Tuesday, September 17 |
07:00 - 09:00 |
Breakfast ↓ Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building. (Vistas Dining Room) |
09:00 - 09:30 |
Eric Lacourse: Understanding the use of mixture models with cross-sectional and longitudinal data ↓ During the past 25 years, the methodological literature on mixture models has expanded and it is now extensively used in practice to capture unobserved heterogeneity of specific parameters in the population. We use a pedagogical review presenting different mixture models and their relationships to each other. We first present the results of a Latent Transition Analysis of political behaviors. Secondly, we will present the results of a Latent Transition Mixture Model that combines Latent Class Analysis to identify profiles based on an important number of risk factors, Latent Class Growth Analysis to identify longitudinal profiles of a behavior, and their interaction in predicting some distal outcomes. We will present these prototypic mixture models in a unified format, based on familiar probability laws, common assumptions, and interpretation. Each mixture model can be divided into a within class model and a between class model. The within class model accounts for the data-generating mechanism for persons in class K. The between class model helps define the probability that a person will be in a class vs. another.
Co-authors: Félix Laliberté, Mathieu Pelletier-Dumas, Jean-Marc Lina and Roxane de la Sablonnière (TCPL 201) |
09:30 - 10:00 |
Jean-Marc Lina: From structural equation modeling to Kalman (Online) |
10:00 - 10:30 |
Mathieu Caron-Diotte: Missing Responses in Modeling Social Behaviour ↓ Missing data is inherent in the study of social phenomena, and can introduce bias and result in diminished statistical power. However, missingness can also be an indication of interesting dynamics. For instance, some individuals might be dissatisfied about the efficacy of a treatment and thus abandon a study. Other individuals could be unreachable because of an unstable social and political context. This communication is aimed to argue that mathematical modelling of social behaviour must take into account missingness in order to deepen the understanding of the phenomenons under study. To this end, we will outline the theory behind missing data, present on some of its causes, and provide pointers on the introduction of missingness into the mathematical modelling of social behaviour.
Co-authors: Mathieu Caron-Diotte, Mathieu Pelletier-Dumas, Éric Lacourse, Anna Dorfman, Dietlind Stolle, Jean-Marc Lina, and Roxane de la Sablonnière (TCPL 201) |
10:30 - 11:00 |
Coffee Break (TCPL Foyer) |
11:00 - 11:30 |
Mathieu Pelletier-Dumas: Navigating the Complex Dynamic of Compliance to Public Policy During a Dramatic Social Change: Insights from Three Canadian Studies ↓ The United Nations anticipates that over 500 dramatic social changes (DSC) like the COVID-19 pandemic will occur by 2030 (i.e., climate catastrophes). DSC requires the implementation of public policies aimed at shaping collective behavior, and in particular compliance. Understanding the adaptation process to DSCs entails complex methodologies and analyses that consider the dynamic nature of DSC. However, to date, most research has focused on cross-sectional “static” designs. We aim at answering this limit by exploring compliance with public policies during the COVID-19 pandemic. Specifically, we use a comprehensive investigation spanning three studies that involves a representative sample of Canadian citizens (N=3617) who participated in a 12-wave longitudinal study (April 2020 to April 2022). All studies use LGCA (i.e., trajectory analysis) to account for the dynamic aspect of the pandemic. Study 1 seeks to identify longitudinal patterns of compliance behaviour with preventive measures during the first year of the pandemic and their relation with factors like used sources of information and level of understanding. Study 2 uses different indicators (e.g., trust in the Prime Minister) to predict transitions in memberships to varying patterns of compliance across time. Finally, Study 3 furthers our understanding of transition patterns during a different moment of the COVID-19 pandemic (July 2020 to March 2021). By delving into dynamic processes, this investigation offers valuable insights for understanding how individuals react to the public policies put in place to face the DSCs of today and of those anticipated in the coming decades.
