may help to respond to three main challenges: how to overcome complexity in social research; how to reduce its uncertainty; and how to reinforce its theoretical foundations. We sketch a model-based research programme for demography, looking specifically at interactions between various population systems. We then show how this approach might conform to the classical scientific research programme, in order to take advantage of its benefits.

Agent-based computational models are gaining traction as a means for modelling the complexities of designing and implementing health interventions in our rapidly-changing society. When such models are integrated with an interactive virtual environment they o er a way to investigate complex conditions including social and environmental de- terminants, while also facilitating participation and interaction from re- search users and policy-makers. Here we present a prototype Agent-Based Virtual Environment which features an early-stage model of obesity in- tended to support planners and local authority members in the develop- ment of environments that encourage healthy diets and higher physical exertion. We illustrate the construction of the model and its intended role in raising awareness of the role of the built environment in prevent- ing obesity. We also describe future extensions and ways to extend this framework to other areas of concern in public health.

In this paper, the relations between live weight (LW) gain of the grazing cows and rangeland condition (RC), LW gain and concentrate supplement and LW gain and genetics of the grazing cows were investigated to quantify LW and body condition score (BCS) losses driven by rangeland degradation to estimate the threshold RC over which rangelands can compensate these losses. This study was conducted in Erzurum Province, Turkey. LW gains were estimated using farmer-animal-and rangeland-related variables during June-August and June-October periods. RC was calculated employing the classical condition assessment method. Ordinary least squares (OLS) were used in data analyses. The results show that a 10% enhancement or setback in RC can result in about 10 kg LW gain or loss per head. The financial equivalent of these LW gain or losses was 314.6 Turkish lira (TRY) or 59.0 USD (I TRY = 0.1875 USD) per farm, which accounts 15.5 TRY or 2.9 USD per hectare of rangeland. It is concluded that rangeland with an RC value below 4.3 requires restoration and that cows of higher genetic merit more than crossbred genotypes are not suitable for extensive production in the study area and areas sharing similar agroecological conditions.

We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique-the network regression, Multiple Regression Quadratic Assignment Procedure-in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scalefree, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.

The emergence of coupled natural and human landscapes marked a transformative interval in the human past that set our species on the road to the urbanized, industrial world in which we live. This emergence enabled technologies and social institutions responsible for human-natural couplings in domains beyond rural, agricultural settings. The Mediterranean Landscape Dynamics Project (MedLand) is studying the interacting social and biophysical processes associated with these novel socioecological systems and their long-term consequences using a new form of 'experimental socioecology' made possible by recent advances in computation. We briefly describe the MedLand modeling laboratory, a hybrid simulation environment that couples models of small-holder farming and herding, landscape evolution, and vegetation change managed through an interaction model. We then review three examples of experimental socioecology carried out in this laboratory. These examples offer new insights for scaledependent thresholds in agropastoral productivity, long-term sustainability of alternative land-use strategies, and identifying signatures of human and climate-driven landscape dynamics. We conclude with an overview of new directions for this interdisciplinary research on Anthropocene human-earth systems, including: modeling more diverse decision-making strategies for land-use, developing more sophisticated models of vegetation dynamics and fire ecology, and generating digital proxy data for more robust model validation against the empirical record.

The archaeological record has been described as a key to the long-term consequences of human action that can help guide our decisions today. Yet the sparse and incomplete nature of this record often makes it impossible to inferentially reconstruct past societies in sufficient detail for them to serve as more than very general cautionary tales of coupled socio-ecological systems. However, when formal and computational modeling is used to experimentally simulate human socioecological dynamics, the empirical archaeological record can be used to validate and improve dynamic models of long term change. In this way, knowledge generated by archaeology can play a unique and valuable role in developing the tools to make more informed decisions that will shape our future. The Mediterranean Landscape Dynamics project offers an example of using the past to develop and test computational models of interactions between land-use and landscape evolution that ultimately may help guide decision-making.

