Model Of Individual Behavior Research Articles

A socioecological view toward an understanding of how to prevent overweight in children

PurposeThis study aims to contribute to the understanding of how elements of the socioecological system shape individual behaviors. The problem of childhood overweight and obesity is analyzed as existing within a complex system of relationships at different levels by means of system dynamics (SD).Design/methodology/approachSecondary sources and primary information collected from an elementary school were used to analyze the influences of several social environmental factors on the dietary habits and physical activity of children. The major elements that influence these behaviors were identified via a socioecological framework (SEF), and the interrelationships among these elements were described using an SD model. Then, several scenarios corresponding to social marketing actions oriented toward modifying the influence of specific elements in the socioecological system were proposed to evaluate how effective they are at reducing the percentages of overweight and obesity among children.FindingsThe current research shows the existence of counteracting efforts at the micro (family) and macro (governmental policies) levels that need to be aligned to reduce rates of obesity and overweight.Practical implicationsThe systems perspective supports decision makers in defining social marketing strategies to modify alimentary behaviors based on the understanding of what elements of the SEF influence behavior and how they interrelate. To the authors’ knowledge, a detailed analysis of the influences of the socioecological environment has not been performed based on Latin American countries to seek solutions to the public health problems of overweight and obesity.Originality/valueThe application of SD enhances the value of the SEF suggested by Collins et al. (2010) for modeling individual behaviors. Moreover, the use of the systems approach for framing and understanding how the interrelationships of socioecological elements derive in synergic or antagonistic effects helps to predict the long-term effect of governmental actions and school interventions.

Patient-specific modeling of individual sickle cell behavior under transient hypoxia.

Sickle cell disease (SCD) is a highly complex genetic blood disorder in which red blood cells (RBC) exhibit heterogeneous morphology changes and decreased deformability. We employ a kinetic model for cell morphological sickling that invokes parameters derived from patient-specific data. This model is used to investigate the dynamics of individual sickle cells in a capillary-like microenvironment in order to address various mechanisms associated with SCD. We show that all RBCs, both hypoxia-unaffected and hypoxia-affected ones, regularly pass through microgates under oxygenated state. However, the hypoxia-affected cells undergo sickling which significantly alters cell dynamics. In particular, the dense and rigid sickle RBCs are obstructed thereby clogging blood flow while the less dense and deformable ones are capable of circumnavigating dead (trapped) cells ahead of them by choosing a serpentine path. Informed by recent experiments involving microfluidics that provide in vitro quantitative information on cell dynamics under transient hypoxia conditions, we have performed detailed computational simulations of alterations to cell behavior in response to morphological changes and membrane stiffening. Our model reveals that SCD exhibits substantial heterogeneity even within a particular density-fractionated subpopulation. These findings provide unique insights into how individual sickle cells move through capillaries under transient hypoxic conditions, and offer novel possibilities for designing effective therapeutic interventions for SCD.

Network assemblage of regime stability and resilience: comparing Europe and China

AbstractThis article demonstrates that the network structures of historical regimes influence the way information is spread, which in turn circumscribe the behaviors of the different groups that make up the system. It advances two central claims. The first is a methodological one showing that patterns of long-term historical change are best studied at the system level, rather than by a traditional equilibrium framework grounded in models of individual behavior. Then, an empirical claim is established by comparisons of China's hub-and-spoke hypernetwork with Europe's multi-hub hypernetwork to show that their different patterns of interconnectivity forged their respective capacities to weather intermittent socioeconomic transitions.

Identifying Human Behaviors Using Synchronized Audio-Visual Cues

In this paper, a human behavior recognition method using multimodal features is presented. We focus on modeling individual and social behaviors of a subject (e.g., friendly/aggressive or hugging/kissing behaviors) with a hidden conditional random field (HCRF) in a supervised framework. Each video is represented by a vector of spatio-temporal visual features (STIP, head orientation and proxemic features) along with audio features (MFCCs). We propose a feature pruning method for removing irrelevant and redundant features based on the spatio-temporal neighborhood of each feature in a video sequence. The proposed framework assumes that human movements are highly correlated with sound emissions. For this reason, canonical correlation analysis (CCA) is employed to find correlation between the audio and video features prior to fusion. The experimental results, performed in two human behavior recognition datasets including political speeches and human interactions from TV shows, attest the advantages of the proposed method compared with several baseline and alternative human behavior recognition methods.

The Guiding-Forces Model: A Step toward Developing a Longitudinal, Multilevel Model of Individuals' Work Behavior

The ultimate goal of this paper is to provide an interpretation of the behaviors of individuals in the global work force. Using the well-established contingency theory as a jumping-off point, I begin this paper by developing a longitudinal, multilevel Global Contingency Theory of Work Behavior as the overarching framework. Then, using this framework as a foundation, I present the development of its crucial and complex micro-level component, the Guiding-Forces Model of individual behavior. Next, I present a parsimonious design that provides a set of sample propositions that provide ways to empirically investigate the various parts of the Guiding-Forces Model. I conclude by presenting a set of holistic issues, relevant to the Guiding-Forces Model, which also require future empirical investigation.