Co-authors: Sahar Ramazan Ali, Éric Lacourse, Jean-Marc Lina, Jacques Bélair, Dietlind Stolle, and Roxane de la Sablonnière (TCPL 201) |
11:30 - 12:00 |
Simon Bacon: Modelling behaviour change using theory - an example from the COVID-19 pandemic ↓ There are now a number of ways in which behaviour and behaviour change can be modelled. One of the most popular is the Capability-Opportunity-Motivation: Behaviour (COM-B) model. This provides an excellent structure to be able to understand behaviours. COVID-19 was, and still is, a disease which requires a specific set of behaviours to occur to reduce its spread and impact. As an example of the kind of work we do at the Montreal Behavioural Medicine Centre (www.mbmc-cmcm.ca), we applied the COM-B to an ongoing COVID-19 study, the iCARE study (www.iCAREStudy.com), which has collected over 170,000 responses since the start of the pandemic, to explore the drivers of various pandemic behaviours, e.g., mask wearing, vaccine uptake, and physical distancing. Mapping the data collected onto the COM-B provides concrete information on how to better structure public health interventions to fight future pandemics. However, there are notable challenges in analysing this kind of data, e.g., changes in trends overtime, accounting for impacts of multiple concurrent behaviours, etc., which would benefit from more advanced statistical techniques. (Online) |
12:00 - 13:30 |
Lunch ↓ Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building. (Vistas Dining Room) |
13:30 - 13:45 |
Elissa Schwartz: Epidemic control and vaccine hesitancy: What vaccine efficacy levels are needed? ↓ Throughout the last two centuries, vaccines have been helpful in mitigating numerous epidemic diseases. However, vaccine hesitancy has been identified as a substantial obstacle in healthcare management. We examined the epidemiological dynamics of an emerging infection under vaccination using an SVEIR model with differential morbidity. We mathematically analyzed the model, derived R0, and provided a complete analysis of the bifurcation at R0 = 1. Sensitivity analysis and numerical simulations were used to quantify the tradeoffs between vaccine efficacy and vaccine hesitancy on reducing the disease burden. Our results indicated that if the percentage of the population hesitant about taking the vaccine is 10%, then a vaccine with 94% efficacy is required to reduce the peak of infections by 40%. If 60% of the population is reluctant about being vaccinated, then even a perfect vaccine will not be able to reduce the peak of infections by 40%. (TCPL 201) |
13:45 - 14:00 |
Rebecca Claire Tyson: The role of committed minorities in climate change action ↓ It is well-established that human activity is driving extreme weather patterns, and that these extreme events influence human behaviour. However, few models allow for human behaviours and the climate to dynamically interact. The models presented in this talk expand on previous work and serve as an initial framework to extend current models by using a dynamic social-climate feedback loop. First, we introduce a social model to determine the conditions under which a committed minority can overturn a pre-established social convention. Second, we modify an existing climate model to include climatic variability. Lastly, we formulate a social-climate feedback loop to study the interplay between human behaviour and the climate. Our results demonstrate that the social-climate feedback loop may be important in accurately predicting future temperatures, in contrast to the standard approach where human behaviour is considered fixed. Additionally, we find that a committed minority plays a vital role in shifting public opinion towards climate action and that the time at which the social convention of climate inaction is overturned has a large impact on future temperatures. (TCPL 201) |
14:00 - 14:15 |
Brian Beckage: A framework for putting human behavior into socio-ecological models ↓ Problems in environmental sustainability, climate change, and spread of contagious disease involve biophysical, economic, and human social and behavioral systems that dynamically interact. We present a framework for modeling human social and behavioral systems through the processes of contagion and cognition with perceived risk as the state variable determining the human response. We briefly present examples from epidemiology, solar geoengineering, and anthropogenic climate change.