Indigenous people of the Rupununi region of Amazonian Guyana interact with their natural environment through hunting and subsistence agriculture. To date the sustainability of indigenous livelihoods has been analyzed by modeling either hunting or forest clearing. Here we develop a holistic model framework with agent-based modeling to examine interactions between demographic growth, hunting, subsistence agriculture, land cover change, and animal population in the Rupununi. We use an extensive field dataset from social surveys, animal observation records and hunting kill locations along with satellite images. The model exhibits feedback loops between a growing human population and depletion of local natural resources. Our model can reproduce the population size of two different villages along with landscape patterns without further calibration. Our model can be used for understanding the conditions of sustainability for indigenous communities relying on subsistence agriculture and hunting, and for scenario analyses to examine the implications of external interventions.

Modeling and in silico simulations are of major conceptual and applicative interest in studying the cell cycle and proliferation in eukaryotic cells. In this paper, we present a cell cycle checkpointoriented simulator that uses agent-based simulation modeling to reproduce the dynamics of a cancer cell population in exponential growth. Our in silico simulations were successfully validated by experimental in vitro supporting data obtained with HCT116 colon cancer cells. We demonstrated that this model can simulate cell confluence and the associated elongation of the G1 phase. Using nocodazole to synchronize cancer cells at mitosis, we confirmed the model predictivity and provided evidence of an additional and unexpected effect of nocodazole on the overall cell cycle progression. We anticipate that this cell cycle simulator will be a potential source of new insights and research perspectives. Doubling Time Cell cycle distribution Virtual flow cytometry VIRTUAL CELL CYCLE ANALYSIS HYPOTHESIS TESTING Cell cycle parameters Dynamic view Challenging cell cycle checkpoint M S G1 G2 R G1/S G2/M iM INPUT BIOLOGICAL DATA SIMULATION

This article proposes digital simulations of a bacterial communication system termed quorum sensing, and investigates the design of artificial networks build on the behavior of bacteria societies that tweet using quorum sensing signals. To this end, this article proposes a cell-based model that uses a “bottom-up” agent-based model coupled with ordinary differential equations, and develops the abstraction of intracellular dynamics as a basis underlying cooperative artificial network formation. Results show the emergence of self-sustainable behaviors thanks to the proposed model of metabolism that permits bacteria to grow, reproduce, interact, and coordinate at the population level to exhibit near-perfect bioluminescence behaviors. Moreover, the evolution of cooperation in the subsequent artificial network leads to the emergence of non-predicted coercive strategies. Coercion has been shown to be beneficial to share common interests between variants of cooperators leading the entire po...

This paper describes a tiny business simulator: Alexander Islands developed for Business Simulation course at GSSM, Tsukuba University. Alexander Islands is used (1) to understand principles of both business models and gaming simulators and (2) to help students to develop their own simulators.

Agent Based Modeling (ABM) is a computational paradigm for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) to understand the behavior of a system and the underlying laws governing its outcomes. For the conclusions reached via this technique to be valid, the agent population in the ABM should be representative of – and commensurate with – the population size and rule set under study. Moreover, since the systems under study are complex and hence many of their possible behaviors are unknown, simulations should be executed several times to identify average (and representative) behaviors. From a computational perspective, most research reported in the literature has focused on the impact of performance, scalability and visual representations on the quality of the scientific outcomes obtained with ABM.
The present work details an Feature Space Maturity Model (FSMM) which includes significant sections usually overlooked in current ABM frameworks. Advances in Computer Science and the advent of more powerful hardware make it possible to define a reference architecture capable of supporting – at the framework level – robust computational properties, a set of essential features leading to correctness and fidelity to reality. The purpose of this work is manifold. First, it introduces a maturity model for formal assessments of the feature space of ABM and evaluates the five most used ABM frameworks. Second, it proposes a macroscopic analysis of different implementations as a correctness mechanism. Third, it identifies and proposes a reference architecture based on the frameworks reviewed. Finally, it provides a Proof of Concept (POC) implementation based on the reference architecture to evaluate its quality according to the FSMM.

The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett. 97, 168001 (2006)] and a phenomenon called “crowd turbulence ” can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E 75, 046109 (2007)]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat B...

