Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling

Hysteresis has been used to understand various social phenomena, such as political polarization, the persistence of the vaccination-compliance problem, or the delayed response of employees in a firm to wage incentives. The aim of this article is to show the insights that can be gained from using agent-based models (ABMs) to study hysteresis. To build up an intuition about hysteresis, we start with an illustrative example from physics that demonstrates how hysteresis manifests as collective memory. Next, we present examples of hysteresis in psychology and social systems. We then present two simple ABMs of binary decisions-the Ising model and the q-voter model-to explain how hysteresis can be observed in ABMs. Specifically, we show that hysteresis can result from the influence of various external factors present in social systems, such as organizational polices, governmental laws, or mass media campaigns, as well as internal noise associated with random changes in agent decisions. Finally, we clarify the relationship between several closely related concepts such as order-disorder transitions or bifurcation, and we conclude the article with a discussion of the advantages of ABMs.

A psychosocial network approach studying biomedical HIV prevention uptake between 2017 and 2019

Biomedical HIV-prevention strategies (BmPS) among men who have sex with men (MSM), such as pre-exposure prophylaxis (PrEP) and viral load sorting (VLS), are essential but relatively new and their uptake gradual. Using an extension of the causal attitude network approach, we investigated which beliefs are related to uptake of PrEP and VLS at each time-point. We included 632 HIV-negative MSM from the Amsterdam Cohort Studies from four data-waves between 2017 and 2019. We estimated weighted, undirected networks for each time-point, where we included pairwise interactions of PrEP and VLS uptake and related beliefs. PrEP use increased from 10 to 31% (p < 0.001), while VLS was reported by 7-10% at each time-point. Uptake of both BmPS was directly related to the perceived positive impact of the strategy on one's quality of sex life and perceived supportive social norms. Overall network structure differed between time points, specifically in regard to PrEP. At earlier time points, perceptions of efficacy and affordability played an important role for PrEP uptake, while more recently social and health-related concerns became increasingly important.The network structure differed across data-waves, suggesting specific time changes in uptake motives. These findings may be used in communication to increase prevention uptake.

Organization of psychosocial factors associated with worry about acquiring SARS-CoV-2 among women undergoing cancer treatment: an empirical network comparison approach

OBJECTIVES

Pandemic-induced anxiety can have adverse mental and somatic health consequences on cancer patients (CP). This study aimed to (1) explore the intricate relationships between worry related to potential SARS-CoV-2 infection, COVID-19 perception, sociodemographic factors, and the perceived effectiveness of lockdown measures and (2) investigate if these relationships differ between cancer patients and individuals without a history of cancer (IWHC).

METHODS

We conducted a cross-sectional quantitative study in France between December 1 and 14, 2020. Network analysis was employed on a sample of 1889 women, including 282 cancer patients and 1607 noncancer individuals.

RESULTS

Our findings indicate that CP were 20% more likely to express worry than IWHC. Anxiety is embedded within a complex network involving sociodemographic, cognitive, and emotional factors. The emotional components related to COVID-19 perception were found to play a crucial role. The networks for both groups were observed to be identical.

CONCLUSIONS

Our study underscores the heightened vulnerability of cancer patients to pandemic-induced anxiety, emphasizing the crucial role of emotional components related to COVID-19 perception. The observed similarities in the anxiety network between cancer patients and those without a history of cancer suggest that universal approaches might be effective across groups.

IMPLICATIONS

Utilizing the Causal Attitude Network Model, we propose potential methods for managing and reducing individual anxiety levels.

Idiographic Ising and Divide and Color Models: Encompassing Networks for Heterogeneous Binary Data

The Ising model is a graphical model that has played an essential role in network psychometrics. It has been used as a theoretical model to conceptualize psychological concepts and as a statistical model to analyze psychological data. Using graphical models such as the Ising model to analyze psychological data has been heavily critiqued since these data often come from cross-sectional applications. An often voiced concern is the inability of the Ising model to express heterogeneity in the population. The idiographic approach has been posed as an alternative and aims to infer individual network structures. While idiographic networks overcome population heterogeneity, it is unclear how they aggregate into established cross-sectional phenomena. This paper establishes a formal bridge between idiographic and cross-sectional network approaches of the Ising model. We ascertain unique topological structures that characterize individuals and aggregate into an Ising model cross-sectionally. This new formulation supports population heterogeneity while being consistent with cross-sectional phenomena. The proposed theory also establishes a new statistical framework for analyzing populations of idiographic networks for binary variables. The Ising model and the divide and color model are special cases of this new framework. We introduce a Gibbs sampling algorithm to estimate models from this new framework.

