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Sznajd-Weron K, Jȩdrzejewski A, Kamińska B. Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:511-521. [PMID: 37811605 DOI: 10.1177/17456916231195361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
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.
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Affiliation(s)
- Katarzyna Sznajd-Weron
- Department of Management Systems and Organization Development, Wrocław University of Science and Technology
| | | | - Barbara Kamińska
- Department of Management Systems and Organization Development, Wrocław University of Science and Technology
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van den Ende MWJ, van der Maas HLJ, Epskamp S, Lees MH. Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network. Sci Rep 2024; 14:4499. [PMID: 38402289 PMCID: PMC11052543 DOI: 10.1038/s41598-024-54155-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/09/2024] [Indexed: 02/26/2024] Open
Abstract
We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.
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Affiliation(s)
- Maarten W J van den Ende
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands.
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, 1012 GC, The Netherlands.
| | - Han L J van der Maas
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Sacha Epskamp
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
- Department of Psychology, National University of Singapore, Singapore, 117570, Singapore
| | - Mike H Lees
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, 1012 GC, The Netherlands
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Barnett NP, Light JM, Clark MA, Ott MQ, DiGuiseppi GT, Meisel MK. Dynamic social network analysis of a brief alcohol intervention trial in heavy-drinking college students shows spillover effects. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2024; 48:375-388. [PMID: 38240663 PMCID: PMC10922236 DOI: 10.1111/acer.15237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/08/2024]
Abstract
BACKGROUND Heavy-drinking college students tend to have close social networks, and there is theoretical and empirical support for the idea that behavior change can spread through those networks via close ties. The objective of this research was to determine whether intervention-induced behavior change in a subset of heavy drinkers in a sociometric (whole) college class-year social network is transmitted to other heavy drinkers in the network, resulting in reduced behavioral risk and change in network ties. METHODS We conducted a controlled trial in which most of a first-year college class (N = 1236; 56.9% female) was enrolled, with alcohol use and social network ties measured early in each of three semesters. Following a baseline assessment, the network was divided into two groups, brief motivational intervention (BMI) and natural history control (NHC) according to dormitory residence location. A subset of heavy drinkers in each group was selected, and those in the BMI group received an in-person intervention. RESULTS Using stochastic actor-oriented modeling, we found a significant tendency for participants in the BMI group to shed ties with individuals with similar drinking behaviors between the first and second semesters, relative to the NHC group. Furthermore, heavy drinkers with reciprocal ties to intervention recipients in the BMI group showed a significant reduction in drinks per week. CONCLUSIONS Individual alcohol interventions appear to have effects both on behavior and network connections among individuals who did not receive the intervention. Continued research is needed to identify the optimal conditions for the diffusion of behavior change.
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Affiliation(s)
| | | | | | | | - Graham T. DiGuiseppi
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA
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Kato A, Shimomura K, Ognibene D, Parvaz MA, Berner LA, Morita K, Fiore VG. Computational models of behavioral addictions: State of the art and future directions. Addict Behav 2023; 140:107595. [PMID: 36621045 DOI: 10.1016/j.addbeh.2022.107595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/23/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kanji Shimomura
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan
| | - Dimitri Ognibene
- Department of Psychology, Università degli Studi Milano-Bicocca, Milan, Italy; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Muhammad A Parvaz
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura A Berner
- Center of Excellence in Eating and Weight Disorders, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kenji Morita
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo 113-0033, Japan
| | - Vincenzo G Fiore
- Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Wiers RW, Grasman RP. Editorial special issue addictive behaviors, networks, complexity and addictive behaviors. Addict Behav 2022; 132:107369. [PMID: 35633616 DOI: 10.1016/j.addbeh.2022.107369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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Epskamp S, van der Maas HLJ, Peterson RE, van Loo HM, Aggen SH, Kendler KS. Intermediate stable states in substance use. Addict Behav 2022; 129:107252. [PMID: 35182945 DOI: 10.1016/j.addbeh.2022.107252] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022]
Abstract
Many people across the world use potentially addictive legal and illegal substances, but evidence suggests that not all use leads to heavy use and dependence, as some substances are used moderately for long periods of time. Here, we empirically examine, the stability of and transitions between three substance use states: zero-use, moderate use, and heavy use. We investigate two large datasets from the US and the Netherlands on yearly usage and change of alcohol, nicotine, and cannabis. Results, which we make available through an extensive interactive tool, suggests that there are stable moderate use states, even after meeting criteria for a positive diagnosis of substance abuse or dependency, for both alcohol and cannabis use. Moderate use of tobacco, however, was rare. We discuss implications of recognizing three states rather than two states as a modeling target, in which the moderate use state can both act as an intervention target or as a gateway between zero use and heavy use.
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Affiliation(s)
- Sacha Epskamp
- University of Amsterdam, Department of Psychology, Psychological Methods Program Group, Amsterdam, The Netherlands; University of Amsterdam, Centre for Urban Mental Health, Amsterdam, The Netherlands.
| | - Han L J van der Maas
- University of Amsterdam, Department of Psychology, Psychological Methods Program Group, Amsterdam, The Netherlands
| | - Roseann E Peterson
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Hanna M van Loo
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Steven H Aggen
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Kenneth S Kendler
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
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