A discrete time stochastic model for a multiagent system given in terms of a large collection of interacting Markov chains is studied. The evolution of the interacting particles is described through a time inhomogeneous transition probability kernel that depends on the 'gradient' of the potential field. The particles, in turn, dynamically modify the potential field through their cumulative input. Interacting Markov processes of the above form have been suggested as models for active biological transport in response to external stimulus such as a chemical gradient. One of the basic mathematical challenges is to develop a general theory of stability for such interacting Markovian systems and for the corresponding nonlinear Markov processes that arise in the large agent limit. Such a theory would be key to a mathematical understanding of the interactive structure formation that results from the complex feedback between the agents and the potential field. It will also be a crucial ingredient in developing simulation schemes that are faithful to the underlying model over long periods of time. The goal of this work is to study qualitative properties of the above stochastic system as the number of particles (N ) and the time parameter (n) approach infinity. In this regard asymptotic properties of a deterministic nonlinear dynamical system, that arises in the propagation of chaos limit of the stochastic model, play a key role. We show that under suitable conditions this dynamical system has a unique fixed point. This result allows us to study stability properties of the underlying stochastic model. We show that as N → ∞, the stochastic system is well approximated by the dynamical system, uniformly over time. As a consequence, for an arbitrarily initialized system, as N → ∞ and n → ∞, the potential field and the empirical measure of the interacting particles are shown to converge to the unique fixed point of the dynamical system. In general, simulation of such interacting Markovian systems is a computationally daunting task. We propose a particle based approximation for the dynamic potential field which allows for a numerically tractable simulation scheme. It is shown that this simulation scheme well approximates the true physical system, uniformly over an infinite time horizon.

Agent Based Modeling (ABM) is a powerful method that has been used to simulate potential critical incidents in the infrastructure and built environments. This paper will discuss the modeling of some critical incidents currently simulated using ABM and how they may be expanded and improved by using better physiological modeling, psychological modeling, modeling the actions of interveners, introducing Geographic Information Systems (GIS) and open source models.

In the healthcare domain, human collaboration processes (HCPs), which consist of interactions between healthcare workers from different (para)medical disciplines and departments, are of growing importance as healthcare delivery becomes increasingly integrated. Existing workflow-based process modelling tools for healthcare process management, which are the most commonly applied, are not suited for healthcare HCPs mainly due to their focus on the definition of task sequences instead of the graphical description of human interactions. This paper uses a case study of a healthcare HCP at a Dutch academic hospital to evaluate a novel interaction-centric process modelling method. The HCP under study is the care pathway performed by the head and neck oncology team. The evaluation results show that the method brings innovative, effective, and useful features. First, it collects and formalizes the tacit domain knowledge of the interviewed healthcare workers in individual interaction diagrams. Second, the method automatically integrates these local diagrams into a single global interaction diagram that reflects the consolidated domain knowledge. Third, the case study illustrates how the method utilizes a graphical modelling language for effective tree-based description of interactions, their composition and routing relations, and their roles. A process analysis of the global interaction diagram is shown to identify HCP improvement opportunities. The proposed interaction-centric method has wider applicability since interactions are the core of most multidisciplinary patient-care processes. A discussion argues that, although (multidisciplinary) collaboration is in many cases not optimal in the healthcare domain, it is increasingly considered a necessity to improve integration, continuity, and quality of care. The proposed method is helpful to describe, analyze, and improve the functioning of healthcare collaboration.

The aim of this paper is to reformulate the question of school choice in a voucher system, by moving towards a perspective, assessing competition between schools and the choice of students using Geographic Information Systems (GIS) and Agent-Based Modeling (ABM). How spatial structure affects competition among schools? How do socio-geographic structuration and social interaction affect this process? We show a preview of the proposed model and main social mechanisms of school choice: Quality, Distance, Cost, Expectations, Homophily and Mimesis for students and their families, and Selection, Supply and Local Competition for schools

How should archaeologists and other social scientists tackle the big and little questions of change in socionatural systems? Most nonpractitioners think that the answer is obvious—fieldwork! That is certainly the place to start, and we know very few archaeologists who were not first drawn to their profession because of a love of fieldwork and discovery. Still, it does not take long before we realize that the things we discover, in and of themselves, are not immediately helpful in answering the questions that usually intrigue us most—those involving explanations for change. This realization might start with little questions: why is one raw material for making stone tools prevalent at this site, while right next door, another was more important? But before long, bigger questions surface: how and why do humans cooperate so successfully in large, unrelated groups, and how and why do human societies move from egalitarian to more hierarchical organizations? Every time we ask one of these ...

Collective behavior of the complex socio-economic systems is heavily influenced by the herding, group, behavior of individuals. The importance of the herding behavior may enable the control of the collective behavior of the individuals. In this contribution we consider a simple agent-based herding model modified to include agents with controlled state. We show that in certain case even the smallest fixed number of the controlled agents might be enough to control the behavior of a very large system.