Tailored interventions into broad attitude networks towards the COVID-19 pandemic

This study examines how broad attitude networks are affected by tailored interventions aimed at variables selected based on their connectiveness with other variables. We first computed a broad attitude network based on a large-scale cross-sectional COVID-19 survey (N = 6,093). Over a period of approximately 10 weeks, participants were invited five times to complete this survey, with the third and fifth wave including interventions aimed at manipulating specific variables in the broad COVID-19 attitude network. Results suggest that targeted interventions that yield relatively strong effects on variables central to a broad attitude network have downstream effects on connected variables, which can be partially explained by the variables the interventions were aimed at. We conclude that broad attitude network structures can reveal important relations between variables that can help to design new interventions.

Using a cognitive network model of moral and social beliefs to explain belief change

Skepticism toward childhood vaccines and genetically modified food has grown despite scientific evidence of their safety. Beliefs about scientific issues are difficult to change because they are entrenched within many interrelated moral concerns and beliefs about what others think. We propose a cognitive network model that estimates network ties between all interrelated beliefs to calculate the overall dissonance and interdependence. Using a probabilistic nationally representative longitudinal study, we test whether our model can be used to predict belief change and find support for our model's predictions: High network dissonance predicts subsequent belief change, and people are driven toward lower network dissonance. We show the advantages of measuring dissonance using the belief network structure compared to traditional measures. This study is the first to combine a unifying predictive model with an experimental intervention and to shed light on the dynamics of dissonance reduction leading to belief change.

Objective Bayesian Edge Screening and Structure Selection for Ising Networks

The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian approach to parameter estimation and structure selection for the Ising model. Our methods build on a continuous spike-and-slab approach. We show that our methods consistently select the correct structure and provide a new objective method to set the spike-and-slab hyperparameters. To circumvent the exploration of the complete structure space, which is too large in practical situations, we propose a novel approach that first screens for promising edges and then only explore the space instantiated by these edges. We apply our proposed methods to estimate the network of depression and alcohol use disorder symptoms from symptom scores of over 26,000 subjects.

Moral Judgment as Categorization (MJAC)

Observed variability and complexity of judgments of "right" and "wrong" cannot be readily accounted for within extant approaches to understanding moral judgment. In response to this challenge, we present a novel perspective on categorization in moral judgment. Moral judgment as categorization (MJAC) incorporates principles of category formation research while addressing key challenges of existing approaches to moral judgment. People develop skills in making context-relevant categorizations. They learn that various objects (events, behaviors, people, etc.) can be categorized as morally right or wrong. Repetition and rehearsal result in reliable, habitualized categorizations. According to this skill-formation account of moral categorization, the learning and the habitualization of the forming of moral categories occur within goal-directed activity that is sensitive to various contextual influences. By allowing for the complexity of moral judgments, MJAC offers greater explanatory power than existing approaches while also providing opportunities for a diverse range of new research questions.

A Psychological Network Approach to Attitudes and Preventive Behaviors During Pandemics: A COVID-19 Study in the United Kingdom and the Netherlands

Preventive behaviors are crucial to prevent the spread of the coronavirus causing COVID-19. We adopted a complex psychological systems approach to obtain a descriptive account of the network of attitudes and behaviors related to COVID-19. A survey study (N = 1,022) was conducted with subsamples from the United Kingdom (n = 502) and the Netherlands (n = 520). The results highlight the importance of people's support for, and perceived efficacy of, the measures and preventive behaviors. This also applies to the perceived norm of family and friends adopting these behaviors. The networks in both countries were largely similar but also showed notable differences. The interplay of psychological factors in the networks is also highlighted, resulting in our appeal to policy makers to take complexity and mutual dependence of psychological factors into account. Future research should study the effects of interventions aimed at these factors, including effects on the network, to make causal inferences.

