Testing the phenotypic decanalization hypothesis: social determinants of hyperglycemia and type 2 diabetes in adult urban Argentinian population

Type 2 diabetes, one of the major causes of death and disability worldwide, is characterized by problems in the homeostasis of blood glucose. Current preventive policies focus mainly on individual behaviors (diet, exercise, salt and alcohol consumption). Recent hypotheses state that the higher incidence of metabolic disease in some human populations may be related to phenotypic decanalization causing a heightened phenotypic variance in response to unusual or stressful environmental conditions, although the nature of these conditions is under debate. Our aim was to explore variability patterns of fasting blood glucose to test phenotypic decanalization as a possible explanation of heightened prevalence for type 2 diabetes in some groups and to detect variables associated with its variance using a nation-wide survey of Argentinian adult population. We found patterns of higher local variance for fasting glycemia associated with lower income and educational attainment. We detected no meaningful association of glycemia or its variability with covariates related to individual behaviors (diet, physical activity, salt or alcohol consumption). Our results were consistent with the decanalization hypothesis for fasting glycemia, which appears associated to socioeconomic disadvantage. We therefore propose changes in public policy and discuss the implications for data gathering and further analyses.

Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

The outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

Voter model on adaptive networks

Voter model is an important basic model in statistical physics. In recent years, it has been more and more used to describe the process of opinion formation in sociophysics. In real complex systems, the interactive network of individuals is dynamically adjusted, and the evolving network topology and individual behaviors affect each other. Therefore, we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper, and study the voter model on the adaptive network. We theoretically analyze the properties of the voter model, including consensus probability and time. The evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary game. Finally, a case study of real data is shown to verify the effectiveness of the theory.

How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.

Big data-drive agent-based modeling of online polarized opinions

Under the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case “NeiYuanWaiFang” is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.

The Influence of Neighborhood Characteristics on HIV Treatment Outcomes Among Adults: A Scoping Review Protocol

BackgroundThe HIV pandemic has caused enormous suffering and loss of life in the last forty years. Currently more than 34 million people have died as a result of HIV infection and over 37 million people are currently living with HIV. There have been tremendous global efforts to address the HIV pandemic and although significant progress has been made in treatment and prevention of HIV, many people living with HIV still do not have appropriate access to treatment and care. Current HIV interventions have focused more on individual factors than on community level factors. Community level factors influence individual behavior by enhancing or limiting access to- and utilization of HIV care. The objective of this scoping review is to assess and critically examine the association between neighborhood characteristics and outcomes of HIV treatment.Methods The following databases will be searched for studies published on and prior to May 31, 2021: PubMed, PsycINFO, CINAHL, and EMBASE. We will include published full text studies with both experimental and observational study designs that discussed neighborhood characteristics and HIV treatment outcomes. We will exclude abstracts only publications and studies reporting opinion. Data generated from the search will be managed using Mendeley 1.19.8. The review will be conducted using Arskey and Levac’s methodological framework for scoping reviews. A narrative synthesis will be conducted on the included studies. Discussion Sustainability of HIV interventions that focus on individual behaviors rely on the community level factors that influence those individual behaviors. This review will enhance the understanding of the impact of neighborhood characteristics on HIV treatment outcomes among adults, and possibly shed a light on potential points of intervention that will aid in improving ART initiation, ART adherence, and HIV viral load suppression.Scoping Review Registration: OSF 10.17605/OSF.IO/MD89T

Religion as a Pillar for Establishing a Civilization: Al-Māwardī’s Perspective

This study examines religion in terms of establishing and maintaining a civilization based on Abū al-Ḥasan al-Māwardī’s (d. 1058) contribution, “Kitāb adab al-dunyā wa-al-dīn.” In this paper, the central argument is that religion is a pillar for establishing and maintaining civilization. There are no possibilities for a civilization to be constituted and maintained without a central role for religion. I will elaborate on the meaning of religion from al-Māwardī’s perspective and its role in constructing a civilization. This paper considers several religious notions and concepts proposed by al-Māwardī for this purpose. For instance, ‘al-Targhīb wa al-Tarhīb’ (persuasion and intimidation) and al-ulfah (social affinity) indicate how religion can affect individual behaviors and actions and helps create and maintain civilization. As a methodology, this paper analyses al-Māwardī’s contribution and compares it with significant contributions to civilization Studies. It concludes that a profound understanding of religion's role in any civilization gives a proper direction for understanding civilization and how religion can enhance and improve people's behavior and manners, which reflects positively on religion’s role in establishing and preserving a civilization.