Prehistoric rock carvings are one of Scotland's most enigmatic and poorly understood monument types. This article discusses the pioneering approach used by Scotland's Rock Art Project to enhance understanding of the abstract motifs through multiscalar computational analyses of a large dataset co-produced with community teams. The approach can be applied to suitable rock art datasets from other parts of the world and has international relevance for rock art reserach. Our analysis incorporates data from across Scotland in order to investigate inter-regional differences and similarities in the nature and contexts of the carvings. Innovative application of complementary analytical methods identified subtle regional variations in the character of the rock art and motif types. This variability suggest an understanding of the rock art tradition that was widely shared but locally adapted, and reflects connections and knowledge exchange between specific regions.

Agent based modeling (ABM) and High Performance Computing (HPC) techniques are very popular in investigation and understanding cellular and molecular systems. The complex nature of these systems and the demand for emulation and comprehension at different levels in these models creates the expectation for new effective simulation strategies and tools. The present paper peruses the foresaid demands and the approaches for developing simulation in tumor model and its interactions using ABM and HPC. ABM allows the analysis of the actions and interactions of autonomous agents (cells in this case) to evaluate their effects on the system as a whole in order to re-create and predict the appearance of a complex phenomenon. This is a parametric model and it is necessary to explore the data model space to determine which combinations of adjustments cause the behaviors which are of interest. In this case, HPC is a useful tool to perform experiments in acceptable time.

In Evolutionary Biology and Game Theory, there is a long history of models aimed at predicting strategies adopted by agents during resource foraging. In Artificial Life, the agentbased modeling approach allowed to simulate the evolution of foraging behaviors in populations of artificial agents embodied in a simulated environment. In this paper, different sets of behaviors are evolved from a simple setting where agents seek for food patches distributed on a two-dimensional map. While agents are not explicitly playing a game of chicken, their strategies are found on a spectrum ranging from a frugal strategy (aka Dove) to a greedy strategy (aka Hawk). This phenomenon is due to the fact that moving is both a way for the agents to play or go to get away from an unfavorable area of the environment. It is also observed that by moving away, the agents preserve the ecology, preventing the resource from disappearing locally. Those strategies are shown to be stable if the environment is colonized by one given population. However, post-mortem tournaments among different groups of agents (separately evolved), systematically result in a specific group of agents dominating. The optimal strategy in the simulated tournaments is found to be one with fine-tuned timing for leaving. Further analysis shows how the strategy exploits resources without completely depleting them, producing Volterra-like population tendencies.

In multicellular organisms, relations among parts and between parts and the whole are contextual and interdependent. These organisms and their cells are ontogenetically linked: an organism starts as a cell that divides producing non-identical cells, which organize in tri-dimensional patterns. These association patterns and cells types change as tissues and organs are formed. This contextuality and circularity makes it difficult to establish detailed cause and effect relationships. Here we propose an approach to overcome these intrinsic difficulties by combining the use of two models; 1) an experimental one that employs 3D culture technology to obtain the structures of the mammary gland, namely, ducts and acini, and 2) a mathematical model based on biological principles. The typical approach for mathematical modeling in biology is to apply mathematical tools and concepts developed originally in physics or computer sciences. Instead, we propose to construct a mathematical model based ...

Abstract. Le reti complesse sono state osservate in molti sistemi biologici reali e l'applicazione di tale topologia alle reti neurali artificiali porta a delle considerazioni interessanti. In questo articolo presentiamo uno studio preliminare sull'evoluzione di reti neurali a topologia complessa. Questa struttura viene utilizzata per affrontare un problema di modellazione di un processo chimico in cui sono presenti degli ingressi non conosciuti (disturbi). L'algoritmo evolutivo da noi utilizzato considera una popolazione iniziale di ...

In this paper, we first review some basic concepts associated with a model for social interaction previously proposed by us. Each agent is seen as an array of variables that can be found in different states. The agents are then allowed to interact and form groups based on their variables. We discuss how spin-glass type physics may be appropriate for our model. Several types of variables and costs associated with flipping the variables are discussed. Then some simple graphs are presented to understand the formation of various levels of identities within social clusters. In the end, we analyze events from the French revolution and the Russian revolution to to understand how different variables and identities interact within a hierarchical social structure.