Exploring inclusiveness towards immigrants as related to basic values: A network approach

Using the 9th round of European Social Survey (ESS), we explored the relationship between Europeans' basic values and their attitudes towards immigrants. Employing a latent class analysis (LCA), we classified the respondents based on three items capturing the extent to which participants would support allowing three groups of immigrants to enter and live in their countries: immigrants of same ethnic groups, immigrants of different ethnic groups, and immigrants from poorer countries outside Europe. Four classes of Europeans with mutually exclusive response patterns with respect to their inclusive attitudes towards immigrants were found. The classes were named Inclusive (highly inclusive), Some (selective), Few (highly selective), and Exclusive (highly exclusive). Next, using a network technique, a partial correlation network of 10 basic human values was estimated for each class of participants. The four networks were compared to each other based on three network properties namely: global connectivity, community detection, and assortativity coefficient. The global connectivity (the overall level of interconnections) between the 10 basic values was found to be mostly invariant across the four networks. However, results of the community detection analysis revealed a more complex value structure among the most inclusive class of Europeans. Further, according to the assortativity analysis, as expected, for the most inclusive Europeans, values with similar motivational backgrounds were found to be interconnected most strongly to one another. We further discussed the theoretical and practical implications of our findings.

Attitudes, Habits, and Behavior Change

Efforts to guide peoples' behavior toward environmental sustainability, good health, or new products have emphasized informational and attitude change strategies. There is evidence that changing attitudes leads to changes in behavior, yet this approach takes insufficient account of the nature and operation of habits, which form boundary conditions for attitude-directed interventions. Integration of research on attitudes and habits might enable investigators to identify when and how behavior change strategies will be most effective. How might attitudinally driven behavior change be consolidated into lasting habits? How do habits protect the individual against the vicissitudes of attitudes and temptations and promote goal achievement? How might attitudinal approaches aiming to change habits be improved by capitalizing on habit discontinuities and strategic planning? When and how might changing or creating habit architecture shape habits directly? A systematic approach to these questions might help move behavior change efforts from attitude change strategies to habit change strategies. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Integrating social and cognitive aspects of belief dynamics: towards a unifying framework

Belief change and spread have been studied in many disciplines-from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics-but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.

Bayesian Estimation for Gaussian Graphical Models: Structure Learning, Predictability, and Network Comparisons

Gaussian graphical models (GGM; "networks") allow for estimating conditional dependence structures that are encoded by partial correlations. This is accomplished by identifying non-zero relations in the inverse of the covariance matrix. In psychology the default estimation method uses ℓ1-regularization, where the accompanying inferences are restricted to frequentist objectives. Bayesian methods remain relatively uncommon in practice and methodological literatures. To date, they have not yet been used for estimation and inference in the psychological network literature. In this work, I introduce Bayesian methodology that is specifically designed for the most common psychological applications. The graphical structure is determined with posterior probabilities that can be used to assess conditional dependent and independent relations. Additional methods are provided for extending inference to specific aspects within- and between-networks, including partial correlation differences and Bayesian methodology to quantify network predictability. I first demonstrate that the decision rule based on posterior probabilities can be calibrated to the desired level of specificity. The proposed techniques are then demonstrated in several illustrative examples. The methods have been implemented in the R package BGGM.

Estimating and Examining the Replicability of Belief System Networks

Belief system structure can be investigated by estimating belief systems as networks of interacting political attitudes, but we do not know if these estimates are replicable. In a sample of 31 countries from the World Values Survey (N = 52,826), I find that countries’ belief system networks are relatively replicable in terms of connectivity, proportion of positive edges, some centrality measures (e.g., expected influence), and the estimates of individual edges. Betweenness, closeness, and strength centrality estimates are more unstable. Belief system networks estimated with smaller samples or in countries with more unstable political systems tend to be less replicable than networks estimated with larger samples in stable political systems. Although these analyses are restricted to the items available in the World Values Survey, they show that belief system networks can be replicable, but that this replicability is related to features of the study design and the political system.