Co-authors:
Katherine LaCasse, Rhode Island College
Louis J. Gross, University of Tennessee, Knoxville
Karim Chichakly, ISEE Systems
Sarah Constantino, Stanford University
Travis Franck, Tufts University
Sara Metcalf, University at Buffalo
Fran Moore, UC Davis
Kaitlin Raimi, University of Michigan
Dale Rothman, George Mason University
Daniele Visioni, Cornell University (TCPL 201) |
14:30 - 14:45 |
Louis Gross: Modeling Cognitive Processes for Human Risk Perception: A Climate Change Example ↓ The vast majority of climate models designed to project future global temperature trajectories ignore feedbacks between human behavioral and social system responses and the climate system. Prior research on models linking climate models to human behavior provide evidence that these linkages can significantly modify future trajectories compared to climate models based only on natural system processes. We will describe our efforts to model the interactions of climate systems and human social systems, focusing particularly on risk perception. We will describe how we model human risk perception and associated changes in attitudes as driven by the experience of climate change, for example, from extreme climate events and economic damages, and how this perceived risk motivates willingness to pay for abatement of greenhouse gas emissions and support for ‘green’ policies. This presentation will focus on methods to model cognition, personal experience and memory processing. This is modeled as a balance between sensing and forgetting extreme climate events that allows for habituation, salience, biased assimilation and recency and considers how these factors might vary across a heterogeneous population.
Co-authors: Brian Beckage (University of Vermont), Karim Chichakly (isee systems), Sarah Constantino (Northeastern University), Travis Franck (Tufts University), Katherine LaCasse (Rhode Island College), Sara Metcalf (University at Buffalo), Dale Rothman (George Mason University) (TCPL 201) |
14:45 - 15:00 |
Jane Heffernan (TCPL 201) |
15:00 - 15:15 |
Priscilla (Cindy) Greenwood: How to use stochastic dynamics in social behaviour (TCPL 201) |
15:15 - 15:30 |
Coffee Break (TCPL Foyer) |
15:30 - 15:45 |
Diana Cardenas: Dramatic Social Change and Threatened Identities: An Algorithm to Understand Socio-psychological Processes ↓ COVID-19, wars, and natural disasters exemplify dramatic social changes (DSC), characterized by rapid shifts, disruption in social structures and behaviors, and threats to social identity. While existing models like ViEWS and MASON RebeLand attempt to forecast such events, they fall short by focusing narrowly on specific contexts and types of social change without taking a proper look on individual experiences such as identity threats. To address these gaps, we developed a new typology of social change from an extensive review of over 300 selected papers. This typology identifies four social states: stability, incremental social change, DSC, and collective inertia. Utilizing this framework, we created the Social Change Algorithm (SCA), a predictive tool now undergoing validation and simulation. The SCA forecasts shifts between social states by analyzing patterns and probabilities using a Bayesian model, aiming to improve decision-making in response to events like pandemics. Initial studies have modeled transitions due to coups d’état and elections and have first applied the algorithm to assess the impact of COVID-19 in North America. The SCA, however, requires further theoretical refinement, including the integration of collective memory, to enhance its utility for decision-makers.
Co-authors: Jean-Marc Lina, Jacques Bélair, and Roxane de la Sablonnière (TCPL 201) |
15:45 - 16:00 |
Laura French Bourgeois: In the name of freedom: Using machine learning to identify the factors that influence psychological reactance during the COVID-19 pandemic ↓ The public health measures imposed to curb the spread of the COVID-19 virus created strong opposition among part of the population. The literature points to psychological reactance as a main factor explaining non-compliance with the public health measures, as it prompts individuals to want to restore the freedom that they felt they have lost. While demographic, personal, and contextual factors have been identified as influencing the level of reactance, previous studies have not explored a comprehensive model that includes all these factors together to develop a broad profile of reactant individuals. Using a representative sample of Canadians (N=3617) and machine learning, the present study employs a regression with Lasso regularization to develop a predictive model assessing the most important factors (old and new) related to psychological reactance during the COVID-19 pandemic. Out of the 158 factors considered, the Lasso regularization retained the 34 most important ones. The results reveal that confidence towards scientists, political orientation, and loneliness are among the strongest factors related to psychological reactance. These findings contribute to a better understanding of the complex interplay of factors influencing psychological reactance and can inform public health strategies to enhance compliance with health measures.