The promise of agent-based for explicating properties of social systems has not yet been fully realized. Agent models sometimes provide only a veneer of, rather than substantive engagement with, social behavior. The problem will be illustrated with Axelrod's model for evolution of ethnocentrism (a biological model) versus Schelling's model for spatial segregation based on preferences (a cultural model). The examples show the need to incorporate both the biological and cultural basis for behavior through a schema that includes behavior based on cultural/cognitive processing of information and behavior based on biological/cognitive processing of information. An example of an agent-based model that implements decision making in this manner is discussed. The model accounts for heterogeneity in behavior outcomes and leads to two main predictions: (1) small scale, hunter-gatherer societies in resource scarce environments will have stable adaptations less affected by variation in resource abundance in comparison to groups in resource rich regions where inter-group conflict is more likely and (2) the relationship between community size, population size and administrative complexity will have two distinct patterns, one for patrilineally organized societies and the other for matrilineally organized societies. Both predictions have been verified empirically.

This paper argues that agent-based modeling's innovations in method developed in terms of simulation techniques also involve an innovation in economic methodology. It shows how Epstein's generative science conception departs from conventional methodological reasoning, and employs what I term an open rather than closed approach to economic methodology associated with the roles that reflexivity, counterfactual reasoning, and abduction play in ABM. Central to this idea is that improvements in how we know something, a matter of method, determine whether we know something, a matter of methodology. The paper links this alternative view of economics and economic methodology to a social science model of economics and contrasts this with standard economics' natural science model of economics. The paper discusses what this methodological understanding implies about the concept of emergence.

The individual-level model is extended based on self-propelled Brownian particles and social force model, and it mainly describes how agents/particles interact with each other, and also with surrounding facilities including obstructions and passageways. Most importantly, we introduce a set of arrays to define social relationship of agents/particles in a quantitative manner. Opinion dynamics is integrated with force-based interaction to study complex social phenomena including path-selection activities, social groups and herding effect. Very interestingly, the interaction of agent/particles does not only exist at physics-level, but also at consciousness and unconsciousness level by integrating advanced social-psychological studies in our modeling framework.

Cloud-based health apps must keep patient info safe. This research shows that SSO and OAuth might make it easier for patients to get to their data in safe AWS GovCloud healthcare apps. To provide better care, healthcare must find a balance between patient privacy and data access. Application-specific login password security is available, but it is not safe or efficient. AWS GovCloud healthcare apps should use SSO and OAuth. Use SSO to log in to apps in the healthcare environment. OAuth is in charge of patient info. AWS GovCloud lets government and healthcare groups use cloud services that are HIPAA-compliant. The security design and compliance certifications of AWS GovCloud help healthcare businesses run apps safely and meet data protection standards. Central IdPs are used in the planned healthcare network. Only this IdP checks the users of healthcare apps. After IdP authentication, SAML sends user credentials safely to the AWS GovCloud target application. Next, OAuth is great. The healthcare app gives you tools while the central IdP gives the go-ahead. OAuth lets apps get to patient info safely. Users give apps permission to view their EHRs. The combined method makes things safer. Cut down on login information to stop brute-force attacks and password tiredness. Unified user management in the IdP limits access to patient data to only approved healthcare providers in a complex way. OAuth only lets apps access employment info. Security risks go down when data traces are smaller. Lastly, healthcare data platforms are safe because AWS GovCloud security is built in. Problems with patient safety. Advanced permissions are managed by OAuth flows in suggested design. The patients pick who can see their information. In IdP fine-grained access control rules, entry is limited by the person who has the least privilege. The design meets the strict rules set by HIPAA for keeping data safe. It is legal to use granular consent and authorization boundaries, as well as centralized IdP authentication. AWS GovCloud apps make it easier to follow the rules for cloud design in healthcare. SSO/OAuth makes it easier for healthcare workers to use. Clinicians and others can get to patient info without having to remember a lot of passwords. Less mental stress makes caring for patients easier. To put the plan into action, technical problems must be solved. Apps for health care may need SSO and OAuth APIs. To keep an eye on what users and data do, you need strong logging and tracking.The research is pointed. In the future, researchers may look into how MFA improves control over entry. Blockchain audit trails and history can be looked at.