Stochastic modeling of individual grain behavior during Ostwald ripening at ultra-high volume fractions of the coarsening phase

The evolution of grains during coarsening phenomena like Ostwald ripening is a focus of recent and ongoing research. In the present paper, a new and flexible model is proposed that describes the statistical evolution of the “typical” individual grain size as a function of neighborhood characteristics. The grain size evolution (GSE) model defines a stochastic process based on contemporary mathematical techniques and requires only few (natural) assumptions. It is fitted to time-resolved experimental data of a semisolid Al–Cu alloy, in which the coarsening phase has an ultra-high volume fraction VV=0.93. Evaluation shows that the model describes the experimental data quite closely. The nature of this modeling approach serves to improve the understanding of coarsening processes at the intermediate level between coarsening mechanisms and global statistical properties. Furthermore, the model enables predictive simulations to be performed, based on an extension of an existing 3D microstructure model (Spettl et al., 2015) to 4D.

Modeling and analysis of residential flexibility: Timing of white good usage

Challenges that smart grids aim to address include the increasing fraction of supply by renewable energy sources, as well as plain rise of demand, e.g., by increased electrification of transportation. Part of the solution to these challenges lies in exploiting the opportunity to steer residential electricity consumption (e.g., for flattening the peak load or balancing the supply and demand in presence of the renewable energy production). To optimally exploit this opportunity, it is crucial to have insights on how flexible the residential demand is. Load flexibility is characterized by the amount of power, time of availability and duration of deferrable consumption. Residential flexibility however, is challenging to exploit due to the variation in types of customer loads and differences in appliance usage habits from one household to the other. Existing analyses of individual customer flexibility behavior in terms of timing are often based on inferences from surveys or customer load patterns (e.g., as observed through smart meter data): there is a high level of uncertainty about customer habits in offering the flexibility. Even though some of these studies rely on real world data, only few of them have quantitative data on actual flexible appliance usage, and none of them characterizes individual user behavior. In this paper, we address this gap and contribute with: (1) a new quantitative specification of flexibility, (2) two systematic methodologies for modeling individual customer behavior, (3) evaluation of the proposed models in terms of how accurately the data they generate corresponds with real world customer behavior, and (4) a basic analysis of factors influencing the flexibility behavior based on statistical tests. Experimental results for (2)–(4) are based on a unique data set from a real-life field trial.

Uncovering the political dimension of social-ecological systems: Contributions from policy process frameworks

Abstract Research on the governance of social-ecological systems often emphasizes the need for self-organized, flexible and adaptive arrangements to deal with uncertainty, abrupt change and surprises that are characteristic of social-ecological systems. However, adaptive governance as well as transitions toward alternative forms of governance are embedded in politics and it is often the political processes that determine change and stability in governance systems and policy. This paper analyses five established theoretical frameworks of the policy process originating in political science and public policy research with respect to their potential to enhance understanding of governance and complex policy dynamics in social-ecological systems. The frameworks are found to be divergent in their conceptualization of policy change (focusing on incremental or large-scale, major changes), highlighting different aspects of bounded rationality in their model of individual behavior and focusing their attention on different aspects of the policy process (role of information, attention, beliefs, institutional structure, particular actors, etc.). We discuss the application of these frameworks and their potential contribution to unravelling the political dimension in adaptive governance and transformations.

A Heterogeneous Agent Macroeconomic Model for Policy Evaluation: Improving Transparency and Reproducibility

This paper provides a detailed description of the Eurace@Unibi model, which has been developed as a versatile tool for economic policy analysis. The model explicitly incorporates the decentralized interaction of heterogeneous agents across different sectors and regions. The modeling of individual behavior is based on heuristics with empirical microfoundations. Although Eurace@Unibi has been applied successfully to different policy domains, the complexity of the structure of the model, which is similar to other agent-based macroeconomic models, has given rise to concerns about the reproducibility and robustness of the obtained insights. This paper addresses these concerns by describing the exact details of all decision rules, interaction protocols and balance sheets used in the model. Furthermore, we discuss the use of a virtual appliance as a tool allowing third parties to reproduce and verify the simulation results. The paper provides a systematic and extensive sensitivity analysis of the simulation output with respect to a set of key parameters. Particular emphasis is put on the question which parameter constellations give rise to strong economic fluctuations and high frequencies of sudden downturns in economic activity.