Co-authors: Matthew Fernandez, Sophie Sydoriak, Mathieu Pelletier-Dumas, Eric Lacourse & Roxane de la Sablonnière (TCPL 201) |
16:00 - 16:45 |
Katherine Reynolds: Does COVID-19 herald a new era for the psychology of behaviour change? ↓ All the major challenges humanity faces - climate adaptation, social cohesion, technology adoption, healthy lifestyles depend on behaviour change. A silver lining of COVID-19 may be an awareness of the importance of behaviour change and the contribution of psychology. Different countries engaged with psychology and social psychology in different ways and adopted different ‘theories’ of the human subject and behaviour change (e.g., with an individual approach there was an emphasis on attitudes, risk perception, behavioural fatigue, loneliness and mental health, crowd fear and panic, incentivisation). In some areas there was an increased and novel openness towards group-based processes (social identity and group norms, collective responsibility, aid and solidarity, leadership and social influence). Internally within the field debates emerged about whether psychology was ready for “prime time” and the crisis of generalisability. In this presentation the focus will be on how COVID-19 offers a disciplinary opportunity to advance social psychology. Post-crisis it is important to consider questions such as What have we learned? What are the implications for other imminent crises and human challenges? and where are the gaps in knowledge and advances that are needed in theory and research? (Online) |
16:45 - 17:00 |
Bert Baumgaertner: Standards of Evidence and Deference to Experts ↓ A standard of evidence is a transition from collecting information to acting on that information. In this brief talk I argue for two claims. First, evidentiary standards are heterogenous because they depend on context, domain, and social conventions. Here I will present some results from empirical work about people's evidence gathering behavior when they are asked to assess a causal claim, such as the effectiveness of an anti-viral nasal spray in preventing contraction of COVID. Second, deference to experts is itself subject to standards of evidence, but little is known about how people update their assessments of the reliability or trustworthiness of sources. Consequently, models should capture this uncertainty. (TCPL 201) |
17:00 - 17:15 |
Chenangnon Tovissode: The Relative Impact of Social Influence Cost and Benefit of Prophylaxis on Epidemic Severity ↓ Coupling social influence-based opinion model with disease dynamics knows a growing interest to understanding feedback loops between the distribution of opinions on costly prophylactic behaviors and disease evolution. Recent research has examined how the occurrence of multiple epidemic waves depends on the influence cost of prophylaxis and the rate of opinion changes relative to disease propagation. Building on these results, we combine an attitude spectrum based on a double-prophylaxis with an SIR disease dynamic model that accounts for important disease states from the COVID-19 pandemic context. In this model, attitude changes are governed by the effective rate of mutual influence which is determined by the perceived risk of becoming infected.
Under this framework, the distribution of opinions in disease-free conditions depends on the influence cost of prophylaxis. We explore how epidemic severity measures such as epidemic peak and final size, and the occurrence of multiple epidemic waves are related to the disease-free influence cost of prophylaxis and the influence benefit of adherence to prophylactic behaviors during an epidemic. (TCPL 201) |
17:15 - 17:16 |
Jacques Bélair: Knowledge as an infection: a model for variable compliance with NPIs ↓ In the deployment of non-pharmaceutical interventions (NPIs) for the control of infectious disease propagation, the level of compliance in the target population is crucial for their success. We present deterministic compartmental models in which subgroups of the population have variable degrees of i. information about the NPIs and ii. compliance with the NPIs. We explore the dynamical consequences of movements between the corresponding subgroups. (TCPL 201) |
17:16 - 17:31 |
Julien Arino: A few naive experiments in phenomenological modelling of media-induced behavioural changes (TCPL 201) |
17:50 - 19:00 |
Dinner ↓ A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building. (Vistas Dining Room) |
19:00 - 20:30 |
Poster Session ↓ Posters will be presented by (in no particular order):
• Laura French Bourgeois
• Teri Garstka
• Betsy Varughese
• Azadeh Aghaeeyan
• Monica Cojocaru
• Lindsey McConnell-Soong
• Priscilla (Cindy) Greenwood
• Lou Gross
• Md Mijanur Rahman
• Grégoire Ranson
• Chadi Saad-Roy
• Elissa Schwartz
• Rebecca Tyson
• Madeline Ward (TCPL Foyer) |