How Exposure to Different Opinions Impacts the Life Cycle of Social Media
Rad AA, Jalali MS, Rahmandad H. How Exposure to Different Opinions Impacts the Life Cycle of Social Media. Annals of Operations Research 2017;268:63–91.Abstract

As a lot of communication and media consumption moves online, people may be exposed to a wider population and more diverse opinions. However, individuals may act differently when faced with opinions far removed from their own. Moreover, changes in the frequency of visits, posting, and other forms of expression could lead to narrowing of the opinions that each person observes, as well as changes in the customer base for online platforms. Despite increasing research on the rise and fall of online social media outlets, user activity in response to exposure to others’ opinions has received little attention. In this study, we first introduce a method that maps opinions of individuals and their generated content on a multi-dimensional space by factorizing an individual–object interaction (e.g., user-news rating) matrix. Using data on 6151 users interacting with 287,327 pieces of content over 21 months on a social media platform we estimate changes in individuals’ activities in response to interaction with content expressing a variety of opinions. We find that individuals increase their online activities when interacting with content close to their own opinions, and interacting with extreme opinions may decrease their activities. Finally, developing an agent-based simulation model, we study the effect of the estimated mechanisms on the future success of a simulated platform.

Exposure to different opinions.pdf
Measuring Stakeholders’ Perceptions of Cybersecurity for Renewable Energy Systems
Madnick S, Jalali MS, Siegel M, Lee Y, Strong D, Wang R, Ang WH, Deng V, Mistree D. Measuring Stakeholders’ Perceptions of Cybersecurity for Renewable Energy Systems. In: Lecture Notes in Artificial Intelligence 10097. Springer; 2017 p. 67–77.
A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans
Ghaffarzadegan N, Ebrahimvandi A, Jalali MS. A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans. PLOS ONE 2016;11:e0161405.Abstract

Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a “system of systems.” This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.

A dynamic model for PTSD.pdf
Estimating the parameters of system dynamics models using indirect inference
Hosseinichimeh N, Rahmandad H, Jalali MS, Wittenborn AK. Estimating the parameters of system dynamics models using indirect inference. System Dynamics Review 2016;32:156-180.Abstract

There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation‐based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error‐generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights.

Parameter estimation using indirect inference.pdf
Preventive behaviors and perceptions of influenza vaccination among a university student population
Hashmi S, D’Ambrosio L, Diamond DV, Jalali MS, Finkelstein SN, Larson RC. Preventive behaviors and perceptions of influenza vaccination among a university student population. Journal of Public Health 2016;38(4):739–745.Abstract

Background: Every year during influenza season, preventable illnesses occur due to lack of vaccination and failure to adopt the preventive behaviors known as non-pharmaceutical interventions (NPIs). In an effort to study the impact of preventive strategies and policies on behavioral changes during the spread of the H1N1 pandemic in 2009, we examined a sample of undergraduate, graduate and business students at the Massachusetts Institute of Technology (MIT).

Methods: An online survey was completed by 653 students to assess NPI use, perceptions of influenza vaccinations and effectiveness of preventive health policy strategies during the 2009 H1N1 outbreak. Strategies included e-mails and text messages, posters in corridors and restrooms, and videos. These strategies were implemented during both the first and second waves of the 2009 H1N1 pandemic.

Results: Despite the widespread campaign, fewer than half of the respondents reported modifying their behaviors. We discovered that >70% of the respondents did not practice any NPIs, and more than half showed lack of knowledge of flu vaccinations.

Conclusions: Our study results indicate a need for more effective strategies to encourage NPI practices in student populations during outbreaks of infection.