A Framework for Developing the Structure of Public Health Economic Models

BackgroundA conceptual modeling framework is a methodology that assists modelers through the process of developing a model structure. Public health interventions tend to operate in dynamically complex systems. Modeling public health interventions requires broader considerations than clinical ones. Inappropriately simple models may lead to poor validity and credibility, resulting in suboptimal allocation of resources. ObjectiveThis article presents the first conceptual modeling framework for public health economic evaluation. MethodsThe framework presented here was informed by literature reviews of the key challenges in public health economic modeling and existing conceptual modeling frameworks; qualitative research to understand the experiences of modelers when developing public health economic models; and piloting a draft version of the framework. ResultsThe conceptual modeling framework comprises four key principles of good practice and a proposed methodology. The key principles are that 1) a systems approach to modeling should be taken; 2) a documented understanding of the problem is imperative before and alongside developing and justifying the model structure; 3) strong communication with stakeholders and members of the team throughout model development is essential; and 4) a systematic consideration of the determinants of health is central to identifying the key impacts of public health interventions. The methodology consists of four phases: phase A, aligning the framework with the decision-making process; phase B, identifying relevant stakeholders; phase C, understanding the problem; and phase D, developing and justifying the model structure. Key areas for further research involve evaluation of the framework in diverse case studies and the development of methods for modeling individual and social behavior. ConclusionsThis approach could improve the quality of Public Health economic models, supporting efficient allocation of scarce resources.

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment.

IntroductionSmartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models.MethodUsing a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.ResultsAmple compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.ConclusionA typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.

Modeling of Individual and Group Behavior in P-adic Coordinate Systems in Order to Solve Information Security Problems

In this paper, we propose a formal model of individual and group behavior based on p-adic coordinate system. The model developed allows description and prediction of behavioral reactions of the personnel of critically important objects under conditions of external destructive informational impacts.

Data-driven agent-based modeling, with application to rooftop solar adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income households. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.

Individual Moving Behavior Modeling based on People Flow Data

Individual Moving Behavior Modeling based on People Flow Data

No longer take a tree for the forest: A cross-level learning-related perspective on individual innovative behavior

AbstractUsing trait activation theory as a framework, this study developed and tested a cross-level model of individual innovative behavior. Data from a sample of 334 employees within 75 work teams were used to examine the hypothesized model. Results showed that employee learning goal orientation was positively related to innovative behavior only when the team structure was more organic. Additionally, the relationship between employee learning goal orientation and innovative behavior would be strongest when both the team structure was more organic and team mean learning goal orientation was higher.

Toward a new generic behavior model for human centered system simulation

The simulation of Human Centered Systems (HCS) has attracted increasingly attention in recent research. These systems involve individuals playing key roles, such as workers in manufacturing systems, soldiers in military operations, and investors in stock markets. The complexity simulating such systems is due to the need for modeling individual and group behavior and the integration of psychological and socio-technical aspects that can affect individual and HCS global performance. Although several models have been proposed to simulate such systems, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed and elaborated on in this paper. The current study presents a new model for HCS simulation based on recent social and psychological theories. A model implementation example involving the simulation of a manufacturing system, considered as a HCS, is presented.

Predicting Self-Initiated Preventive Behavior Against Epidemics with an Agent-Based Relative Agreement Model

Human self-initiated behavior against epidemics is recognized to have significant impacts on disease spread. A few epistemic models have incorporated self-initiated behavior, and most of them are based on a classic population-based approach, which assumes a homogeneous population and a perfect mixing pattern, thus failing to capture heterogeneity among individuals, such as their responsive behavior to diseases. This article proposes an agent-based model that combines epidemic simulation with a relative agreement model for individual self-initiated behavior. This model explicitly represents discrete individuals, their contact structure, and most importantly, their progressive decision making processes, thus characterizing individualized response to disease risks. The model simulation and sensitivity analysis show the existence of critical points (threshold values) in the model parameter space to control influenza epidemic including minimum values for the initially positive population size, the communication rate, and the attitude uncertainty. These threshold effects shed insights on preventive strategy design to deal with the current circumstances that new vaccines are often insufficient to combat emerging communicable diseases.

A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease

Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.

An Evacuation Model for Risk Analysis in Spanish Road Tunnels

In this paper we present EvacTunnel 3.0 an evacuation model design specifically for road tunnels. The model combines coarse network (space discretization) and a microscopic approach for modeling individual behaviors. Based on Monte Carlo methods, the model has the capability to perform multiple simulations by changing random variables of tunnel users such as pre-movement times and walking speeds. The proposed model allows the user to incorporate the effects of fire into the evacuation simulation (importing fire data/results from another model) and, therefore, predicts the number of people directly affected by the smoke (victims). Three verification cases of the proposed model are shown in this paper: 1) risk analysis application, 2) comparison with other evacuation models and 3) a real case application.

Can cognitive science create a cognitive economics?

Cognitive science can intersect with economics in at least three productive ways: by providing richer models of individual behaviour for use in economic analysis; by drawing from economic theory in order to model distributed cognition; and jointly to create more powerful ‘rational’ models of cognitive processes and social interaction. There is the prospect of moving from behavioural economics to a genuinely cognitive economics.