Preventive behaviors and perceptions of vaccination.pdf
Social influence in childhood obesity interventions: a systematic review
Jalali MS, Sharafi‐Avarzaman Z, Rahmandad H, Ammerman AS. Social influence in childhood obesity interventions: a systematic review. Obesity Reviews 2016;17:820-832.Abstract

The objective of this study is to understand the pathways through which social influence at the family level moderates the impact of childhood obesity interventions. We conducted a systematic review of obesity interventions in which parents' behaviours are targeted to change children's obesity outcomes, because of the potential social and environmental influence of parents on the nutrition and physical activity behaviours of children. PubMed (1966-2013) and the Web of Science (1900-2013) were searched, and 32 studies satisfied our inclusion criteria. Results for existing mechanisms that moderate parents' influence on children's behaviour are discussed, and a causal pathway diagram is developed to map out social influence mechanisms that affect childhood obesity. We provide health professionals and researchers with recommendations for leveraging family-based social influence mechanisms to increase the efficacy of obesity intervention programmes.

social influence in childhood obesity interventions.pdf
Information diffusion through social networks: The case of an online petition
Jalali MS, Ashouri A, Herrera-Restrepo O, Zhang H. Information diffusion through social networks: The case of an online petition. Expert Systems with Applications 2015;44:187–197.Abstract

People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers. Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have ‘disproportionate’ effects in increasing the total number of signatures. The model is fully documented for further development and replications.

    Information diffusion.pdf
    Using the method of simulated moments for system identification
    Jalali MS, Rahmandad H, Ghoddusi H. Using the method of simulated moments for system identification. Analytical Methods for Dynamic Modelers 2015;:39-69. MSM book chapter.pdf
    Dynamics of Obesity Interventions inside Organizations
    Jalali M, Rahmandad H, Bullock S, Ammerman A. Dynamics of Obesity Interventions inside Organizations. The 32nd International Conference of the System Dynamics Society 2014;:69.
    How individuals weigh their previous estimates to make a new estimate in the presence or absence of social influence
    Jalali MS. How individuals weigh their previous estimates to make a new estimate in the presence or absence of social influence. International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction 2014;:67-74.Abstract

    Individuals make decisions every day. How they come up with estimates to guide their decisions could be a result of a combination of different information sources such as individual beliefs and previous knowledge, random guesses, and social cues. This study aims to sort out individual estimate assessments over multiple times with the main focus on how individuals weigh their own beliefs vs. those of others in forming their future estimates. Using dynamics modeling, we build on data from an experiment conducted by Lorenz et al. [1] where 144 subjects made five estimates for six factual questions in an isolated manner (no interaction allowed between subjects). We model the dynamic mechanisms of changing estimates for two different scenarios: 1) when individuals are not exposed to any information and 2) when they are under social influence.

    Social influence on making estimations.pdf
    Public and health-professionals’ misconceptions about the dynamics of body weight gain/loss
    Abdel-Hamid T, Ankel F, Battle-Fisher M, Gibson B, Gonzalez-Parra G, Jalali M, Kaipainen K, Kalupahana N, Karanfil O, Marathe A. Public and health-professionals’ misconceptions about the dynamics of body weight gain/loss. System Dynamics Review 2014;30(1-2):58–74.Abstract

    Human body energy storage operates as a stock‐and‐flow system with inflow (food intake) and outflow (energy expenditure). In spite of the ubiquity of stock‐and‐flow structures, evidence suggests that human beings fail to understand stock accumulation and rates of change, a difficulty called the stock–flow failure. This study examines the influence of health care training and cultural background in overcoming stock–flow failure. A standardized protocol assessed lay people's and health care professionals’ ability to apply stock‐and‐flow reasoning to infer the dynamics of weight gain/loss during the holiday season (621 subjects from seven countries). Our results indicate that both types of subjects exhibited systematic errors indicative of use of erroneous heuristics. Indeed 76% of lay subjects and 71% of health care professionals failed to understand the simple dynamic impact of energy intake and energy expenditure on body weight. Stock–flow failure was found across cultures and was not improved by professional health training. The problem of stock–flow failure as a transcultural global issue with education and policy implications is discussed. 

    misconceptions about obesity.pdf